Digital Commons Network^™

Centralized, Decentralized And Hybrid Wastewater Management Approaches – Gap Study, Zeyad Kadry, Ahmed M. Ebid Dr., Ibrahim Mahdi

Future Engineering Journal

This literature review highlights advancements in wastewater management, emphasizing hybrid systems that integrate centralized and decentralized approaches. It underscores the cost-effectiveness and scalability of decentralized wastewater management (DWWM) for small urban and rural areas, despite higher land and capital needs. Optimization frameworks utilizing advanced computational tools, such as genetic algorithms and GIS, have proven effective in reducing costs and enhancing resource recovery, with decision-support frameworks tailored for rural planning. The integration of synergistic technologies like vertical farming and photovoltaic energy enhances the sustainability and resilience of decentralized systems in urban contexts. Additionally, innovative treatment methods, including nano-disinfection and advanced coagulation, …

Symposium Introduction: Interrogating The Hidden Curriculum: Implications For Formation Of Professional Identity, David I.C. Thomson

The Journal of Law Teaching and Learning

No abstract provided.

The Ferguson Effect, Ayleen A. Suarez

Quest

Research Essay

Research in progress for CRIJ 2328: Police Systems and Practices

Faculty Mentor: Stephanie Abramoske-Austin, PhD

This article began from a term paper assignment in CRIJ 2328, Police Systems and Practices—a required course for Criminal Justice students. The assignment asks students to research a current policing topic and write an essay that includes the history of the topic, its importance for modern-day policing, a review and analysis of the empirical evidence on the topic, and proposed solutions. Ayleen Suarez chose to research the “Ferguson effect.” The Ferguson effect began circulating online and in a variety of news programs after …

Ole Miss Engineering, University Of Mississippi. School Of Engineering

University Website Migration from WordPress

No abstract provided.

Leetquest, Sanvir Bal, Vladimir Ceban, Matty Herzig, Daryl Ho

Computer Science and Engineering Senior Theses

The technical interview process for software engineering position has drastically changed in recent times to focus on Data Structures and Algorithms (DSA). Although, data structures and algorithms are a foundational aspect of several programming languages, the questions asked in the technical interview process do not accurately reflect, in terms of style and difficulty, the experience students and professionals have with DSA from school and work respectively. As a result, many have looked to online resources in order sharpen their DSA problem solving skills. However, the existing solutions are riddled with flaws. In particular, the learning resources they provide are locked …

Real-Time 3d Automated Spotlight Tracking System, Francine Garcia, Mary Hemker

Computer Science and Engineering Senior Theses

Commercially available automated spotlight systems tend to be expensive and unattainable for smaller school or community theaters. This project provides a cheap alternative to automated spotlight systems on the market and is compatible with most stage lighting architecture by using the Digital Multiplex (DMX) Protocol.

Circuit boards with Ultra-Wideband (UWB) radio frequency track an actor’s coordinates in a 3D space defined by four UWB anchors. A Raspberry Pi single-board computer acts as a controller and converts the coordinates into pan and tilt angles and maps these calculations to values that the spotlight can read in order to move. These values …

Early Lane Change Prediction For Mixed Traffic With V2x Communication, Muhammed Fatih Koc, Nouman Ashraf, Pramod Pathak, Sachin Sharma

SAML-25 Workshop on Statistical and Machine Learning

Lane change prediction is essential for ensuring road safety and effective decision-making in autonomous vehicles (AVs). AVs will probably take several decades to penetrate new vehicle sales. As AVs and human-driven vehicles (HDVs) will coexist in traffic for the long term, AVs must understand the lane change intentions of surrounding HDVs. Lane changing is a critical manoeuvre that can cause a crash if it is performed late or if incorrect lane adjustments are made. Therefore, forecasting surrounding vehicles’ lane change intentions in advance is essential to ensure safe driving in mixed traffic environments having both AVs and HDVs. The unpredictability …

Pros & Cons Of Reinforcement Learning - Illustrated By The Problem Of Controlling Gantry Robots, Horst Zisgen

SAML-25 Workshop on Statistical and Machine Learning

In this talk a solution for the dynamic scheduling of flexible flow shop systems using gantry robots for material handling by means of simulation and Reinforcement Learning (RL) is presented. Subsequently the pros and cons of a RL approach are briefly discussed and illustrated at the robot control problem.

