Engineering Commons^™

Dynamic Data Driven Simulation Based On Macro-Microscopic Hierarchical Simulation Models, Xu Xie, Yuqing Ma

Journal of System Simulation

Abstract:This paper proposed a dynamic data driven simulation approach based on macro-microscopic hierarchical simulation models. This approach enabled the measurement data from the real system to affect the macroscopic simulation and microscopic simulation in sequence and made the two simulations evolve together so that it could provide decision makers with the state evolution prediction of the real system at the macroscopic level to assist decision making and provide a microscopic testbed similar to the real system, on which decision makers could deduce and evaluate their strategies. This paper established a formal description of the approach and designed a data …

Research On High-Performance Optimization Methods For Fastdds In Heterogeneous Real-Time Simulation, Congping Liu, Wei Song, Jian Fang, Fei Liu

Journal of System Simulation

Abstract:FastDDS faces limitations under high-frequency data streams, such as lock contention, performance overhead from frequent context switching, and configuration complexity of multiple nodes under strict real-time constraints, which affect the experimental efficiency. This paper proposed a performance optimization method based on a batch-scalable circular queue (BSCQ). The approach replaced the traditional mutex mechanism with a lock-free algorithm to reduce lock contention and avoid deadlocks, while batch processing improved data locality, cache hit rates, and memory utilization, effectively reducing data transmission delay and improving system throughput. Hazard pointers were introduced to ensure safe memory management during batch processing and eliminate …

Reflections On Innovative Approaches To Autonomous Simulation Software, Feng Tian

Journal of System Simulation

Abstract: Given China's weak foundation in simulation software, simply replicating the development paths of these global leading companies offers limited potential for leapfrog development. Based on an analysis of pitfalls in the independent innovation of domestic simulation software, this paper proposed and elaborated on six strategic approaches: avoiding established paths, aligning with national realities, pursuing extreme performance, leveraging special needs to advance technology, building new systems from the ground up, and embracing artificial intelligence (AI)-native architectures. These strategies aim to provide new perspectives for the independent innovation and development of domestic simulation software.

Reliability Simulation Testing And Verification Technologies For Intelligent Systems: Frontiers, Progress, And Challenges, Zili Wang, Yuntian Gao, Dezhen Yang, Yeyang Liu, Yi Ren

Journal of System Simulation

Abstract: Intelligent systems are extensively deployed in domains such as transportation, energy and water resources, smart healthcare, and aerospace, where their reliability is directly linked to public safety and social stability, thus requiring thorough and scientifically rigorous verification. This article conducted an in-depth exploration of the current state of reliability simulation and verification techniques for intelligent systems. It defined the concept of reliability specific to intelligent systems and identified key challenges they face in areas including mission scenario modeling, characteristic modeling and simulation, evaluation and verification, and simulation platforms. Future development requirements were proposed to guide research toward more trustworthy, …

Modeling And Simulation Of Complex Systems Based On Graph Neural Networks, Jinhu Lü, Hongyi Jiang, Deyuan Liu, Shaolin Tan

Journal of System Simulation

Abstract: Modeling and simulation of complex systems are critical issues for understanding their structural and functional properties. The ability of graph neural networks (GNNs) to learn and represent the internal correlations within data provides a new approach for modeling and simulating complex systems. Currently, there are various types of GNN models involving frequency-domain, spatial-domain, generative, heterogeneous, and spatio-temporal models. These models are widely applied in complex system modeling and simulation research in multiple fields such as industrial internet, social networks, and supply chains based on specific tasks and scenarios. Starting from three representative tasks: network topology representation, dynamic evolution modeling, …

Intelligent Transition Of Automotive Industry Driven By Autonomous Driving Simulation Testing Technology, Jianping Wu, Guanzhou Li, Shuai Zhao, Ling Huang

Journal of System Simulation

Abstract: As a pivotal approach supporting the safety verification and commercial implementation of intelligent driving systems, autonomous driving simulation testing has achieved remarkable progress in technical methodologies and application scenarios. Conventional real-world road testing faces critical limitations including prohibitive costs, inadequate coverage of corner case scenarios, and efficiency bottlenecks, rendering it insufficient for safety validation of high-level autonomous driving systems (L4 and above). To address these challenges, simulation testing frameworks have evolved into a multi-layered verification system encompassing mathematical modeling, virtual scenarios, hardware-in-the-loop (HIL), mixed reality, and cloud-based simulation clusters. Specifically, mathematical modeling accelerates algorithm development; virtual scenario simulation enhances …

