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Advances In Research On The Quality, Influential Factors, And Contamination Prevention And Control Technologies Of Mine Water In Representative Coal Mining Areas Of China, Xu Guangquan, Zhang Haitao, Liang Miao, Chen Xiaoqing, Li Zixuan, Li Xu, He Jianghui

Coal Geology & Exploration

Background Mine water represents a major type of water body produced during coal mining. Investigating its quality, influential factors, and contamination prevention and control technologies is of great significance for environmental protection and the sustainable utilization of water resources in coal mining areas.Methods Based on field surveys and literature review, this study systematically analyzed the quality and formation mechanisms of mine water in representative coal mining areas of China. Furthermore, it explored the advances and primary challenges in current research on technologies for the prevention and control of mine water contamination and proposed major directions for future research. Advances …

Advances In Research On Eco-Geological Environment Monitoring Of Underground Coal Mining Areas Based On Airborne Remote Sensing, Li Jun, Zou Zhaohui, Zhang Chengye, Wang Yi, Cheng Yang, Xu Lianhang, Zhang Jinhe, Liu Chuanrui, Liang Lixia

Coal Geology & Exploration

Background Timely and effective monitoring of the current status of eco-geological environment holds great significance for scientific decision-making regarding both geological environment protection and ecological restoration in underground coal mining areas. Owing to its advantage of large-scale and rapid monitoring, airborne remote sensing technology has emerged as an important approach to the eco-geological environment monitoring of mining areas. Advances This study first clarifies relevant concepts of eco-geological environment monitoring, as well as the monitoring requirements and contents specific to underground coal mining areas. Accordingly, it presents a summary of the advances in research on airborne remote sensing technology for the …

Development Path And Prospects Of Technologies For Detecting Hidden Hazards In Coalfield Fire Zones, Deng Jun, Wang Jinrui, Ren Shuaijing, Song Zeyang, Wang Caiping, Li Yaqing, Lu Junhui, Qu Gaoyang

Coal Geology & Exploration

Background Hidden hazards in coalfield fire zones are characterized by limited detectability, rapid evolution, and multi-hazard coupling, posing major challenges to the safe coal mining and related ecological restoration. Methods This study aims to achieve the fine-scale identification and risk control of hidden hazards in coalfield fire zones. Based on a systematic review of the genetic mechanisms and spatial distribution patterns of coalfield fire zones, this study summarizes the characteristics of three primary fire zone types: outcrop/surface fire zones, shallowly buried fire zones, and deeply buried underground burnt-out areas and fire zones within old coal pits. Multi-dimensional hazards caused by …

Biogeochemical Mechanisms Behind Water Quality Evolution And Valuable Element Reutilization Of Abandoned Mine Drainage: A Review, Wu Pan, Huang Jiangxun, Li Qingguang, Zhang Ruixue, Li Bo

Coal Geology & Exploration

Background Abandoned mine drainage, also known as acidic mine drainage (AMD), represents a major pollution source in mining areas while also serving as an important carrier of strategic critical metals and underground space utilization. In China, water pollution stemming from coal mining is more severe in the south than in the north. This spatial pattern is jointly shaped by climatic conditions, geological settings, and mining history. Advances From the perspective of the environmental geochemical frontier, this study presents a systematic review of AMD formation and evolution mechanisms, as well as technologies for water pollution prevention and control, resource recovery, and …

Pathways And Modes For Prevention And Control Of Acid Mine Drainage-Induced Pollution In China, Liu Guo, Chen Qian, Gan Xinzhu, Tang Jie, Zhu Mingtan, Fan Jiajun, Ren Shuang

Coal Geology & Exploration

Background Mining activities in polymetallic sulfide mines significantly disrupt regional hydrogeochemical equilibrium, leading to the formation of acid mine drainage (AMD). Consequently, severe pollution of aquatic environments occurs, posing a threat to regional water resource security. To lay the foundation for the prevention and control of AMD-induced pollution, it is necessary to investigate the formation and evolution mechanisms of AMD and establish a low-cost, sustainable risk management and control mode. Methods Based on field sampling and literature research, this study made statistics of the hydrochemical characteristics of AMD from different polymetallic sulfide mining areas across China and elucidated the processes …

