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Comparative Computational Study Of Sulfur-Donor Additives For Stabilization Of Fapbi3 Perovskites, Ilnar Nurgaliev, Akbarxon Hamzayev, Murad Marasulov, Zavkiddin Julliev, Akhmad Oblakulov, Nigmat Ashurov

Turkish Journal of Chemistry

A multiscale computational investigation integrating density functional theory (DFT) and molecular dynamics (MD) simulations was conducted to elucidate the mechanisms through which sulfur-containing donor molecules stabilize the photoactive α-phase of formamidinium lead iodide (FAPbI3) perovskites. The binding energetics, charge-transfer behavior, and hydrogen-bonding interactions of thiourea (TU), thiosemicarbazide (TSC), thiocyanate (SCN–), and diethyldithiocarbamate (DTC) were systematically analyzed. DFT results revealed pronounced Pb–S coordination and multidentate hydrogenbonding, with binding energies following the trend SCN–>TSC>DTC>TU. Thermodynamic analysis demonstrated that these additives lower the Gibbs free energy difference, thereby stabilizing the black α-phase, with TSC, TU, and to a lesser extent …

Zoledronic Acid-Loaded Hap–Sio2–Camoo4:Eu3+ With Luminescent Properties As A Novel Drug Delivery System, Emi̇ne Kutlu, Muhammad Asim Ali, Fati̇h Mehmet Emen, Canan Vejselova Sezer, Hati̇ce Mehtap Kutlu

Turkish Journal of Chemistry

CaMoO4:Eu3+-functionalized hydroxyapatite–silica (HAp–SiO2–CaMoO4:Eu3+) core–shell nanocomposites were synthesized for the first time and evaluated as a multifunctional drug delivery and imaging platform. HAp–SiO2 nanocomposites were prepared via a hydrothermal route and subsequently functionalized with a luminescent CaMoO4:Eu3+ shell using the Pechini sol–gel method. Structural analyses confirmed the successful coexistence of HAp, SiO2, and CaMoO4:Eu3+ phases, while electron microscopy revealed spherical core–shell morphologies. Dynamic light scattering measurements showed average hydrodynamic particle sizes of approximately 1085 nm for HAp–SiO2 and 1427 nm for HAp–SiO2–CaMoO4:Eu3+ nanocomposites, indicating particle clustering in aqueous media, which is consistent with the low surface charge of the particles. The …

Development And Optimization Of A Novel Spectroscopic Method To Monitor Crystallization Behavior Of Bcs-Ii Drug, Uditi Handa, Anuj Malik, Kumar Guarve

Turkish Journal of Chemistry

This study investigated the crystallization behavior of dextromethorphan hydrobromide, a class II drug, to improve understanding of its supersaturation potential and formulation design. Conventional methods often lack the sensitivity or scalability needed for accurate detection of early nucleation events. To overcome these limitations, a novel process tracks real-time absorbance below turbidity levels to detect early molecular aggregation and phase separation. Crystallization kinetics of the drug was assessed at concentrations of 0.1, 0.2, and 0.3 mg/mL using three analytical techniques, namely, isothermal crystallization (baseline), ultraviolet (UV) absorbance (turbidity monitoring, UV-TM), and an optimized UV absorbance graphical method (UV-GM) (novel process). Induction …

Efficient Electrocatalytic Activity Of A Fe-Mof Based Cathode Catalyst In Pemfcs, Susmita Singh, Tanya Nishad, Debasmita Paul, Sumi Ganguly, Soumyabrata Goswami

Turkish Journal of Chemistry

Advancements in cost-effective and efficient alternative energy sources are critical for socioeconomic development. In this study, Iron-based metal–organic framework (Fe-MOF) electrocatalyst was fabricated using an Fe salt and ligand. Several surface characterization methods, including Fourier Transform-Infrared Spectroscopy (FTIR), Scanning Electron Microscope (SEM), and X-ray diffraction (XRD), were used to characterize the electrocatalyst. Additionally, using Chronoamperometry (CA), Electrochemical Impedance Spectroscopy (EIS), Linear Sweep Voltammetry (LSV), and Cyclic Voltammetry (CV), the electrocatalytic behaviour in a cathodic reaction in PEMFCs is investigated. The study showed the material’s exceptional chemical or surface stability, and great surface morphology of the material that facilitated the cathodic …