A Review Of Intelligent Generation Of Combat Simulation Scenarios, Zhiming Dong, Zhongqi Hu, Zhaoyang Liu, Heyang Zhou

Journal of System Simulation

Abstract: In order to improve the efficiency of combat simulation, this paper provided a theoretical reference for the research on the intelligent generation of combat simulation scenarios. It systematically reviewed the intelligent generation methods of combat simulation scenarios based on large language models (LLMs). It began by introducing the basic content of combat simulation scenarios, analyzed the shortcomings of current mainstream scenario generation methods, and discussed how to leverage LLMs to address these issues. Next, it outlined the application paradigms and key supporting technologies for the intelligent generation of combat simulation scenarios based on LLMs. Finally, it pointed out the …

Digital Twinned Industrial Robot: Conceptual Framework, Key Technologies, And Case Study, Yongkui Liu, Kang Yang, Benben Tuo, Yaduo Pan, Xinyu Wang, Yihan Wang, Yongqian Gong, Lin Zhang, Lihui Wang, Tingyu Lin, Bin Zi, Yuan Li, Wei You, Xun Xu

Journal of System Simulation

Abstract: To effectively enhance the value and full life cycle management level of industrial robots, this paper integrated deeply digital twin with industrial robots and discussed a new concept, namely digital twinned industrial robot (DTIR). It defined the concept, composition, and typical characteristics of DTIRs and proposed their system architecture. From the perspective of the full life cycle of "design, manufacturing, operation and maintenance, and decommissioning", the key technologies of DTIRs were systematically sorted out. Furthermore, the validity of the proposed conceptual framework was verified through a case study. Finally, the paper summarized the findings and discussed the future development …

Large-Scale Social Simulator: Frontiers And Perspectives, Jinghua Piao, Chen Gao, Fang Zhang, Jun Su, Yong Li

Journal of System Simulation

Abstract: Social experiments, as a typical research method in social sciences, aim to study specific social phenomena or the impacts of policies by observing the behaviors of individuals, organizations, or social groups in real or simulated environments. However, traditional social experiment methods often face challenges such as random bias, high costs, and ethical risks, making them inadequate to address increasingly complex research demands. Against this backdrop, computational social experiments have emerged, enabling researchers to conduct social experiments within computational simulation environments that are free from random bias, cost-efficient, and ethically manageable. Meanwhile, China is currently undergoing a critical period of …

Combat-Oriented Comprehensive Simulation And Verification Technology For Equipment System Rms, Yue Zhang, Wenliang Zhang, Qiang Feng, Xing Guo, Yi Ren, Zili Wang

Journal of System Simulation

Abstract:Existing reliability maintainability supportability (RMS) simulation and verification methods for equipment systems are typically conducted under standard conditions and suffer from weak combat environments and task modeling capabilities. To address this limitation, a multi-agent RMS simulation and verification framework was proposed. Key breakthroughs included agent modeling techniques for complex environments and variable tasks, interaction mechanisms among environmental agents, task agents, equipment, and support systems, and a simulation-based comprehensive RMS evaluation method. Case studies demonstrate that the proposed method effectively models complex environments and variable tasks, supports combat-oriented simulation and verification and design scheme evaluation, and meets combat-ready development requirements.

Multi-Scenario Multi-Satellite Mission Planning Method Based On Adaptive Large Neighborhood Search, Xiutian Li, Ling Wang, Yingwu Chen, Lining Xing, Yingguo Chen

Journal of System Simulation

Abstract: To further improve the execution efficiency of remote sensing satellites, an integrated optimization framework combining adaptive large neighborhood search (ALNS) and a constraint programming-boolean satisfiability problem (CP-SAT) solver monitor was proposed, addressing the challenges of complex constraints, dynamic scale, and resource heterogeneity in multi-scenario multi-satellite mission planning. A unified multi-objective mixed-integer programming model was established, coupling heterogeneous constraints of point targets and area tasks. A time-domain rolling mechanism dynamically decomposed the problem scale, and a priority screening strategy enhanced the search efficiency of ALNS. Solution feasibility was verified in real time through the CP-SAT monitor. Results show that compared …