Geophysical Responses And Their Statistical Relationships With Lithium And Kaolinite Contents For Lithium-Rich Coals In The Yangquan Mining Area, Chen Tongjun, Xu Haicheng, Li Wan

Coal Geology & Exploration

Objective China, representing the world's largest consumer of lithium resources, suffers from a limited endowment of conventional lithium resources, rendering it inevitable for China to seek to develop and utilize abundant coal-hosted lithium resources. Through a comparative analysis of coal samples from the lithium-rich No. 8 and the ordinary No. 15 coal seams in the Taiyuan Formation, Yangquan mining area, Shanxi Province, this study explored the statistical relationships between the lithium and kaolinite contents and geophysical responses. Accordingly, the feasibility of the indirect detection of coal-hosted lithium resources using geophysical methods was assessed. Methods First, by testing the lithium content …

Construction Project Administration: Course Portfolio, Andre Messner

UNL Faculty Course Portfolios

This portfolio presents a structured framework for the design and implementation of a construction project administration course within a Construction Management program. Emphasis is placed on aligning student learning objectives, instructional strategies, and assessment methods with current industry expectations and professional standards. The course is intentionally designed to bridge the gap between theoretical knowledge and practical application by incorporating real-world scenarios, collaborative learning environments, and experiential activities that mirror industry practices.

In addition to outlining the course structure, this paper examines the pedagogical foundations that support student-centered learning, including constructivism, scaffolding, and project-based learning. These approaches are used to foster …

Influence Of Polyvinyl Alcohol Content On Starch-Based Bioplastic Reinforced With Chitosan And Microcrystalline Cellulose, Bagus Kharisma Putra, Andoko Andoko, Mochamad Viky Afandy, Muhammad Faizullah Pasha, Riduwan Prasetya

Chimica et Natura Acta

Starch-based bioplastics are promising sustainable alternatives to petroleum-based plastics; however, their limited mechanical strength and stability restrict broader packaging applications. This study investigates the influence of polyvinyl alcohol (PVA) content on the structural and functional properties of starch-based bioplastic films reinforced with chitosan and microcrystalline cellulose (MCC). Films prepared by solution casting with varying PVA compositions were characterized in terms of density, mechanical properties, thermal stability, biodegradation behavior, antibacterial activity, and structural features. Increasing PVA content produced denser film structures with improved tensile strength and thermal stability, while elongation at break reached its maximum at the intermediate formulation, indicating a …

Decision Making At Triage Classification Using Svm With Smote Technique, Mehanas Shahul, Pushpalatha Kp

Northeast Journal of Complex Systems (NEJCS)

The efficient functioning of triage gates in overcrowded emergency departments (EDs) occurs in the context of the complex adaptive system (CAS) framework, where diverse system elements – patients, medical personnel, resources, patients’ inflow patterns, and patients themselves – simultaneously and dynamically influence the decision process. This study addresses the automated incorporation of machine learning triage algorithms as part of the system triage process to support automated classified risk-level recognition based on a limited set of vital signs. Patients are dynamically subsumed under high and low-risk categories enhanced by sensitivity, which enables optimal diagnosis and triage response to the critical clinician …

Uncovering Discrete States From Multimodal Psychophysiological Data Using Gaussian Latent Dirichlet Allocation (Glda), Congyu Wu, Aaron Fisher, David Schnyer

Northeast Journal of Complex Systems (NEJCS)

In this article we explore and validate the utility of an unsupervised probabilistic model, Gaussian Latent Dirichlet Allocation (GLDA), for discovering discrete states from repeated, multimodal psychophysiological samples collected from multiple individuals. Psychology and medical research heavily involves measuring potentially related but individually inconclusive variables from a cohort of participants to derive diagnosis, necessitating clustering analysis for state identification. Traditional probabilistic clustering models such as Gaussian Mixture Model (GMM) assume a global mixture of component distributions, which may not be realistic for observations from different patients. The GLDA model borrows the individual-specific mixture structure from a popular topic model Latent …

