Physical Sciences and Mathematics Commons^™

Hybrid Jaya-Transformer-Based Task Scheduling Algorithm For Super-Agile Satellites, Haoyu Zhao, Guanghui Zhang, Lanting Hao, Meijia Guo

Journal of System Simulation

Abstract: Super-agile satellite task scheduling is characterized by complex constraints and tight coupling among task transitions. To address the relatively long online solution times of traditional heuristic algorithms, this paper establishes a super-agile satellite task scheduling model that aims to maximize the task profit ratio while considering constraints on visible time windows, attitude transition time, energy, and storage capacity. A Transformer is used to learn task requirements, visible time windows, and global correlations among tasks, and a dynamic mask and a task scheduler are incorporated to generate feasible task sequences. To enhance the model's ability to learn complex task correlations …

Aerial Refueling Scheduling Plan Generation Method For Multi-Position Task Management *, Jiachen Hu, Huanyu Li

Journal of System Simulation

Abstract: An aerial refueling scheduling plan generation method based on hierarchical hybrid strategy is proposed to address the high complexity of aerial refueling scheduling for multi-position tasks and the difficulty for traditional manual operations or single-position-oriented solutions to manage tasks effectively. A multi-level strategic framework based on fixed-rule heuristics, rolling horizon optimization, and steady-state cyclic scheduling is developed and integrated into a hierarchical hybrid strategy to balance scheduling efficiency, optimization capability, and emergency response capability. Simulation results demonstrate that the proposed method effectively generates feasible refueling scheduling plans, producing viable plans under both undisturbed rotational operations and disturbed dynamic support …

Flight Test Mission Planning Method Based On Adaptive Large Neighborhood Search, Luofu Wu, Xinyang Wu, Lunhao Ju, Guopeng Song, Rui Wang, Jiang Jiang

Journal of System Simulation

Abstract: Flight test mission planning is critical to aircraft performance evaluation, but traditional manual methods often struggle with extensive solution spaces and complex constraints. This paper constructs an overall flight test mission planning model and introduces a flight test planning algorithm (FTPA) based on adaptive large neighborhood search. FTPA utilizes Kahn topological sorting to generate an initial test-item priority sequence, which is then decoded into an initial solution, and jointly schedules flight test items and test aircraft within the adaptive large neighborhood search framework. A comparison with the CPLEX solver shows that the FTPA algorithm yields solutions with a gap …

Large Language Model-Assisted Control Framework For Mixed Remote Sensing Satellite Constellations, Guohua Wu, Ziyan You, Lei Li, Yi Gu, Shuanglin Li

Journal of System Simulation

Abstract: A LLM-assisted control framework for mixed remote sensing satellite constellations is proposed to address the problem that the control and scheduling of mixed remote sensing satellite constellations cannot process users' raw natural language observation requirements and realize full-process automation from requirement input to scheduling schemes. LLMs are utilized to enhance the construction of domain knowledge graphs, requirement understanding and task characterization, and realize intelligent generation of scheduling schemes and optimal solution of scheduling models. A scheduling scheme generation method based on the "planning and execution" dual-Agent architecture is designed. Combined with the constellation scheduling toolset, the capability boundary of …

On-Orbit Refueling Mission Planning Of Satellites Based On Improved Nsga-Ii, Guozheng Li, Rui Wang, Yi Lu

Journal of System Simulation

Abstract: An improved NSGA-II solution framework based on multi-tree encoding is proposed to address the problems of high modeling complexity under large-scale scenarios and the tendency of traditional algorithms to fall into local optima. Based on multi-tree modeling, the complex mixed-integer programming problem is transformed into a multi-tree configuration optimization problem, thus reducing the constraint handling difficulty and improving the search efficiency of the solution space. Multi-tree crossover and mutation operators as well as the calculation method for objective functions are designed. A large neighborhood search (LNS) strategy is introduced, and the global exploration and local exploitation capabilities of the …

High-Fidelity Dynamic Digital Twin Modeling Method For Permanent Magnet Synchronous Motor, He Lai, Jinquan Yang, Cong Peng, Bei Li