Enhanced Restoration Of Covid-19 Ct Scan Images Utilizing Advanced Wiener Filtering Techniques, Warqaa Shaher Alazawee, Marwa Subhi Ibrahim, Raghda Salam Al Mahdawi, Ali Albu-Rghaif, Ahmed Sabri Altaie

Iraqi Journal for Computer Science and Mathematics

COVID-19, caused by the SARS-CoV-2 virus, was declared a global pandemic by the World Health Organization (WHO) and rapidly spread worldwide from late 2019. While Reverse Transcription Polymerase Chain Reaction (RT-PCR) is the primary diagnostic tool, its sensitivity ranges from only 60% to 70%, leading to false negatives. Computed Tomography (CT) imaging has emerged as a valuable alternative for accurate diagnosis; however, the quality of CT images is often degraded by motion-induced blur and additive noise, particularly in children, individuals with mental health conditions, or those with phobias of CT scans. This study aims to enhance COVID-19 CT image quality …

Motionfusion: A Robust Ensemble Learning Framework For Accurate Sensor-Based Human Activity Recognition, Hussein K. Almulla, Hussam J. Mohammed, Alaa S. Al-Waisy, Shumoos Al-Fahdawi, Ahmed Adnan Had, Bourair Al-Attar

Iraqi Journal for Computer Science and Mathematics

Human activity Recognition (HAR) has emerged as an important research area due to its potential applications in health, sport, and recreation. The widespread availability of smartphone sensors has facilitated data collection for HAR systems. Although machine learning and deep learning models have proven to be effective in detecting human activity from sensor data, their performance may be limited, this study proposes MotionFusion which is an ensemble learning model to increase HAR accuracy utilizing accelerometer and gyroscope data from a smartphone. By combining Histogram-Based Gradient Boosting, Random Forest, and Extra Trees models with a Support Vector Machine classifier and using feature …

Retracted: A Quantum Convolutional Neural Network Approach For Early And Accurate Diagnosis Of Parkinson's Disease, Aiesha Mahmoud Ibrahim, Mazin Abed Mohammed, Omar Al-Boridi

Iraqi Journal for Computer Science and Mathematics

Parkinson's disease (PD) is a progressive neurological disorder that primarily affects individuals over the age of 55. It is characterized by a range of motor and non-motor symptoms that can significantly impact various aspects of daily life. Despite notable advancements in medical science, there is currently no permanent cure or definitive treatment for PD. This therapeutic gap underscores the critical importance of early diagnosis, which remains a major focus of ongoing research. Due to the disease's gradual progression, PD symptoms may take years to fully develop, making early detection essential for improving patient outcomes and quality of life. Moreover, the …

Retracted: Solving Time-Fractional Nonlinear Variable-Order Delay Pdes Using Feedforward Neural Networks, Hala S. Alruhaili, Adel S. Hussain, Abdullah M. S. Ajlouni, Funda Türk, Emad A. Az-Zo’Bi, Mohammad A. Tashtoush

Iraqi Journal for Computer Science and Mathematics

This study presents an innovative application of Feedforward Neural Networks ‘FNNs’ to solve Variable-Order Fractional Partial Differential Equations ‘VO-FPDEs’ with time delays. Utilizing the Caputo definition, the variable-order fractional derivatives are approximated in terms of integer-order derivatives. The problem is reformulated as a system of partial differential equations with delay terms, which is then addressed using ‘FNNs’ to achieve explicit approximate solutions. Comprehensive error and convergence analyses validate the method’s precision and reliability. The effectiveness of the proposed approach is highlighted through numerical examples, with graphical and tabular representations showcasing minimal absolute errors and robust convergence. These results demonstrate the …

The Future Of Intelligent Industrial Systems: Plc, Node-Red, And Iot/Iiot, Firas Ahmed Hussein, Mohammad Tariq Yaseen, Mohammed Obaid Mustafa

AUIQ Technical Engineering Science

Integrating Programmable Logic Controllers (PLCs) with Node-RED and IoT/IIoT has emerged as a transformative technique for developing intelligent industrial systems as industrial automation improves. This narrative review brings together more than 70 research sources on industrial PLC, Node-RED, IoT, and IIoT applications. Case studies, experimental implementations, and industry reports are used to discover trends, challenges, and opportunities. The review focuses on three main topics: PLCs in modern industrial systems and their evolution and integration with IoT/IIoT; Node-RED as a middleware for industrial automation and its ability to connect PLCs to cloud and edge computing; and IIoT and smart manufacturing and …