Accelerating Wound Healing Rates With Cucurbita Pepo Leaf Extract Loaded Electrospun Poly(Methyl Methacrylate)/Halloysite/Chitosan/ Caco₃ Composite Nanofibers Through In Vitro And In Vivo Assessments, Samar A. Salim

Nanotechnology Research Centre

Cucurbita pepo leaf extract (CPE) was incorporated into electrospun poly(methyl methacrylate)/ halloysite/chitosan/ CaCO₃ (PMMA/Hal/CS/CaCO₃) composite nanofibers to develop a novel biomaterial for accelerating wound healing rates. The different nanofibrous scaffolds were successfully fabricated and characterized through scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD). SEM analysis revealed uniform, smooth nanofibers, while FTIR and XRD confirmed the integration of CPE into nanofiber matrix, indicating an amorphous structure and effective dispersion of the extract. In vitro agar well-diffusion and antibiofilm assays revealed that the optimized formulation exhibited potent antimicrobial activity against wound-associated pathogens. The nanofibers composite based on …

Real-Time Fraud Detection, Haidi Aly Fahmy, Abdelhadi Nait-Zerrad, Shiva Krishana Reddy Ravuula, Sangwhan Cha

Harrisburg University Other Works

Financial fraud detection is a high-volume, high-velocity analytics problem. Traditional rule-based systems are often easy to deploy, but they are limited by static thresholds, delayed response, high false-positive rates, and weak explainability. This report presents a formalized end-to-end Big Data architecture for real-time fraud and anomaly detection in financial transaction streams.

The proposed architecture ingests transaction events through AWS Kinesis, enriches them through an Apache Flink stream-processing layer, scores them with an XGBoost classifier, explains model outputs using SHAP, and converts structured evidence into human-readable summaries through a controlled GPT explanation layer. Results are persisted through a hybrid storage strategy …

Sentience, Sheetal Agrawal

Masters Theses

The increasing urgency for sustainable and adaptive systems has driven research toward embedding intelligence directly into materials rather than relying solely on external sensing and control systems. This thesis explores how smart material1 embedded systems can be designed to recognize and respond to environmental signatures, defined as measurable patterns such as temperature fluctuations and mechanical forces. Central to this investigation is the integration of shape memory alloys, particularly Nitinol, with geometry-based actuation mechanisms that amplify material behavior into functional system responses.

The central argument is that designing with smart materials is a design problem, not primarily a materials science problem …

Somatic Prosthetics For Planetary Resonance, Disha Dharesh Kumar R

Masters Theses

This thesis investigates how industrial design can transcend extractive technological paradigms in favor of relational interfaces that foster planetary attunement. Framing the climate crisis as a 'crisis of imagination', the research challenges the Western bifurcation of nature and culture by drawing on Indic cosmologies- which recognize stones, plants, and ecosystems as conscious at different levels and participants in a shared cosmic field. By synthesizing research in Biosemiotics, Quantum Information Pansycishm, and Neuroscience, the project redefines intelligence as a distributed, more-than-human phenomenon.

The research materializes as a speculative design artifact: a device that functions as a somatic prosthetic for planetary resonance. …

Characterizing Stiffness Dynamics Of Normal And Malignant Breast Spheroids Using Brillouin Microscopy, Razanne Rafat Zaghloul, Karlin Hilai, Chenjun Shi, Jitao Zhang

Medical Student Research Symposium

Background: Breast cancer progression and metastasis are closely linked to alterations in the mechanical properties of tumor cells and their microenvironment. Softer, more deformable cells are often associated with higher metastatic potential. While atomic force microscopy (AFM) is the current gold standard for mechanical characterization, it is limited to surface measurements and can damage 3D cultures. It remains unclear how the mechanical properties evolve over time in normal versus malignant spheroids. This study utilizes Brillouin light-scattering microscopy, a non-contact and label-free optical technique, to assess stiffness changes in normal and malignant breast epithelial spheroids over time. Understanding these mechanical signatures …

Characterization And Potential Suitability Of Some Egyptian Clays For Ceramic Building Applications, Marwa Askar, Medhat Sobhy El-Mahllawy, Mohamed Hamed Abdel-Aal, Ali Mohamed Ali Abd-Allah, Safia Gaber Al Menoufy

