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How to enable the snapping for a layer with the tolerance value with python programming

How to enable the snapping for a layer with the tolerance value with python programming


I have a vector polygon and vector polyline layer. I want to enable the snapping for both the layer with tolerance value? How can i do that using python programming. I have tried this code but getting the following error:

snapper = QgsMapCanvasSnapper(canvas) snapper.setSnapSettingsForLayer(aLayer,True,SnapToVertexAndSegment,pixels,0.0001‌​,True)

Traceback (most recent call last): File "", line 1, in AttributeError: 'QgsMapCanvasSnapper' object has no attribute 'setSnapSettingsForLayer'

Can anyone help me in setting the snapping options to the currentLayer using python programming?


You must define it first:

result3 = QgsVectorLayer("LineString", "ligne", "memory") #it's to create your layer ligneid=result3.id() # it allows you to have the idvalue for your layer QgsProject.instance().setSnapSettingsForLayer(ligneid,True,2,1,1000,True) # it defines the snapping options ligneid : the id of your layer, True : to enable the layer snapping, 2 : options (0: on vertex, 1 on segment, 2: vertex+segment), 1: pixel (0: type of unit on map), 1000 : tolerance, true : avoidIntersection)

Python scripting libraries for subsurface fluid and heat flow simulations with TOUGH2 and SHEMAT

Numerical simulations of subsurface fluid and heat flow are commonly controlled manually via input files or from graphical user interfaces (GUIs). Manual editing of input files is often tedious and error-prone, while GUIs typically limit the full capability of the simulator. Neither approach lends itself to automation, which is desirable for more complex simulations.

We propose an alternative approach based on the use of scripting. To this end we have developed Python libraries for scripting subsurface simulations using the SHEMAT and TOUGH2 simulators. For many problems the entire modeling process including grid generation, model setup, execution, post-processing and analysis of results can be carried out from a single Python script.

Through example problems we demonstrate some of the potential power of the scripting approach, which does not only make model setup simpler and less error-prone, but also facilitates more complex simulations involving, for example, multiple model runs with varying parameters (e.g. permeabilities, heat inputs, and the level of grid refinement). It is also possible to apply the developed methods for extending the functionality of graphical user interfaces.

Basing our approach on the Python language makes it simple to take advantage of other libraries available for scientific computation, with sophisticated analysis of results often a matter of a single function call. We envisage many other possible applications of the approach, including linking with geological modeling software, running stochastic ensembles of models and hybrid modeling using multiple interacting simulators.

Highlights

► We describe a novel scripting approach to fluid and heat flow simulations. ► Python modules were defined for TOUGH2 and SHEMAT. ► They complement and extend the functionality of graphical user interfaces. ► Mesh studies and simple inverse problems are possible with some lines of code. ► New research questions can be addressed with exceptional flexibility.


What is the history of Apache Spark?

Apache Spark started in 2009 as a research project at UC Berkley’s AMPLab, a collaboration involving students, researchers, and faculty, focused on data-intensive application domains. The goal of Spark was to create a new framework, optimized for fast iterative processing like machine learning, and interactive data analysis, while retaining the scalability, and fault tolerance of Hadoop MapReduce. The first paper entitled, “Spark: Cluster Computing with Working Sets” was published in June 2010, and Spark was open sourced under a BSD license. In June, 2013, Spark entered incubation status at the Apache Software Foundation (ASF), and established as an Apache Top-Level Project in February, 2014. Spark can run standalone, on Apache Mesos, or most frequently on Apache Hadoop.

Today, Spark has become one of the most active projects in the Hadoop ecosystem, with many organizations adopting Spark alongside Hadoop to process big data. In 2017, Spark had 365,000 meetup members, which represents a 5x growth over two years. It has received contribution by more than 1,000 developers from over 200 organizations since 2009.


It is often difficult to find real data for use with tutorials so first of all a hat tip to Eric Pimpler, the author of ArcGIS Blueprints, for pointing me towards accessing crime data for Seattle. To follow this tutorial you will need the neighborhoods of Seattle Shapefile which you can download from here and burglary data for 2015 which I have provided a link to here. Use the Projecttool from Data Management Tools > Projections and Transformations to project the data into a Projected Coordinate System. For this tutorial I have used UTM Zone 10N. Open, view and if you want style the data in ArcMap.


It is often difficult to find real data for use with tutorials so first of all a hat tip to Eric Pimpler, the author of ArcGIS Blueprints, for pointing me towards accessing crime data for Seattle. To follow this tutorial you will need the neighborhoods of Seattle Shapefile which you can download from here and burglary data for 2015 which I have provided a link to here. Use the Projecttool from Data Management Tools > Projections and Transformations to project the data into a Projected Coordinate System. For this tutorial I have used UTM Zone 10N. Open, view and if you want style the data in ArcMap.


All Research Projects (89)

3D Forming of Advanced Composites for Automotive and Sports Applications

The Manufacturing Design Laboratory (MDLab) at Purdue University is driven by today’s fast growing demands for cost-effectiveness and more sustainable solutions in the aerospace, automotive, and sports industries. Our research focuses on integrating next-generation composite manufacturing approaches with a full-scale Industry 4.0 Digital Manufacturing Testbed. As the utilization of advanced composites expands from the aerospace industry to high volume applications such as automotive and sports industries, increased complex forming, and cost-effective manufacturing has been increasingly demanded. The MDLab has integrated advanced robotics to automate the fiber preforming process which has led to a significant reduction of cycle times for complex shaped structures.
One of the equipment in the lab is the FREESTYLETM machine which is used to form M-TOW® (overbraided composite tow) into any desired shape and is synonymous to metal roll forming methods. The method is a free-forming method, no mold required, and raises the issues of dimensional and forming accuracy, which highlights our research focus in this area.
The student’s project will focus on mastering the forming of thermoplastic composites into 3D shapes. The student should have a desire to work with novel manufacturing equipment which may require modifying equipment for better performance. The results from this research will contribute to a deeper understanding of the dimensional stability of thermoplastic composites and will serve as a preform for over-molded components to be used in the automotive industry.

4D Materials Science - X-ray Microtomography, Image Analysis, and Machine Learning

The student will be working on state-of-the-art characterization techniques, such as x-ray microtomography and correlative microscopy of high performance materials. The project will involve image analysis and machine learning algorithms for efficiently and accurately analyzing the x-ray tomography datasets.

4D Printer Project

The project's goal is to use a Hyrel 3D printer to print out highly complex electronic circuits without any human interaction. To show the complexity of our printing method, our final print job will be a human neuron in the form of a computer chip. Undergraduate student goals will include fixing the many problems that come along the way, such as improving on the mechanics of the current printer, updating or adding a new software for printing, changing the cartridge material used, etc.

Accelerator Architecture Lab at Purdue (AALP): Optimizing Simulators for Advanced Processor Development

Modern processor design and research in both industry and academia rely on early-stage modeling and simulation. The ideas that make up every CPU, GPU, and accelerator you have ever used started their life in cycle-level C++ processor simulation. Today, much of the progress we see in the processor industry comes from specialization (i.e. Google’s TPU) and acceleration (i.e. GPGPU computing, such as NVIDIA’s CUDA). The Accelerator Architecture Lab at Purdue (AALP) develops a popular open-source GPU simulator called Accel-Sim that models modern NVIDIA GPUs executing compute workloads, like those commonly used in machine learning. Intimate details of the actions taken on each cycle of a real processor are modeled in Accel-Sim’s C++ code, such that new architectural ideas can be explored and empirically evaluated on real workloads. Simulating such an advanced, scaled system consumes a significant amount of CPU-time and memory (for some workloads - on the order of a TB!). This summer project involves understanding the high-level design of GPUs, the basic operation of CUDA, and optimizing the simulator infrastructure to consume orders of magnitude less memory at runtime, enabling larger and more complex workloads to be simulated. The successful completion of the project will see the student contribute to a highly-visible piece of open-source software and develop foundational skills to work at hardware design companies like Intel, AMD, NVIDIA, Qualcomm, Microsoft, and many others.

More information: https://accel-sim.github.io
Group Website: https://engineering.purdue.edu/tgrogers/group/aalp.html

Accelerator Architecture Lab at Purdue (AALP): Modeling Diverse GPU Architectures in C++ Simulation

Modern processor design and research in both industry and academia rely on early-stage modeling and simulation. The ideas that make up every CPU, GPU, and accelerator you have ever used started their life in cycle-level C++ processor simulation. Today, much of the progress we see in the processor industry comes from specialization (i.e. Google’s TPU) and acceleration (i.e. GPGPU computing, such as NVIDIA’s CUDA). The Accelerator Architecture Lab at Purdue (AALP) develops a popular open-source GPU simulator called Accel-Sim that models modern NVIDIA GPUs executing compute workloads, like those commonly used in machine learning. Intimate details of the actions taken on each cycle of a real processor are modeled in Accel-Sim’s C++ code, such that new architectural ideas can be explored and empirically evaluated on real workloads. Although the basic design of GPUs share many similarities across generations and vendors, each part and company have subtle differences that can greatly affect their performance on critical applications, such as those found in machine learning. This summer project involves understanding the high-level design of GPUs, the basic operation of CUDA, and modeling the performance of bleeding-edge GPU parts from both NVIDIA (an Ampere A100) and AMD. The successful completion of the project will see the student contribute to a highly-visible piece of open-source software and develop foundational skills to work at hardware design companies like Intel, AMD, NVIDIA, Qualcomm, Microsoft, and many others.

More information: https://accel-sim.github.io
Group Website: https://engineering.purdue.edu/tgrogers/group/aalp.html

Additive Manufacturing of Lightweight Metallic Alloys

The student will work on microstructural characterization and mechanical properties of new aluminum-based additive manufactured alloys. Corrosion testing and analysis are also part of the existing project.

