Academic Journal of Applied Sciences
type: channel
description: Academic Journal of Applied Sciences (AJAS) is an international, peer-reviewed journal. The mission of the journal is to provide an academic platform for scholars and practitioners to discuss the issues of applied sciences across the world. All articles published are rigorously and fast reviewed meeting the Journal Quality standards.
publisher: Darcy & Roy Press
language: en
publication name: Academic Journal of Applied Sciences
issn: 3105-0549
items: Conformal-AEDL: Uncertainty-Calibrated Neuro-Symbolic Multiagent Causal Inference for Non-Stationary Time Series
items: Conformal Uncertainty-Aware Physics-Informed Neural Networks for Reliable Electromagnetic Field Prediction in VLSI Interconnects
items: ConformalSafe-Routing: Uncertainty-Aware Safe Deep Reinforcement Learning for Adaptive Routing Convergence
items: TalentFlow: Explainable Internal Talent Mobility Prediction Using Graph Representation Learning
items: Research Progress on Multi-Hazard Disaster Mechanisms, Monitoring, Early Warning and Control in Underground Engineering
items: A Multi-Source Monitoring Data Fusion and Early Warning Framework Based on LightGBM-SHAP and Weibull Distribution
items: Numerical Simulation of Alpha Particle Scattering Based on the Symplectic Euler and Heun Methods
items: Passive Shading Optimization for Buildings Based on Differential Evolution and Non-Dominated Sorting Multi-Objective Evolutionary Algorithm
items: Bayesian Inverse Sampling and Shapley-Driven Calibration for Dual-Channel Preference Aggregation
items: Toward 6G-Empowered Multi-Machine Collaboration in Smart Agriculture: A Communication-Control Co-Design Survey
items: Additive Tree Latent Variable Models for Insurance Loss Prediction
items: DFPSE: Diagnostic-Feature-Preserving Signal Enhancement for Robust Bearing Fault Diagnosis under Compound Noise
items: Physics-Based Data Generation with SVR Surrogate Modeling for PDMS Thin Film Emissivity Prediction
items: Cross-Domain Classification for Hyperspectral Imagery: A Comprehensive Review of Domain Adaptation, Domain Generalization, and Multimodal Approaches
items: Study on Abnormal Vibration Cause and Blade Damage Fractal of Induced Draft Fan Burning Low-quality Coal
items: Research on a Dynamic Behavior Analysis Framework Based on Random Forest and Markov Chain
items: Artificial Intelligence in Instrumentation: A Balanced Analysis of Potential Benefits and Risks
items: Research Progress on Algal-Bacterial Symbiotic Systems for the Treatment of Livestock Wastewater
items: Simulation and Optimization of Structural Parameters of Nozzle for Abrasive Water Jet Stubble Breaking
items: Design and Optimization of Feeding Device for Pneumatic Seeder of Legume-Gramineae Mixed Sowing
items: Research on a YOLOv8-Based Metal Residue Detection Algorithm
items: Research on Wind Energy Data Quality Control Methods in Qinghai Province on the Qinghai-Tibet Plateau (2019–2021)
items: Learning When to Reason: Gating LLM Inference for Cost-Efficient Serverless Function Scheduling at Scale
items: Robust Structural Response Prediction Under Coupled Thermo-Mechanical Effects via Hierarchical Attention-Based Sensor Fusion
items: Study on Overlying Rock Deformation and Surface Subsidence Prediction under Layered Grouting Filling Conditions
items: Analysis of the Dilemmas and Causes of “Supervisor-Counselor” Collaborative Education in Universities in the Context of AI
items: Innovative Practice of Game Level Design and Scene Narrative Driven by AI Technology
items: Exploratory Research Based on AGIL Model: Application Path Practice of "Fengqiao Experience" in Improving College Students' Mental Health in Zhejiang Universities' "One-Stop" Student Communities
items: An Optimization Path for "Supervisor-Counselor" Collaborative Education in Universities Empowered by Large Language Models
items: From "Disciplinary Space" to "Life Harbor": Dilemmas and Path Optimization of the "One-Stop" Student Community Construction in Medical Universities Based on the "Fengqiao Experience"

Conformal-AEDL: Uncertainty-Calibrated Neuro-Symbolic Multiagent Causal Inference for Non-Stationary Time Series
type: item
description: Event-driven causal labeling in non-stationary time series is useful only when the system can state when its own root-cause decision is ambiguous. Existing neuro-symbolic multiagent pipelines improve semantic and structural consistency, but their confidence scores need not retain a stable error meaning after regime changes. This paper presents Conformal-AEDL, a resource-efficient extension that couples heterogeneous residual and propagation agents with reliability-weighted consensus, an explicit symbolic compliance layer, and online conformal prediction sets. The method outputs a set of plausible root causes rather than forcing a singleton, and adapts the conformal error budget when score surprises indicate rapid drift. Evaluation uses a nonlinear six-node structural causal model with four regimes and a semi-synthetic U.S. macroeconomic benchmark whose dynamics and residuals are estimated from 203 public quarterly observations while intervention labels remain controlled. Across ten seeds, Conformal-AEDL attains 0.929±0.022 and 0.917±0.029 coverage at a nominal 0.90 level, with average set sizes of 1.186 and 1.890. Relative to static split conformal prediction, this reduces set size by 26.0% and 29.5%, respectively. The results show that calibrated abstention can be added without inventing causal ground truth or relying on proprietary language-model calls, while also exposing an important limitation: immediate post-switch coverage remains difficult in the macro benchmark.
subject: Articles
  topic: section
subject: Adaptive conformal inference
  topic: keywords
subject: causal attribution
  topic: keywords
subject: concept drift
  topic: keywords
subject: multiagent systems
  topic: keywords
subject: neuro-symbolic artificial intelligence
  topic: keywords
subject: non-stationary time series
  topic: keywords
subject: uncertainty calibration
  topic: keywords
creator: Elena Rossi
creator: Lars Johansen
rights: Copyright (c) 2026 Academic Journal of Applied Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-21
publication date: 2026-07-21
starting page: 133
ending page: 140
doi: 10.54097/49f82931

Conformal Uncertainty-Aware Physics-Informed Neural Networks for Reliable Electromagnetic Field Prediction in VLSI Interconnects
type: item
description: Electromagnetic field prediction in very-large-scale integration (VLSI) interconnects is increasingly constrained by two competing requirements: field solvers must remain physically faithful near conductor edges and material interfaces, yet they must also provide calibrated reliability indicators for fast design-space exploration. This paper proposes a conformal uncertainty-aware physics-informed neural network (UQ-PINN) for quasi-static electromagnetic field approximation in a two-layer interconnect cross-section. The method combines a random-feature neural representation with boundary, sparse observation, and electroquasistatic residual constraints, and then converts ensemble uncertainty into finite-sample prediction intervals using split conformal calibration. The scope is deliberately reproducible: no measured silicon data are claimed. A deterministic finite-difference solution of div (epsilon grad phi)=0 is used as the reference benchmark, and all reported values are generated by the accompanying code. On the benchmark, the physics-informed ensemble obtains a potential MAE of 0.02057 V and an electric-field magnitude relative L2 error of 17.69%, improving the sparse data-only neural baseline. The conformalized interval achieves 90.60% empirical coverage for a 90% target, whereas the uncalibrated ensemble interval reaches only 66.63%. These results indicate that physics constraints and conformal calibration can jointly support reliable electromagnetic surrogates for early-stage EDA analysis without overstating the substitute for sign-off full-wave verification.
subject: Articles
  topic: section
subject: Physics-informed neural networks
  topic: keywords
subject: conformal prediction
  topic: keywords
subject: uncertainty quantification
  topic: keywords
subject: VLSI interconnects
  topic: keywords
subject: electromagnetic field prediction
  topic: keywords
subject: parasitic extraction
  topic: keywords
subject: EDA
  topic: keywords
creator: Sofia Duarte
creator: Benjamin Hart
rights: Copyright (c) 2026 Academic Journal of Applied Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-21
publication date: 2026-07-21
starting page: 126
ending page: 132
doi: 10.54097/56shkz94