Communication Assistance Using A Large Language Model, Justin Y. Li

Electrical and Computer Engineering Senior Theses

The number of people diagnosed with Autism Spectrum Disorder is growing annually at a rapid pace. Autistic people are known to have multiple problems with communication, requiring assistance and coaching which is already limited in availability due to the amount of training required. At the same time, there has been a recent exponential rise in usage of LLMs like ChatGPT which is widely available. This project aims to prototype a solution using ChatGPT to provide communication assistance to those diagnosed with autism. The project would record voice input through a microphone and transcribe it into text. The text would be …

Recent Advances In Modelling Of Frost Formation For Mechanical Systems, Yong Tao

Mechanical Engineering Faculty Publications

The physics underlying frost and ice formation has been extensively studied over the past few decades, with significant contributions to our understanding of this phenomenon. These insights have primarily been applied to engineering systems with refrigerated surfaces, such as refrigerators, freezers and heat pumps of various sizes. Despite considerable progress, the dynamic and complex mechanisms governing frost and ice formation remain an active area of research, as competing factors continue to challenge predictive accuracy. The increasing interest from stakeholders in reducing energy consumption and carbon footprints in mechanical systems further underscores the importance of advancing modelling and simulation capabilities in …

A Predictive Framework For Early Detection And Personalised Monitoring Of Parkinson’S Disease Using Artificial Intelligence And Large Language Models, Priyadharshini S

Theses and Dissertations

Parkinson’s Disease (PD) is a multifaceted and progressive neurodegenerative disorder that presents a spectrum of motor and non-motor symptoms. Early and accurate diagnosis is essential for effective disease management and improved patient outcomes, yet remains clinically challenging due to symptom overlap and diagnostic limitations. This thesis proposes a comprehensive and interpretable artificial intelligence (AI)-driven diagnostic framework that aims to transform the early detection, personalised monitoring, and treatment recommendation process for PD. The proposed solution integrates deep learning, radiomics, evolutionary optimisation, and large language models (LLMs), ensuring a highly accurate and clinically adaptable system.

The research begins by analysing T2-weighted 3D …

Predicting Sleep And Sleep Stage In Children Using Actigraphy And Heartrate Via A Long Short-Term Memory Deep Learning Algorithm: A Performance Evaluation, Robert Weaver Med, Phd, James White, Olivia Finnegan, Hongpeng Yang, Zifei Zhong, Keagan Kiely, Catherine Jones, Yan Tong, Srihari Nelakuditi, Rahul Ghosal, David E. Brown, Russell R. Pate Ph.D., Gregory J. Welk, Massimiliano De Zambotti, Yuan Wang, Sarah Burkart, Elizabeth L. Adams Phd, Bridget Armstrong, Michael Beets Med, Mph, Phd

Faculty Publications

Children's ambulatory sleep is commonly measured via actigraphy. However, traditional actigraphy measured sleep (e.g., Sadeh algorithm) struggles to predict wake (i.e., specificity, values typically < 70) and cannot predict sleep stages. Long short-term memory (LSTM) is a machine learning algorithm that may address these deficiencies. This study evaluated the agreement of LSTM sleep estimates from actigraphy and heartrate (HR) data with polysomnography (PSG). Children (N = 238, 5–12 years,52.8% male, 50% Black 31.9% White) participated in an overnight laboratory polysomnography. Participants were referred be-cause of suspected sleep disruptions. Children wore an ActiGraph GT9X accelerometer and two of three consumer wearables(i.e., Apple Watch Series 7, Fitbit Sense, Garmin Vivoactive 4) on their non-dominant wrist during the polysomnogram. LSTM estimated sleep versus wake and sleep stage (wake, not-REM, REM) using raw actigraphy and HR data for each 30-s epoch. Logistic regression and random forest were also estimated as a benchmark for performance with which to compare the LSTM results. A 10-fold cross-validation technique was employed, and confusion matrices were constructed. Sensitivity and specificity were calculated to assess the agreement between research-grade and consumer wearables with the criterion polysomnography. For sleep versus wake classification, LSTM outperformed logistic regression and random forest with accuracy ranging from 94.1to 95.1, sensitivity ranging from 94.9 to 95.9 across different devices, and specificity ranging from 84.5 to 89.6. The addition of HR improved the prediction of sleep stages but not binary sleep versus wake. LSTM is promising for predicting sleep and sleep staging from actigraphy data, and HR may improve sleep stage prediction.