A Method For Diffracted Wave Separation And Imaging Based On The Gwa-Vmd, Lin Peng, Liu Yulin, Peng Suping, Cui Xiaoqin, Guo Changbo, Yang Jie

Coal Geology & Exploration

Background Small-scale discontinuous geobodies such as faults, collapse columns, and pinch-out points are widely present in underground spaces. They are closely associated with the safe production and development of underground resources such as coals, oil, and gas. As the wave field responses of small-scale discontinuous geobodies, diffracted waves can overcome the shortcomings of traditional reflected wave imaging, being capable of accurately identifying and localizing small-scale geobodies. Objective and Method This study aims to achieve diffracted wave imaging of discontinuous geobodies. Based on the differences in kinematic and dynamic characteristics between reflected and diffracted waves, this study leveraged the precise self-adaptive …

Optimization Of Image Quality Of Simulated Multiple Detectors Computed Tomography Acquisition Parameters Using Machine Learning And Catphan Phantom, Ali O. Masoud, Najat K. Mohammed, Khamis O. Amour, Ahmed M. Jusabani, Denise Mwalongo, Mwingereza John Kumwenda

Tanzania Journal of Science

The study successfully employed Monte Carlo (MC) simulation and a Machine Learning (ML) approach using a Random Forest Regression (RFR) model to develop optimized Multi-Detector CT (MDCT) protocols that significantly reduce radiation dose while maintaining diagnostic image quality. The MC engine accurately modeled X-ray spectra, and the RFR model demonstrated high predictive power for key metrics, achieving R2 scores of 0.97 for CTDIvol and over 0.92 for image quality metrics (Noise, CNR). Through multi-objective optimization guided by the RFR, the final protocol (Optimization-3) was found on the Pareto front, achieving a notable 35% dose reduction (from 15.5 mGy to 9.9 …

Reliable Eigenspace Error Estimation Using Source Error Estimators, Jay Gopalakrishnan, Gabriel Pinochet-Soto

Mathematics and Statistics Faculty Publications and Presentations

We introduce a framework for repurposing error estimators for source problems to compute an estimator for the gap between eigenspaces and their discretizations. Of interest are eigenspaces of finite clusters of eigenvalues of unbounded nonselfadjoint linear operators with compact resolvent. Eigenspaces and eigenvalues of rational functions of such operators are studied as a first step. Under an assumption of convergence of resolvent approximations in the operator norm and an assumption on global reliability of source problem error estimators, we show that the gap in eigenspace approximations can be bounded by a globally reliable and computable error estimator. Also included are …

Note: Greenvent – Revolutionizing Methane Management With Hybrid Energy Processing In Mine Ventilation, Roman J. Jędrzejczyk, Damian K. Chlebda, Jacek Dańczak, Piotr Rosikowski, Monika A. Koperska, Robert E. Hayes, Joseph P. Mmbaga, Robert Hildebrandt, Joanna Profic-Paczkowska

Journal of Sustainable Mining

Methane plays a significant role in intensifying the greenhouse effect, possessing a global warming potential more than twenty times greater than that of CO₂ when measured on a carbon-dioxide-equivalent basis. Hence, identifying efficient methods to mitigate methane emissions is crucial for safeguarding the environment, ensuring economic viability, and maintaining practicality. Globally, research initiatives are dedicated to crafting an effective catalyst system for methane oxidation, which involves developing catalysts, comprehending their characteristics, and determining the best way of bonding them to structural supports to enhance functionality. Notably, systems that incorporate metals from the non-noble d-block of the periodic table as …

A Probabilistic Deep Learning Framework For Retrieving Chlorophyll-A From Hyperspectral Imagery: Integrating Channel Attention And Mixture Density Networks, Wenzhao Li, Surendra Maharjan, Rejoice Thomas, Junde Chen, Hesham Morgan, Michael J. Garay, Olga V. Kalashnikova, Shahryar Fazli, Charles Ichoku, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Accurate monitoring of Chlorophyll-a (Chla) is critical for assessing aquatic ecosystem health, yet ecological complexity often leads to ambiguous spectral signatures in satellite data. Traditional deterministic models assume a one-to-one mapping between spectra and pigments, often failing to capture these high-dimensional analytical challenges. In this study, we propose a novel deep learning architecture, the Channel Attention-Mixture Density Network (CA-MDN), to retrieve Chla from the National Aeronautics and Space Administration (NASA) Earth Surface Mineral Dust Source Investigation (EMIT) hyperspectral mission. The CA-MDN integrates an attention mechanism to dynamically select ecologically relevant spectral bands and employs a probabilistic output layer to quantify …

Biological Opinion Monitoring Of Riparian Habitat And Forest Stand Resources, Peter D. Howe

Funded Research Records

No abstract provided.

