Physical Sciences and Mathematics Commons^™

Νgan: A Deep Learning Emulator For Cosmic Web Simulations With Massive Neutrinos, Neerav Kaushal, Elena Giusarma, Mauricio Reyes

Michigan Tech Publications

Understanding the impact of neutrino masses on the evolution of the Universe is a central goal of modern cosmology. Due to their large free-streaming lengths, neutrinos significantly affect structure formation at nonlinear scales, requiring accurate theoretical predictions to fully exploit current and future galaxy surveys. However, generating such predictions through large ensembles of cosmological simulations is computationally expensive. In this work, we introduce a deep learning-based generative adversarial network, νGAN, to emulate the Universe across a range of neutrino masses from 0.0 to 1.2 eV. The generated 2D cosmic web maps are statistically independent, show no correlations with the training …

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 …

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 …

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 …

Https://Digitalcommons.Mtech.Edu/Superfund_Silverbowbutte/1787/, Josh Bryson

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

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 …

Ueof, Nick Truong, Pritam P. Karkomar, William J. Beksi

Event-Based Vision - Archive

UEOF is the first synthetic underwater event-based optical flow dataset derived from physically-based ray-traced RGBD sequences. It was constructed using a modern video-to-event pipeline applied to rendered underwater videos. It consists of realistic event data streams with dense ground-truth flow, depth, and camera motion. The dataset is composed of 12 minutes and 51 seconds of data across 13,714 RGB frames. This results in a total of 4.94 billion events across all scenes. UEOF exhibits a high dynamic range of motion with a mean flow magnitude of 6.1 px and a median of 3.6 px. The motion distribution is heavy-tailed. While …

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 …

L. Dots To Data: Manufacturing Digital Seismic Traces From Scanned Images Of The Paper Sign Bit Sections From The 1970 Flathead Lake Seismic Survey, Robert W. Lankston

1970 Flathead Lake Seismic Survey

PowerPoint slides and the recorded audio of a lecture presented by Robert Lankston on October 13, 2014 in the University of Montana, Department of Geosciences Colloquium lecture series.

M. Epilogue For The 1970 Flathead Lake Seismic Project Collection, Robert W. Lankston

1970 Flathead Lake Seismic Survey

Re-introduction of the 1980 Qamar-Kogan seismic survey at Flathead Lake with recommendations for building an archive for that project. Article includes a proof-of-concept for converting scanned images of seismic sections recorded in variable density to digital seismic traces.

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), …

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 …

2026 February 19 - Tennessee Weekly Drought Summary, Tennessee Climate Office, East Tennessee State University

Tennessee Climate Office Weekly Drought Summaries

No abstract provided.

Research On System And Application Framework Of Tactical Wargaming Simulation Driven By Ai4s, Dayong Liu, Qisheng Guo, Zhiming Dong, Xuehuan Qiu, Zhuoli Liu

Journal of System Simulation

Abstract: Tactical wargaming simulation, as a crucial tool for combat analysis, simulation training, and equipment demonstration and test, has become a significant means for generating combat effectiveness. Integrating AI into simulation not only enhances simulation efficiency but also diminishes reliance on humans. To assist professionals engaged in tactical wargaming simulation in mastering AI application methods, fostering a systematic mindset, and understanding evolving trends, this paper provided a concise overview of the principles behind AI for science (AI4S). Subsequently, it conducted an analysis of AI4S's application effectiveness in tactical wargaming simulation, established an AI4S-driven wargaming simulation system, and elucidated its composition, …

Social Cognition Simulation With Large Language Model-Driven Agents, Mingxin Zhang, Jinxuan Wu, Rui Zhu, Yunlong Wang, Wenjuan Meng, Zhe Liu, Xu Li, Xiaolei Chen, Yuxuan Liang, Yi Zheng, Xiangyang Xue

