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.
Conformal-AEDL: Uncertainty-Calibrated Neuro-Symbolic Multiagent Causal Inference for Non-Stationary Time Series
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.
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Conformal Uncertainty-Aware Physics-Informed Neural Networks for Reliable Electromagnetic Field Prediction in VLSI Interconnects
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.
ConformalSafe-Routing: Uncertainty-Aware Safe Deep Reinforcement Learning for Adaptive Routing Convergence
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.
TalentFlow: Explainable Internal Talent Mobility Prediction Using Graph Representation Learning
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, fairness-aware promotion models such as FairPromote by extending explainable talent analytics from flat classification to relational, structurally grounded mobility prediction.
Research Progress on Multi-Hazard Disaster Mechanisms, Monitoring, Early Warning and Control in Underground Engineering
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.
A Multi-Source Monitoring Data Fusion and Early Warning Framework Based on LightGBM-SHAP and Weibull Distribution
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.
Numerical Simulation of Alpha Particle Scattering Based on the Symplectic Euler and Heun Methods
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.
Passive Shading Optimization for Buildings Based on Differential Evolution and Non-Dominated Sorting Multi-Objective Evolutionary Algorithm
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.
Non-Dominated Sorting Multi-Objective Evolutionary Algorithm
Bayesian Inverse Sampling and Shapley-Driven Calibration for Dual-Channel Preference Aggregation
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.
Toward 6G-Empowered Multi-Machine Collaboration in Smart Agriculture: A Communication-Control Co-Design Survey
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.
Additive Tree Latent Variable Models for Insurance Loss Prediction
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 prediction accuracy through the integration of machine learning and latent variable modeling.
DFPSE: Diagnostic-Feature-Preserving Signal Enhancement for Robust Bearing Fault Diagnosis under Compound Noise
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 assumption that better denoising necessarily yields better diagnosis and argues for task-aware evaluation in signal enhancement research.
Physics-Based Data Generation with SVR Surrogate Modeling for PDMS Thin Film Emissivity Prediction
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.
Cross-Domain Classification for Hyperspectral Imagery: A Comprehensive Review of Domain Adaptation, Domain Generalization, and Multimodal Approaches
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.
Study on Abnormal Vibration Cause and Blade Damage Fractal of Induced Draft Fan Burning Low-quality Coal
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.
Research on a Dynamic Behavior Analysis Framework Based on Random Forest and Markov Chain
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.
Artificial Intelligence in Instrumentation: A Balanced Analysis of Potential Benefits and Risks
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.
Research Progress on Algal-Bacterial Symbiotic Systems for the Treatment of Livestock Wastewater
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 biomass valorization and comprehensive environmental benefit assessment frameworks. These efforts will promote the transition of livestock wastewater treatment from pollution reduction toward resource recycling.
Simulation and Optimization of Structural Parameters of Nozzle for Abrasive Water Jet Stubble Breaking
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.
Design and Optimization of Feeding Device for Pneumatic Seeder of Legume-Gramineae Mixed Sowing
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.
Research on a YOLOv8-Based Metal Residue Detection Algorithm
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.
Research on Wind Energy Data Quality Control Methods in Qinghai Province on the Qinghai-Tibet Plateau (2019–2021)
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.
Learning When to Reason: Gating LLM Inference for Cost-Efficient Serverless Function Scheduling at Scale
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.
Robust Structural Response Prediction Under Coupled Thermo-Mechanical Effects via Hierarchical Attention-Based Sensor Fusion
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.
Study on Overlying Rock Deformation and Surface Subsidence Prediction under Layered Grouting Filling Conditions
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.
Analysis of the Dilemmas and Causes of “Supervisor-Counselor” Collaborative Education in Universities in the Context of AI
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.
Innovative Practice of Game Level Design and Scene Narrative Driven by AI Technology
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.
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
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.
An Optimization Path for "Supervisor-Counselor" Collaborative Education in Universities Empowered by Large Language Models
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.
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"
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.