ConformalSafe-Routing: Uncertainty-Aware Safe Deep Reinforcement Learning for Adaptive Routing Convergence
type: item
description: Adaptive routing controllers based on deep reinforcement learning can shorten recovery after failures, but a policy optimized only for expected reward may select timer and damping configurations whose control overhead or route oscillation risk is poorly estimated under rare or shifted conditions. This paper presents ConformalSafe-Routing, a runtime safety layer for adaptive routing convergence. A dueling double deep Q-network ranks bounded routing profiles, while an independently trained risk model estimates one-step operational cost from topology, failure, load, and convergence-state features. Split conformal calibration converts point predictions into finite-sample upper bounds. A horizon-aware allocation uses a per-decision miscoverage budget of 0.0125 for an eight-step episode, and the shield selects the highest-value action whose upper bound satisfies the safety envelope; a balanced static profile is used when the certified set is empty. Experiments use four real telecom topologies from the Internet Topology Zoo and a fully released topology-driven event simulator. Across 300 paired episodes per condition, the proposed method reduces episode-level safety violations from 56.7% to 4.3% in-domain and from 64.0% to 2.0% under high-load distribution shift relative to unshielded DQN. It also reduces route flaps by 73.0% and 76.6%, respectively. Compared with a balanced static profile, it shortens mean convergence by 22.5% in-domain and 9.9% under shift while maintaining low violation rates. The results support conformal shielding as a practical mechanism for exposing and controlling the safety-speed trade-off in learning-based routing, while also identifying the limits of guarantees under topology and load shift.
subject: Articles
  topic: section
subject: Adaptive routing
  topic: keywords
subject: conformal prediction
  topic: keywords
subject: deep reinforcement learning
  topic: keywords
subject: network convergence
  topic: keywords
subject: runtime assurance
  topic: keywords
subject: safe reinforcement learning
  topic: keywords
creator: Fatima Rahman
creator: Eric Nolan
rights: Copyright (c) 2026 Academic Journal of Applied Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-20
publication date: 2026-07-20
starting page: 118
ending page: 125
doi: 10.54097/dfh9nx76

TalentFlow: Explainable Internal Talent Mobility Prediction Using Graph Representation Learning
type: item
description: Internal talent mobility—the movement of employees into new roles within the same organization—is central to workforce planning, yet most data-driven talent models treat each employee as an isolated feature vector and predict a single binary outcome such as promotion or attrition. This ignores a defining property of mobility: an employee’s next role is shaped not only by individual attributes but by relational context—the skills shared with target roles, the career ladders typical of a function, and the social capital embedded in a managerial and peer network. We present TalentFlow, a framework that reformulates internal mobility as next-role link prediction over a heterogeneous talent graph whose nodes are employees, roles, skills, departments and managers, connected by typed relations. A relational graph convolutional encoder learns employee and role representations by message passing over these relations, and a mobility decoder ranks candidate next roles for each employee. To make recommendations auditable, TalentFlow couples two complementary explanation modalities: SHAP attribution over employee attributes and a structural occlusion method that quantifies how much each relational channel contributed to a recommendation—an explanation flat-feature models cannot provide. Because real internal-mobility trajectories are proprietary, we construct TalentFlow-Sim, a benchmark that anchors employee attribute distributions to the public IBM HR Analytics dataset while generating a mobility layer driven by skill fit, career-ladder priors and managerial sponsorship. Across five seeds, relational message passing improves next-role ranking over strong non-graph baselines and over homogeneous-graph and structure-only embedding methods, and ablations show that removing the managerial/peer relation causes the largest degradation—direct evidence that social-capital signals carry predictive value recoverable only through the graph. TalentFlow complements recent explainable, fairn…
subject: Articles
  topic: section
subject: Talent mobility
  topic: keywords
subject: graph neural networks
  topic: keywords
subject: heterogeneous graphs
  topic: keywords
subject: link prediction
  topic: keywords
subject: explainable AI
  topic: keywords
subject: human resource analytics
  topic: keywords
creator: Daniel Brooks
creator: Priya Raman
creator: Lina Chen
rights: Copyright (c) 2026 Academic Journal of Applied Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-20
publication date: 2026-07-20
starting page: 111
ending page: 117
doi: 10.54097/7zspnn42

Research Progress on Multi-Hazard Disaster Mechanisms, Monitoring, Early Warning and Control in Underground Engineering
type: item
description: Underground engineering is increasingly exposed to complex geological conditions, intensive urban constraints, and long service periods. Disasters rarely arise from a single defective component; they usually develop through interactions among geological uncertainty, excavation disturbance, groundwater migration, support response, construction organization, and delayed deterioration. This paper presents a critical review of the mechanisms, monitoring methods, and prevention strategies associated with major underground-engineering hazards, including collapse, water and mud inrush, squeezing deformation, rockburst, ground settlement, harmful-gas and fire events, and structural deterioration. A source–pathway–response framework is used to explain how local damage evolves into system-level failure and cascading consequences. Analytical, numerical, physical-model, field-monitoring, and data-driven approaches are compared with respect to applicability, interpretability, and uncertainty. The review shows that conventional single-index warning and static safety assessment are inadequate for rapidly changing ground conditions. More reliable practice requires multi-source geological prediction, continuous deformation and seepage sensing, physics-informed data fusion, and decision thresholds linked directly to construction actions. An integrated closed-loop framework is therefore proposed that connects investigation, scenario analysis, monitoring, diagnosis, adaptive control, emergency response, and post-event model updating. The principal research needs are uncertainty-aware prediction, transferable warning models, coupled multi-hazard simulation, resilient support systems, and life-cycle digital twins. The review provides a structured basis for transforming underground disaster control from isolated hazard treatment to adaptive, risk-informed, and resilience-oriented management.
subject: Articles
  topic: section
subject: Underground engineering
  topic: keywords
subject: tunnel disaster
  topic: keywords
subject: multi-hazard coupling
  topic: keywords
subject: monitoring and early warning
  topic: keywords
subject: grouting
  topic: keywords
subject: digital twin
  topic: keywords
subject: resilience
  topic: keywords
creator: Lin Gao
creator: Jiyang Jin
rights: Copyright (c) 2026 Academic Journal of Applied Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-19
publication date: 2026-07-19
starting page: 100
ending page: 110
doi: 10.54097/2g9xeh35