Hybrid Vision Transformer–Cnn Framework For Alzheimer’S Disease Cell Type Classification: A Comparative Study With Vision–Language Models, Md Easin Hasan, Md Tahmid Hasan Fuad, Omar Sharif, Amy Wagler

School of Mathematical & Statistical Sciences Faculty Publications

Accurate identification of Alzheimer’s disease (AD)-related cellular characteristics from microscopy images is essential for understanding neurodegenerative mechanisms at the cellular level. While most computational approaches focus on macroscopic neuroimaging modalities, cell type classification from microscopy remains relatively underexplored. In this study, we propose a hybrid vision transformer–convolutional neural network (ViT–CNN) framework that integrates DeiT-Small and EfficientNet-B7 to classify three AD-related cell types—astrocytes, cortical neurons, and SH-SY5Y neuroblastoma cells—from phase-contrast microscopy images. We perform a comparative evaluation against conventional CNN architectures (DenseNet, ResNet, InceptionNet, and MobileNet) and prompt-based multimodal vision–language models (GPT-5, GPT-4o, and Gemini 2.5-Flash) using zero-shot, few-shot, and chain-of-thought …

Anomalous Spontaneous Induction Of Magnetic And Electric Fields In Dense Quark Matter, Efrain J. Ferrer, Jose Miguel Perez-Fernandez

Physics & Astronomy Faculty Publications

In this paper, we will demonstrate that a dense quark-matter system in the dual chiral density wave (DCDW) phase behaves as a ferromagnet in the sense that its magnetic-field dependent magnetization remains different from zero even at 𝐵 →0. The corresponding permanent magnetization is a function of the baryonic chemical potential 𝜇, decreasing up to zero as 𝜇 increases in the range of intermediate densities (312  MeV⩽𝜇⩽342  MeV) and then increasing from zero in the higher density interval 490  MeV⩽𝜇⩽550  MeV. We will show that this system’s ability to generate permanent magnetization, together with the existence of the axial anomaly, …

Trends In Self-Reported Responses To Nutrition Facts Labels Before And After Nutrition Labeling Policy Implementation: A Comparison Of Adults In The United States And Mexico, Jim Thrasher Ph.D., Ma, Ms, Victor Vilalobos, Dai Fang, Alejandra Jáuregui, Alejandra Contreras-Manzano, Rachel Davis Ph.D., Lana Vanderlee, Christine M. White, Rachel B. Acton, James W. Hardin, Edward A. Frongillo Jr. Ph.D., Simón Barquera, David Hammond

Faculty Publications

Background

Objectives

Methods

Practices, Experiences, And Insights From Major Developed Economies On Green And Low-Carbon Technology Innovation Policies, Baihe Gu, Hongqian Wang, Yuling Sun, Dan Chen

Bulletin of Chinese Academy of Sciences (Chinese Version)

The transition toward green and low-carbon development has become a significant global trend for future growth. This study focuses on policy frameworks that support innovation in green and low-carbon technologies. It reviews the practices of major developed economies, such as the United States, the European Union, the United Kingdom, and Japan in this field, and synthesizes their experiences across key dimensions such as industrial policy, innovation actors, innovation ecosystems, funding mechanisms, and international cooperation. Drawing on these insights, and taking into account China’s unique advantages and challenges in green and low-carbon technological innovation, the paper offers policy recommendations aimed at …

Promoting Water Security Of South-To-North Water Diversion Project For National Water Network, Quanfa Zhang, Guihua Liu, Chesheng Zhan, Xiaobo Ren

Bulletin of Chinese Academy of Sciences (Chinese Version)