Impact Of Spatial Diversity And Subject Variability On Wifi-Based Human Activity Recognition, Amany Elkelany, Robert J. Ross, Susan Mckeever

SAML-25 Workshop on Statistical and Machine Learning

In recent years, WiFi-based Human Activity Recognition (HAR) has gained substantial attention due to the ubiquity of WiFi infrastructure and advancements in wireless communication. Unlike camera-based systems that raise privacy concerns or wearable sensors that require user compliance, WiFi-based HAR provides a noninvasive and practical alternative that operates seamlessly with existing infrastructure. WiFi-based HAR leverages fluctuations in wireless signals, particularly Channel State Information (CSI), to passively detect and classify human activities. WiFi-based HAR models often achieve high accuracy in a single environment but suffer significant performance drops when applied to new environments due to variations in spatial settings, human movement, …

Multi-Omic Spatially Resolved Analysis Of The Neuroinflammatory Response To Intracortical Microelectrode Arrays, E. Ricky Chan, Jeffrey R. Capadona

Faculty Scholarship

Intracortical microelectrode arrays (MEAs) are devices implanted into the brain's cortex with the ability to record or stimulate neuronal activity. Unfortunately, MEAs tend to fail over chronic time points, limiting their clinical utility. Chronic failure has largely been attributed to the brain's neuroinflammatory response. Until recently, most of what was understood about the neuroinflammatory response to MEAs was learned through immunohistochemical analysis of small numbers of proteins. More recently, gene expression studies have sequenced thousands of mRNA molecules that contribute to neuroinflammation, but few studies have performed large-scale proteomic analyses. To expand the knowledge of molecular mechanisms involved, we have …

Electrochemically-Assisted Low Power Density Laser Writing On Stainless Steel Via Enrichment Of Chromium Oxides, Zechariah J. Pfaffenberger, Lianlian Liu, Tae Kyong John Kim, Vignesh Venkataramani, Lydia Kisley

Faculty Scholarship

Laser color marking produces nearly permanent, environmentally friendly, vibrant colors on surfaces. However, previous work has used high-power-density pulsed lasers to induce the physicochemical reactions for marking. Here, laser color marking on stainless steel 304 (SS304) is performed with a less expensive continuous wave (CW) laser and a power density five orders of magnitude below that previously reported by combining an electrochemical cell with a fluorescence microscope. Using a combination of optical microscopy, x-ray photoelectron spectroscopy, and bulk electrochemistry, it is demonstrated that the laser-induced luminescence and colors are due to enrichment (32 ± 9% increase) of Cr₂O₃ in the …

Ascertion: Middleware For Content Moderation, Mia Lassiter, Vani Aggarwal

Computer Science and Engineering Senior Theses

The Internet is dominated by a handful of social media platforms that disproportionately control content curation and moderation. As a result, it is increasingly difficult for users to discern verified and credible information from misinformation and disinformation. Middleware is a specialized software with mechanisms to decentralize platform power and reinstate users with autonomy over their online experiences. We propose middleware that gives users the autonomy to create and consume authentic and trusted content. We achieved a proof of concept for a middleware with mechanisms for verifiable content posting and authentication. Our solution is implemented as a Chrome plug-in that provides …

Exploitation Of Plasma Treatment-Assisted Monocarboxylic Cobalt Phthalocyanine Nanorod Growth For High-Efficiency Photodetection Applications, Ahmed Ramzy, Ahmed M. El-Mahalawy, M A. Abd El Ghaffar, Wael A. Abbas

Nanotechnology Research Centre

Here, the effect of cobalt phthalocyanine (CoPC) functionalization by a monocarboxylic group in the form of thermally evaporated thin films followed by plasma treatment for variable time intervals is introduced. The effect of plasma treatment on crystalline structure, molecular structure, and morphology was examined using the XRD, FT-IR, and FE-EEM techniques. Significant modifications were observed in the crystal structure and morphology with increasing plasma treatment time intervals. The plasma treatment for 5 min enhanced the formation of the nanorods along the deposited surface with improved crystallinity. Tracking the optical properties variation of the deposited MCCoPc thin films under plasma treatment …

