Chem 125-103: General Chemistry I, Sean Lee

Chemistry, Environmental and Forensic Science Syllabi

No abstract provided.

Eps 202-451: Technology, And Environment, Michael Bonchonsky

Chemistry, Environmental and Forensic Science Syllabi

No abstract provided.

Chem 339-101: Analytical/Physical Lab, Chunyan Liu

Chemistry, Environmental and Forensic Science Syllabi

No abstract provided.

Chem 301-101: Chemical Technology, Lecture & Lab, Xiaokai Zhang

Chemistry, Environmental and Forensic Science Syllabi

No abstract provided.

Chem 221-001: Analytical Methods, Mustansar Hussain

Chemistry, Environmental and Forensic Science Syllabi

No abstract provided.

Robust Ai Solutions For Financial Markets Through Generative Modeling, Dynamic Graph Learning, And Reinforcement-Based Portfolio Optimization, Jingyi Gu

Dissertations

Financial markets are inherently uncertain and dynamic, driven by complex factors such as macroeconomic signals, investor sentiment, and evolving inter-asset relationships. While machine learning has advanced financial modeling, existing approaches often fall short in addressing the real-world intricacies of finance. This dissertation confronts two critical challenges, human-driven stochasticity and risk-intensive decision-making under real-world trading constraints, while seizing a pivotal opportunity, the structural dynamics of evolving financial systems. These elements are foundational to advancing robust and practical financial intelligence.

To this end, this dissertation develops a unified framework for robust financial modeling and decision-making. The framework is architected as a progressive, …

Large-Scale Graph Algorithms And Applications With An Emphasis On Fintech Data, Fuhuan Li

Dissertations

Graph algorithms are essential analytical tools with applications spanning cybersecurity, biology, social media, and increasingly, financial technology (FinTech). The complex and interconnected nature of financial data, particularly in cryptocurrency networks, presents unique opportunities for graph-based analysis in fraud detection and anomaly identification.

This dissertation presents the design and implementation of scalable graph algorithms tailored for large-scale networks, with particular emphasis on FinTech applications. The primary contributions include: (1) novel cover-edge based triangle counting algorithms that significantly reduce computational overhead through breadth-first search preprocessing, achieving substantial speedups over traditional methods and dramatic communication reduction in distributed settings, (2) optimized parallel implementations …

Bodipy-Based Photocages For The Controlled Release Of Hydrogen Sulfide And Persulfides, Yanmei Li

Dissertations

Reactive sulfur species (RSS) such as hydrogen sulfide (H2S) and hydropersulfides (RSSH) are crucial mediators in a variety of physiological and pathological processes. Their regulatory roles span from maintaining cellular redox balance to influencing signaling pathways critical for homeostasis and stress responses. This dissertation investigates H2S and RSSH as biologically important molecules and explores the development of boron-dipyrromethene (BODIPY)-based photocages for its controlled, light-triggered release. Given H2S and RSSH's critical role in redox regulation and cellular protection, achieving spatiotemporal control over its delivery is essential for both therapeutic and research applications. BODIPY scaffolds offer favorable photophysical properties, including visible-light activation …

Reduced Order Models Of Hydrodynamically Interacting Flapping Wings, Jose Pabon

Dissertations

Fish schools exhibit a collective behavior and self-organization that is mediated by hydrodynamic interactions between individual fish. However, the long-time evolution of hydrodynamically interacting collectives is challenging to investigate due to the persistent influence of long-lived vortical structures, and the high-resolution requirements of direct numerical simulation at large Reynolds numbers. Reduced-order models have therefore played an important role in theoretical investigations of collectives of swimming bodies. The main results detailed herein are several new reduced-order models of swimmers that self-propel by flapping, i.e., by executing a prescribed periodic rigid body motion. The models are extensions of a discrete-time dynamical system …

Optimizing Hip And Knee Assistance For Walking And Sit-To-Stand Transitions: An Intrinsic Muscle Mechanics Based Predictive Approach, Neethan Ratnakumar

Dissertations

As the global population ages, the demand for wearable assistive technologies continues to rise, driven by their potential to enhance mobility and independence in older adults. Effectively designed controllers for lower-limb exoskeletons to assist sit-to-stand (STS) and walking are crucial for delivering efficient, safe, and comfortable assistance during daily activities. Traditionally, controller optimization involves biomechanical modeling and user-specific customization. Musculoskeletal simulations play a central role in this process by providing insights into human-exoskeleton interaction dynamics, thereby informing and refining control strategies.