A Multi-Source Monitoring Data Fusion and Early Warning Framework Based on LightGBM-SHAP and Weibull Distribution
type: item
description: This paper addresses the need for multi-source data fusion and risk early warning in slope monitoring by proposing a modeling method that integrates data calibration, feature interpretation, and hierarchical early warning. First, linear regression is used to calibrate fiber-optic displacement data, and data consistency is improved through anomaly removal, multiple interpolation, and wavelet denoising. Subsequently, a LightGBM regression model is constructed to characterize the nonlinear relationship between surface displacement and factors such as rainfall, pore water pressure, microseismic events, and deep-seated displacement. The SHAP method is introduced to explain the contributions of each factor, thereby enhancing the interpretability of the model results. Building on this, Lasso regression is used to screen for key variables and reduce redundant information, followed by the establishment of a probabilistic representation of displacement velocity and graded early warning thresholds based on the Weibull distribution. This method balances prediction accuracy, physical plausibility, and engineering applicability, and can serve as a reference for identifying complex slope deformations, analyzing key controlling factors, and making risk warning decisions.
subject: Articles
  topic: section
subject: LightGBM-SHAP
  topic: keywords
subject: Graded Early Warning Framework
  topic: keywords
subject: Lasso Feature Selection
  topic: keywords
creator: Haiben Lin
creator: Jiekai Li
creator: Qiqi Li
rights: Copyright (c) 2026 Academic Journal of Applied Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-16
publication date: 2026-07-16
starting page: 93
ending page: 99
doi: 10.54097/nhwtyp92

Numerical Simulation of Alpha Particle Scattering Based on the Symplectic Euler and Heun Methods
type: item
description: This paper constructs a two-dimensional numerical model of α-particle scattering based on Coulomb forces and compares simulations using the Rutherford concentrated-charge model and the Thomson uniform-charge model. The Symplectic Euler method and the Heun prediction-correction algorithm were employed to calculate particle trajectories, scattering angles, and energy changes, which were then validated against theoretical scattering angles. The results show that in the Rutherford model, the scattering angle exhibits an approximately linear relationship with 1/b, with a maximum deflection angle of approximately 160°; in the Thomson model, the overall deflection is weaker, and the scattering angle is generally less than 3.5°. The Symplectic Euler method exhibits smaller energy fluctuations and superior long-term stability, while the Heun algorithm provides better trajectory fitting accuracy. This study establishes a general simulation framework applicable to charged particle scattering processes, which can serve as a reference for classical scattering modeling and numerical calculations of conservative systems.
subject: Articles
  topic: section
subject: Symplectic Euler Method
  topic: keywords
subject: Heun Method
  topic: keywords
subject: Coulomb Force Model
  topic: keywords
creator: Wenyu Zou
rights: Copyright (c) 2026 Academic Journal of Applied Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-15
publication date: 2026-07-15
starting page: 85
ending page: 92
doi: 10.54097/4t2nwa44

Passive Shading Optimization for Buildings Based on Differential Evolution and Non-Dominated Sorting Multi-Objective Evolutionary Algorithm
type: item
description: This paper proposes an hourly-scale evaluation and multi-objective optimization method for passive shading retrofits in buildings. Using typical meteorological annual data as input, the study couples solar radiation, heat transfer through building envelopes, ventilation heat exchange, window heat gain, and daylighting and glare constraints to construct a computational framework for “design variables—thermal response—energy consumption evaluation.” The study employs the Differential Evolution algorithm and the Non-Dominant Sorting Multi-Objective Evolution algorithm to obtain a Pareto optimal solution balancing energy consumption, comfort, and retrofit costs, while incorporating thermal mass coupling and adjustable shading strategies to enhance the model’s adaptability. The results indicate that the optimized solution reduces the annual cooling load by 9.4%, heating season energy consumption by 14.7%, and the number of overheating hours by 82.1%. This method is applicable to building shading retrofit decisions across different climate zones and future climate scenarios, demonstrating good universality and engineering application value.
subject: Articles
  topic: section
subject: Differential Evolution Algorithm
  topic: keywords
subject: Non-Dominated Sorting Multi-Objective Evolutionary Algorithm
  topic: keywords
subject: Hourly Weather-Driven Model
  topic: keywords
creator: Binhe Wang
creator: Ran Cheng
creator: Haoran Wu
rights: Copyright (c) 2026 Academic Journal of Applied Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-14
publication date: 2026-07-14
starting page: 77
ending page: 84
doi: 10.54097/6zkr1k53

Bayesian Inverse Sampling and Shapley-Driven Calibration for Dual-Channel Preference Aggregation
type: item
description: This paper presents a dual-channel preference aggregation framework that reconstructs latent user voting distributions and calibrates heterogeneous scoring channels through Bayesian inverse sampling and gradient boosting attribution. The target setting is a mixed-evaluation ranking system where expert scores and user votes drive elimination jointly. Three coupled problems are addressed: latent preference recovery from elimination outcomes, normalization-induced bias quantification across scoring schemes, and dynamic weight allocation under temporal drift. A Monte Carlo inverse sampling module reconstructs latent voting distributions across 34 episodes by enforcing elimination-consistency constraints with Bayesian priors, raising the simulation acceptance rate from 28.6% to 44.6%. A share dispersion metric quantifies variance convergence near elimination boundaries, with eliminated candidates registering dispersion 0.0252 against 0.0601 for advancing candidates. The Fan Dominance Index measures normalization-induced bias. Percentage-based aggregation amplifies user signals more aggressively than rank-based aggregation, while a normalized expert override neutralizes excessive popularity influence. An XGBoost regression with Shapley attribution reaches R-squared of 0.906, with candidate age (weight 0.324) as the dominant judging predictor and a per-expert efficacy hierarchy that registers marginal premiums up to +0.603. The D.A.R.E. system integrates dual-quantile calibration and dynamic weighting, and remains robust under 275 weeks of simulation with extreme voting fluctuations.
subject: Articles
  topic: section
subject: Bayesian Inverse Sampling
  topic: keywords
subject: Shapley Additive Attribution
  topic: keywords
subject: Gradient Boosting Regression
  topic: keywords
subject: Dual-Quantile Calibration
  topic: keywords
subject: Dynamic Weight Allocation
  topic: keywords
subject: Heterogeneous Preference Aggregation
  topic: keywords
creator: Jiawei Liu
creator: Kai Li
creator: Qisheng Su
rights: Copyright (c) 2026 Academic Journal of Applied Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-13
publication date: 2026-07-13
starting page: 71
ending page: 76
doi: 10.54097/erpkp016

Toward 6G-Empowered Multi-Machine Collaboration in Smart Agriculture: A Communication-Control Co-Design Survey
type: item
description: As communication and sensing capabilities grow, smart agriculture is shifting from single-machine automation to coordinated fleets. Cooperative harvesting, multi-UAV spraying, and fleet logistics demand bounded latency, reliability, and synchronized state that traditional best-effort networks cannot guarantee. Control algorithms validated under ideal communication assumptions degrade when deployed on real field networks with partial coverage and thin backhaul, and this gap cannot be closed by a faster network or a smarter controller alone. Taking as its organizing line that the network deadline and the controller deadline must be treated as one design surface, this review systematically surveys the communication-control co-design of multi-machine collaboration in smart agriculture, covering the joint engineering of deterministic communication, integrated sensing and communication, edge intelligence, and distributed control. We organize the literature around four pillars: deterministic networks, multi-agent coordination, co-design substrates, and enablers. Stability-region analysis already bounds control cost as a function of communication parameters, whereas integrated sensing and digital twins remain at the architecture stage, lacking quantitative validation in agricultural scenarios. It follows that standardization, rural coverage, and safety are constraints that fix the co-design operating point rather than afterthoughts.
subject: Articles
  topic: section
subject: Smart agriculture
  topic: keywords
subject: multi-machine collaboration
  topic: keywords
subject: communication-control co-design
  topic: keywords
subject: deterministic networking
  topic: keywords
subject: integrated sensing and communication
  topic: keywords
subject: edge intelligence
  topic: keywords
subject: digital twin
  topic: keywords
creator: Baofeng Ji
creator: Yangyang Li
creator: Ran Zhang
creator: Saibing Wang
creator: Yifan Liu
creator: Wenjing Xu
creator: Kaipeng Sun
creator: Xin Zhang
creator: Du Cui
creator: Yanchao Guo
creator: Peipei Wang
creator: Song Chen
creator: Jinguang Li
creator: Zheng Tu
creator: Shusheng Lü
rights: Copyright (c) 2026 Academic Journal of Applied Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-13
publication date: 2026-07-13
starting page: 55
ending page: 70
doi: 10.54097/99gpx908