Journal of System Simulation

Abstract: With the continuous evolution of the capabilities of generative LLMs, their application in social cognition simulation is demonstrating paradigm-shifting potential. Traditional social simulation methods predominantly rely on static rules and simplified behavioral models, making it difficult to capture the dynamic evolution and cultural complexity of human social behavior. LLM-driven agents, equipped with contextual understanding and natural language generation capabilities, are emerging as novel tools for modeling social cognitive mechanisms, enabling the simulation of complex sociopsychological processes such as identity construction, value judgment, and intentional reasoning. This paper briefly introduced the technical foundations of LLMs and highlighted their suitability for …

Integrated Development Environment For Digital Test Applications Based On Cloud-Edge-End Architecture, Wenguang Yu, Qun Li, Hongjie Dang, Hao Chen, Ping Yang

Journal of System Simulation

Abstract: Digital test applications need to be constructed using the unified digital test development tool. After analyzing the features of digital test applications such as large-sample autonomous run, high computational efficiency requirement, and diverse task scenarios, this paper proposes the integrated development environment (IDE) for digital test applications based on cloud-edge-end architecture. The layered expandable architecture, the hybrid integration framework of multi-source heterogeneous models, and the cloud-edge-end collaborative deployment architecture are designed for the IDE of digital test applications. The IDE supports the rapid development, integration, and execution of digital test models and enables development of digital test applications on …

Research On Chain-Of-Thought Technology For Situational Awareness Based On Modular Reasoning, Hongyuan Ji, Duzheng Qing

Journal of System Simulation

Abstract: To address issues such as insufficient intelligence of situational understanding in traditional simulation systems, a situational visual question answering dataset was constructed, and a modular reasoning framework was proposed. The SACoT was built, which, under a zero-shot setting, employed expert prompts to guide the model in task decomposition and multimodal information fusion, generating reasoning chains to enhance semantic cognition and interpretability and offering a scalable solution with low computation cost. Experimental results indicate that SACoT improves task allocation, enables models to focus on query-relevant image details, mitigates the fragmentation of chain-of-thought induced by multi-step reasoning, and reduces long-form …

Simulation Of Robotic Arm Ball-Catching Strategy Based On Curriculum Rl Of Transformer, Ziyao Zhang, Yunfeng Ji

Journal of System Simulation

Abstract: Method integrating the PPO algorithm with Transformer network architecture is proposed, and curriculum learning strategy is introduced to solve the difficult training convergence and low efficiency of traditional RL methods in complex and dynamic high-degree-of-freedom tasks such as robotic arm ball catching. The Transformer is employed to effectively capture the complex high-dimensional dependency between the robotic arm's state space, ball trajectory, and environmental physical parameters. Curriculum learning progressively increases catching difficulty by designing training tasks from simple to complex objectives. The experimental results show this method increases the ball-catching success rate by over 60% compared to the traditional …

Research On Uav Target Tracking Algorithm For Simulation Scenarios, Xinyi Li, Zhenfei Wang, Han Wu

Journal of System Simulation

Abstract: To address the need for automatic UAV tracking of moving targets in simulated experiments, this paper proposed a long-term automatic tracking method based on an improved channel and spatial reliability-aware tracker (CSRT) algorithm. The target edge features were detected using the Laplacian of guided filter (LOGF) through guided filtering and then fused with the histogram of oriented gradient (HOG) and color names (CN) features to enhance the algorithm's discriminative ability for the target. To evaluate the target state, the paper used average peak correlation energy and perceptual hash Hamming distance. When the target was occluded, the paper employed YOLOv8 …

Distributed Optimization For Integrated Energy Based On Multi-Agent Reinforcement Learning, Caixia Tao, Naikun Chen, Fengyang Gao, Jiangang Zhang

Journal of System Simulation

Abstract: To address the energy management and privacy preservation problems faced by the coordinated optimization of distributed integrated energy systems, a distributed coordinated optimization strategy based on the multi-agent proximal policy optimization algorithm was proposed. An energy management model was established under the MDP framework; the electrical and thermal heterogeneous energy characteristics were considered; a multi-region two-layer interaction mechanism was constructed. Under the framework of centralized training and decentralized execution, homomorphic encryption was utilized to avoid privacy leakage during the coordination process, while accurately quantifying individual contributions to mitigate the problem of variance explosion in multi-agent policy evaluation. In the …