The South-to-North Water Diversion Project is a crucial infrastructure initiative aiming at optimizing nation-wide distribution of water resources, meanwhile fostering sustainable economic and social development, and ensuring the well-being of the people. The project, with its extensive scope and high technical complexity, particularly in the middle and west routes located in biodiversity hotspots such as the Qinling Mountains and the Qinghai-Xizang Plateau, may alter local microclimates, potentially triggering meteorological disasters such as droughts, thunderstorms, and hail, along with secondary hazards. The implementation of the project may also disrupt the local hydrological conditions, disturb the balance of the existing atmospheric-vegetation-soil system, …

Artificial Intelligence-Driven Materials Science: Evolution, Framework, Dilemmas, And Breakthroughs, Yanglili Zhou, Weihua Wang, Ziwei Zhao

Bulletin of Chinese Academy of Sciences (Chinese Version)

Artificial intelligence-driven materials science (AIMS) represents a revolutionary and disruptive paradigm in materials research, promising to fundamentally break through the traditional bottlenecks of research cycles and efficiency. Historically, the evolution of materials science research paradigms from empirical trial and error, theoretical modeling, and computational simulation to the new data-driven stage has been driven by innovations in cognitive tools and methods. Currently, artificial intelligence, as a disruptive cognitive tool, is fundamentally reconstructing the core elements and interaction logic of materials science: the research process achieves intelligent iteration and full-process closed-loop; the capabilities of researchers are reshaped and teams are organized; and …

Unraveling Patch Size Effects In Vision Transformers: Adversarial Robustness In Hyperspectral Image Classification, Shashi Kiran Chandrappa, Sidike Paheding, Abel A. Reyes-Angulo

Michigan Tech Publications

Highlights: This work investigates the effect of spatial patch size on the classification accuracy and adversarial robustness of Vision Transformer-based architectures for hyperspectral image analysis. What are the main findings? Smaller patch sizes generally exhibit stronger adversarial robustness while maintaining comparable clean classification performance. Larger patch sizes tend to reduce robustness by increasing sensitivity to localized adversarial perturbations, with some dataset-dependent variations. What are the implications of the main findings? Spatial patch size is an important design consideration when applying Vision Transformers to hyperspectral image classification tasks. The findings provide practical guidance for informed patch-size selection in robust, deployment-aware transformer-based …

Study On Strategies And Measures And Insights For Introducing Artificial Intelligence Talents In Japan, Fangxin Hu, Ru Ma, Yujun Su

Bulletin of Chinese Academy of Sciences (Chinese Version)

In response to the aging population and the need for innovation in science and technology development, Japan regards AI as a key technology to solve social problems. In addition to accelerating the training of domestic AI talents, Japan is also vigorously introducing overseas AI talents. This study sorts out and analyzes Japan’s long-term, annual, and AI-specific strategic planning for the introduction of AI talents, including Basic Plan for Science, Technology and Innovation, Comprehensive Innovation Strategy, Strategic Plan for Artificial Intelligence Technology, and AI Strategy, and explores Japan’s specific implementation measures such as updating the national residence management system, improving the …

A Decolonial Approach To International Education: Insights From A Cal Poly Global Program In Palermo, Sicily, Elvira Pulitano, Iyad Jamaly

csuglobalaction

This article presents a critical intervention in the current debates about decolonizing international education. It is based on a Global Program in Palermo, Sicily, offered by Cal Poly San Luis Obispo in summer 2023. As both program creator/director and student participant in the program, the authors rely on their personal experience and insights along with expertise in coloniality and decolonial theories. The article focuses on a series of pedagogical activities led by a young group of migrants and refugees who, in the city of Palermo, have come together to form two associations designed and structured to offer new contemporary models …

Monitoring Strategies In Cave Microclimate Studies, Nenad Buzjak, Dalibor Paar, Aurel Persoiu, Christos Pennos, Franci Gabrovšek, Valerija Rossi

International Journal of Speleology

Cave atmospheres are spatially confined but dynamical systems that influence subterranean ecosystems, geomorphological processes, registration and preservation of climatic signals in sedimentary archives, cultural heritage, and conservation management. Despite major advances in cave climatology, methodological approaches to microclimate monitoring remain fragmented and are in some cases still shaped by the long-standing assumption that cave air temperature simply reflects the mean annual surface temperature. This paper revisits that assumption and develops an experience- and literature-based methodological framework for cave microclimate research. Key terminology is addressed by distinguishing between cave climate, cave microclimate, and cave meteorology, and by situating microclimatic variability within …