Additive Tree Latent Variable Models for Insurance Loss Prediction
type: item
description: Accurate prediction of insurance losses is essential for premium pricing, reserve estimation, and risk management in the property and casualty insurance industry. Traditional actuarial models, such as generalized linear models, often rely on predefined distributional assumptions and struggle to capture complex nonlinear interactions among risk factors. Meanwhile, tree based ensemble methods have demonstrated strong predictive power but typically lack the ability to model latent heterogeneity in policyholder risk profiles. This paper proposes an Additive Tree Latent Variable (ATLV) model that integrates gradient boosted additive tree structures with a latent variable framework to improve insurance loss prediction. A Latent Risk Index (LRI) is developed to capture unobserved policyholder risk heterogeneity through variational inference. A Tree Ensemble Loss Predictor (TELP) is formulated to combine the latent representations with observed covariates in a gradient boosting framework. A Distributional Calibration Module (DCM) is introduced to ensure that predicted loss distributions are well calibrated under the Tweedie compound Poisson family. Empirical analysis is conducted on the French Motor Third Party Liability (MTPL) insurance dataset comprising 678,013 policies. The results demonstrate that the ATLV model significantly outperforms both traditional generalized linear models and standard gradient boosting approaches across multiple evaluation metrics. The ATLV model achieves a mean Deviance of 1.247, a Gini coefficient of 0.312, and a calibration error of 0.034, representing improvements of 8.6%, 14.3%, and 41.4% respectively over the best performing baseline. An ablation study further reveals that the LRI module contributes most significantly to overall performance, confirming the importance of modeling latent risk heterogeneity in insurance loss prediction. These findings provide both theoretical insights and practical tools for actuaries seeking to enhance loss…
subject: Articles
  topic: section
subject: Additive Tree Models
  topic: keywords
subject: Latent Variable Models
  topic: keywords
subject: Insurance Loss Prediction
  topic: keywords
subject: Gradient Boosting
  topic: keywords
subject: Tweedie Distribution
  topic: keywords
creator: Jiajun Li
rights: Copyright (c) 2026 Academic Journal of Applied Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-09
publication date: 2026-07-09
starting page: 48
ending page: 54
doi: 10.54097/94tgta52

DFPSE: Diagnostic-Feature-Preserving Signal Enhancement for Robust Bearing Fault Diagnosis under Compound Noise
type: item
description: Rolling bearing fault diagnosis using deep learning (DL) achieves high accuracy under clean laboratory conditions, yet performance collapses under the compound noise typical of industrial environments — Gaussian white noise, harmonic interference, transient impulses, and sensor drift. Existing signal denoising methods optimize for waveform reconstruction fidelity, but we demonstrate a critical and counterintuitive finding: higher signal-to-noise ratio (SNR) does not imply better diagnostic accuracy, and reconstruction-only enhancement can actively degrade classification performance below the unenhanced noisy baseline. To address this disconnect, we propose Diagnostic-Feature-Preserving Signal Enhancement (DFPSE), a framework in which a frozen, pretrained diagnostic model (teacher) guides a lightweight Residual Temporal Convolutional Network (Residual TCN) enhancer via three complementary losses: waveform reconstruction, diagnostic classification, and feature-level alignment. Trained under a single Gaussian drift noise condition (SNR in [-10, 8] dB), the enhancer is evaluated across six unseen noise conditions spanning five Gaussian drift levels and a compound interference scenario. Experiments on the Huazhong University of Science and Technology (HUST) and Paderborn University (PU) bearing datasets with three architecturally diverse teachers — ResNet1D (CNN), TCN (dilated convolution), and Vanilla Transformer (self-attention) — demonstrate that DFPSE consistently recovers diagnostic accuracy with gains of 22–29 percentage points on HUST (average across conditions) and 9–22 points on PU. Critically, on the HUST dataset at -2 dB input SNR, the reconstruction-only variant achieves 5.59 dB output SNR but only 65.4% accuracy (below the 86.7% noisy baseline), whereas DFPSE achieves 2.72 dB SNR with 98.4% accuracy — a 33-point gap that reveals a fundamental trade-off between waveform fidelity and diagnostic discriminability. This finding challenges the conventional assumpt…
subject: Articles
  topic: section
subject: Bearing fault diagnosis
  topic: keywords
subject: signal enhancement
  topic: keywords
subject: knowledge distillation
  topic: keywords
subject: compound noise
  topic: keywords
subject: deep learning
  topic: keywords
creator: Jiajun Liu
creator: Baishun Su
rights: Copyright (c) 2026 Academic Journal of Applied Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-08
publication date: 2026-07-08
starting page: 35
ending page: 47
doi: 10.54097/5rk1xm44

Physics-Based Data Generation with SVR Surrogate Modeling for PDMS Thin Film Emissivity Prediction
type: item
description: This study combines Transfer Matrix Method (TMM) simulation with Support Vector Regression (SVR) to predict the infrared emissivity of PDMS/SiO₂ thin films across λ=2.0-14.0μm and thicknesses of 100–1000 nm. Physical consistency is enforced during data generation: every TMM spectrum is validated against ε=1-R-T, and points violating |R+T+ε-1|>10-3 are excluded from training. The SVR surrogate, trained on 82,938 validated spectra with an RBF kernel and Halton hyperparameter search, achieves hold-out R2 = 0.959 (RMSE = 0.047, MAE = 0.026) and runs 275× faster than direct TMM evaluation. An overlap-aware multi-region partitioning scheme reduces boundary prediction errors below 1%. The atmospheric-window band (8–13 μm) reaches R2 = 0.985. The complete pipeline, including data, model, and configuration, is publicly archived.
subject: Articles
  topic: section
subject: Radiative Cooling
  topic: keywords
subject: PDMS Thin Films
  topic: keywords
subject: Transfer Matrix Method
  topic: keywords
subject: Support Vector Regression
  topic: keywords
subject: Surrogate Modeling
  topic: keywords
creator: Yifan Wu
rights: Copyright (c) 2026 Academic Journal of Applied Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-07
publication date: 2026-07-07
starting page: 28
ending page: 34
doi: 10.54097/2as25961