Adhesives at the Beach

The oceans are home to a diverse collection of animals producing intriguing materials. Mussels, barnacles, oysters, starfish, and kelp are examples of the organisms generating adhesive matrices for affixing themselves to the sea floor. Our laboratory is characterizing these biological materials, designing synthetic polymer mimics, and developing applications. Characterization efforts include experiments with live animals, extracted proteins, and peptide models. Synthetic mimics of these bioadhesives begin with the chemistry learned from characterization studies and incorporate the findings into bulk polymers. For example, we are mimicking the cross-linking of DOPA-containing adhesive proteins by placing monomers with pendant catechols into various polymer backbones. Adhesion strengths of these new polymers can rival that of the cyanoacrylate “super glues.” Underwater bonding is also appreciable. Future efforts are planned in two different areas: A) Using biobased and biomimetic adhesives as the basis for making new plastic materials. This project will be more in the realm of materials engineering. B) Developing gel-based adhesives for wound closure. Work here will involve some aspects of biomedical engineering.

Advanced Textile based Wearable Devices

We are developing advanced textile materials towards next generation comfortable and wearable devices. The student will be involved in the design, fabrication and demonstration of the wearable devices including sensors, circuit components, power generators, etc.

Advanced Vehicle Automation and Human-Subject Experimentation

Vehicle automation is developing at a rapid rate worldwide. While fully autonomous vehicles will be pervasive on the roadway for the next several years, many research initiatives are currently underway to understand and design approaches that will make this technology a future reality. This work ranges from the development of sensors and controls algorithms, to schemes for networks and connectivity, to the creation of in-vehicle driver interfaces. Here, one component that is key to the effective design of next-generation autonomous driving systems is the human driver and, thus studying human-vehicle interactions and defining driver’s roles/tasks will be important.

The goal of this project is to describe and measure the ways in which a person interacts with advanced vehicle automation. Students will assist with multiple activities and will learn a combination of the following: how to a) develop/code advanced driving simulation scenarios, b) collect driving performance data, c) analyze driver and performance data (using methods via software packages), and d) write technical reports and/or publications. Students may also gain experience collecting and analyzing complementary physiological measures, such as eye movement data, brain activity, skin conductance, and heart rate. The students will work closely with graduate student mentors to enhance learning.

Advancing Pharmaceutical Manufacturing through Process Modeling and Novel Sensor Development

The limitations of batch processes to manufacture pharmaceutical products such as tablets, coupled with advances in process analytical technology (PAT) tools have led to a shift towards continuous manufacturing (CM), which represents the future of the pharmaceutical industry.

The flexibility of continuous processes can reduce wasted materials and facilitate scale-up more easily with active plant-wide control strategies. Ultimately, this results in cheaper and safer drugs, as well as a more reliable drug supply chain.

To fully realize the benefits of continuous manufacturing, it is important to capture the dynamics of the particulate process, which can be more complex than common liquid-based or gas-based chemical processes. In addition, effective fault detection and diagnostic systems need to be in place, so intervention strategies can be implemented in case the system goes awry.

All of these require the development of process models that leverages knowledge of the process and big data. Students in this part of the research would have a chance to gain experience in industry-leading software for process modeling (e.g. Simulink, gProms, OSI PI) and machine learning (e.g. Matlab, Python, .NET).

Most importantly, they would be able to test the models in Purdue's Newly Installed Tablet Manufacturing Pilot Plant at the FLEX Lab in Discovery Park.

Another important aspect of the research are sensors. In this project, we will be investigating the feasibility of two novel sensors: a capacitance-based sensor to measure mass flow, and a particle imaging sensor that directly captures images of the powder particles to give you a particle size distribution. We will be testing these sensors together with NIR and Raman sensors, and use data analytics to determine their feasibility of application in a drug product manufacturing process.

Analyzing educational teamwork dataset using quantitative and NLP techniques

Teamwork is an essential competency highly valued by both academia and industry, especially for engineers who usually work in a small group. With tens of years' development, our research group, the Comprehensive Assessment of Team Member Effectiveness (CATME), had collected millions of survey data, including peer comments. The selected SURF student will join our research group to assist with data cleaning, preparation, and analysis for educational or technical research related to teamwork, and perhaps NLP (NLP is not necessary but a plus).

Automatically Detecting and Fixing Software Bugs and Vulnerabilities

In this project, we will develop cool machine learning approaches to automatically learn bug/vulnerability patterns and fix patterns from historical data to detect and fix software bugs and security vulnerabilities. This project is partially funded by a Facebook Research Award (https://research.fb.com/programs/research-awards/proposals/probability-and-programming-request-for-proposals/).

Earlier work can be found here: https://www.cs.purdue.edu/homes/lintan/publications/deeplearn-tse18.pdf

Bio-inspired Radiative Cooling Nanocomposites

Radiative cooling is a passive cooling technology without power consumption, via reflecting sunlight and radiating heat into the deep space. Compared to conventional air conditioners, radiative cooling not only saves energy, but also combats global warming. Recently, our group has invented commercial-like particle-matrix paints that cool below the surrounding temperature under direct sunlight. The Purdue cooling paints attracted remarkable global attention. Read, for example, the BBC News coverage here: https://www.bbc.com/news/science-environment-54632523. Currently we are working to improve the performance and create new radiative cooling solutions using bio-inspired concepts.

In this SURF project, we look for a self-motivated student to work with our PhD students. The student will first synthesize bio-inspired nanocomposites via some wet chemistry and/or nanoscale 3D printing methods. The optical, mechanical, and other relevant properties will then be characterized with spectrometers and specialized equipment, with a particular focus on the effect of different particle alignment/processing techniques on the optical and mechanical properties. Field testing will be performed to measure the cooling performance of the materials and devices. The work is expected to results in journal paper(s) of high quality. Students who make substantial contributions to the work can expect to be co-authors of the paper(s).

Building Software for Environmental Modeling

Agricultural and Biological Engineering Department has contributed several tools for environmental modeling community. This project involves building Graphical User Interface (GUI) to connect with various components of a recently updated environmental model. The SURF student will be required to understand the current application, create a list of changes to be made in collaboration with the project supervisor, get a head start on developing new GUIs and document the process. The SURF student will work with a staff programmer.

Bursting of Leading Edge Vortices on Swept Wings

The vortex generated at the sharp leading edge of a swept wing at high angles of attack maintains lift by preventing the free stream from separating from the upper surface of the wing. This is important for reducing landing speeds and for enhancing the maneuver performance of fighter aircraft. However this benefit is limited to moderate angles of attack because the vortex breaks down, or bursts, at some point along its length where it changes from a strong spiral motion to a disorganized turbulent eddy with a consequent loss of lift.

The purpose of this SURF project will be to visualize the bursting of the vortex at the leading edge of a delta wing in order to investigate methods of preventing bursting. The experiment will be performed in the water tunnel at the Purdue Aerospace Research Laboratories, which will be adapted for this purpose. The ideal candidate will understand wing aerodynamics and have some experience with wind tunnel testing and the design of electro-mechanical drive mechanisms. This research project will be performed under the guidance of Dr. Paul Bevilaqua, a Purdue AAE graduate, retired Chief Engineer of the Lockheed Martin Skunk Works, and Neil Armstrong Distinguished Visiting Fellow in AAE.

Catalytic Conversion of Methane to Chemicals and Fuels

Methane is the most abundant component of natural and shale gas. The ability to convert methane to chemicals and fuels using catalytic technologies would enable developing lower CO2-footprint energy sources to power our society. This project will involve catalyst design, research and development to selectively convert methane into alcohol and aromatic products. The student will learn how to synthesize, characterize and evaluate novel catalytic materials and conduct research at the interface of materials science and heterogeneous catalysis.

Computational investigation of mechanosensitive behaviors of motile cells

Cell migration plays an important role in physiology and pathophysiology. Migrating cells are able to sense surrounding mechanical environments. For example, a number of experiments have demonstrated that nano- and micro-patterns can guide migration of cells. This migratory behavior is called the contact guidance and is of great importance in various physiological processes, such as cancer metastasis. In this research project, we aim to use a rigorous computational model and collaborate with experimentalists in order to investigate intrinsic mechanisms of the contact guidance. A participating student will run computer simulations and analyze data from the simulations to perform the research. If necessary, everything for this project can be done remotely.

Crawling the Internet for Denial of Service Vulnerabilities

A student will explore the Internet looking for denial of service (DoS) vulnerabilities. DoS vulnerabilities prevent legitimate users from accessing a service, with implications ranging from financial costs to personal safety. In this project, the student will focus on algorithmic complexity vulnerabilities, where a particular input is particularly expensive for a web service to process. Such inputs will unfairly direct resources away from legitimate users and towards the attacker. To identify these vulnerabilities, the student will synthesize state-of-the-art web crawlers, analysis tools, and probing techniques to discover novel security vulnerabilities.

Deep Learning applications in agriculture

This project will use public and custom datasets of images/sounds/videos for training deep learning models and explore methods to improve generalization abilities of the trained models.

Defining Chemical Modifications on Histones that Control Chromosome Integrity

The student will join a multi-disciplinary team investigating epigenetic processes, chromatin structure and gene regulation. This project will involve learning and applying biochemical, genetic and molecular biology strategies to build and characterize customized budding yeast (Saccharomyces cerevisiae) strains or mammalian cell lines for the investigation of evolutionarily conserved protein-protein interactions and post-translational modifications using state-of-the-art detection and quantification strategies. Biological targets may include histone modifying enzymes, histone modifications, histone variants and chromatin assembly and DNA replication factors.