Journal of System Simulation

Abstract: Conventional mechanism models often suffer from model mismatch due to the absence of nonlinear mechanical damping representation and the dynamic drift of electromagnetic parameters. To address this issue, a high-fidelity dynamic digital twin modeling method for permanent magnet synchronous motors is proposed. In the static mechanism modeling dimension, a friction energy decoupling strategy is proposed, which fundamentally overcomes the inherent conflict between steady-state accuracy and transient response from a physical logic perspective. For dynamic model updating, a distributed cascaded sensing architecture based on the multi-innovation recursive least squares and extended Kalman filter (MIRLS-MIEKF) algorithms is proposed. This architecture enables …

Scheduling Optimization For Cascaded Hybrid Flow Shop Integrating Llm And Rl, Fuqing Zhao, Bin Wu, Tianpeng Xu, Ningning Zhu, Houbin Song, Yukai Yao

Journal of System Simulation

Abstract: For the cascaded hybrid flow shop scheduling problem with AGV transportation, a multiobjective optimization model is built to minimize the makespan and total energy consumption. A hybrid algorithm framework integrating the LLM, covariance matrix adaptation evolution strategy, and RL strategy selection mechanism is proposed. LLMs are employed to generate heuristic priority rules and construct high-quality initial solutions. Covariance matrix adaption evolution strategy is adopted to optimize the search in continuous priority space, and an enhanced scalar fitness function integrating hypervolume gain and solution diversity is introduced. An RL strategy selection module based on the upper level information boundary is …

Q-Learning Enhanced Memetic Algorithm For Robust Scheduling Of Space Launch Missions, Rui Lin, Xiaolu Liu, Yonghao Du, Mingyang Geng, Zhenshun Shi

Journal of System Simulation

Abstract: A robust optimization mathematical model and an enhanced memetic algorithm (MA) for space launch mission scheduling are proposed to address the practical challenges in space launch mission scheduling under the influence of multiple uncertainties such as equipment failure, process rework due to quality issues, and technological changes. A robust optimization scheduling model for space launch missions is built by integrating the makespan under the worst-case scenario, the average maximum makespan, and the average deviation of process start times. A Q-learning enhanced memetic algorithm (QMA) is proposed, which can dynamically select parent solutions based on the population state. Simulation experiments …

The Structure And Regulatory Mechanism Of The Emrb Rna Motif In Lactobacillus Acidophilus, Isabella J. Mezzenga, Brian M. Lee

Journal of the South Carolina Academy of Science

Major facilitator superfamily (MFS) transporters aid bacteria in expelling toxic compounds such as antibiotics, bile acids, and other substances. Previous bioinformatic studies identified the emrB motif as a potential regulatory RNA element. In Lactobacillus, this RNA motif is generally found in the intergenic region upstream of an MFS transporter gene with homology to the emrB gene from Escherichia coli. Secondary structure predictions of the intergenic region were used to define the structural features of this RNA motif. The emrB motif comprises two hairpins, though inclusion of neighboring regions is predicted to stabilize alternative secondary structures. A potential rho independent terminator …

Exploring The Dynamics Of Tilted, Truncated Accretion Disks, José L. Montés, Chris Fragile, Deepika Bollimpalli, Matthew Liska

Journal of the South Carolina Academy of Science

Tilted accretion disks are theoretically not uncommon in stellar mass black hole systems. The dynamics of tilted disks display interesting physical behaviors that could explain certain observational phenomena such as quasi-periodic oscillations. We present results of recent numerical simulations of tilted, truncated accretion disks around rotating black holes. Our simulations model systems composed of a thick inner torus surrounded by a thin outer disk and were run with the goal of investigating the behaviors of the torus because of the exchange of angular momentum between it and the outer disk. Multiple simulations produced surprising results, such as the potential tearing …

From Classification To Segmentation: Evaluating Machine Learning Defect Localization And Inspector Performance In Manufacturing, Gavin Smithson, Dakota Fulp, William M. Jones, Sharmistha Chakrabarti