This work presents a simulation-driven approach for developing exoskeleton controllers for walking and STS using two distinct methods: optimal …

From Chemistry To Conscious Code: How Ai Is Shaping The Next Generation Of Sustainable Innovation, Shehan S. Makani

STEM for Success Showcase

In a world racing toward an uncertain future, we stand at the thrilling intersection of chemistry, artificial intelligence, and human resilience. For many of us in STEM, this is no longer just about mastering equations or coding algorithms - it’s about engineering the future itself. At NJIT, we are not just students and researchers - we are builders, thinkers, and dreamers. And the truth is, the world needs dreamers with tools now more than ever.

Smart Manufacturing In Chemicals, Shehan S. Makani

STEM for Success Showcase

The chemical and pharmaceutical manufacturing sectors are evolving toward intelligent, adaptive production systems. This white paper explores how AI-enabled machinery and connected equipment are transforming chemical process efficiency, product quality, and sustainability. Drawing from conceptual case studies at ChemRich USA and ChemeNova LLC, we propose a framework where AI models interface with real-time machine data from centrifuges, filters, reactors, and mixers to enhance formulation, reduce downtime, and enable on-demand customization. The result is a more agile, data-driven, and sustainable manufacturing paradigm.

From Digital Natives To Ai-Natives: Rethinking Assessment And Instructional Design In The Age Of Autonomous Ai Tools, James Lipuma Ph.D., Cristo Leon Ph.D., Yi Meng Ph.D.

STEM for Success Resources

No abstract provided.

Artificial Intelligence In Stem Education: A Transdisciplinary Framework For Engagement And Innovation, Cristo Leon Ph.D., James Lipuma Ph.D., Xavier Oviedo Torres

STEM for Success Resources

This peer-reviewed article, Artificial Intelligence in STEM Education: A Transdisciplinary Framework for Engagement and Innovation by Cristo León, James Lipuma, and Xavier Oviedo-Torres, published in Frontiers in Education, examines how artificial intelligence (AI) is transforming STEM education through a transdisciplinary lens. Based on a systematic review of 41 scholarly studies (2020–2025), it analyzes AI integration in STEM classrooms, focusing on learner agency, adaptive assessment, and ethical challenges such as algorithmic transparency, equity, and inclusion. Guided by a Transdisciplinary Communication (TDC) framework and grounded in PRISMA protocols, the study applies tools like Nvivo, Excel, and VOSviewer for thematic coding and …

Continuum Of Academic Collaboration: Issues Of Inconsistent Terminology In Multilingual Context, Cristo Leon Ph.D., James Lipuma Ph.D., Marcos O. Cabobianco, Maria B. Daizo

STEM for Success Resources

Journal of Systemics, Cybernetics and Informatics
1690-4524 (Online)

Methodological Memorandum For Michelle Llado-Wrzos' Phd Dissertation, Michelle Llado-Wrzos Ed.D., Cristo Leon Ph.D.

Journal of Roleplaying Studies and STEAM

This methodological memorandum outlines the research approach for Michelle Llado-Wrzos' PhD dissertation, which explores the underrepresentation of women in Science, Technology, Engineering, and Mathematics (STEM) fields. Despite multiple efforts to reduce the gender disparity, female students remain significantly less likely to pursue STEM majors compared to their male peers. The research focuses on assessing the impact of extrinsic motivators—specifically financial incentives and exposure to female role models—on the STEM major choices of female high school students. By adopting a mixed-methods approach, this study thoroughly integrates quantitative surveys and qualitative interviews to examine these motivators.

A quasi-experimental design will assess the …

Narrativas Y Convergencias Transdisciplinarias: Contribuciones Académicas Relevantes, Desafíos Y Horizonte Editorial, Cristo Leon Ph.D.

Journal of Roleplaying Studies and STEAM

La Revista de Estudios sobre Juegos de Rol y STEAM (JRPSSTEAM) se ha consolidado como un espacio transdisciplinario, multilingüe y de acceso abierto que articula investigación, práctica y comunidad en torno a los juegos de rol. En esta editorial, se reflexiona sobre los aprendizajes y patrones emergentes de los tres volúmenes y cinco números publicados hasta la fecha, abordando tanto aciertos como áreas de mejora. Se ofrecen lineamientos prácticos para autores potenciales, destacando errores comunes, criterios de relevancia temática y aspectos que enriquecen la comunicación entre disciplinas. Además, se revaloriza el carácter abierto e inclusivo del JRPSSTEAM, que acoge desde …

Journal Of Roleplaying Studies And Steam (Jrpssteam) Vol. 4 [2025], Número 1 (Issue 1), Ivan Ávila González Phd, Cristo Leon, Romano Ponce-Díaz Phd, Diogo A. Camelo, Alfonso Rincón Vaquero, Edgar Ávila González, Daira Pando Tamayo, Michelle Llado-Wrzos, Leonid Moyzhes, Andrea A. Echeverría Barrios