Describing the collective motion of dislocations in metals

The collective behavior of dislocations (line defects) in crystals is not well understood. This is somewhat strange considering that this collective behavior is the physical origin of deformation in many crystalline materials. The only tool that we currently have to study this involves simulating how individual dislocations move in a crystal. However, we are creating a theory that treats these dislocations like a fluid, as a density field.

We have two projects available, please apply for this position if you are interested in either one.

• One project will involve simulating dislocations in face centered cubic metals to extract statistical information about how they form junctions. This junctions are the physical basis of work-hardening, and this statistical information will allow us to incorporate junctions into the density-based, fluid-like model.

• Another project will involve simulating x-ray diffraction patterns in face-centered cubic metals containing dislocations in order to identify signals relevant to the fluid-like properties of the dislocations. Basic machine learning techniques will be used to identify these signals. No experience with x-ray diffraction or machine learning is needed. These results will allow experimentalists at our national labs to measure the fluid-like properties of dislocations in a lab rather than through simulations.

Design, construction and simulation of scaled test facility for gas cooled reactor cavity building blowdown

The main goal of the research is to develop a scaled experimental facility to study a High Temperature Gas-cooled Reactor (HTGR) building response in the event of a depressurization accident caused by a break in the primary coolant boundary and obtain first-of-a-kind data on the oxygen concentration distribution for validation of reactor safety codes and Computational Fluid Dynamics (CFD) models. It is proposed to conduct experiments in a well-scaled test facility representing reference GA-MHGTR reactor building cavities and obtain oxygen concentration as function of time and space for range of reactor building vent locations, flow paths, and break sizes, locations and orientations. To support the experimental program, it is proposed to perform analysis of the reactor building response with a system level reactor safety code complimented by a CFD analysis for detailed localized predictions. The task under this project include study of the HTGR reactor components, where actual dimensions of the systems components are collected data, using scaling design scaled facility, and perform CFD analysis. Students interested on hands on experience in the laboratory, willing to build test facility, perform experiment, and analyze data are welcome. Great opportunity to develop thermal hydraulics laboratory skills.

Design, fabrication, and testing of an environmental chamber for X-ray characterization

High energy X-rays produced by synchrotron sources can be used to characterize the 3D microstructure and evolution of the lattice strains (and thereby stresses) in each grain during thermo-mechanical loading. For this project, we would use high energy X-rays to characterize the evolution of a fatigue crack in a corrosive environment. This project would entail the design, fabrication, and testing of an environmental chamber. The chamber would enclose the specimen in a corrosive environment, and at the same time, applying loading to the specimen. The design would need to limit the impedance of the incoming/outgoing X-ray sources during characterization.

Designing Epidemic Mitigation Methods with Limited Resources

By the end of 2020, the COVID-19 infection has caused more than one million deaths and a large amount of financial loss globally. To reduce the losses, social planners are implementing appropriate methods to mitigate the spread of the epidemic, such as developing vaccines, maintaining social distancing, quarantine, investing in effective medicines, etc. Meanwhile, each type of mitigation method has different costs and the decision-makers often have to carry out the policies under limited budgets. Our plan is to design epidemic mitigation methods with limited resources.

In the project, the students will participate in designing a dynamic epidemic model for COVID-19 spreading in a community. Further, the students will fill in the role of a decision-maker of the community. Given a restricted budget, the students will try to alternate the system parameters which correspond to actions such as allocating medical equipment, imposing lock-down, and distributing vaccines, so that the virus will be eradicated quickly. Once the virus is eradicated, we will study how to prevent the occurrence of subsequent waves with relatively moderate policies. Furthermore, we will extend the problem to study how to mitigate the spreading of the virus with the lowest budget possible. The students will learn to apply geometric programming ideas to solve these problems.

Developing Computational Methods to Classify Unlabeled Reactions Using Large Data Sets

The ability to understand how chemical structure and conditions (i.e., chemical reaction class) affect reactions is fundamental to generalizing chemical transformations to new conditions and substrates. This ability opens up new ways to simulate and predict chemical behavior. Although reaction classes have historically been based on hypothetical mechanisms or the presence of specific combinations of reactive groups, there is a pressing need to develop empirical methods for extracting reaction classes from reaction data generated by automated experimentation and computations. In this research project, students will learn how to use data science techniques to develop computational methods to automatically extract reaction classes from chemical data in a manner that can be used to predict reactivity in other contexts. Several approaches are possible and encouraged for reaching this goal, including unsupervised learning algorithms, supervised predictive models, or heuristic models that use a mixture of chemical expertise and automation to classify reactions. Participation in this project will provide exposure to research in machine learning and data science including training in programming, model training, and utilization of large data sets. Participants do not need to have prior experience in data science.

Developing IoT sensors for real-time concrete strength monitoring

EMI technique is a nondestructive testing (NDT) method that makes use of the piezoelectric nature of lead zirconate titanate (PZT) sensor that vibrates and interacts with the host structure, thereby tuning the electrical characteristics of PZT through mechanical interaction. Inversion algorithm is then used to extract mechanical properties of host structure from using electrical characteristics of PZT sensor. EMI technique has been evolving for decades and demonstrated to be a good in-situ method to determine bulk concrete properties, e.g. Young’s modulus, in lieu of tedious molding and compression test. However, current EMI studies in modulus measurement are mostly established on the statistical relationship between EMI spectrum and conventional compression test, and the variation of sensors can lead to a bad repeatability.
In this work, a novel EMI method for concrete modulus measurement will be reported. This novel NDT method can extract the dynamic modulus of concrete cylinder using only one PZT sensor. The specific activities include: (a) embedding PZT sensor in cylinder mold (b) casting concrete in mold (c) measuring the electrical impedance spectrum of sensor (d) reading the resonance frequencies of the spectrum in low frequency band and (e) calculating the modulus using resonance frequencies. The orientation of sensor, the sensing range and the repeatability between different sensors will be discussed in this project. The investigation of the nature of EMI sensor-structure interaction has a broad interest to NDT and piezoelectric material community.

Developing Simple Mathematical Models to Track the Mass and Energy Flows in a Natural Gas Processing System

Chemical engineers routinely use computational modeling to improving the efficiency and sustainability of manufacturing and energy conversion processes. In this research experience, you help develop simple mathematical models to track the mass and energy flows in a natural gas processing/upgrading system. We will use these models to simulate and optimize the system in an interaction Python (Jupyter) notebook. These models will help identify the key opportunities to improve the economics and sustainability of the process as well as set quantitative performance targets for more fundamental CISTAR research (e.g., catalysts, separations).

Developing and Studying Activities for Localized Engineering Curricula

Engineering programs provide a unique pathway for learners to reassert control over their environment, demonstrate agency and decision making, build strong social connections, and take on crucial roles in their communities, all while developing complementary professional (“21st century”) skills. We have demonstrated through our Localized Engineering in Displacement (LED101) course that authentic engineering learning opportunities can serve as a vehicle for community development while simultaneously expanding the representation of engineers to explicitly include marginalized, displaced learners. The course has run multiple times, each cohort with a central “authentic” (real-world) challenge that is the context for all learning activities. The class for which our undergraduate researcher would develop activities, assess the implementation process, and study the impact will offer as its “authentic problem” the need for students to design, build, optimize, and implement a solar-powered lighting solution for girls, mothers, and the community studying at home during COVID in Senegal.

Development of an anti-deterrent formulation against opioid abuse

Prescription analgesics such as opioids are an indispensable resource for managing pain. While these drugs may provide relief from the discomfort that occurs after a medical procedure, opioids are highly addictive. If taken as prescribed, the overall risk to the patient’s health is minimal. However, some addicts alter the method of ingestion in order to feel the effects as quickly as possible. These alternative ingestion strategies result in a rapid and dangerous increase in the concentration of the drug in the blood that can lead to death. In fact, overdose deaths caused by prescription drug abuse now exceed the total number of deaths caused by heroin or cocaine combined. To help minimize the risk of overdose, we are developing an advanced pill formulation designed to deter addicts from using alternative ingestion strategies.

Dynamic contractile behaviors of active cytoskeletal networks

The actin cytoskeleton is a dynamic structural scaffold used by eukaryotic cells to provide mechanical integrity and resistance to deformation while simultaneously remodeling itself and adapting to diverse extracellular stimuli. The actin cytoskeleton with molecular motors also generates tensile mechanical forces with contractile behaviors in various biological processes of cells such as migration, cytokinesis, and morphogenesis. Although microscopic properties of key constituents of the actin cytoskeleton have been well characterized, it still remains elusive how the actin cytoskeleton contracts and generates mechanical forces. In this research project, we aim to illuminate the mechanisms, using a well-established computational model. A participating student will run computer simulations and analyze data from the simulations to perform the research. If necessary, everything for this project can be done remotely.

Efficient and renewable water treatment

Water and energy are tightly linked resources that must both become renewable for a successful future. However, today, water and energy resources are often in conflict with one another, especially related to impacts on electric grids. Further, advances in material science and artificial intelligence allow for new avenues to improve the widespread implementation of desalination and water purification technology. This project aims to explore nanofabricated membranes, artificial intelligence control algorithms, and thermodynamically optimized system designs. The student will be responsible for fabricating membranes, building hydraulic systems, modeling thermal fluid phenomenon, analyzing data, or implementing control strategies in novel system configurations.