HBRC Journal

This study investigates the suitability of five Egyptian clay deposits—Wadi El-Natrun, Qasr El-Sagha, Kafr Homied, Fayed, and Suez Road—for ceramic building applications. Representative samples were characterized chemically and mineralogically, with grain size distribution and Atterberg limits determined to assess their industrial potential. Mineralogical analysis identified montmorillonite, illite, and kaolinite as the dominant clay minerals. Grain size and plasticity data, plotted on industrial diagrams, revealed that all samples are highly plastic clays with elevated clay fractions and minimal sand content. Such properties, while beneficial for molding, require modification to reduce excessive plasticity. The addition of non-plastic materials is recommended to enhance …

In Silico Molecular Docking Study Of Antidiabetic Bioactive Compounds From Brotowali (Tinospora Cordifolia) Targeting Glut4 In Type Ii Diabetes Mellitus, Gita Euaggelion Tarigan, Surya Dwira

Indonesian Journal of Medical Chemistry and Bioinformatics

Type 2 diabetes mellitus (T2DM) is a global metabolic disorder characterized by insulin resistance and impaired glucose uptake. Despite the availability of pharmacological therapies, limitations such as adverse effects and high costs highlight the need for alternative therapeutic candidates. Tinospora cordifolia has been widely reported to contain bioactive compounds with antidiabetic potential; however, comparative evaluation of their interaction with glucose transporter type 4 (GLUT4) remains limited.

This study aimed to identify the most promising bioactive compounds from Tinospora cordifolia targeting GLUT4 using an in silico molecular docking approach, followed by pharmacokinetic and toxicity (ADMET) prediction. Molecular docking was performed using …

Assessment Of Buildings' Energy-Saving Strategies Resilience To Climate Change: The Case Of Mediterranean Climate, Aya S. Mohamed, Bakr M. Gomaa, Alaa Eldin N. Sarhan

HBRC Journal

The Earth's climate is changing, and projections indicate that global warming will continue throughout this century, leading to increased occurrences of extreme temperatures. This raises critical questions about the performance and resilience of different energy-saving design strategies in buildings under future climatic conditions. To address this, the present study investigates the impact of passive design strategies, including building orientation, window-to-wall ratio, south and east/west shading devices, and thermal insulation on a prototype building's energy performance across four timeframes: 2002, 2020, 2050, and 2080, using validated computer-based thermal simulations. The results indicate that individual strategies vary significantly in their effectiveness, with …

Explainable Tree-Based Ensemble Models For Diabetes Prediction Using Shap, Maan Y Anad Alsaleem, Omar Shakir Hasan, Yahya Albugg

AUIQ Technical Engineering Science

Due to the generally unqualified nature of prediction data and the difficulty of interpreting predictions, predicting diabetes remains a significant hurdle in the adoption of machine learning within the medical domain. In this study, several tree-based machine learning techniques (LightGBM, XGBoost, CatBoost, and Gradient Boosting) were applied to predict diabetes using the 2015 BRFSS dataset, while two ensemble methods (soft voting and stacking) were employed to improve predictive accuracy. The performance analysis of the individual models and ensemble approaches indicates that CatBoost achieved the highest accuracy among the single classifiers (0.871), with an F1-score of 0.871 and a ROC–AUC of …

An Open-Source Linear Actuated-Quartz Tube Furnace With Programmable Ceramic Heater Movement For Laboratory-Scale Studies Of Combustion And Emission, Casey Coffland, Ryan Bixler, Elliott T. Gall

Mechanical and Materials Engineering Faculty Publications and Presentations

The Linear Actuated Quartz Tube Furnace (LA-QTF) is an instrument engineered to heat and combust materials under controlled conditions, capable of achieving flaming and smoldering states. A ceramic ring furnace is linearly actuated parallel to the length of a quartz tube. The mode of combustion depends on temperature, fuel composition, and oxygen availability; the LA-QTF regulates combustion by controlling the temperature and position of the ring furnace, and airflow within the tube. The LA-QTF can maintain temperatures between 23 °C and 530 °C for extended time periods, with stable temperatures over long-duration (∼120 min) experiments. Flow rate is dependent on …