Prediction Of Inflow Wind Field For Large-Scale Wind Turbines Based On Multimodal Hybrid Deep Learning, Jiheng Wang, Yang Hu, Ziqiu Song, Fang Fang, Jizhen Liu

Journal of System Simulation

Abstract: To address the demand for high-precision inflow wind field prediction in large-scale wind turbines, traditional CFD methods suffer from high computational costs and poor real-time applicability. This paper proposed a multimodal hybrid deep learning-based wind field prediction method. The proposed method took turbine operating parameters and far-range wind field images as inputs and generated short-range wind field images as outputs. By employing a U-Net-Transformer-GAN hybrid architecture, the model achieved multi-scale feature extraction, temporal dependency modeling, and highresolution wind field image generation. The vorticity transport equation and Kármán-Howarth turbulence statistics were incorporated as weak constraints to enhance physical consistency, while …

Llm-Driven Multi-Agent Social Network Simulation: Interdisciplinary Integration And Cutting-Edge Development, Jiting Li, Yi Sun, Yirong Wang, Yiqin Lin, Jun Jia, Gangsong Ding

Journal of System Simulation

Abstract: The breakthrough of LLMs has provided powerful tools for social network research, advancing multi-agent social network simulation into a new era. This review systematically examined recent progress in LLM-driven multi-agent social network simulation research through a integrated perspective of multi-disciplines such as artificial intelligence, psychology, communication studies, and sociology. A three-tiered research system, which has gradually formed in this field and encompassed micro-level individual behaviors, meso-level interactive relations, and macro-level system emergence, was summarized. At the micro-level, research focuses on individual human behavior simulation, and numerous studies are dedicated to developing human-like agents with complex cognitive and affective architectures …

An Adaptive Robot Path Planning Based On Improved Rea* Algorithm, Ling Zhu, Jing Li, Zhaohui Zhang

Journal of System Simulation

Abstract: In order to improve the computational efficiency and path smoothness in a robot's global path planning, an adaptive robot path planning strategy based on an improved unilateral rectangle expansion A*(REA*) algorithm was proposed. The robot's operational safety was ensured by setting a buffer around obstacles. A passable interval formed by unilateral rectangle expansion was used as the operation unit, and bidirectional alternating search was combined to enhance the path planning efficiency. Inspired by potential field theory, the evaluation function was optimized by introducing a vector form to achieve fast adaptive obstacle avoidance. A new path planning strategy was proposed …

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 …

Intelligent Air Combat Decision-Making Method Based On Bigru And Priority Dynamic Sampling, Zhengkun Ding, Jiaqi Liu, Junzheng Xu, Yuezhu Xu, Xingmei Wang

Journal of System Simulation

Abstract: Current multi-agent reinforcement learning algorithms suffer from low efficiency in utilizing experience data and difficulties in setting appropriate learning rates. To address these issues, this paper proposed a BiGRU multi-agent PPO with priority sampling and dynamic learning rate. The algorithm incorporated a BiGRU network to enhance the policy network's ability to model temporal information. A priority partial sampling mechanism was introduced to improve the utilization efficiency of high-value experience data. Additionally, an improved Adam optimizer with dynamic learning rate adjustment was employed to address the challenge of learning rate configuration. Simulation experiment results demonstrate that the algorithm significantly …

Knowledge-Enhanced Llm-Based Method For Regional Traffic Signal Control, Risheng Xu, Linyao Yang, Yuanqi Qin, Xiao Wang, Changyin Sun

Journal of System Simulation

Abstract: Adaptive traffic signal control (ATSC) is crucial for alleviating regional traffic congestion, yet it faces severe challenges in real-time response to unexpected events and global coordination. The DRL method relies on pure data-driven approaches, suffering from core limitations such as poor generalization, weak interpretability, and a lack of guidance from emergency disposal knowledge, which makes them difficult to meet the demands of complex traffic scenarios. A control system that integrates knowledge-driven and data-optimized approaches was proposed. The GraphRAG was used to construct a dynamic traffic knowledge graph, providing LLMs with real-time updated historical emergency disposal experience and road …