Journal of Roleplaying Studies and STEAM

La cultura es un diálogo constante con el pasado, el presente y el futuro. Todos nuestros objetos culturales son el resultado causal de las personas que estuvieron antes que nosotros, se actualizan en nuestro proceder en el presente y, les proyectamos al futuro con la esperanza de que aporten algo positivo a las generaciones que estarán delante de nosotros; de tal forma, este Volumen 4 [2025], número 1 (Issue 2) es el resultado directo de la labor de los editores fundadores Mauricio Rangel Jiménez, Miguel Angel Bastarrachea Magnani y todas aquellas personas que trabajaron antes que nosotros para que existiera …

Colloidal Quantum Dots: A Path Toward Making Mid-Wave Infrared Sensing A Ubiquitous Technology, Mohammad Mostafa Al Mahfuz

Dissertations

Reducing the size, weight, power consumption, and cost (SWaP-C) of infrared detectors could make infrared sensing more widely accessible. In the critical mid-wavelength infrared (MWIR) spectral range of 3-5 gm, commercially available detectors are limited by the high costs associated with epitaxial growth and hybridization, as well as the need for cryogenic cooling. These factors restrict their use to defense and space applications.

Colloidal quantum dots present a promising material for overcoming these challenges, with wafer-scale monolithic integration and Auger suppression being the key material capabilities to minimize the sensor's SWaP-C. Infrared sensors based on colloidal quantum dots have been …

Childhood Neuroanatomical Markers Of Familial And Nonfamilial Attention-Deficit/Hyperactivity Disorder, Rahman Baboli

Dissertations

Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent neurodevelopmental disorder, characterized by developmentally inappropriate levels of inattention, hyperactivity, and impulsivity. Children with family history of ADHD are at an elevated risk of having ADHD as well as a higher risk of persistent ADHD into adulthood, reflecting a source of etiological heterogeneity in ADHD. This heterogeneity in terms of both biological and environmental risk factors may explain differences in neural correlates, outcomes, cognitive, behavioral as well as developmental trajectories. It is therefore critical to understand the influence of having, or not having positive family risk factors on the neuroanatomical structures of the …

On The Design Of A Framework For Large-Scale Exploratory Graph Analytics, Oliver Andres Alvarado Rodriguez

Dissertations

Large-scale exploratory graph analytics merges data science with high-performance computing to extract critical insights from network-representable data. Data scientists routinely analyze data from the natural, social, and computing sciences by representing it as networks, or graphs, where objects become vertices and their relationships become edges. This representation allows data scientists to add graph analytics to their toolbox. However, designing tools for large-scale exploratory graph analytics is challenging due to the complexities of graph algorithms, such as high communication in distributed systems and large memory demands. These challenges can lead to overly complex software, which limits usability and development to a …

Machine Learning And Optimization For Intelligent Decision-Making, Elson Cibaku

Dissertations

This dissertation presents a series of innovative machine learning and optimization model designs that address complex operational challenges across logistics and power systems. By integrating advanced neural architectures with robust optimization techniques, the work delivers scalable solutions designed to improve efficiency, reliability, and decision-making in dynamic and real-world environments. The first study introduces a two-stage approach to effective vaccine distribution. This framework tackles the capacitated vehicle routing problem by combining adaptive clustering techniques with reinforcement learning and a simulated annealing pickup policy. Through extensive computational experiments, the approach demonstrates substantial improvements in routing efficiency, reducing both computational time and logistical …

Model-Based Reinforcement Learning And Deep Learning For Power Converter Circuit Design Automation, Shaoze Fan

Dissertations

This dissertation presents a comprehensive automated framework for power converter design, leveraging reinforcement learning (RL) and graph-transformer networks (GTN) to address critical inefficiencies in traditional manual topology optimization. Motivated by the combinatorial increase of circuit design spaces and the computational cost of iterative simulations, this work develops a robust framework for generating energy-efficient topologies requiring rapid and reliable circuit design.

The framework integrates three key components: (1) an upper-confidence-bound-tree-based (UCT-based) RL model for circuit topology space exploration, (2) parallelized UCT algorithms to accelerate exploration processes, (3) a Graph-Transformer-based Network enabling fast circuit performance evaluation. Experimental validation demonstrates the whole framework …

The Interplay Of Accommodation And Oculomotor Vergence Within Young Adults With Binocularly Normal Vision And Typically-Occurring Convergence Insufficiency, Sebastian Fine

Dissertations

Concerted binocular coordination evoking oculomotor and refractive responses to visual stimuli are essential to daily function. Oculomotor dysfunctions can inhibit binocular responses to visually-near stimuli and have high comorbidities to accommodative dysfunctions. Three visual cues for inward (convergent) and outward (divergent) oculomotor movements, when presented concertedly create natural-viewing conditions: disparity- the binocular difference in light cast onto the fovea due to differing ocular perspectives, blur- the acuity of a visual target which stimulates accommodation, and proximal- the perceived distance of a visual stimuli based on size.