Cerebral Documents And Algorithmic Sensemaking: Searching For Expressions In Human And Artificial Cognitive Collaborations, Rebekah L. Cowell

Proceedings from the Document Academy

Generative Artificial Intelligences (AIs) and current advanced large language models (LLMs) are algorithmically designed to generate text-based conversations as conversational agents (CAs), by replicating human language and conversational communication. Pairing human cognition with generative computationally coded cognition. We have never been here before: cerebral and artificial information collaborations and processing producing expressions that may or may not become visible as second-hand/secondary source documents.

Sensemaking or sense(un)making is a unique autonomous human drive cognitively, our information processing is sensemaking in action and expressions and articulations are evidence of the sensemaking cycle. Documentation [expressed or articulated through various mediums] are a product …

Representing The Apparatus In Nineteenth And Twentieth Century Chemical Abstracting Literature, Evan F. Kuehn

Proceedings from the Document Academy

The rise of abstracting services in the 20th century is usually associated with the explosion of original scientific research and its attendant journal literature. The abstract was a tool of information dissemination and access for increasingly expansive and international work. What is often overlooked, however, is the correlation between original research and the technologies that made these advances available. Scientific apparatuses were, however, documented from the beginning, and were also a part of the rise of abstract literature in the 19^th and 20^th centuries. In this paper I examine the development of abstracts for apparatuses and patents in …

Ai-Guided Binding Mechanisms And Molecular Dynamics For Mers-Cov, Pradyumna Kumar, Lingtao Chen, Rachel Yuanbao Chen, Yin Chen, Seyedamin Pouriyeh, Progyateg Chakma, Abdur Rahman Mohd Abul Basher, Yixin Xie

Faculty Articles

The MERS-CoV (Middle East respiratory syndrome coronavirus) is a zoonotic virus with a high mortality rate and a lack of antiviral drugs, underscoring the need for effective therapeutic methods. Viral entry depends on interactions between viral surface proteins and human receptors, with Dipeptidyl Peptidase-4 (DPP4), a transmembrane glycoprotein, acting as the receptor for MERS-CoV. We employed Molecular Dynamics (MD) Simulations to identify critical interface residues under a high-performance computing (HPC) workflow for accelerated results. Target residue pairs were identified through analysis of salt bridge and hydrogen bond occupancy. The stability of these residues was confirmed through three independent MD Simulations …

A Transcriptional Program Associated With Neurotransmission In The Living Human Brain, Alexander W. Charney, Lora E. Liharska, Eric Vornholt, Alissa Valentine, Anina Lund, Alice Hashemi, Ryan C. Thompson, Terry Lohrenz, Jessica S. Johnson, Nicole Bussola, Esther Cheng, You Jeong Park, Salman Qasim, Alisha Aristel, Lillian Wilkins, Kimia Ziafat, Hannah Silk, Lisa M. Linares, Brendan Sullivan, Claudia Feng, Beniamino Hadj-Amar Ph.D.

Faculty Publications

At the foundation of neurotransmission, and by extension at the foundation of brain function, are coordinated programs of gene expression involving many thousands of genes. These programs are poorly defined in humans because most modern studies that characterize human brain gene expression use tissue obtained in the postmortem state when neurotransmission and brain function have ceased. Here, to advance knowledge of the gene expression programs at the foundation of neurotransmission in the human brain, gene expression was characterized in 130 prefrontal cortex (PFC) samples obtained from participants of the Living Brain Project (LBP) during neurosurgical procedures in conjunction with intracranial …

Survival On Image Regression With Application To Partially Functional Distributional Representation Of Physical Activity, Rahul Ghosal, Sunwoo Emma Cho, Marcos Matabuena

Faculty Publications

Technological advancements in wearable devices and medical imaging often lead to high-dimensional physiological signals in the form of images or surfaces. To address these data structures, we develop a novel survival on image regression model with a specific focus on partially functional distributional representation of wearable data. The existing approaches for functional data and survival outcomes have been primarily developed for uni-dimensional functional predictors. Drawing on recent developments in distributional data analysis, we model temporally varying distributional patterns of physical activity (PA) as a partially functional distributional predictor within a semiparametric Cox model framework. We use tensor product splines to …