A Framework For Scalable And Controlled Hallucination Data Collection, Lin Ting Liang

Computer Science Senior Theses

This thesis addresses a key bottleneck in hallucination research: the scarcity and limitations of hallucination benchmark datasets. Existing datasets typically focus on a single type of hallucination and are expensive to produce due to the need for manual prompt creation and annotation. To overcome these challenges, we propose a novel mixture-of-experts (MoE) adversarial framework that actively induces hallucinations. Our framework employs three large language model (LLM) agents that iteratively and adversarially revise prompts to provoke hallucinated responses from a target question-answering model. It automates the generation of both intrinsic hallucinations (logical inconsistencies) and extrinsic hallucinations (inclusion of unverifiable external information). …

Highway-Transportation-Asset Criticality Estimation Leveraging Stakeholder Input Through An Analytical Hierarchy Process (Ahp), Kwadwo Amankwah, Sarah Hernandez, Suman Kumar Mitra

Civil Engineering Faculty Publications and Presentations

Transportation agencies face increasing challenges in identifying and prioritizing which infrastructure assets are most critical to maintain and protect, particularly amid aging networks, limited budgets, and growing threats from climate change and extreme events. However, existing prioritization approaches often lack consistency and fail to adequately incorporate diverse stakeholder perspectives. This study develops a systematic, stakeholder-informed method for ranking transportation assets based on their criticality to the overall transportation system. As a novel approach, we use the analytical hierarchy process (AHP) and present a case study of the applied approach. Six criteria were identified for ranking assets: annual average daily traffic …

Benchmarking Energy And Performance Of Parallel Machine Learning Models Using Hardware And Software Power Meters, Urooj Asgher, Tania Malik

SAML-25 Workshop on Statistical and Machine Learning

The growing reliance on machine learning algorithms across domains such as healthcare, transportation, and finance has led to their increased deployment on high-performance computing platforms. While performance optimization remains a central concern, energy efficiency is emerging as a critical design consideration, particularly in light of global sustainability goals. This study presents a comparative analysis of the energy consumption and performance of serial and parallel implementations of four machine learning algorithms, K-means clustering, Ant Colony Optimization, Logistic Regression, and Random Search. Experiments were conducted on an HPC testbed using both hardware-based and software-based power meters to measure energy consumption. The results …

Pace Financing: A Legal Guide To Energy And Water Efficiency Funding, James P. Cargas

PEEL Alumni Scholarship

No abstract provided.

Thin And Ephemeral Snow Shapes Melt And Runoff Dynamics In The Peruvian Andes, Catriona L. Fyffe, Emily Potter, Evan Miles, Thomas E. Shaw, Michael Mccarthy, Andrew Orr, Edwin Loarte, Katy Medina, Simone Fatichi, Rob Hellström, Michel Baraer, Emilio Mateo, Alejo Cochachin, Matthew Westoby, Francesca Pellicciotti

School of Geography, Earth and Environmental Sciences

The snow and glaciers of the Peruvian Andes provide vital water supplies in a region facing water scarcity and substantial glacier change. However, there remains a lack of understanding of snow processes and quantification of the contribution of melt to runoff. Here we apply a distributed glacio-hydrological model over the Rio Santa basin to disentangle the role of the cryosphere in the Andean water cycle. Only at the highest elevations (>5000 m a.s.l.) is the snow cover continuous; at lower elevations, the snowpack is thin and ephemeral, with rapid cycles of snowfall and melt. Due to the large catchment …

Method For Power Grid Nodal Inertia Estimation Based On Maximum Likelihood Identification, Xinfan Jiang, Yonggang Liu, Mingrui Sun, Jinbo Wu, Jingwen Wang, Yunfeng Wen

Journal of Electric Power Science and Technology

In recent years, with the large-scale integration of power electronic interfaced power sources such as wind power and photovoltaics into the power grid, the overall inertia level of the power grid has decreased, and the nodal inertia has shown spatial distribution differences, significantly increasing the risk of system frequency instability. It is urgent to quickly evaluate the distribution of inertia in the power grid so that dispatch and operation personnel can timely formulate effective inertia control measures. Therefore, a method for estimating the power grid nodal inertia based on maximum likelihood identification is proposed. Firstly, by using frequency and active …