Cross-Domain Classification for Hyperspectral Imagery: A Comprehensive Review of Domain Adaptation, Domain Generalization, and Multimodal Approaches
type: item
description: Cross-domain hyperspectral image (HSI) classification has emerged as a critical challenge in remote sensing, driven by the inherent spectral variability across different acquisition conditions, sensors, and geographical regions. Traditional supervised classification methods rely heavily on abundant labeled data, which is expensive and time-consuming to obtain for each new scene. This review provides a comprehensive synthesis of 23 representative works published between 2015 and 2025, organized into three methodological paradigms: (1) domain adaptation (DA)—13 methods that align source and target distributions through statistical matching, adversarial learning, contrastive learning, or active sample selection; (2) domain generalization (DG)—5 methods that learn domain-invariant representations without target data exposure; and (3) cross-modality and graph-based approaches—5 methods that leverage multimodal data or capture nonlocal topological structures. Our cross-cutting synthesis yields four key findings: (i) feature-level alignment alone is insufficient—class-conditional, decision-boundary, and topological alignment each provide complementary signals; (ii) contrastive learning has emerged as a unifying paradigm since 2023, adopted by 5 of the 10 most recent works; (iii) only 2 of 23 methods support open-set transfer, revealing a critical gap between research assumptions and real-world deployment; and (iv) domain generalization methods have narrowed the accuracy gap with DA while offering zero-shot deployment capability. We identify seven research gaps, each assessed for urgency and impact, with foundation model adaptation, benchmark standardization, and open-set domain adaptation rated as near-term, high-impact priorities. This review provides researchers with a structured taxonomy, a 23-method comparative matrix across six design dimensions, and a prioritized roadmap for future investigations in cross-domain remote sensing image classification.
subject: Articles
  topic: section
subject: Hyperspectral image classification
  topic: keywords
subject: domain adaptation
  topic: keywords
subject: domain generalization
  topic: keywords
subject: cross-scene classification
  topic: keywords
subject: transfer learning
  topic: keywords
subject: contrastive learning
  topic: keywords
subject: graph neural networks
  topic: keywords
subject: multimodal learning
  topic: keywords
creator: Jiashuo Hou
rights: Copyright (c) 2026 Academic Journal of Applied Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-06
publication date: 2026-07-06
starting page: 17
ending page: 27
doi: 10.54097/c0s57j81

Study on Abnormal Vibration Cause and Blade Damage Fractal of Induced Draft Fan Burning Low-quality Coal
type: item
description: With increasingly fierce competition in the electricity market and the gradual recovery of coal prices, the co-firing of low-quality coal is becoming increasingly popular among large-capacity thermal power units as a means to reduce fuel costs and improve economic efficiency. This study first conducted an abnormal vibration analysis of the induced draft fan blades in a thermal power plant that follows the principles of low-quality coal blending. The results are as follows: By recording vibration data inside the bearing housing during operation, it was found that severe bearing looseness caused excessive vibration. Upon disassembling the induced draft fan and inspecting its condition, it was discovered that, due to the high silicon and sulfur content in the low-quality coal, a significant amount of ammonium bisulfate crystals had formed on the hub. Additionally, the blades exhibited severe corrosion and wear, ultimately leading to dynamic imbalance and resulting vibration. Next, fractal theory was applied. Threshold segmentation was performed using ImageJ software to identify areas of pitting, wear, and microcracks in the segmented images. Fractal dimensions were calculated at different locations on the blades to determine their specific damage states and priority maintenance areas. Finally, corresponding measures to prevent excessive induced draft fan vibration were proposed for operating and maintenance personnel at power plants burning low-quality coal.
subject: Articles
  topic: section
subject: Low-quality coal
  topic: keywords
subject: induced draft fan
  topic: keywords
subject: fractal theory
  topic: keywords
creator: Shaocong Fu
rights: Copyright (c) 2026 Academic Journal of Applied Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-05
publication date: 2026-07-05
starting page: 12
ending page: 16
doi: 10.54097/3ddbyd54

Research on a Dynamic Behavior Analysis Framework Based on Random Forest and Markov Chain
type: item
description: This paper constructs a comprehensive analytical framework integrating constrained optimization, machine learning and dynamic transition modeling based on multi-season competition data. First, statistical feature analysis and correlation tests are adopted to model the stability of the scoring system. On this basis, a constrained convex optimization model is established to inversely estimate unobservable audience voting ratios, and the Spearman correlation coefficient is used for result verification to enhance the reliability and consistency of estimations. Next, the random forest regression model combined with the SHAP interpretation method is applied to quantitatively analyze key factors affecting scoring and voting results, realizing the importance assessment of multi-dimensional features and interpretable modeling. Furthermore, a Markov chain-based dynamic transition model is developed. With the Softmax probability allocation mechanism and utility function incorporated, this model simulates the transition of audience support across different stages and characterizes the dynamic evolution of popularity in competitive scenarios. Experimental results demonstrate that the proposed model can accurately recover hidden voting distributions and effectively capture result discrepancies and dynamic variations under different rules. Featuring good universality, stability and scalability, this method can serve as a reference for complex behavior prediction and dynamic decision analysis.
subject: Articles
  topic: section
subject: Random Forest
  topic: keywords
subject: Markov Chain
  topic: keywords
subject: Dynamic Migration Modeling
  topic: keywords
creator: Xiyao Liu
creator: Xiangrui Li
creator: Yuxuan Qiu
rights: Copyright (c) 2026 Academic Journal of Applied Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-02
publication date: 2026-07-02
starting page: 6
ending page: 11
doi: 10.54097/456q6p62

Artificial Intelligence in Instrumentation: A Balanced Analysis of Potential Benefits and Risks
type: item
description: The integration of artificial intelligence into instrumentation and measurement systems promises transformative improvements in accuracy, efficiency, and operational intelligence. However, this technological convergence also introduces novel risks that challenge established practices in measurement science, legal metrology, and industrial safety. This article provides a balanced analysis of the potential benefits and risks associated with AI-driven instrumentation. We examine key benefits including enhanced measurement accuracy through intelligent compensation, predictive maintenance capabilities, self-diagnostics and adaptive calibration, and operational efficiency gains. Concurrently, we analyze critical risks: the “black box” problem and lack of interpretability, data quality dependence, cybersecurity vulnerabilities, workforce displacement and skills gaps, and regulatory and standardization challenges. We argue that realizing the full potential of AI in instrumentation requires a balanced approach that combines technological innovation with robust governance frameworks, human oversight, and rigorous uncertainty quantification. This analysis serves as a reference for researchers, practitioners, and policymakers navigating the complex landscape of AI-empowered instrumentation.
subject: Articles
  topic: section
subject: Artificial intelligence
  topic: keywords
subject: instrumentation
  topic: keywords
subject: measurement
  topic: keywords
subject: benefits
  topic: keywords
subject: risks
  topic: keywords
subject: uncertainty quantification
  topic: keywords
subject: interpretability
  topic: keywords
subject: cybersecurity
  topic: keywords
subject: standardization
  topic: keywords
creator: Fujie Lu
rights: Copyright (c) 2026 Academic Journal of Applied Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-07-02
publication date: 2026-07-02
starting page: 1
ending page: 5
doi: 10.54097/95pfb480