Engineering human stem cells for targeted cancer therapy

Cancer is a major threat for humans worldwide, with over 18 million new cases and 9.6 million cancer-related deaths in 2019. Although most common cancer treatments include surgery, chemotherapy, and radiotherapy, unsatisfactory cure rates require new therapeutic approaches. Recently, adoptive cellular immunotherapies with chimeric antigen receptor (CAR) engineered T and natural killer (NK) cells have shown impressive clinical responses in patients with various blood and solid cancers. However, current clinical practices are limited by the need of large numbers of healthy immune cells, resistance to gene editing, lack of in vivo persistence, and a burdensome manufacturing strategy that requires donor cell extraction, modulation, expansion, and re-introduction per each patient. The ability to generate universally histocompatible and
genetically-enhanced immune cells from continuously renewable human pluripotent stem cell (hPSC) lines offers the potential to develop a true off-the-shelf cellular immunotherapy. While functional CAR-T and NK cells have been successfully derived from hPSCs, a significant gap remains in the scalability, time-consuming (5 or more weeks), purity and robustness of the differentiation methods due to the cumbersome use of serum, and/or feeder cells, which will incur potential risk for contamination and may cause batch-dependency in the treatment. This project thus aims to develop a novel, chemically-defined platform for robust production of CAR-T and CAR-NK cells from hPSCs.

Enhancing Human-Robot Interaction Using Wearable Technologies

While intelligent systems promise to extend human capabilities within occupational settings, workers must increasingly collaborate with artificial intelligence (AI) to achieve desired outcomes. This research aims at enhancing bi-directional interaction between workers and robots at the construction jobsites by obtaining continuous neurophysiological and psychophysiological data from workers. The developed personalized AI will measure, adapt, and enhance the skill performance of the next generation of the workforce to work safely and communicate effectively in the future automated jobsites.

Epidemic Analysis Via Social Networks

Social media has significantly increased the rate at which news spreads through the population, enabling shifts in people’s beliefs towards the news. One such example is the disagreement on the severity of the disease over different communities during the COVID-19 pandemic. The contention over COVID-19 affects people’s attitudes and behaviors towards the policies and suggestions from the government and scientific institutions, respectively. Our question is if it is possible to mitigate the spreading of the epidemic by impacting the opinions over the social networks. Our proposed solution is to capture the opinions of the COVID-19 pandemic through dynamical social networks with both cooperative and antagonistic interactions. We will validate the network model with social network data. Through the data-based model, we will explore the role of opinion dissemination on epidemic spreading in reality. The undergraduate researchers will learn to model signed social networks via the opinions on COVID-19. The students will gain fundamental knowledge in systems and control, social network modeling and analysis, and hands-on experience in data collection, analysis, and model validation.

Epidemic Modeling and Prediction with COVID-19 Dataset

COVID-19 has been a major challenge in the year 2020 and the epidemic modeling community has yet to come up with an accurate and reliable method for epidemic spread prediction. Some difficulties of the epidemic spread prediction problem include testing delays, testing inaccuracy and feedback effects from local health authorities’ disease mitigation policies. These complexities in the dataset will lead to inaccurate prediction and poor disease mitigation strategies if not resolved properly.

There are abundant well-organized Covid-19 datasets available online, including the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. By leveraging these datasets, we plan to design a project-based learning experience that participants will model and predict epidemic spread over a nine-week schedule. The project includes five major stages: 1) data collection, 2) model selection, 3) parameters optimization, 4) model verification, and 5) prediction. The participants will learn to model and analyze epidemic processes with compartmental models, and they will get the first-hand experience using a programming language of their choice to implement the modeling, optimization, and prediction pipeline.

Evaluation of a Prototype Membrane Heat Exchanger for Efficient Buildings

Buildings are the largest source of energy consumption in the U.S., constituting roughly 48% of our primary energy consumption, and air conditioning is one of the largest uses of energy within buildings. As global temperatures rise from global warming, populations grow, and greater emphasis is put on indoor air quality and comfort, cooling energy demand will grow too. The long-standing conventional technologies we rely on for space cooling are inherently inefficient in warm, humid climates where a large portion of the cooling energy goes to the condensation dehumidification process instead of air cooling. Thus, there is a great need for innovative, disruptive technological development that can challenge the way we’ve provided space cooling for decades. In this project, we are developing a novel technology that mechanically separates water vapor out of air using water vapor selective membranes, which is much more efficient than condensing water out of air. Additionally, we are exploring innovative heat and mass transport phenomena using novel materials. The student who joins this project will have the opportunity to contribute to important experimental work, will learn about energy use and the thermodynamics and heat transfer in buildings, and will learn about material development, too.

Evaluation of early changes in a non-surgical post-traumatic osteoarthritis model

Osteoarthritis affects over 32.5 million American adults, impacting mobility and quality of life, and costs over $16.5 billion in direct medical costs in hospitals within the United States. Knee osteoarthritis is most common among these, and approximately 1 in 8 cases of osteoarthritis are considered post-traumatic, meaning that degeneration of the tissues in the joint is precipitated from an injury, such as tearing of the anterior cruciate ligament (ACL). Unfortunately, about half of people who tear their ACLs go on to develop post-traumatic osteoarthritis, whether or they had ACL repair surgery. An understanding of the early biological response of the joint after an injury could help identify targets for treatment and rehabilitation to be prescribed in conjunction with ACL repair and physical therapy. In order to learn more about the early inflammation in the joint after an injury, we need to develop a non-surgical ACL tear model for mice that replicates key conditions of the human injury. This project involves development and testing of a new system to perform the single tibial compression model of ACL rupture. This model will enable the examination of the early inflammatory response in the mouse knee.

High Performance Concrete from Recycled Hydrogel-Based Superabsorbent Materials

Concrete that is internally cured by water-swollen superabsorbent polymer (SAP) particles has improved strength and durability. Widespread adoption of SAP-cured concrete is hindered by the lack of commercial SAP formulations that maintain their absorbency in cement’s high-pH environment. Most commercial SAP formulations are designed for disposable diapers and other absorbent hygiene products (AHPs), which account for

12% (3.4M tons) of all non-durable goods in landfills. Over 70% of a diaper’s weight is composed of absorbent materials – mainly cellulose and polyacrylamide(PAM)-based SAP particles – the latter being chemically equivalent to the SAP particles that perform well in concrete research. Thus, a sustainable strategy to create effective concrete curing agents is to recycle the absorbent materials from AHPs and reprocess for use in concrete. AHP recycling efforts are already underway, including a plant in Italy with a 10,000-tonne annual capacity for AHP recycling. However, synthetic strategies must be developed to convert recycled AHPs into absorbent particles that perform well in concrete. Hypothesis and Objectives: We hypothesize that the PAM and cellulose components of AHPs can be separated and chemically crosslinked to form particles that display high absorption capacity in alkaline environments. The SURF student will: (1) obtain recycled absorbent materials and characterize the structures of the materials including composition, particle morphology, and swelling behavior (2) design and synthesize absorbent particles by combining different ratios of recycled absorbent materials with a crosslinking agent and grinding/sieving to create particles with dry sizes of 10-100 micron (3) identify the dosages of absorbent particles required to create internally cured concrete with good workability and mechanical strength and (4) perform cost-benefit analysis of concrete cured by recycled particles and commercial SAP.

High Performance Halide Perovskite Solar Cells

Sunlight is the most abundant renewable energy resource available to human beings, and yet it remains one of the most poorly utilized sources of clean energy. Solar cell modules incorporating single crystalline silicon and gallium arsenide currently provide the highest efficiencies for solar energy conversion to electricity but remain limited due to their high costs.

In the past few years, perovskite solar cell technology has made significant progress, improving in efficiency to

25%, while maintaining attractive economics due to the use of inexpensive soluble materials coupled with ultra low-cost deposition technologies. However, the real applications of these devices requires new breakthroughs in device performance, large-scale manufacturing, and improved stability. Among these, stability and degradation are among the most significant challenges for perovskite technologies. Perovskite absorber layer and organic charge transport materials can be sensitive to water, oxygen, high temperatures, ultraviolet light, and even electric field, all of which will be encountered during operation. To address these issues, significant efforts have been made, including mixed dimensionality and surface passivation alternative absorber materials and formulations, new charge transport layers, and advanced encapsulation techniques, etc. Now, T80 lifetimes (i.e., the length of time in operation until measured output power is 80% of original output power) of over 1,000 hours have been demonstrated. However, it is still far below the industry required 20 years lifetime, indicating the ineffectiveness of current approaches. To make this advance, non-incremental and fundamentally new strategies are required to improve the intrinsic stability of perovskite active materials.

In this project, we propose a new paradigm to develop intrinsically robust perovskite active layers through the incorporation of multi-functional semiconducting conjugated ligands. In preliminary work, we have demonstrated that semiconducting ligands can spontaneously organize within the active layer to passivate defects and restrict halide diffusion, resulting in dramatic improvements in moisture and oxygen tolerance, reduced phase segregation, and increased thermal stability. Combining a team with expertise spanning the gamut of materials synthesis, computational materials design, and device engineering, we will develop a suite of multi-functional semiconducting ligands capable of improving the intrinsic stability perovskite materials while preserving and even enhancing their electronic properties. Through this strategy, we aim to achieve over 25% cell efficiency with operational stability over 20 years for future commercial use.

Human Factors: Enhancing Performance of Nurses and Surgeons

High physical and cognitive workload among surgeons and nurses are becoming more common. The purpose of this project is to examine the contributors to these and develop technology to understand and enhance their performance.

The SURF student will participate in data collection in the operating room at Indiana University School of Medicine, data analysis and interpretation, and write his/her results for a journal publication. The student will regularly communicate his/her progress and results with faculty, graduate mentors, and surgeon collaborators.