This study aims to quantitatively investigate oculomotor vergence and accommodation performances between individuals with …

Modulation Of Cerebellar Cells By Transcranial Ac Stimulation In Anesthetized Rats, Qi Kang

Dissertations

Noninvasive brain stimulation (NIBS) is increasingly utilized in clinical trials for the treatment of neurological disorders. Each NIBS technique offers distinct advantages. Transcranial electrical stimulation (tES) is easy to apply and requires only simple equipment, while transcranial magnetic stimulation (TMS) can penetrate deeper than tES into the brain and it is more focal. Transcranial focused ultrasound stimulation (FUS) is superior to both in terms of penetration and focal stimulation. This study focuses on the modulation of the cerebellum, traditionally believed to be associated with motor coordination but increasingly recognized for its role in cognition and emotion as well. While tES …

Adversarial Robustness In Advanced Machine Learning Models Integrating Graph Neural Networks And Large Language Models, Mahmoud Nazzal

Dissertations

Artificial intelligence (AI) has achieved remarkable performances across various domains. In most real-world applications, data often takes relational forms, such as graphs and networks, or sequential forms, such as text and time series. As AI evolves, specialized models have emerged to handle these structures; Graph Neural Networks (GNNs) for relational mining and Large Language Models (LLMs) for sequential understanding. Despite their success, these models face challenges in security, robustness, and interpretability. GNNs excel in relational reasoning but are vulnerable to adversarial manipulation and lack interpretability, while LLMs are strong in linguistic reasoning and generalization yet struggle with relational data and …

An Inquiry Into The Physics Of Mixing And Floc Filtration, Andrew P. Pennock

Dissertations

Flocculation and clarification are two essential processes to deliver safe water at a reasonable cost to consumers. There are two major thrusts to the research presented in this dissertation. The first is to better characterize the physics and mixing parameters used for the design of hydraulic flocculators in the context of drinking water treatment plants. The second major thrust is to investigate floc filtration as a mechanism for the removal of primary particles during floc blanket clarification.

The intensity of mixing in environmental and chemical engineering applications is often characterized by the Camp and Stein velocity gradient. This parameter has …

Fact-Checking As A Multi-Step Process: From Ambiguity Resolution To Claim Validation, Wenbo Wang

Dissertations

The spread of misinformation and disinformation has become a major concern, particularly with the rise of social media as a primary source of information for many people. Fact-checking—the process of verifying claims against credible evidence—has emerged as a critical safeguard against misinformation. Yet, the task is fraught with challenges: claims are often ambiguous, context-dependent, or composed of multiple intertwined assertions, while automated systems struggle to replicate the nuanced reasoning of human experts. This dissertation addresses these challenges by reimagining fact-checking as a multi-step, knowledge-guided process that systematically resolves ambiguity, decomposes complexity, and validates claims through structured reasoning. Additionally, the proposed …

Next-Generation Extended Reality Systems With Real-Time Edge Artificial Intelligence And Mobile Computing, Pedro H. Regalado

Dissertations

Mixed reality (MR) and augmented reality (AR) systems are reshaping digital experiences by seamlessly integrating physical and virtual environments. This dissertation presents a comprehensive framework for next-generation immersive systems, combining advances in real-time data processing, multi-user synchronization, and secure communication. The core contributions are structured around three interconnected systems: MediVerse, TeleAvatar, and MultiAvatarLink, each addressing critical challenges in mobile MR.

MediVerse is a secure and scalable framework for real-time health and performance monitoring, integrating intelligent IoT sensors, wearable technologies, and MR interfaces. It supports multi-camera fusion, adaptive compression, and real-time three-dimensional (3D) point cloud generation, enhancing data accuracy and responsiveness …

From Neural Networks To Large Language Models: Innovations In Financial Ai, Mathematical Reasoning, And Structured Data Representation, Junyi Ye

Dissertations

This dissertation explores the evolution and application of artificial intelligence techniques across three critical domains: financial modeling, mathematical reasoning, and structured data analysis. The dissertation presents seven research projects that chart a progression from specialized neural architectures to sophisticated large language models (LLMs), contributing novel methodologies and frameworks at each stage.

In the financial domain, the research first introduces TS-Mixer, a MLP-based architecture for time-series forecasting that captures both feature relationships and temporal dependencies through a simple yet effective design, outperforming more complex models in S&P500 index prediction. The dissertation then presents DySTAGE, a dynamic graph representation learning framework that …