Research Progress on Algal-Bacterial Symbiotic Systems for the Treatment of Livestock Wastewater
type: item
description: Livestock wastewater is characterized by high organic loading, elevated concentrations of ammonia nitrogen and phosphorus, high suspended solids, and the coexistence of antibiotics, heavy metals, antibiotic resistance genes, and other emerging contaminants. Improper treatment may lead to eutrophication, ecological toxicity, and the dissemination of antimicrobial resistance in aquatic environments. Algal-bacterial symbiotic systems couple organic matter degradation, nitrogen and phosphorus transformation, pollutant adsorption and immobilization, and biomass valorization through the synergistic interaction between microalgal photosynthesis and bacterial heterotrophic metabolism, providing a promising pathway for low-carbon treatment and nutrient recovery from livestock wastewater. This review systematically summarizes the major pollution characteristics of livestock wastewater and the applicability of algal-bacterial systems, and discusses the key mechanisms involved in algal-bacterial symbiosis, including oxygen-carbon dioxide exchange, nutritional complementarity, extracellular polymeric substance-mediated floc formation, and nitrogen transformation driven by aerobic-anoxic microzones. Furthermore, recent progress in the removal of organic matter, nitrogen, phosphorus, antibiotics, antibiotic resistance genes, heavy metals, persistent organic pollutants, microplastics, and other emerging contaminants by algal-bacterial systems is reviewed. The treatment performance of algal-bacterial systems is mainly influenced by algal species and bacterial community composition, algal-bacterial inoculation ratio, light conditions, pH, temperature, dissolved oxygen, wastewater pretreatment, and pollutant loading. Future studies should focus on long-term operation under real livestock wastewater conditions, regulation of algal-bacterial communities, clarification of pollutant migration and transformation among the aqueous phase, biomass, and sediments, and the establishment of safe…
subject: Articles
  topic: section
subject: Algal-bacterial symbiosis
  topic: keywords
subject: livestock wastewater
  topic: keywords
subject: nitrogen and phosphorus recovery
  topic: keywords
subject: heavy metals
  topic: keywords
subject: emerging contaminants
  topic: keywords
creator: Kaiwei Xu
rights: Copyright (c) 2026 Academic Journal of Applied Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-16
publication date: 2026-06-16
starting page: 65
ending page: 76
doi: 10.54097/hkqgg658

Simulation and Optimization of Structural Parameters of Nozzle for Abrasive Water Jet Stubble Breaking
type: item
description: Aiming at the demand for removing maize stubble in sticky soil areas of Southwest China, this study conducts simulation and optimization on nozzle structural parameters to improve the impact capacity and energy concentration of abrasive water jet. With garnet particles of 0.2 mm in diameter and an abrasive inlet velocity of 15 m/s as basic working conditions, the single-factor simulation method is adopted to investigate the influences of outlet length, convergence angle and mixing chamber length on jet velocity, beam convergence and energy distribution. The results show that the optimal parameter combination is determined as the outlet length of 20 mm, convergence angle of 30° and mixing chamber length of 30 mm. Under this structural configuration, the impact velocity of the jet reaches 180 m/s at a standoff distance of 200 mm. The jet features good beam convergence and slow kinetic energy attenuation along the path, which can effectively enhance the crushing performance of maize stubble. The research findings can provide theoretical references for the design and engineering application of nozzles used in agricultural abrasive water jet stubble breaking.
subject: Articles
  topic: section
subject: Abrasive water jet
  topic: keywords
subject: Stubble breaking
  topic: keywords
subject: Nozzle
  topic: keywords
subject: Flow field simulation
  topic: keywords
subject: Parameter optimization
  topic: keywords
creator: Kun Yu
creator: Ran Zhang
creator: Dongming Gao
creator: Hanbing Wu
rights: Copyright (c) 2026 Academic Journal of Applied Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-15
publication date: 2026-06-15
starting page: 60
ending page: 64
doi: 10.54097/kxb79g81

Design and Optimization of Feeding Device for Pneumatic Seeder of Legume-Gramineae Mixed Sowing
type: item
description: To address the problems of uneven velocity and seed separation during the mixed feeding of alfalfa and smooth brome, which have significant differences in physical properties, a dedicated feeding device was designed based on the Venturi principle. The EDEM-Fluent gas-solid two-phase flow coupled simulation method was adopted to explore the effects of nozzle cross-sectional size, nozzle length and air inlet velocity on seed motion characteristics and flow field distribution. Multiple sets of simulation tests were carried out with the outlet velocity difference between the two grass seeds and airflow energy loss as evaluation indexes. The results show that the optimal parameter combination is a nozzle cross-section of 26 mm × 16 mm, a nozzle length of 30 mm, and an air inlet velocity of 14 m/s. Under this working condition, the outlet velocity difference between alfalfa and smooth brome is only 0.29 m/s, featuring high airflow conveying efficiency, low pressure loss and excellent gas-solid mixing performance. The research results can provide a theoretical reference for the structural design and operating parameter matching of the feeding device of legume-gramineae mixed pneumatic seeders.
subject: Articles
  topic: section
subject: Feeding device
  topic: keywords
subject: Venturi structure
  topic: keywords
subject: Gas-solid two-phase flow
  topic: keywords
subject: Simulation optimization
  topic: keywords
creator: Dongming Gao
creator: Ran Zhang
creator: Kun Yu
creator: Hanbing Wu
rights: Copyright (c) 2026 Academic Journal of Applied Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-15
publication date: 2026-06-15
starting page: 55
ending page: 59
doi: 10.54097/b2b35g96

Research on a YOLOv8-Based Metal Residue Detection Algorithm
type: item
description: To address the recognition challenges caused by the tiny size of metal residues, high reflectivity, and severe background texture interference in industrial production environments, this paper proposes a YOLOv8-based metal residue recognition algorithm. First, to overcome the scarcity of high-quality samples in industrial sites, a metal residue dataset containing multiple complex backgrounds such as welds and oil stains is constructed by simulating actual working conditions. Targeted data augmentation techniques are employed to enhance the model's generalization ability in extreme environments. Second, to tackle the problems of extremely low pixel occupancy of metal targets and the difficulty of feature extraction, a lightweight coordinate attention mechanism is introduced into the backbone network. By capturing cross-channel directional and positional information, the model's perception and extraction capabilities of subtle metal edge features are strengthened. Experimental results show that the improved algorithm achieves a mean average precision (mAP) of 0.724 on the self-built dataset. While maintaining real-time inference speed, it can accurately identify metallic foreign objects with extremely small diameters, effectively reducing the miss rate and false alarm rate under complex working conditions, thus providing reliable technical support for the deployment of industrial precision inspection and automated rejection systems.
subject: Articles
  topic: section
subject: Metal Residue Recognition
  topic: keywords
subject: YOLOv8
  topic: keywords
subject: Small Object Detection
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Feature Fusion
  topic: keywords
creator: Qingqi He
creator: Siyuan Zhao
creator: Yunlong Liu
creator: Yongxing Li
creator: Yi Yang
creator: Fangfang Hou
rights: Copyright (c) 2026 Academic Journal of Applied Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-12
publication date: 2026-06-12
starting page: 50
ending page: 54
doi: 10.54097/d3r2cg88