Identification, Verification and Validation of a Surfactant Formulation for Chemical Enhanced Oil Recovery in the Illinois Basin

Challenge: The Enhanced Oil Recovery (EOR) Lab has an ardent interest in developing a practical and economical program for the Illinois Basin. The Illinois basin is characterized as a mature asset that is typified by its shallow depths and low temperatures. Many of the fields have been waterflooded for the last several decades to aid in the recovery of the stranded oil within the sandstone and carbonate reservoirs. Significant progress has been made in understanding the brine constituents, oil viscosity/API gravity and reservoir mineralogy of the Illinois Basin however, suitable chemical formulations, primarily surfactant/polymer combinations are still elusive. Considerable chemical testing is necessary to complement the Illinois Basin reservoir characteristics in order to move a project to pilot scale implementation.
The most pressing technical challenge is the design of a surfactant formulation that provides technical confidence (performance) for the reservoir brine and the crude oil. Notwithstanding, the areas of low/ultralow IFT, phase behavior and core flood are all key areas that need to demonstrate performance before implementing a field pilot program. Once a suitable surfactant formulation is determined, its stability, compatibility and performance with respect to the addition of polymer must also be understood and evaluated.

Targeted Goal: This project will focus on using the library of commercial surfactant products available in the EOR lab to find a suitable formulation for a target reservoir in the Illinois Basin. Once a surfactant formulation is determined through satisfactory phase behavior testing, Interfacial tension testing followed by core flood validation experiments will be carried out. Students should expect to learn about chemical enhanced oil recovery while performing experiments with surfactants, various brine solutions and oils.

Image-based computational modeling of tissue interface mechanics

Osteoarthritis affects over 32.5 million American adults, impacting mobility and quality of life, and costs over $16.5 billion in direct medical costs in hospitals within the United States. Tissue trauma, such as focal cartilage defects, can lead to osteoarthritis if not properly treated. Although cartilage tissue engineering has potential to repair or regenerate tissues in the joint, long-term success of these strategies hinge on the ability of clinicians to monitor the repair process. Imaging techniques currently allow for assessment of structure and even some biochemical changes, but these measures poorly reflect the mechanical properties of the repair. The repair not only must match the depth-dependent mechanical behavior of the surrounding tissues but also needs to be securely integrated with the native tissue. Our lab has developed a magnetic resonance imaging-based method to measure tissue biomechanics. However, integration of these images into computational models is necessary to evaluate how forces are distributed to the repair tissue and how strong the interface between the repair and native tissues is. This project is an important step towards this goal and involves developing and imaging phantoms that mimic the repair interface. Then, the researcher will subsequently generate a computational biomechanics model based on the image data.

Immunohistochemical characterization of mouse secondary visual areas

Humans perceive motion and location in different areas of the visual cortex. This is called the “what” and “where” pathways in the human brain. The ‘ventral stream’ is used for object vision while the ‘dorsal stream’ is used for spatial vision. Mice, although having smaller brains, also have primary and secondary parts of their visual cortex, but the functional roles of their secondary visual cortices remain unclear. One of the overarching goals of our laboratory is to investigate the secondary visual cortical areas in mice to determine which areas are responsible for perceiving motion. To achieve this goal graduate students in the laboratory use in vivo 2-photon calcium imagine to simultaneously record the visual response from secondary visual cortices. We would like to teach undergraduate students to help with different parts of this process, including stereotaxic brain surgeries, behavioral habituation and training, immunohistochemical characterization of the changes in the mouse brains following visual experience, and fluorescent microscopy to visualize these changes. Developing these skills will be invaluable for students in their future development as life scientists and will open the new horizons in neuroscience research.

In vitro tissue engineering scaffold maturation and integration for longitudinal MRI

Osteoarthritis affects over 32.5 million American adults, impacting mobility and quality of life, and costs over $16.5 billion in direct medical costs in hospitals within the United States. Tissue trauma, such as focal cartilage defects, can lead to osteoarthritis if not properly treated. Although cartilage tissue engineering has potential to repair or regenerate tissues in the joint, long-term success of these strategies hinge on the ability of clinicians to monitor the repair process. Imaging techniques currently allow for assessment of structure and even some biochemical changes, but these measures poorly reflect the mechanical properties of the repair. The repair not only must match the depth-dependent mechanical behavior of the surrounding tissues but also needs to be securely integrated with the native tissue. Our lab has developed a magnetic resonance imaging-based method to measure tissue biomechanics, a technique that has potential for monitoring the longitudinal processes of tissue maturation and integration. In order to evaluate the ability of our imaging technique to measure these two factors, an in vitro model of cartilage tissue repair is needed. This project includes the development of a mechanobioreactor, in which a cartilage tissue repair model can be housed under standard culture conditions, as well as preliminary studies to image the maturing and intergrating scaffolds.

Indoor Air Chemistry & Physics

We spend 90% of our time indoors. Indoor air quality has a significant impact on human health and well-being. Our research group studies the physics and chemistry of indoor air. We use state-of-the-art measurement techniques to explore the dynamics of indoor air pollutants in diverse indoor environments. We are seeking a motivated student to assist with ongoing research projects related to indoor air chemistry - dynamics of volatile organic compounds and ozone in buildings and indoor air physics - emissions and filtration of airborne particles (aerosols). Your role will involve assisting graduate students with indoor air measurements and data analysis in MATLAB.

IoT4Ag P1: Autonomous recharging of ground and aerial mobile agricultural robot platforms

By 2050, the US population is estimated to grow to 400 million and the world population to 9.7 billion. Current agricultural practices account for 70% of global water use, energy accounts for one of the largest costs on a farm, and inefficient use of agrochemicals is altering Earth’s ecosystems. With finite arable land, water, and energy resources, ensuring food, energy, and water security will require new technologies to improve the efficiency of food production, create sustainable approaches to supply energy, and prevent water scarcity.

A new Engineering Research Center on the Internet of Things for Precision Agriculture (IoT4Ag) has recently been established to ensure food, energy, and water security by advancing technology to increase crop production, while minimizing the use of energy and water resources and the impact of agricultural practices on the environment. The center will create novel, integrated systems that capture the microclimate and spatially, temporally, and compositionally map heterogeneous stresses for early detection and intervention to better outcomes in agricultural crop production. The Center will create internet of things (IoT) technologies to optimize practices for every plant from sensors, robotics, and energy and communication devices to data-driven models constrained by plant physiology, soil, weather, management practices, and socio-economics. We are looking to hire a cohort of SURF students to work on different activities in the center.

IoT4Ag 1: Autonomous recharging of ground and aerial mobile agricultural robot platforms
# students: 2 - US Citizens or permanent residents only

In this project, students will be tasked to design and implement an autonomous battery recharging system for ground and aerial mobile agricultural robot platforms.

Student 1:
Survey of state of the art - robot battery swapping systems
Mechanical design of battery swapping system - on robot/at charging station
Research and implement robot path planning to charging nearest charging station
Develop and test visual servoing algorithms for robot to dock in charging station

Student 2:
Survey of state of the art - wireless charging techniques
Waveform design for wireless charging
Proof of concept demonstration of candidate system(s)
Electrical system design/specifications for robot-side wireless charging system

IoT4Ag P2: IsoBlue integration to UGV/UAV platforms

By 2050, the US population is estimated to grow to 400 million and the world population to 9.7 billion. Current agricultural practices account for 70% of global water use, energy accounts for one of the largest costs on a farm, and inefficient use of agrochemicals is altering Earth’s ecosystems. With finite arable land, water, and energy resources, ensuring food, energy, and water security will require new technologies to improve the efficiency of food production, create sustainable approaches to supply energy, and prevent water scarcity.

A new Engineering Research Center on the Internet of Things for Precision Agriculture (IoT4Ag) has recently been established to ensure food, energy, and water security by advancing technology to increase crop production, while minimizing the use of energy and water resources and the impact of agricultural practices on the environment. The center will create novel, integrated systems that capture the microclimate and spatially, temporally, and compositionally map heterogeneous stresses for early detection and intervention to better outcomes in agricultural crop production. The Center will create internet of things (IoT) technologies to optimize practices for every plant from sensors, robotics, and energy and communication devices to data-driven models constrained by plant physiology, soil, weather, management practices, and socio-economics. We are looking to hire a cohort of SURF students to work on different activities in the center.

IoT4Ag P2: IsoBlue integration to UGV/UAV platform
# students: 1, US Citizens or permanent residents only

In this project, the student will be tasked with system architecture design, integration, and mechanical design for an integrated IsoBlue communications module with existing UGV and UAV platforms. IsoBlue is an on-going project for an open source telematics and edge computing device, which connects to the CANbus of agricultural machines in order to read and log machine sensors and to create the capability for machine control. IsoBlue is also a general purpose sensor hub capable of communications using WiFi, Bluetooth Low Energy, TV whitespaces, and LoRa and it creates a bridge to the cloud using LTE cellular.

The ECE student on this project will:
Survey the state of the art in telematics, edge computing, and sensor networking
Specify the electrical and mechanical interfaces needed to integrate with the UGV platform
Modify the existing IsoBlue design to implement IsoBlue/UGV integration

IoT4Ag P3: Biophysical modeling and integration with in-situ and remotely sensed data

By 2050, the US population is estimated to grow to 400 million and the world population to 9.7 billion. Current agricultural practices account for 70% of global water use, energy accounts for one of the largest costs on a farm, and inefficient use of agrochemicals is altering Earth’s ecosystems. With finite arable land, water, and energy resources, ensuring food, energy, and water security will require new technologies to improve the efficiency of food production, create sustainable approaches to supply energy, and prevent water scarcity.

A new Engineering Research Center on the Internet of Things for Precision Agriculture (IoT4Ag) has recently been established to ensure food, energy, and water security by advancing technology to increase crop production, while minimizing the use of energy and water resources and the impact of agricultural practices on the environment. The center will create novel, integrated systems that capture the microclimate and spatially, temporally, and compositionally map heterogeneous stresses for early detection and intervention to better outcomes in agricultural crop production. The Center will create internet of things (IoT) technologies to optimize practices for every plant from sensors, robotics, and energy and communication devices to data-driven models constrained by plant physiology, soil, weather, management practices, and socio-economics. We are looking to hire a cohort of SURF students to work on different activities in the center.