Journal of the South Carolina Academy of Science

High-volume manufacturing at Los Alamos National Laboratory requires rigorous quality assurance; however, sustained manual inspection can be inconsistent, costly, and fatiguing. This paper presents the model-evaluation component of a larger human-in-the-loop artificial intelligence project for manufacturing anomaly detection. MicroSentryAI uses pretrained models to identify potential anomaly regions, while AnnoMate provides accessible tools for annotating, validating, and comparing those regions. The study emphasizes the distinction between classification, which determines whether a sample is anomalous, and segmentation, which identifies the location of an anomaly. Using Intersection over Union (IoU), precision, recall, and Euclidean distance between centroids, experiments with simulated mask errors demonstrated …

Cropland Change And Prime Farmland In Calloway County, Kentucky, 2015–2025: A Gis Analysis Of Corn And Soybean Fields, Shiba Hari Dhakal Shiba

Scholars Week

Understanding where cropland change occurs relative to soil quality helps indicate whether farmland is being used on the most productive land. This study used GIS-based change detection analysis to evaluate changes in corn and soybean fields in Calloway County, Kentucky, between 2015 and 2025 and to test whether new corn and soybean fields were located on prime farmland. USDA Cropland Data Layer maps for 2015 and 2025 (30 m resolution) were reclassified into seven classes and compared in ArcGIS Pro, and the results were overlaid on USDA NRCS gSSURGO farmland classification. Combined corn and soybean area declined about 19% from …

A Macroergonomic Analysis Of Autonomous Robotic Construction For Affordable Housing, Ricardo Leon Jimenez

Sustainability Conference

As communities seek to increase housing supply, the construction sector continues to grapple with rising costs, skilled labor shortages, and growing pressure to improve environmental sustainability. Meeting these demands will require new approaches that increase productivity while lowering environmental impact throughout the building lifecycle. Recent advances in autonomous systems developed for space exploration offer alternative ways to design, assemble, and maintain residential buildings. This study examines the potential application of NASA's Automated Reconfigurable Mission Adaptive Digital Assembly Systems (ARMADAS) technology from space construction to terrestrial affordable housing construction.

While most construction automation research focuses on technical performance, this study considers …

Pentagonal Pdte2 Monolayer For Sustainable Solar-Driven Hydrogen Production, Narender Kumar, Shambhu Bhandari, Dario Alfè, Ravindra Pandey, Nacir Tit

Michigan Tech Publications

This investigation demonstrates that the pentagonal PdTe2 (penta-PdTe2) monolayer is a highly tunable two-dimensional (2D) photocatalyst, characterized by the bandgap of 1.87 eV and high hole mobility. Using density functional theory calculations with the HSE06 functional, we show that tensile strain engineering (particularly at +2% and +3%) is essential for enabling spontaneous water splitting. At these strain values, the valence-band maximum and conduction-band maximum straddle the water redox potentials (H+/H2 and O2/H2O) under both acidic (pH = 0) and neutral (pH = 7) conditions. The monolayer's low hole effective mass facilitates rapid charge extraction, mitigating recombination and driving the oxygen …

Confluence: Linking Citizen Science And Extension Education To Inspire Environmental Stewardship, Dave Smaldone, Evan Harms, Jason Burnside, Chad Pierskalla, Jinyang Deng

Journal of Extension

Citizen science has rapidly moved beyond simply crowdsourcing data to become a novel platform for environmental and informal science education. However, few studies document the informal learning outcomes of citizen science, particularly in youth or Extension education. This quantitative study evaluated individual learning outcomes of an environmental education and citizen science experience about water quality at 4-H summer camps. Outcomes of interest, knowledge, and stewardship intentions were evaluated using pre- and post-participation surveys. The results showed an overall success of the program. By understanding these outcomes, we can better plan citizen-science-focused Extension learning experiences to meet strategically targeted outcomes.

Growing Climate Literacy In Urban Green Spaces: An Extension Youth Climate Education Experience, Brad Bessler, Justin Hougham

Journal of Extension

Capturing Your Environment (CYE) is an environmental education framework that engages youth in place-based climate exploration. CYE was implemented with youth participating in a Milwaukee summer internship program funded by the Children, Youth, and Families at Risk (CYFAR) Sustainable Community Projects grant. The primary objective of CYE was to create an engaging climate-centered experience for youth. The experience aimed to improve science efficacy, climate content knowledge, science skills, and confidence using scientific tools. A paired t-test compared the learners' pre- and post-experience responses on a 16-item self-report survey. Following participation in CYE, youth reported increases across all measured outcomes.