Research on Wind Energy Data Quality Control Methods in Qinghai Province on the Qinghai-Tibet Plateau (2019–2021)
type: item
description: This paper presents the construction methodology and quality assessment results of the “Qinghai-Xizang Plateau Multi-source Integrated Wind Energy Basic Dataset (V1.0)”. Addressing the challenges in wind energy resource assessment over the complex terrain of the Qinghai-Xizang Plateau, this study establishes a comprehensive data quality control system. The dataset spans from 2019 to 2021 and integrates observations from 12 meteorological towers and 53 national-level weather stations across Qinghai Province, along with multi-source fused analysis products at a spatial resolution of 5 kilometers. A tiered quality control process is applied, including range checks, temporal consistency checks, and vertical consistency checks. Additionally, dynamic threshold adjustment rules are designed based on historical climate extremes, and a multi-factor correlation logic incorporating temperature-humidity coupling is introduced, leading to an innovative algorithm for wind tower data quality control that significantly improves data quality in high-altitude regions of Qinghai Province. Assessment results show that the overall accuracy of the data after quality control reaches 93.28%, substantially higher than the national average. This dataset provides solid foundational support for wind energy resource evaluation, climate research, and low-carbon transition efforts in northwestern China, and demonstrates the effectiveness of multi-source data fusion techniques in enhancing the accuracy of meteorological observations in high-altitude areas.
subject: Articles
  topic: section
subject: Multi-source Fusion
  topic: keywords
subject: quality control
  topic: keywords
subject: wind energy resource
  topic: keywords
subject: Qinghai Province
  topic: keywords
creator: Bingyu Zhao
creator: Yizhe Han
creator: Xinhua Huang
creator: Jing Wang
creator: Xiaoyan Ma
creator: Haowei Yang
creator: Xuanyu Zhang
creator: Qiong Chao
creator: Yan Guo
rights: Copyright (c) 2026 Academic Journal of Applied Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-10
publication date: 2026-06-10
starting page: 46
ending page: 49
doi: 10.54097/30mfjf97

Learning When to Reason: Gating LLM Inference for Cost-Efficient Serverless Function Scheduling at Scale
type: item
description: Serverless computing platforms face a fundamental tension when integrating large language model based scheduling agents: invoking full chain-of-thought reasoning for every function placement decision is computationally prohibitive, yet relying exclusively on lightweight heuristics cannot capture complex workload semantics. This paper presents GateLLM, a learned gating framework that selectively routes incoming serverless scheduling requests to either a lightweight fast-path handler or a deliberative large language model reasoning pipeline based on a compact feature vector extracted from request metadata. The gating classifier is a multilayer perceptron trained through a combination of offline oracle labeling and online reinforcement feedback derived from observed scheduling outcomes, enabling continuous adaptation to workload distribution shifts. Evaluated on a large-scale production function trace, GateLLM reduces total large language model inference cost by 61.3% relative to a full-reasoning baseline while incurring only a 2.1% degradation in average job completion time and a 0.25 percentage point increase in service level objective violation rate. Analysis demonstrates that over 68% of real-world scheduling events are structurally simple and resolvable without large language model involvement, establishing inference gating as an essential primitive for operationally viable intelligent schedulers in production cloud environments.
subject: Articles
  topic: section
subject: Large language model inference
  topic: keywords
subject: serverless computing
  topic: keywords
subject: function scheduling
  topic: keywords
subject: gating mechanism
  topic: keywords
subject: cost efficiency
  topic: keywords
subject: adaptive computation
  topic: keywords
subject: cloud resource management
  topic: keywords
creator: Megan Sullivan
creator: Boyang He
creator: Patrick Evans
rights: Copyright (c) 2026 Academic Journal of Applied Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-09
publication date: 2026-06-09
starting page: 39
ending page: 45
doi: 10.54097/gwmv0761

Robust Structural Response Prediction Under Coupled Thermo-Mechanical Effects via Hierarchical Attention-Based Sensor Fusion
type: item
description: Accurate prediction of structural responses under coupled thermo-mechanical loading remains a persistent challenge in structural health monitoring (SHM) and reliability engineering. Conventional approaches typically treat thermal and mechanical effects in isolation, failing to capture the nonlinear interactions that arise when temperature gradients and mechanical loads coexist. This paper introduces a hierarchical attention-based sensor fusion (HASF) framework that integrates multi-modal sensor data—encompassing strain gauges, thermocouples, and acceleration sensors—to deliver robust structural response predictions under such coupled conditions. The hierarchical attention mechanism operates at two levels: an intra-modal level that captures temporal dependencies within each sensor modality, and an inter-modal level that adaptively fuses information across modalities according to learned relevance weights. A physics-informed loss function is incorporated to enforce consistency with established thermo-mechanical governing equations, thereby improving generalization in data-sparse regimes. Validation on both a laboratory steel frame structure and a publicly available benchmark dataset confirms that HASF reduces root mean square error (RMSE) by 31.4% compared with state-of-the-art baselines, while achieving competitive inference speeds suitable for real-time monitoring. These findings establish HASF as a viable approach for structural prognosis and health monitoring across aerospace, civil, and mechanical engineering applications.
subject: Articles
  topic: section
subject: Structural health monitoring
  topic: keywords
subject: thermo-mechanical coupling
  topic: keywords
subject: hierarchical attention mechanism
  topic: keywords
subject: sensor fusion
  topic: keywords
subject: physics-informed deep learning
  topic: keywords
subject: response prediction
  topic: keywords
creator: Elena Rossi
rights: Copyright (c) 2026 Academic Journal of Applied Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-08
publication date: 2026-06-08
starting page: 32
ending page: 38
doi: 10.54097/2thg6m48

Study on Overlying Rock Deformation and Surface Subsidence Prediction under Layered Grouting Filling Conditions
type: item
description: To investigate the movement mechanism of overlying rock and surface under layered filling extraction conditions, scientifically evaluate the applicability of traditional prediction models under layered filling conditions, and improve the accuracy of surface subsidence prediction parameters, this paper takes the 1605 layered filling working face of Guhanshan Coal Mine as the engineering background. A comprehensive approach combining long-term field precise levelling measurements, non-contact microwave remote sensing (SBAS-InSAR) monitoring, and parameter optimisation inversion using the probability integral method was employed to conduct an in-depth study of overlying rock and surface movement patterns under layered filling. The study shows that under the disturbance of filling excavation, surface deformation exhibits distinct staged evolution characteristics of "grouting disturbance," "dynamic pressure balance," and finally "rheological stability." The high-pressure grouting filling area shows significant local atypical settlement different from the traditional caving method. SBAS-InSAR technology was used to obtain multi-dimensional surface deformation monitoring results for the entire field, achieving a leap from "point-line" unidirectional control to full "area" coverage in mining area settlement monitoring. On this basis, the limitations of the probability integral method in layered filling mining were explored, and an equivalent mining thickness correction model and optimisation fitting inversion algorithm were established. Engineering verification shows that the corrected probability integral model after parameter inversion can highly fit the measured scatter points, successfully achieving refined prediction of the atypical surface subsidence under layered filling, providing scientific data support for disaster prevention, loss reduction, and optimisation of filling parameters in mining areas.
subject: Articles
  topic: section
subject: Layered filling
  topic: keywords
subject: surface movement patterns
  topic: keywords
subject: SBAS-InSAR monitoring
  topic: keywords
subject: probability integral method
  topic: keywords
subject: parameter inversion
  topic: keywords
creator: Qinghua Liu
rights: Copyright (c) 2026 Academic Journal of Applied Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-07
publication date: 2026-06-07
starting page: 27
ending page: 31
doi: 10.54097/92622v37