IoT4Ag P3: Biophysical modeling and integration with in-situ and remotely sensed data
# of students: 3, US Citizens or permanent residents only

This interdisciplinary project will focus on acquisition and processing of remotely sensed data acquired by sensors on UAVs and wheel-based vehicles, developing empirical models, and working collaboratively with teams in the College of Agriculture to integrate empirical machine learning models with biophysical modeling to detect plant stress and predict yield. The project will provide opportunities for students to learn about sensors via field-based data acquisition from remote sensing platforms, expand their understanding of techniques for processing data, use data products for applications related to cropping systems (plant breeding, production management, in-season treatments) and engage in development of hybrid models that include both data analytics and biophysically based approaches. Use of existing models may require use of APIs for data acquisition, familiarity with file types, and aptitude for functions and systems thinking.

The project will involve both field-based and computer laboratory focused research. Courses /experience in python programming, data analytics and image processing, and particularly related to remote sensing technologies, are desirable. Interest in interdisciplinary research is essential.

IoT4Ag P4: Frontiers in Thermal Stress Sensing

By 2050, the US population is estimated to grow to 400 million and the world population to 9.7 billion. Current agricultural practices account for 70% of global water use, energy accounts for one of the largest costs on a farm, and inefficient use of agrochemicals is altering Earth’s ecosystems. With finite arable land, water, and energy resources, ensuring food, energy, and water security will require new technologies to improve the efficiency of food production, create sustainable approaches to supply energy, and prevent water scarcity.

A new Engineering Research Center on the Internet of Things for Precision Agriculture (IoT4Ag) has recently been established to ensure food, energy, and water security by advancing technology to increase crop production, while minimizing the use of energy and water resources and the impact of agricultural practices on the environment. The center will create novel, integrated systems that capture the microclimate and spatially, temporally, and compositionally map heterogeneous stresses for early detection and intervention to better outcomes in agricultural crop production. The Center will create internet of things (IoT) technologies to optimize practices for every plant from sensors, robotics, and energy and communication devices to data-driven models constrained by plant physiology, soil, weather, management practices, and socio-economics. We are looking to hire a cohort of SURF students to work on different activities in the center.

IoT4Ag P4: Frontiers in Thermal Stress Sensing
2 students - US Citizens or permanent residents only

Crop canopy temperatures are modulated by transpiration of water vapor from leaf surfaces when water exits via leaf stomata. Although thermal sensors are being deployed on drones and autonomous robots, too little is known about the relationship between evaporative cooling and stomatal conductance that can be measured directly via leaf photosynthesis assessment (e.g. with a LiCor 6400 or 6800 portable photosynthesis system) . The simultaneous and direct measurement of thermal properties of corn canopies from above the canopy and below the canopy is suggested here to coincide with leaf photosynthesis measurements. The project goal is to investigate the differential between air temperatures and both upper and lower leaf temperatures via both thermal sensors and leaf stomatal conductance assessment for corn plots under a range of water deficit conditions. Knowing these relationships could help guide the timing (diurnal and weekly frequency) for thermal canopy assessments at different growth stages. Field experiments will be established in spring 2021 at the Agronomy Center for Research and Education. Corn treatments may include both hybrid and management variables intended to create a spectrum of crop water stress. Corn biomass measurements will also be taken to study crop growth rates occurring in the actual range of “water productivity” treatments.

Laboratory study of key thermal characteristics of common pharmaceutical reagents

The understanding of chemical reactivity plays a key role in the design of pharmaceutical facilities. This project will entail taking calorimetric measurements using an Advanced Reactive System Screening Tool (ARSST). The systems studied will include various reagents commonly used in the pharmaceutical industry. Work will begin with lab safety training and familiarization with the use of the ARRST, while conducting a literature search of existing heat of reaction data for the chemical systems to be studied. Overall, the work will entail making a series of measurements of various systems at a variety of conditions and then analyzing the data using computer models.

This project is well-suited for chemical engineers interested in the pharmaceutical industry and process safety. Very few students have the opportunity to use such a calorimeter, which will stand out on resumes.

Lake Michigan Shoreline Erosion - Measurements and Modeling

In the Great Lakes, water levels have been at record highs in the last few years , and the damage to the shorelines has been immense and costly (just google "Lake Michigan erosion" to see newspaper articles and videos). As engineers, we need to be better able to predict this erosion and design resilient shorelines that can withstand the huge variations in water levels that may be a consequence of climate change. The aims of this research are two-fold: (1) Quantify recent erosion along Lake Michigan's shoreline, using both direct measurements and remote sensing (2) Develop a computational model that can predict this erosion.

With these aims in mind, this summer research project aims to leverage students' strengths to contribute to the best of their abilities. Research activities can include boat work on Lake Michigan, beach surveys with LiDAR-equipped drones, data analysis using Matlab and/or Arc-GIS, laboratory experiments involving water flumes and acoustic instrumentation, and setting up/running sophisticated computer models that aim to simulate how waves and currents move sand along the shoreline. This project is best suited for a student really interested in water, potentially setting you down a path to become a hydraulic (water) or coastal engineer, working to create more sustainable and resilient coastlines and waterways.

Laser diagnostics for studying shock-heated gases

The student will learn how to use mid-infrared laser diagnostics to measure the temperature of gases that are heated to 1000s of degrees by high-Mach shock waves in our shock tube. This will be used to improve our understanding of non-equilibrium processes that occur behind shock waves and play an important role in governing heat transfer to space vehicles entering the atmosphere.

Lithium-ion Battery Analytics

Lithium-ion (Li-ion) batteries are ubiquitous. Thermo-electrochemical characteristics and porous electrode structures of these systems are critical toward safer and high-performance batteries for electric vehicles. As part of this research, physics-based modeling and experimental data-driven analytics will be performed over a wide range a normal and anomalous operating conditions of Li-ion cells.

Low-cost user-friendly biosensors for animal health

Infectious diseases are a leading cause of economic burden on food production from animals. For example, bovine respiratory diseases lead to a loss of

$1 billion annually. Current methods for tackling these diseases includes the administration of antibiotics by trial-and-error. This approach leads to failure of treatment in up to one-third of the cases. In addition, it also leads to a proliferation of antibiotic resistance in pathogens.
Our research project focuses on developing a low-cost user-friendly biosensor based on paper that can detect which pathogen is causing the disease and whether it exhibits antibiotic resistance. Such a biosensor would provide a readout to the farmer or the veterinary physician and suggest which antibiotics are likely to be successful.
Lab members working in the team have three objectives: i) design, test, and optimize primers for detecting pathogens associated with bovine respiratory diseases, ii) build a paper-based device for conducting loop-mediated isothermal amplification, and iii) build a heating/imaging device for conducting the paper-based assay in the field.
The SURF student will work on the third objective to build a heater coupled to an imager for detecting colorimetric/fluorometric output from the biosensor.

Magnetic RAM for space applications

Radiation in outer space can greatly affect the operation and long-term performance of microelectronics. Radiation hardening is making electronic components and circuits resistant to damage or malfunction caused by high levels of ionizing radiation in this environment. Transient effects include single-event effects like memory bit flips permanent effects include single-event latchups that prevent individual devices from operating.

In this project, the student will develop a model to predict failures for new and emerging types of memories and logic. It will consist of models for radiation in the space environment, as well as the susceptibility of devices to various types of ionizing radiation. The end goal will be to predict failures for certain classes of devices for validation in a beam-line, which may ultimately be used to adapt off-the-shelf electronics to space applications.

Mass spectrometry of biomolecules and nanoclusters

We are using mass spectrometry to study the localization of lipids, drugs, and proteins in biological tissues and to prepare novel functional interfaces using well-defined polyatomic ions. The student will work with a graduate student mentor to either perform nanocluster synthesis and characterization using mass spectrometry and electrochemical measurements or to develop new analytical approaches for quantitative analysis of biomolecules in biological samples. In both projects, the student will be trained to operate state-of-the-art mass spectrometers and perform independent data acquisition and analysis. The student will also work with the scientific literature to obtain a broader understanding of the field.

Measurement and Modeling of Mass Transfer Characteristics During Pharmaceutical Lyophilization

Freeze-drying, also called lyophilization, is widely used in manufacturing of injectable pharmaceuticals, vaccines, biotech products, chemical reagents, food and probiotic cultures. The SURF undergraduate researchers will have an opportunity to be involved in one of the ongoing projects in LyoHUB technology demonstration facility in Discovery Park in collaboration with one or more of 20+ LyoHUB industry members.

Lyophilization is a desiccation method whereby a solvent is removed from a frozen system via sublimation. In industry, the process is typically performed at a slow rate due to large uncertainties associated with key mass transfer mechanisms. Current data is highly scattered and valid for a tightly constrained set of operating points. Frequently, these points are not optimal and the data provides little benefit to the user. Students will be responsible for computationally and experimentally characterizing the mass transfer properties of various representative pharmaceutical formulations over a range of process conditions. The goal of the project is to generalize and consolidate key results into a standardized database which will be directly integrated into LyoHUB’s LyoPronto simulation tool (http://lyopronto.rcac.purdue.edu/). The software is freely available and widely used among major pharmaceutical companies. The student will also perform benchmarking studies against current published mass transfer models.

The student will learn the basics of the freeze drying process and will get the skills of experimental work in the lab with different lyophilizers.

This project will include online meetings and hands-on lab work.

Measurement and Modeling of Vial Heat Transfer Characteristics During Pharmaceutical Lyophilization

Freeze-drying, also called lyophilization, is widely used in manufacturing of injectable pharmaceuticals, vaccines, biotech products, chemical reagents, food and probiotic cultures. The SURF undergraduate researchers will have an opportunity to be involved in one of the ongoing projects in LyoHUB technology demonstration facility in Discovery Park in collaboration with one or more of 20+ LyoHUB industry members.