Optimization And Energy Efficiency Analysis Of An Automatic Feed Mixer With A Rotating Drum Mechanism, Kris Witono, Talifatim Machfuroh, Nurlia Pramita Sari, Lisa Agustriyana, Aini Lostari

Journal of Mechanical Engineering Science and Technology (JMEST)

Energy-efficient feed mixer machines are important for improving the productivity and sustainability of small and medium-scale livestock farms. Previous studies primarily focused on either structural performance or mixing efficiency, with limited studies integrating both aspects. Therefore, this study evaluated an automatic rotating-drum feed mixer by combining Finite Element Method (FEM) analysis and energy modeling. The study used FEM simulations for different materials, namely A36 steel alloy, stainless steel 304, aluminium 6061, and galvanized steel, with thicknesses of 3 mm and 4 mm, as well as different drum systems. The FEM results showed that all evaluated materials met the minimum safety …

Predictive Natural Language Metrics Of Alzheimer's Disease And Cognitive Decline Trend Analysis, Zerui Ma

Computer Science and Engineering Theses and Dissertations

Inspired by Dr. David Snowden's Nun Study, which linked early-life Propositional Idea Density (PID) to later-life Alzheimer's disease, this thesis investigates two questions: whether fine-tuned Transformer-based large language models (LLM) can detect cognitive decline from patient speech transcripts with meaningful feature attribution, and whether longitudinal PID trends are observable across large-scale internet and academic text corpora. We evaluate dementia prediction on the DementiaBank Pitt Corpus and conduct an exploratory longitudinal PID analysis across seven diverse datasets spanning up to 29 years and over 12.6 million documents. This work suggests that linguistic ability metrics, traditional PID metrics and novel LLM-based analysis, …

Machine Learning And Formal Methods In Quantum Chemistry: Theory And Application, Ishna Satyarth

Computer Science and Engineering Theses and Dissertations

In recent years, the progress in inter-disciplinary application of machine learning and artificial intelligence (ML/AI) have truly transformed various fields, from weather forecasting and drug development to medical diagnostics, energy, and sustainability. Computational chemistry uses computational tools to model, predict, analyze, and explain chemical phenomena, while the Quantum chemistry specifically uses techniques based on quantum mechanics (as opposed to classical mechanics or empirical models). Quantum chemistry or Computational chemistry has also observed a momentum in application of ML techniques over the past decade significantly accelerating results and providing valuable insights into vast datasets, often surpassing traditional methods.

This dissertation explores …

Assessing Uranyl-Specific Dnazymes And Dna-Aptamers For Uranium Binding In Environmentally Relevant Waters, Ashley R. Apodaca-Sparks

Civil Engineering ETDs

The legacy of uranium mining affects many communities in the western United States, resulting in elevated levels of uranium in surface water. The ability to quickly and accurately detect uranium on site in affected communities can lead to real-time water quality analysis that informs risk assessment and remediation efforts. The objective of this work is to advance the application of biosensing technologies for the measurement of uranium in waters affected by mining legacy. The third chapter of this thesis compares ANDalyze by AlpHa Instruments, a commercially available uranium biosensor, and inductively coupled plasma mass spectrometry (an EPA-verified method for uranium …

Brain-Based Mechanisms Of Behavioral Impairment In Fetal Alcohol Spectrum Disorder (Fasd): The Neuroimaging Biomarkers Of Inhibitory Control, Zinia Pervin

Biomedical Engineering ETDs

The developing brain is highly susceptible to alcohol-induced toxicity, often resulting in long-term deficits in executive function and behavioral regulation. Inhibitory control impairments are among the most prominent deficits observed in Fetal Alcohol Spectrum Disorder (FASD). This study investigated the neural mechanisms of inhibitory dysfunction using a multimodal MEG–DTI approach in 67 children aged 6–8 years (34 with FASD, 33 controls) who performed a Go/No-Go task. Source-level MEG analyses revealed reduced stimulus-locked cortical activation in the anterior cingulate cortex and significant group-by-hemisphere interactions in the superior parietal cortex and cuneus. Time–frequency analyses showed diminished response-locked beta power in the sensory-motor …