A Comprehensive Review On The Role Of Deep Learning For Detecting And Identifying Obscured Objects, Baraa Hasan Hadi, Jamila Harbi Saud

Karbala International Journal of Modern Science

Human operators frequently encounter eye fatigue that typically comes from workload and sleep deprivation. This could affect the efficiency and performance of vigilance significantly. In safety-sensitive applications such as traffic systems, healthcare, and security, this issue is essential. To address this issue, there is a need for situational awareness and maintaining continuous monitoring of the workload of the person. To this end, advanced artificial intelligence (AI) methods, denoted by a deep learning (DL) based approach, can be used. Such methods are also supported by computer vision (CV) to detect and identify obscured objects. Combining CV with AI can overcome the …

Astrophysical Radiative Capture Reaction Rates In Light Nuclei: A Review Of Results From The Modified Potential Cluster Model, S. B. Dubovichenko, B. M. Yeleusheva, A. S. Tkachenko, R. Ya. Kezerashvili

Publications and Research

A review of results obtained within a single theoretical framework—the Modified Potential Cluster Model (MPCM)—is presented for astrophysical radiative capture reactions involving the lightest and 1p-shell nuclei. The approach has been applied to reactions relevant to the r-process, the CNO and HCNO cycles, and parts of the Big Bang nucleosynthesis (BBN) and post-BBN scenarios. The model incorporates the classification of orbital states according to Young’s diagrams and has been successfully applied to describe the available experimental data on the total cross sections of the radiative capture of neutrons, protons, and the lightest clusters by 40 light nuclei at astrophysical energies. …

Seasonal Variations In Radar Backscatter And Corn Vegetation In The South Fork Iowa River Watershed During The Growing Season Of 2025, Monika Bhusal

Scholars Week

Remote sensing provides complementary information on crop development and surface conditions, but radar backscatter over agricultural areas can be influenced by vegetation and soil characteristics. This study examines seasonal relationships between Sentinel-1 radar backscatter and Landsat-derived vegetation greenness over corn-growing areas in the South Fork Iowa River Watershed, Iowa, during the 2025 growing season. Landsat 8/9 was used to derive the Normalized Difference Vegetation Index (NDVI), while OPERA RTC-S1 imagery provided VV and VH backscatter and a VH/VV polarization measure. USDA Cropland Data Layer was used to identify corn growing areas, and analysis was restricted to areas with common satellite …

Shadowable Diffeomorphisms With Dominated Splitting, Naoya Sumi

Journal of Stochastic Analysis

We study the shadowability for diffeomorphisms with dominated

splitting. In this paper we show that if a diffeomorphism f has a periodic

saddle q whose unstable manifold is compatible with dominated splitting in

some sense and if f is shadowable, then any other periodic point p has a large

local stable set. Our result does not require the hyperbolicity of p.

Assessor Experiences In Cmmc Level 2 Certification Assessments: An Interpretative Phenomenological Analysis Of Role Expectations, Samuel Heuchert, John Hastings

Research & Publications

The Cybersecurity Maturity Model Certification program requires that third-party assessments be conducted under a non-consultative model. The model is intended to ensure impartiality for organizations seeking certification. While this structure defines expectations for assessor behavior, assessor experiences and interpretations of these constraints remain underexamined. The study examines the lived experiences of CMMC-Certified Assessors and how they navigate role expectations within the non-consultative model. Using Role Conflict Theory as a guiding framework, the study applied Interpretative Phenomenological Analysis (IPA) to semi-structured interviews to explore how assessors make sense of their roles. The analysis identified experiential themes that describe how assessors construct …

Beyond Probability: An Analytical Exploration Of Fuzzy And Neutrosophic Paradigms In Binomial Acceptance Sampling, Uma G, Vela Muruga Anurahini P