Lyophilization is a desiccation method whereby a solvent is removed from a frozen system via sublimation. In industry, the process is typically performed over the course of days for weeks due to large uncertainties associated with key heat transfer mechanisms. Accurate determination of these heat transfer characteristics is therefore critical to fully understanding and optimizing the process. Students will be responsible for computationally and experimentally characterizing the heat transfer properties of various vial geometries under a range of process conditions. The goal of the project is to consolidate key results into a standardized database which will be directly integrated into LyoHUB’s LyoPronto simulation tool (http://lyopronto.rcac.purdue.edu/). The software is freely available and widely used among major pharmaceutical companies. The student will also perform benchmarking studies against current published heat transfer models.

The student will learn the basics of the freeze drying process and will get the skills of experimental work in the lab with different lyophilizers.

This project will include online meetings and hands-on lab work

Measuring glutamate release in real time following traumatic brain injury with flexible printed biosensors

Following traumatic brain and spinal cord injury, damaged cells release toxic levels of excitatory neurotransmitter glutamate, which further damages cells through a secondary injury mechanism. This pathology is called glutamate excitotoxicity. The mechanism for sustained high levels of extracellular glutamate remains unclear, and a better understanding of glutamate excitotoxicity may lead to novel therapeutic interventions to minimize secondary injury following traumatic brain injury. Our lab has developed printed glutamate biosensors that we have used to measure glutamate release following simulated traumatic spinal cord injury with explanted rat spinal cord segments.

The student will work on integrating glutamate biosensors with anti-biofouling coating and wireless electronics, so the biosensors can be implanted in the brain and measure glutamate release following traumatic brain injury in anesthetized rats. Specific research tasks include printing biosensor devices by direct ink writing, electrochemical characterization, applying anti-biofouling coatings, and operating implanted biosensors. The student will collect, analyze, and interpret data, and write the results for a journal publication.

More information: https://engineering.purdue.edu/LIMR/research/

Measuring wetland greenhouse gas emissions with environmental Internet of Things sensors.

Wetlands in agricultural landscapes are important sites for maintaining water quality in streams, rivers, and reservoirs that are downstream of farmland. Despite these benefits, such wetlands can be a large source of potent greenhouse gasses—primarily methane (CH4) and nitrous oxide (N2O). Yet, data on the amount of greenhouse gasses produced by agricultural wetlands and the environmental factors that cause these differences are not widely available. For this project, we will leverage environmental internet of things (IoT) technology to deploy networks of gas sensors in agricultural wetlands. We will use these gas sensors to determine what local environmental factors (e.g., water inundation length, elevation, soil organic matter content) cause CH4 and N2O emissions to increase and decrease from wetland soils.

The student working on this project would be responsible for deploying gas sensors, which will involve fieldwork at wetlands located near Purdue. This student will also have the opportunity to analyze the data collected from these sensors with the assistance of faculty and graduate student mentors.

Microbiological Dynamics of Drinking Water during Stagnation

The pipes that deliver drinking water to individual taps develop into complex ecosystems. Most of the bacteria that live on these pipes and in the water are harmless, but several are capable of causing disease. For example, Legionella pneumophila is a bacterium that causes a potentially fatal pneumonia in immunocompromised individuals. It is thus critical to understand and ultimately control the ecosystem within these pipes. This work will contribute to policies (e.g., the minimum required temperature in a water heater) and technologies (e.g., auto-flushing sinks) that will limit needless disease.

In this project, the student will utilize bench scale experiments, a pilot-scale piping rig, and full-scale plumbing systems to test hypotheses regarding establishment of biofilm and relationships between biofilm and water over time. The student will collect and analyze water samples, using a variety of tools to fully characterize the physiochemical and biological dynamics within the system. They will also learn how to write a scientific report and will present it at the SURF symposium.

Mobile Air Quality Sensors and the Internet of Things

The project goal is to design and develop a hardware, software and cloud computing system for the acquisition of air quality data from mobile platforms such as taxis, backpacks, and drones. The sensors will be deployed around Purdue and eventually in the city of Arequipa, Peru. Data will be used to assess the spatial and temporal changes in air pollutions in Peru's 2nd largest city. The research is a collaboration between Purdue and the University of San Augustin (UNSA) as part of the NEXUS project.

Modeling High Efficiency Thermophotovoltaic Systems

This project studies by numerical simulation the impact of optical multilayer structure on improving the efficiency of thermophotovoltaic (TPV) devices. TPV devices convert heat to electricity using thermal radiation to illuminate a photo-voltaic (PV) diode made from semiconductor materials. Typically, this radiation is generated by a blackbody-like emitter. Thermal radiation includes a broad range of wavelengths, but only high energy photons can be converted to heat by the PV diode, which severely limits efficiency. Thus, introducing a selective emitter and filter to recycle unwanted photons can greatly enhance performance.

In this project, the student will develop/upgrade a GUI-based tool to calculate the emittance spectrum and efficiency of a multilayer structure based TPV device. The tool is hosted and run through nanoHUB.org - an open-access science gateway for cloud-based simulation tools and resources in nanoscale science and technology. The student will also work with graduate students and use this tool to study how to improve the TPV efficiency based on physical models.

Modeling of Wound Mechanobiology Following Lumpectomy

The goal of this project is to model the coupled mechanics and mechanobiology of lumpectomy wounds. Lumpectomy, or breast conserving surgery, is becoming the first choice of treatment for breast cancer due to the advances in imaging and diagnosis which allow detection of early tumors. However, this surgical treatment creates a wound void in the breast upon resection of the tumor and a surrounding margin of healthy tissue. The wound heals in a process resembling the healing of other connective tissue organs like the skin. In particular, healing of lumpectomy wounds can lead to permanent contraction of the tissue and change in mechanical properties as the wound gets filled with scar tissue instead of the native breast tissue. Mechanics and mechanobiology of this process are key to understand how these wounds heal. To address this need, our groups (PI Buganza-Tepole from ME and PI VoytikHarbin from BME) are using a combination of experiments and mathematical modeling to improve scaffold design for lumpectomy wounds. The undergrad sought for this project will work in this interdisciplinary group, with a focus on the computational model. PI Buganza-Tepole has proposed a computational model of wound healing that combines large deformation tissue mechanics, reaction-diffusion for cells and cytokine dynamics, and permanent remodeling and growth processes that link the mechanics and mechanobiology. The undergraduate working in this project will learn about C++, finite elements, mechanics of soft materials, mechanobiology modeling, growth and remodeling.

Nanostructural Evaluation of Human Bone Under Applied Loading

Student will design a test method for collecting small angle x-ray scattering data for bone specimen under in-situ loading conditions. Test parameters will be optimized for human bone and other associated materials. Data will be analyzed to determine extent of internal damage related to applied stress/strain conditions.

Neural recording and stimulation using a wireless single-chip system

In this project, we aim to implement an implant that can record and stimulate neural activities of a live mouse brain. We will take advantage of wireless powering and wireless data transfer to miniaturize the neural implant, such that it does not require battery or wires. Students will help develop the Reader for testing and collecting data from in-vitro and in-vivo experiments.

On-Line Programming Assessment

Computer programs are difficult to evaluate due to the large number of possibilities. Existing evaluation systems are restricted to simple programs or impose restrictions to limit possibilities. This project aims to build an online assessment system that can evaluate non-trivial programs and assist students learning computer programming.

Operation and characterization of SPT-100 Hall thruster

Hall thrusters are widely utilized for spacecraft propulsion. Mars exploration missions currently planned by NASA utilize Deep Space Transport which is going to be propelled by Hall thruster technology. The technology has been originally developed in Soviet Union and got adopted in the U.S. in 1990s. In Hall thruster neutral gas propellant is ionized and accelerated in ExB-field configuration to reach high propellant exhaust velocities in the range 10 - 50 km/s.
In this project student will work with Hall thruster SPT-100. The project will include operating the thruster and hollow cathode neutralizer, and measurements of electrical parameters of the thruster, exhaust plasma jet properties, and thrust. The student will use Langmuir probes for measurements of plasma parameters and hanging pendulum thrust stand for the thrust measurements. In addition, the student is going to prepare and update related documentation for AAE 521 Plasma Lab.

Printable functional filaments and sensor for biomedical devices

Current surgical mesh implants require manual size adjustment from pre-fabricated sheets that can lead to improper fitting and thus post-surgical complications. 3D printing surgical mesh would avoid manual errors in addition to providing surgeons and hospitals with an increased number of choices for mesh design. This allows for greater personalization of treatment for patients suffering from hernias.
Design and characterize a novel 3D printable filament imbued with an antibacterial and piezoelectric electrical stimulating agent. This mesh should be biocompatible, flexible, and biodegradable over a period of years.
A second part of project will be focused on printing low-cost biosensors for detecting covid virus.

Process Synthesis and intensification of Shale Gas Valorization

The assignment focuses on the creation of transformative process systems to convert light hydrocarbons from shale resources to liquid chemicals and transportation fuels in smaller, modular, local, and highly networked processing plants. The students will have the opportunities to learn cutting-edge technologies in process synthesis, intensification and optimization, as well as widely-used simulation tools such as Aspen Plus, Matlab, Chemkin, etc.

Real time analysis of viral particles for continuous processing approach

The increasing worldwide demand for vaccines along with the intensifying economic pressure on health care systems underlines the need for further improvement of vaccine manufacturing. In addition, regulatory authorities are encouraging investment in the continuous manufacturing processes to ensure robust production, avoid shortages, and ultimately lower the cost of medications for patients. The limitations of in-line process analytical tools are a serious drawback of the efforts taken in place. In line analysis of viral particles are very limited, due to the large time required for the current techniques for detection, qualitative and quantitative analysis. Therefore, there is a need for new alternatives for viral detection.