Kinetic Study Of Oil–Water Separation In A Dual Port Inlet Cyclone Separator, Ikhwanul Qiram, Agung Nugroho

Journal of Mechanical Engineering Science and Technology (JMEST)

In this study, a Computational Fluid Dynamics method is used to investigate the oil–water separation kinetics in a dual-port inlet cyclone separator. This was achieved using the Reynolds Stress Model coupled to an Eulerian multiphase framework. Three Reynolds numbers were studied (Re = 1.41×10⁵, 1.94×10⁵ and 2.52×10⁵) to analyse the flow; axial velocity distribution, vortex stability, radial migration velocity and separation efficiency were examined individually. Results indicate that both the radial migration velocity (vᵣ) and separation probability (p_remove) grow with Reynolds number, especially for larger oil droplets (10–100 µm). The best condition concerned is that …

Nuclear Deterrence: Enhancing The Mission Through Smart Factory Predictive Solutions, Jarrod Matthew Ronquillo

Chemical and Biological Engineering ETDs

To meet the stewardship and modernization initiatives set by the Department of Energy new technologies must enter the manufacturing facilities within the nuclear deterrent complex. Predictive solutions begin with collecting data. An equipment health monitoring device was created to streamline data collection and organization for equipment and processes. A predictive and process performance dashboard was developed for a deionized water system. The dashboard used Western Electric statistical process rules and a machine learning regression algorithm to predict when the resistivity would fall out of specification. Lastly, a remaining useful life calculation was developed for all equipment related to nuclear deterrent …

Complex-Valued Convolutional Neural Network With Time-Frequency Representation For Electrocardiogram-Based Arrhythmia Detection, Kajeeth Kumar Gurusamy, Muthurajkumar Sannasy

Turkish Journal of Electrical Engineering and Computer Sciences

This research proposes an end-to-end procedure for arrhythmia detection based on electrocardiogram (ECG) signals using complex-valued convolutional neural network (CVCNN) incorporated with time-frequency representation. The proposed model leverages complex numbers to capture amplitude and phase information that enhances the ability of the model for detecting time-frequency variation in cardiac signals. First, signal preprocessing techniques---including normalization, wavelet denoising, and R-peak detection---are applied. Subsequently, the model extracts complex features from raw ECG data by employing the Hilbert transform to derive the analytic signal and the short-time Fourier transform (STFT) to generate a time–frequency representation. The proposed CVCNN framework effectively learns spatial-temporal features …

Accurate Diagnosis Of Diseases By A Novel Ai Pipeline Based On Feature Extraction, Feature Ranking, And Feature Selection From Medical Images, Tuğba Nur Bozkurt, Mehmet Emi̇n Yüksel

Turkish Journal of Electrical Engineering and Computer Sciences

The rapid growth of the global population has led to a substantial increase in the number of patients, while the availability of healthcare professionals has not expanded at a comparable rate. This imbalance highlights the urgent need for efficient and reliable computer-aided decision support systems that can reduce clinical workload while maintaining high diagnostic accuracy. In this study, a novel and systematically integrated artificial intelligence-based pipeline is proposed for medical image classification, combining statistical significance-driven feature ranking with evolutionary feature selection in a unified framework. The proposed pipeline consists of four sequential stages: feature extraction, ranking, selection, and classification. Features …

Chaotic Artificial Bee Colony-Optimized Stacking Ensemble For Robust Multifault Diagnosis Of Wind Turbines, Veilraj Revathi, Solaimalai Jeyadevi, Madasamy Sudalaimani

Turkish Journal of Electrical Engineering and Computer Sciences

The complex electromechanical structure of wind turbines, along with harsh operating conditions, poses significant challenges for precise and robust fault diagnosis. To address this challenge, an ensemble multifault diagnostic framework based on an adaptive chaotic artificial bee colony (C-ABC)-optimized support vector machine (SVM) and gradient boosting machine (GBM) is proposed. In the proposed framework, data redundancy and overfitting are reduced through a two-stage hybrid filter-transformer-based feature reduction approach using ReliefF, followed by Principal Component Analysis. The chaos function of the proposed C-ABC maintains an adaptive balance between the exploration and exploitation phases, thereby preventing premature convergence, which is a common …