Neutrosophic Systems with Applications

Acceptance sampling plans are widely used in Statistical Quality Control to determine the acceptance or rejection of production lots based on sample inspection. Classical sampling plans under the binomial distribution assume precise parameter values, which may not adequately represent uncertainty, vagueness, and indeterminacy encountered in practical inspection environments. This review summarizes acceptance probability formulations for single and double sampling plans developed using fuzzy and neutrosophic logic under the binomial distribution. The focus is on studies that model the fraction defective and related parameters as fuzzy or neutrosophic numbers, allowing imprecise and indeterminate quality information to be incorporated into sampling decisions. …

Reliability-Calibrated Q-Rung Orthopair Neutrosophic Soft Ranking Under Partial Observability: An Auditable Industrial-Process Benchmark, Nagham Ahmed Abdelmegeed, Zeshan Aslam Khan

Neutrosophic Systems with Applications

Multi-criteria decision systems often interpret disagreement among criteria as evidence that some criteria are unreliable. This is problematic when the disagreement reflects a genuine operational trade-off. We develop a reliability-and-admissibility-calibrated q-rung orthopair neutrosophic soft ranking procedure (RAC-qRON) that distinguishes recorded evidence quality from the direction of criterion preferences. An explicitly bounded interval for each alternative–criterion observation is mapped to truth, indeterminacy and falsity memberships, with an admissibility proof for every integer rung q ≥ 1. Criterion weights are adjusted according to recorded information coverage rather than cross-criterion correlation, and a fixed-reference ideal-distance calculation produces an auditable rank. The method is …

Risk Prioritization Of ``Digital Drug'' And Consumer Neurostimulation Modalities: A Reproducible Interval-Valued Fermatean Fuzzy Entropy–Topsis Framework, Rawan Emad El-Saadany, Ahmed Samy, Mohamed M. Abdelhafeez, Mahmoud M. Ismail

Neutrosophic Systems with Applications

Consumer audio and multisensory stimulation technologies are increasingly accessible through streaming platforms, mobile applications, and immersive media. The expression digital drug is sometimes used for binaural-beat content intended to alter subjective states, but the term is a consumer/cultural label and does not imply pharmacological equivalence. Risk prioritization in this area is difficult because evidence is heterogeneous, several relevant criteria are qualitative, and judgments may be imprecise rather than point-valued. This paper develops a transparent interval-valued Fermatean fuzzy (IVFF) group decision framework that combines interval-preserving aggregation, Shannon entropy weighting, and TOPSIS for prioritizing digital drug risk scenarios under uncertainty. The method …

Risk-Based Cybersecurity Threat Prioritization In Digital Twin-Enabled Cyber-Physical Systems, Nada Mohamed, Alshaimaa A. Tantawy, Ahmed M. Abdelmouty

Neutrosophic Systems with Applications

Digital Twin (DT)-enabled cyber-physical systems (CPS) face cybersecurity risks due to their complex connections and high levels of interdependence. Traditional methods, for assessing risk often fail to rank these threats. This is especially true when expert opinions include ambiguity, uncertainty or unclear information. A Type 2 Neutrosophic Entropy– Measurement of Alternatives and Ranking according to COmpromise Solution (Entropy- MARCOS) framework for cybersecurity threat prioritization in DT-enabled CPS is proposed in this paper. The uncertainty and indeterminacy present in expert evaluations are represented by Type 2 Neutrosophic Numbers (T2NNs). While the MARCOS technique is used to assess and rank cybersecurity threats …

Context-Regulated Q-Neutrosophic Soft Model Selection Under Input Perturbation: Evidence From Three Classification Benchmarks, Poom Kumam, Supak Phiangsungnoen

Neutrosophic Systems with Applications

Selecting a classifier using a single average metric can conceal instability across validation splits, poor lower-tail behavior, and sensitivity to input perturbations. This study formulates a context-regulated Q-neutrosophic soft model-selection procedure with two fixed evaluation conditions. For each classifier and performance criterion, mean attainment (truth), across-split dispersion (indeterminacy), and the complement of a lower empirical quantile (falsity) form an auditable three-component descriptor. A bounded lower-context regulator aggregates their penalized utilities; monotonicity and perturbation bounds follow directly from its convex representation. Four fixed classifiers are compared using five benefit-oriented criteria, three repetitions of stratified five-fold cross-validation, and paired test-feature perturbations. Three …