Numerical Simulation Of Frost Formation And Heat Transfer On Fin-And-Tube Heat Exchangers In Turbulent Cross-Flow, Mahsan Farzaneh, ‪Nadim Zgheib, S. Balachandar, S. A. Sherif

Mechanical Engineering Faculty Publications

Frost formation in fin-and-tube heat exchangers in turbulent cross-flow presents significant challenges in industrial refrigeration applications, affecting heat transfer efficiency and operational reliability. The purpose of this work is to investigate frost deposition and growth on a staggered bank of a fin-and-tube freezer coil under turbulent forced convection conditions. The focus here is on investigating conditions that closely replicate real-world scenarios in large walk-in industrial freezers. Using a direct numerical simulation approach, we examine the flow dynamics and thermal behaviour in the presence of frost, considering turbulent regimes characterized by a Reynolds number in the range 1050≤𝑅⁢𝑒𝐷,avg≤4800, with the characteristic …

An Acoustofluidic Device For Sample Preparation And Detection Of Small Extracellular Vesicles, Jessica F. Liu, Jianping Xia, Joseph Rich, Shuaiguo Zhao, Kaichun Yang, Brandon Lu, Ying Chen, Tiffany Wen Ye, Tony Jun Huang

Faculty Publications

Small extracellular vesicles (sEVs) have emerged as powerful vectors for liquid biopsy, offering a noninvasive window into the dynamic physiological and pathological states of the body. However, to fully leverage the clinical potential of sEV biomarkers, it is imperative to develop robust and efficient technologies for their isolation and analysis. In this study, we introduce a novel sharp-edge acoustofluidic platform designed for rapid and effective sample preparation, coupled with sensitive detection of specific sEV populations based on their surface markers. Our approach utilizes acoustically activated sharp-edge microstructures to concentrate bead-bound sEVs within the microfluidic device, facilitating immediate visualization by fluorescence …

Solar Absorption Cooling Systems – A Case Study In Egypt, Hesham Safwat, Iman El-Mahallawi

Mechanical Engineering

Climate change with Egypt’s increasingly hot weather and plans towards energy transition, addressing an approach for clean HVAC (Heating, Ventilation, and Air Condition) solutions is becoming requisite. This paper examines the potential of utilizing solar absorption cooling systems in institutional buildings by presenting a case study of a proposed solar absorption cooling system for a library building with an area of 4402 m2, located at the British University in Egypt. The proposed solution is to replace 30% of the existing conventional air conditioning units with a hot-water driven single-effect absorption chiller powered by solar thermal vacuum tube solar collectors, coupled …

A Rigorous Framework For An Improved Messinger/Myers Model Of Ice Accretion Under Conditions Of Variable Property And Unsteady Aircraft Icing, Hashnayne Ahmed, Arash Shad, ‪Nadim Zgheib, S. A. Sherif, S. Balachandar

Mechanical Engineering Faculty Publications

We analyse the Messinger/Myers model by critically evaluating simplifying assumptions through a rigorous formulation of the rime ice accretion process. We explore the effects of both constant and variable ice density and thermal conductivity, along with the effects of sublimation from the ice surface. The effects of key factors such as droplet impact rate, ambient temperature relative to the freezing temperature and the temperature difference between the ambient air and the airfoil surface are examined. Under these varying conditions, the present rigorous formulation is used to assess the significance of unsteady effects, variable ice properties and sublimation. We observe that …

Stokes-Dependent Droplet Collection Efficiency On A Naca 0012 Airfoil From Droplet-Informed Simulations With Statistical Overloading, Arash Shad, Hashnayne Ahmed, ‪Nadim Zgheib, S. Balachandar, S. A. Sherif

Mechanical Engineering Faculty Publications

Accurate modelling of ice accretion on aircraft wings requires analysing droplet impingement on the surface to optimize the design of ice-protection systems. We perform Euler–Lagrange simulations of a droplet-laden flow impinging on a NACA 0012 airfoil. Our study includes water droplets with eight discrete sizes ranging from 1 to 160 microns. We vary the free-stream velocity of the incoming airflow in the range 60≤⁢𝑈≤⁢240 m s−1 and the chord length of the airfoil in the range 0.5≤⁢𝑐≤⁢2 m. Due to the dilute nature of supercooled clouds, one-way coupling is used in the simulations. The effects of droplet breakup and collision …