Power Grid Fault Diagnosis Method Based On Improved Bayesian Network Model And Hht, Chongchong Wu, Jian Wang, Lihuiqian Gong

Journal of Electric Power Science and Technology

The safety and stable operation of power grid is the prerequisite for reliable transmission, transformation, and distribution. Therefore, when the power grid fails, it is very important to locate the fault quickly and accurately and shorten the fault time. Firstly, the information of component switching value and electrical quantity is obtained from the relevant monitoring system of the power grid. The initial decision table of relevant switching value information is formed according to the fault area, and the effective signal of electrical quantity information is extracted. Then, the rough set theory, Bayesian network, Hilbert-Huang transform (HHT), and other theories are …

A Novel Distance Protection Method For High‑Voltage Line In Substation With Integration Of Inverter‑Interfaced Distributed Generator, Zhengfei Lu, Minghao Wen, Yu Zhou, Longxing Jin, Shuai Ma

Journal of Electric Power Science and Technology

The strong variability, spatiotemporal randomness, and nonlinear controlled characteristics during faults of inverter-interfaced distributed generators (IIDGs) such as direct-drive wind turbines and photovoltaic generators pose significant challenges to the existing relay protection systems in power grids. When faults occur in high-voltage lines of substations with integration of IIDGs, the fault characteristics are more complex compared to those in the case of the access of traditional synchronous machine sources to high-voltage lines, which makes it difficult for conventional positive-sequence voltage-polarized phase-comparison distance protection to adapt to. As a result, the performance of protection for power grids deteriorates. This paper analyzes the …

Analysis Of Distributed Generator Consumption Capacity Of Distribution Networks Considering System Flexibility, Bo Tang, Yilong Liu, Ruijin Zhu, Xuejiao Gong

Journal of Electric Power Science and Technology

With the continuous growth of electricity demand, the high penetration level of renewable energy has become a trend in the development of distribution networks. However, the large-scale integration of distributed generators (DGs) has results in more prominent problems of power flow, congestion, and voltage fluctuations, which brings challenges to the flexible operation of distribution networks. At the same time, it is necessary to further consider the capacity of lines and the voltage deviation of terminal nodes, so that distribution networks can still retain schedulable space, that is, system flexibility. Therefore, it is necessary to rationally analyze the DG consumption capacity …

Transient Voltage Instability Identification Based On Koopman Operator In Power Grid With A High Proportion Of Renewables, Xiuqi Zhang, Hongqing Liu, Liqiang Wang, Yong Li, Han Gao

Journal of Electric Power Science and Technology

Transient voltage instability is one of the important factors that threaten the stability of power system. The dynamic reactive power reserve and supporting capacity of the power grid with a high proportion of renewables decrease sharply, and the control models and operation characteristics of grid-connected renewables are diverse. Thus, the reactive power voltage of the system often fluctuates rapidly after a fault occurs, which leads to a more prominent voltage stability problem. In response, a transient voltage instability identification method based on the Koopman operator is proposed in this paper to avoid power system outage accidents caused by voltage instability …

Transient Stability Assessment Of Power System In Combination With Update Mechanism, Songkai Liu, Xiao Gong, Chao Yang, Longcheng Liu, Yanzhang Li, Lei Zhang, Yating Zhang

Journal of Electric Power Science and Technology

Power system is a time-varying complex system. In recent years, data-driven machine learning method has been widely used in the field of transient stability assessment of power system. However, when the power system is subjected to a large disturbance and the working condition changes, the machine learning model needs to be trained according to the new operating data. Thus, it is difficult to timely respond to transient stability assessment of the system under the new topology structure. To solve this problem, a model update mechanism is proposed in this paper, which updates the model according to different conditions. In addition, …

Fault Line Selection And Section Location Method For Asymmetric Distribution Network Based On Fast Switch Arc Suppression Device, Fangyu He, Yanru Ni, Xiangjun Zeng, Kun Yu, Chao Zeng