Electrocatalytic Oxidation Of Ethylene Glycol To Glycolic Acid: From Mechanism And Catalyst Design To Emerging Neutral Electrolyte Systems, Man Zhang, Xue Li, Xin-Yu Peng, Lin Wang, Wen-Wen Chen, Yao-Yue Yang

Journal of Electrochemistry

Polyethylene terephthalate (PET) waste represents both an environmental burden and a loss of carbon resources. Alkaline depolymerization of PET yields terephthalic acid and ethylene glycol (EG). Electrocatalytic oxidation of EG to glycolic acid (GA), the monomer of biodegradable polyglycolic acid (PGA), offers a sustainable route driven by renewable electricity. However, the ethylene glycol oxidation reaction (EGOR) involves competing pathways toward C2 products (GA and oxalic acid) and C1 products (formic acid and CO₂), making selective GA production a central challenge. This review provides a comprehensive overview of recent progress in selective EG-to-GA electrooxidation from catalyst design, mechanistic understanding, …

Regularization-Based Crystallite Size Distribution Analysis Of Pt/C Catalysts From X-Ray Diffraction, Cheng-Long Ma, Wei-Yu Shi, Zhong-Jun Hou

Journal of Electrochemistry

Accurate determination of the platinum crystallite size distribution in carbon-supported platinum (Pt/C) catalysts is critical for evaluating both the initial performance and long-term durability of proton exchange membrane fuel cells (PEMFCs). Conventional characterization approaches—including transmission electron microscopy (TEM), small-angle X-ray scattering (SAXS), and the Scherrer equation—are limited by restricted statistical sampling, sensitivity to support interference, or the inability to resolve the full breadth of the size distribution. In this work, we present a regularization-based inversion framework that extracts the volume-weighted crystallite size distribution directly from X-ray diffraction (XRD) line profiles, without requiring any a priori assumption regarding the functional form …

Metal Oxide Semiconductor Gas Sensors For Exhaled Breath Cancer Biomarker Detection, Guang-Lan Chen, Hui-Hui Luo, Wen-Wen Peng, Shi-Yu Xu, Yu-Ting Li, Hui-Jie Ke, Si-Hui Zhong, Jia-Ao Wei, Tong Chen, Xiao-Kun Li, You-Lin Zhang, Wei Chen

Journal of Electrochemistry

Early cancer screening can significantly improve therapeutic outcomes, volatile organic compounds (VOCs) and other related gases in exhaled breath, as cancer-specific biomarkers, offer a novel approach for non-invasive diagnosis. Metal oxide semiconductor (MOS)-based gas sensors are pivotal for exhaled breath VOCs detection, featuring compact size, cost-effectiveness, and rapid response. This review summarizes the most recent five-year research advances in cancer biomarker detection, and clarifies the core mechanisms of electrical property changes from sensing material-VOCs interactions and key performance metrics including sensitivity, selectivity, humidity tolerance, response/recovery time, stability, and operating temperature. This review also highlights the optimization strategies of sensing materials …

Autofield-Poa: A Hybrid Automl–Metaheuristic Framework For Automated Feature Selection In Recommender Systems, Mohammed Dheyaaldeen Taha, Waleed Kareem Ahmed, Rana Saad Mohammed, Zied O. Ahmed

Karbala International Journal of Modern Science

The problem of automated feature selection is one of the most important aspects of modern recommender systems, as overestimating the number of feature fields used, which can increase model complexity, inference latency, and performance degradation due to redundancy and noise. Although differentiable Automated Machine Learning (AutoML) based feature selection methods, including AutoField, are effective, they remain susceptible to initialization and local optima. Conversely, population-based metaheuristic algorithms are effective at global exploration, but are often inefficient in high-dimensional discrete spaces when randomly initialized. This paper presents AutoField-POA, a hybrid AutoML-metaheuristic framework that combines a differentiable controller with the Pelican Optimization Algorithm …