Reliable Deep Learning Software

We will build cool and novel techniques to make deep learning code such as TensorFlow and PyTorch reliable and secure. We will build it on top of our award winning project (https://www.cs.purdue.edu/homes/lintan/publications/variance-ase20.pdf), which won an ACM SIGSOFT Distinguished Paper Award! There may be opportunities to collaborate with Microsoft. See below for more details.

Machine learning systems including deep learning (DL) systems demand reliability and security. DL systems consist of two key components: (1) models and algorithms that perform complex mathematical calculations, and (2) software that implements the algorithms and models. Here software includes DL infrastructure code (e.g., code that performs core neural network computations) and the application code (e.g., code that loads model weights). Thus, for the entire DL system to be reliable and secure, both the software implementation and models/algorithms must be reliable and secure. If software fails to faithfully implement a model (e.g., due to a bug in the software), the output from the software can be wrong even if the model is correct, and vice versa.

This project aims to use novel approaches including differential testing to detect and localize bugs in DL software (including code and data) to address the testing oracle challenge. Good programming skills and strong motivation in research are required. Background in deep learning and testing is a plus.

Remote sensing of soil moisture using Signals of Opportunity: Field Experiments and Validation Studies

Root Zone Soil Moisture (RZSM), defined as the water profile in the top meter of soil where most plant absorption occurs, is an important environmental variable for understanding the global water cycle, forecasting droughts and floods, and agricultural management. No existing satellite remote sensing instrument can measure RZSM. Sensing below the top few centimeters of soil, often through dense vegetation, requires the use of microwave frequencies below 500 MHz, a frequency range known as “P-band”. A P-band microwave radiometer would require an aperture diameter larger than 10 meters. Launching such a satellite into orbit will present big and expensive technical challenge, certainly not feasible for a low-cost small satellite mission. This range for frequencies is also heavily utilized for UHF/VHF communications, presenting an enormous amount of radio frequency interference (RFI). Competition for access to this spectrum also makes it difficult to obtain the required license to use active radar for scientific use.

Signals of opportunity (SoOp) are being studied as alternatives to active radars or passive radiometry. SoOp re-utilizes existing powerful communication satellite transmissions as “free” sources of illumination, measuring the change in the signal after reflecting from soil surface. In this manner, SoOp methods actually make use of the very same transmissions that would cause interference in traditional microwave remote sensing. Communication signal processing methods are used in SoOp, enabling high quality measurements to be obtained with smaller, lower gain, antennas.

Under NASA funding, Purdue and the Goddard Space Flight Center have developed prototype instrumentation using P-band (360-380 MHz) and I-band (137 MHz) SoOp measurements to retrieve soil moisture. These studies have culminated in the planned (2021) launch of the SNOOPI (SigNals Of Opportunity P-band Investigation) satellite to present the first demonstration of these measurements from orbit.

To support this mission, an extensive campaign of experiments are planned in the Purdue agricultural research fields and potentially at some remote locations. We are seeking up to two motivated students to assist with these experiments. One position may involve installing and maintaining remote sensing instruments in the field and on an Unpiloted Aerial Vehicle (UAV), writing software for signal and data processing, and performing quality control checks on the collected data. The other position may involve collecting field measurements of soil and vegetation properties.

Students in Electrical Engineering, Aerospace Engineering or Physics are desired for the first position. Good programming skills, experience with C, python and MATLAB, and a strong background in basic signal processing is required. Experience with building computers or other electronic equipment will also be an advantage.

Students in Agronomy, Agricultural and Biological Engineering or Civil Engineering are desired for the second position. Laboratory or field experience is desired.

In both cases, students must be willing to work outdoors for a substantial amount of time and have an interest in applying their skills to solving problems in the Earth sciences, environment, or agriculture. Students should have their own means of transportation as the experimental sites are in remote locations.


Abstract

The waste management processes typically involve numerous technical, climatic, environmental, demographic, socio-economic, and legislative parameters. Such complex nonlinear processes are challenging to model, predict and optimize using conventional methods. Recently, artificial intelligence (AI) techniques have gained momentum in offering alternative computational approaches to solve solid waste management (SWM) problems. AI has been efficient at tackling ill-defined problems, learning from experience, and handling uncertainty and incomplete data. Although significant research was carried out in this domain, very few review studies have assessed the potential of AI in solving the diverse SWM problems. This systematic literature review compiled 85 research studies, published between 2004 and 2019, analyzing the application of AI in various SWM fields, including forecasting of waste characteristics, waste bin level detection, process parameters prediction, vehicle routing, and SWM planning. This review provides comprehensive analysis of the different AI models and techniques applied in SWM, application domains and reported performance parameters, as well as the software platforms used to implement such models. The challenges and insights of applying AI techniques in SWM are also discussed.


15 Answers 15

Using multiple threads on CPython won't give you better performance for pure-Python code due to the global interpreter lock (GIL). I suggest using the multiprocessing module instead:

Note that this won't work in the interactive interpreter.

To avoid the usual FUD around the GIL: There wouldn't be any advantage to using threads for this example anyway. You want to use processes here, not threads, because they avoid a whole bunch of problems.

The above works beautifully on my machine (Ubuntu, package joblib was pre-installed, but can be installed via pip install joblib ).


3.7.0 (2019-05-09)¶

Refactoring Coastal Vulnerability (CV) model. CV now uses TaskGraph and Pygeoprocessing >=1.6.1. The model is now largely vector-based instead of raster-based. Fewer input datasets are required for the same functionality. Runtime in sycnhronous mode is similar to previous versions, but runtime can be reduced with multiprocessing. CV also supports avoided recomputation for successive runs in the same workspace, even if a different file suffix is used. Output vector files are in CSV and geopackage formats.

Model User Interface ‘Report an Issue’ link points to our new community.naturalcapitalproject.org

Correcting an issue with the Coastal Blue Carbon preprocessor where using misaligned landcover rasters would cause an exception to be raised.

Correcting an issue with RouteDEM where runs of the tool with Flow Direction enabled would cause the tool to crash if n_workers > 0 .

Correcting an issue with Habitat Quality’s error checking where nodata values in landcover rasters were not being taken into account.

Valuation is now an optional component of the InVEST Scenic Quality model.

Fixing a bug in the percentiles algorithm used by Scenic Quality that would result in incorrect visual quality outputs.

Carbon Model and Crop Production models no longer crash if user-input rasters do not have a nodata value defined. In this case these models treat all pixel values as valid data.

Adding bitbucket pipelines and AppVeyor build configurations.

Refactoring Recreation Model client to use taskgraph and the latest pygeoprocessing. Avoided re-computation from taskgraph means that successive model runs with the same AOI and gridding option can re-use PUD results and avoid server communication entirely. Successive runs with the same predictor data will re-use intermediate geoprocessing results. Multiprocessing offered by taskgraph means server-side PUD calculations and client-side predictor data processing can happen in parallel. Some output filenames have changed.

Upgrading to SDR to use new PyGeoprocessing multiflow routing, DEM pit filling, contiguous stream extraction, and TaskGraph integration. This also includes a new TaskGraph feature that avoids recomputation by copying results from previous runs so long as the expected result would be identical. To use this feature, users must execute successive runs of SDR in the same workspace but use a different file suffix. This is useful when users need to do a parameter study or run scenarios with otherwise minor changes to inputs.

Refactoring Habitat Risk Assessment (HRA) Model to use TaskGraph >= 0.8.2 and Pygeoprocessing >= 1.6.1. The HRA Proprocessor is removed and its previous functionality was simplified and merged into the HRA model itself. The model will no longer generate HTML plots and tables.

Adding a software update notification button, dialog, and a link to the download page on the User Interface when a new InVEST version is available.

Migrating the subversion sample and test data repositories to Git LFS repositories on BitBucket. Update the repository URL and fetch commands on Makefile accordingly.

Fixing a bug in Habitat Quality UI where the absence of the required half_saturation_constant variable did not raise an exception.

Adding encoding=’utf-8-sig’ to pandas.read_csv() to support utils.build_lookup_from_csv() to read CSV files encoded with UTF-8 BOM (byte-order mark) properly.


If you're using scikit-learn you can use sklearn.preprocessing.normalize :

I would agree that it were nice if such a function was part of the included batteries. But it isn't, as far as I know. Here is a version for arbitrary axes, and giving optimal performance.

You can specify ord to get the L1 norm. To avoid zero division I use eps, but that's maybe not great.

This might also work for you

but fails when v has length 0.

In that case, introducing a small constant to prevent the zero division solves this.

You mentioned sci-kit learn, so I want to share another solution.

Sci-kit learn MinMaxScaler

In sci-kit learn, there is a API called MinMaxScaler which can customize the the value range as you like.

It also deal with NaN issues for us.

NaNs are treated as missing values: disregarded in fit, and maintained in transform. . see reference [1]

Code sample

The code is simple, just type

If you have multidimensional data and want each axis normalized to its max or its sum:

There is also the function unit_vector() to normalize vectors in the popular transformations module by Christoph Gohlke:

If you're working with 3D vectors, you can do this concisely using the toolbelt vg. It's a light layer on top of numpy and it supports single values and stacked vectors.

I created the library at my last startup, where it was motivated by uses like this: simple ideas which are way too verbose in NumPy.

If you work with multidimensional array following fast solution is possible.

Say we have 2D array, which we want to normalize by last axis, while some rows have zero norm.

Without sklearn and using just numpy . Just define a function:.

Assuming that the rows are the variables and the columns the samples ( axis= 1 ):