Journal of Electric Power Science and Technology

In consideration of the problem of high-resistance grounding fault line selection and section location in asymmetric distribution networks, a fault line selection and section location method for asymmetric distribution networks based on the fast switch arc suppression device is proposed. Firstly, the basic principle of the fast switch arc suppression device is analyzed. On this basis, the electrical quantity properties of each feeder in the distribution system prior to and following the operation of the device are analyzed in depth after the high-resistance grounding fault occurs in the system. Through theoretical derivation, it is found that the fault feeder and …

Line Impedance Identification Of Three‑Phase Unbalanced Low‑Voltage Distribution Network Based On Hierarchical Deconstruction And Phase Recursion, Yue Chen, Jian Zhao

Journal of Electric Power Science and Technology

The large-scale integration of distributed photovoltaics and electric vehicles exacerbates the three-phase imbalance in low-voltage distribution network (LVDN), which further leads to issues such as current in neutral wire and voltage fluctuations, preventing the simplification of three-phase line impedance identification into three independently solved single-phase networks with unified power flow. For this reason, a set of line impedance identification methods based on hierarchical deconstruction and phase recursion are proposed, addressing the issue of neutral line current imbalance using the principle of child node voltage correlation. Firstly, three layers are obtained through hierarchical deconstruction: the substation layer, branch layer, and user …

Dominant Feature Identification And Index Construction Of Three‑Phase Voltage Imbalance In Substation Area, Wei Zhu, Ningxiao Huang, Huiying Shu, Jie Hu, Ming Tang

Journal of Electric Power Science and Technology

With a large number of power distribution substations (transformers), three-phase imbalance is a commonly observed issue. Due to the high randomness of single-phase loads in the substation area, the amplitude ranking of three-phase voltages and the imbalance degree according to the traditional algorithm change constantly. Based on the feature analysis of the sampled data, differences in the dominant phase amplitudes of three-phase voltages in each substation area during a certain period of time are obtained, identifying the severely unbalanced substation area for manual intervention. This serves as a feasible method to balance costs and benefits. A time-series matrix of the …

Distribution Network Fault Segment Location Based On Gaf‑Resnet50, Yuxuan Shi, Yanhui Xi, Weijie Zhang

Journal of Electric Power Science and Technology

Distribution lines are an integral part of modern power system, which directly influence the safety and stability of power supply. Distribution network fault location can be classified into precise fault location and fault segment location. Considering the complexity of distribution network structure, this paper proposes a fault segment location method based on Gramian angular field (GAF)-ResNet50. The one-dimensional time series is converted into a two-dimensional GAF image by the GAF algorithm, and the deeper-level fault features of the signal are extracted from the GAF image by using the framework of residual neural network so that the fault areas can be …

A Two‑Stage Recovery Strategy For Damaged Distribution Networks Considering Cold Load Pick‑Up, Yongpeng Zhang, Lizhen Wu, Jianping Wei, Wei Chen

Journal of Electric Power Science and Technology

The development of intelligent distribution networks has improved the self-healing ability and recovery speed of the power grid. However, when the system encounters large-scale power outages, the recovery process becomes more complex. Therefore, in response to the problem of power interruption in damaged distribution networks, this paper proposes a two-stage recovery strategy for damaged distribution networks that takes into account cold load pick-up (CLPU), aiming to generate a power restoration plan for distribution networks with switch control actions. The first stage generates traditional recovery and distributed power-assisted island power supply recovery plans that support feeder reconfiguration. In the second stage, …

Traveling Wave Fault Location Method Of Transmission Line Based On Cpo‑Vmd, Jun Zhou, Zhenguo Qu, Ziang Zhan

Journal of Electric Power Science and Technology

To address the low detection accuracy of traveling wave heads for long-distance transmission line faults as well as the location deviation caused by the clock asynchronization of fault detection devices and the uncertainty of traveling wave velocity, this paper proposes a new fault location method for transmission lines based on improved variational mode decomposition (VMD) combined with Teager-Kaiser energy operator (TKEO) to detect the fault traveling wave head and the double-ended traveling wave location formula independent of timing and wave velocity. Firstly, the crested porcupine optimizer (CPO) is used to optimize VMD parameters. Then the line mode and zero mode …