Environmental Chemicals And Maternal Depression During And After Pregnancy: A Scoping Review, Pengfei Guo, Yunyue Shi, Cindy Nguyen, Haoran Zhuo, Tormod Rogne, Zeyan Liew

Faculty Publications

Purpose of Review

There is increasing evidence that several environmental exposures may pose a risk for depression, including maternal depression. We conducted a scoping review of epidemiological evidence regarding maternal exposure to environmental chemicals and perinatal depression.

Recent Findings

We searched PubMed, Embase, Web of Science, Dimensions, and Scopus, and summarized the findings from 27 articles that examined environmental chemical exposures and maternal depression. Studies of ambient air pollutants (N = 11) showed exposure to NO₂ and PM10 to be most consistently associated with antenatal or postnatal depression. Studies of endocrine-disrupting chemicals, including phthalates (n = 6), …

Evolutionary Game-Based Analysis Of Responses To Hallucinations In Generative Artificial Intelligence, Qiang Yan, Qianyu Zhang, Na Wei

Journal of System Simulation

Abstract: The accelerated deployment of generative artificial intelligence, particularly large language models, has amplified the social risks of hallucinations, posing systemic threats to the credibility of the information ecosystem, the effectiveness of users’ cognitive decision-making, and the governance security in the public domain. Research primarily focuses on hallucination mitigation mechanisms at the technical level or the design of regulatory frameworks at the policy level, lacking a systematic theoretical analysis of the evolutionary logic of strategic interactions among the “large language models, users, and regulators” under conditions of bounded rationality. By introducing evolutionary game theory into the field of generative artificial …

Simulation Method For Multi-Crew Construction Processes Based On Large Language Model-Powered Agent, Yifan Wang, Bin Yang, Congjun Wang

Journal of System Simulation

Abstract: Traditional construction simulation methods typically rely on predefined rules or static scheduling mechanisms, making it difficult to simulate interactive decision-making under complex constraints by dynamically adapting multi-crew construction scenarios. As a result, workforce idleness caused by process dependencies and spatial occupation fails to be solved. A simulation method for multicrew construction processes based on a LLM-powered agent was proposed. The distributed crew agents endowed with construction scenario understanding and reasoning capabilities were constructed, as well as a centralized project manager agent. A “single-manager and multiple-crew” decision-making mechanism for multi-agent interaction in construction was designed. Autonomous decision-making and coordination mechanism …

Symmetric Orthogonalization And Probabilistic Weights In Resource Quantification, Gökhan Torun

Turkish Journal of Physics

Transforming nonorthogonal bases into orthogonal ones often compromises essential properties or physical meaning in quantum systems. Here, we demonstrate that Löwdin symmetric orthogonalization (LSO) outperforms the widely used Gram–Schmidt orthogonalization (GSO) in characterizing and quantifying quantum resources, with particular emphasis on coherence and superposition. We employ LSO both to construct an orthogonal basis from a nonorthogonal one and to obtain a nonorthogonal basis from an orthogonal set, thereby mitigating ambiguity related to the basis choice in defining quantum coherence. Unlike GSO, which depends on the ordering of input states, LSO applies a symmetric transformation that treats all vectors equally and …

Optical Textures Of A Uniform Liquid Crystal Lattice: Monte Carlo Simulations With Newton Colors And Mueller Matrix Analysis, Yakup Emül

Turkish Journal of Physics

The optical textures of a uniform liquid crystal (LC) molecule lattice structure were explored through Monte Carlo (MC) simulations, combined with Mueller matrix (MM) formalism and Newton’s color theory. These simulations utilized a Lebwohl-Lasher (LL) type interacting LC lattice under periodic boundary conditions, generating ensemble-averaged configurations at two temperature regimes (kBT = 0.9 and 1.2), corresponding respectively to nematic and isotropic states. These configurations were then incorporated into the MM framework to evaluate the polarization states of transmitted light. To visually interpret the resulting interference phenomena, we propose a new visualization method based on a modern interpretation of Newton’s color …