Analysis of the Dilemmas and Causes of “Supervisor-Counselor” Collaborative Education in Universities in the Context of AI
type: item
description: The deep application of large language model (LLM) technology has injected new momentum into the reform of graduate education while also posing profound challenges to the traditional “supervisor-counselor” collaborative education model. Based on synergy theory and game theory, and drawing on existing literature and typical practical cases, this paper systematically analyzes the practical dilemmas and underlying causes of “supervisor-counselor” collaborative education in universities. The study finds that the current “supervisor-counselor” collaborative education is mired in a triple “zero-sum game” dilemma, in which conceptual gaming leads to goal fragmentation, responsibility gaming leads to boundary ambiguity, and evaluation gaming leads to dispersed motivation. This paper further analyzes the deep logic of dilemma formation from the institutional, technological, and cultural dimensions. Breaking the zero-sum game of “supervisor-counselor” collaboration and moving toward a technology-empowered positive-sum game has become an urgent task for enhancing the management and service capacity of graduate education.
subject: Articles
  topic: section
subject: Large language models
  topic: keywords
subject: supervisor-counselor collaboration
  topic: keywords
subject: collaborative education
  topic: keywords
subject: zero-sum game
  topic: keywords
subject: graduate education
  topic: keywords
creator: Dongxia Wei
creator: Ying’ao Qi
rights: Copyright (c) 2026 Academic Journal of Applied Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-05
publication date: 2026-06-05
starting page: 23
ending page: 26
doi: 10.54097/spegdh15

Innovative Practice of Game Level Design and Scene Narrative Driven by AI Technology
type: item
description: As Artificial Intelligence Technology has advanced rapidly, a great deal of change has happened on the stage of Game Development concerning Level Design and Scene Narration. This paper systemically examines the new application scenarios of AI-powered technologies, including Procedural Content Generation (PCG), Generative Adversarial Networks (GANs), large language models (LLM), and dynamic difficulty adjustment (DDA) in game level Design and Narrative creation. Referring to verifiable market Data, peer-Reviewed Researches and documented Industry applicationss To identify The essential Technical Pathways Through Which A I Enhances Content Diverse City Playengement And Narrative Immersion. The global artificial intelligence (AI) in games' market is worth about $5.85 billion in 2024, expected to reach US$37.89 billion by 2034, showing its enormous Scale of Transformation. The limitations of current artificial intelligence techniques have been discussed; therefore, the Future Direction of Game Design Will Be Human-AI Collaboration That Preserves Authoritative Intent While Enhancing Scalability and Adaptation Abilities of The System.
subject: Articles
  topic: section
subject: Procedural Content Generation
  topic: keywords
subject: Game Level Design
  topic: keywords
subject: Scene Narrative
  topic: keywords
subject: Large Language Models
  topic: keywords
subject: Dynamic Difficulty Adjustment
  topic: keywords
subject: Generative AI
  topic: keywords
subject: Human-AI Collaboration
  topic: keywords
creator: Zhiqi Zhu
rights: Copyright (c) 2026 Academic Journal of Applied Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-04
publication date: 2026-06-04
starting page: 19
ending page: 22
doi: 10.54097/jr9mft61

Exploratory Research Based on AGIL Model: Application Path Practice of "Fengqiao Experience" in Improving College Students' Mental Health in Zhejiang Universities' "One-Stop" Student Communities
type: item
description: In the new era, the "Fengqiao Experience", with the core essence of "no conflicts handed over to higher authorities, no safety incidents, and no gaps in services", provides local practical guidelines and value guidance for the innovation of university "one-stop" student community governance. Based on Talcott Parsons' AGIL functional model and combined with empirical survey data from multiple undergraduate and vocational colleges in Zhejiang Province, this study systematically analyzes the mechanism of each functional dimension of the "one-stop" student community on college students' mental health. The research shows that there is a structural mismatch between resource input and education effectiveness in the current community construction: although considerable resources are invested in life services and ideological and political education, their efficiency in alleviating students' psychological anxiety is limited; cultural activities and organizational systems are the core key factors to resolve students' conflicts and relieve psychological pressure. Accordingly, this paper proposes an integrated education path of cultural infiltration – organizational collaboration – service sinking – source governance, promoting the in-depth implementation and precise empowerment of the "Fengqiao Experience" in the governance of university "one-stop" student communities, and comprehensively improving the quality and efficiency of college students' mental health education.
subject: Articles
  topic: section
subject: One-stop student community
  topic: keywords
subject: AGIL model
  topic: keywords
subject: Fengqiao Experience
  topic: keywords
subject: College students' mental health
  topic: keywords
subject: Education path
  topic: keywords
creator: Ran Wang
creator: Yanbin Sun
creator: Yingao Qi
rights: Copyright (c) 2026 Academic Journal of Applied Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-03
publication date: 2026-06-03
starting page: 15
ending page: 18
doi: 10.54097/9faxgh54

An Optimization Path for "Supervisor-Counselor" Collaborative Education in Universities Empowered by Large Language Models
type: item
description: The deep integration of large language model (LLM) technology offers a transformative opportunity to break the long-standing zero-sum game dilemma in graduate "supervisor-counselor" collaborative education. This paper proposes a value orientation grounded in the concepts of "precision, synergy, integration, and symbiosis," and guided by the practical principles of "technological support, multi-stakeholder cooperation, resource sharing, and dynamic adjustment," to construct an optimized path system for university "supervisor-counselor" collaborative education in the context of LLMs. On this basis, it proposes reshaping a positive-sum game pattern featuring the deep integration of "academic and ideological-political education" under a fourfold guarantee of institution, platform, capability, and culture, thus providing an operable practical scheme for enhancing the management and service capacity of graduate education.
subject: Articles
  topic: section
subject: Large language models
  topic: keywords
subject: supervisor-counselor collaboration
  topic: keywords
subject: precision education
  topic: keywords
subject: human-machine symbiosis
  topic: keywords
subject: positive-sum game
  topic: keywords
creator: Dongxia Wei
creator: Qiaolian Wang
rights: Copyright (c) 2026 Academic Journal of Applied Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-02
publication date: 2026-06-02
starting page: 11
ending page: 14
doi: 10.54097/m1pg6g15

From "Disciplinary Space" to "Life Harbor": Dilemmas and Path Optimization of the "One-Stop" Student Community Construction in Medical Universities Based on the "Fengqiao Experience"
type: item
description: Students in medical universities are confronted with heavy academic pressure, anticipated high-risk occupational exposure, and complex humanistic and ethical challenges. Their mental health is directly linked to the quality of the future medical and health workforce. At present, the construction of "one-stop" student communities in medical universities generally presents a structural dislocation of "valuing hardware over software, emphasizing management over education". Based on the governance logic of the "Fengqiao Experience" and combined with the psychological characteristics of medical students, this paper proposes to construct a four-dimensional education system of "life education – grid autonomy – precise service – source prevention and control", aiming to build the student community into a "life harbor" that safeguards the physical and mental health of medical students.
subject: Articles
  topic: section
subject: Medical Universities
  topic: keywords
subject: One-stop student community
  topic: keywords
subject: Fengqiao Experience
  topic: keywords
subject: Life education
  topic: keywords
subject: Path optimization
  topic: keywords
creator: Yingao Qi
creator: Yanbin Sun
creator: Ran Wang
creator: Dongxia Wei
rights: Copyright (c) 2026 Academic Journal of Applied Sciences https://creativecommons.org/licenses/by-nc/4.0
date: 2026-06-01
publication date: 2026-06-01
starting page: 8
ending page: 10
doi: 10.54097/4g4k0n13