Fresh And Mechanical Properties Of Self-Compacting Concrete Using Kaolin Limestone Blend And Hybrid Slag Blend, Selesca Devi S

Theses and Dissertations

This study examined the consequences of mono and hybrid natural fibers on the mechanical and fresh properties of self-compacting concrete (SCC), in response to the growing demand for environmentally friendly building materials. In mono fiber, Abaca fiber (AF) at 0.25% and 0.5% dosage, basalt fiber (BF) from 0.25% to 2% at 0.25% increments, and sisal fiber (SiF) from 0.25% to 1.5% at 0.25% increments were evaluated through slump flow diameter, T500, compressive and tensile strength. AF at 0.25% ensured good flow with 6.6% and 4.16% higher compressive and tensile strength over 0.5% AF. SiF up to 1% improved strength but …

Enhancing Winter Climate Simulations Of The Great Lakes: Insights From A New Coupled Lake–Ice–Atmosphere (Cliav1) System On The Importance Of Integrating 3d Hydrodynamics With A Regional Climate Model, Pengfei Xue, Chenfu Huang, Yafang Zhong, Michael Notaro, Miraj Kayastha, Xing Zhou, Chuyan Zhao, Christa Peters-Lidard, Carlos Cruz, Eric Kemp

Michigan Tech Publications

The Laurentian Great Lakes significantly influence the climate of the Midwest and Northeast United States due to their vast thermal inertia, moisture source potential, and complex heat and moisture flux dynamics. This study presents a newly developed coupled lake–ice–atmosphere (CLIAv1) modeling system for the Great Lakes by coupling the National Aeronautics and Space Administration (NASA) Unified Weather Research and Forecasting (NU-WRF) regional climate model (RCM) with the three-dimensional (3D) Finite Volume Community Ocean Model (FVCOM) and investigates the impact of coupled dynamics on simulations of the Great Lakes’ winter climate. By integrating 3D lake hydrodynamics, CLIAv1 demonstrates superior performance in …

Mode Engineering And Functional Enhancement In Piezoelectric-On-Silicon Mems Resonators With Magnetic Field Sensing Applications, Ugur Guneroglu

USF Tampa Graduate Theses and Dissertations

This dissertation significantly advances the field of micro-electro-mechanical systems (MEMS) resonators by exploring novel post-fabrication tuning techniques, electrode configuration effects, and innovative sensor applications for thin-film piezoelectric-on-silicon (TPoS) resonator devices. Driven by the increasing demand for tunable, high-precision and robust MEMS resonators in radio frequency (RF) applications such as sensing, timing, and filtering, this research aim to provide foundational knowledge and practical solutions for overcoming existing technological limitations. The dissertation is organized into three main research thrusts, each addressing critical challenges and opportunities in the design, fabrication, and application of TPoS MEMS resonators.

The first research thrust investigates an innovative …

Retrospective Study And Predictive Modeling Of The Impact Of Social Determinants Of Health On Sepsis Outcomes, Mwembezi Aaron Nyelele

LSU Doctoral Dissertations

Sepsis, a life-threatening organ failure caused by the body’s abnormal and harmful response to an infection, remains a major global and national health challenge. In the United States, it contributes significantly to hospital mortality and imposes a substantial economic burden, with billions spent annually on treatment. Early diagnosis is difficult due to the nonspecific nature of sepsis symptoms, often leading to delays in care and poorer outcomes. As the incidence and severity of sepsis rise, identifying factors that influence outcomes is increasingly critical. Emerging research has shown that Social Determinants of Health (SDOH), including economic stability, education, neighborhood and built …

Towards Leveraging Social Media Data For Fostering Collaborations Among Non-Profits, Monazil Chowdhury

LSU Doctoral Dissertations

Nonprofit organizations serve a crucial role in tackling a wide range of significant social, environmental, and economic issues. But it is often hard to get a clear picture of their work because their information is spread out and it is difficult to see how they are collaborating. To address this issue we developed a web-based tool to collect scattered data—from a variety of sources, such as the IRS, social media, and the Census, into one easy-to-use resource. The tool begins by taking IRS records and geocoding each nonprofit’s physical address With its coordinates. It then retrieves census tract information from …