International Journal of Advanced Engineering and Technology Research
International Journal of Advanced Engineering and Technology Research (IJAETR) 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 engineering and technology across the world. All articles published are rigorously and fast reviewed meeting the Journal Quality standards.
Reducing AutoML Pipeline Failures in Heterogeneous Cloud Environments via Retrieval-Augmented Fault Prediction
Automated Machine Learning (AutoML) pipelines deployed in heterogeneous cloud environments are increasingly susceptible to runtime failures caused by resource contention, hardware diversity, and dynamic workload fluctuations. These failures impose substantial operational overhead and compromise the reliability of large-scale machine learning workflows. This paper introduces a Retrieval-Augmented Fault Prediction (RAFP) framework designed to anticipate AutoML pipeline failures by coupling a dense vector retrieval module over a structured historical failure knowledge base with a gradient-boosted fault classifier. The retrieval module encodes contextually similar past failure records as auxiliary features, enabling the prediction model to condition its output on domain-specific failure patterns rather than relying solely on real-time telemetry signals. The RAFP framework is evaluated against four baseline systems — logistic regression (LR), random forest (RF), gradient boosting (GB), and long short-term memory (LSTM) networks — using the Google Cluster Trace v3 and the Alibaba Cluster Trace 2018. Experimental results demonstrate that RAFP achieves a macro-averaged F1-score of 0.891 and reduces pipeline disruption events by 37.4% relative to the strongest baseline. Ablation studies confirm that the retrieval component yields consistent performance improvements across heterogeneous node configurations. These findings indicate that retrieval-augmented reasoning represents a practically effective complement to existing proactive fault prediction architectures for cloud-hosted AutoML systems.
Copyright (c) 2026 International Journal of Advanced Engineering and Technology Research https://creativecommons.org/licenses/by-nc/4.0
Multi-Resolution Wavelet-Graph Fusion for Early Fatigue Crack Detection in Steel Structures with Limited Labeled Data
Early detection of fatigue cracks in steel structures remains a fundamental challenge in structural health monitoring (SHM), particularly under conditions where labeled training data are scarce. This paper proposes a multi-resolution wavelet-graph fusion framework that integrates continuous wavelet transform (CWT) with graph convolutional networks (GCN) to simultaneously capture time-frequency signal characteristics and spatial inter-sensor dependencies. A semi-supervised learning (SSL) strategy exploits unlabeled vibration measurements to supplement limited annotated samples. Wavelet scalogram features extracted at three resolution levels are encoded onto a sensor graph derived from a five-story shear-building benchmark model, where graph topology reflects physical structural connectivity. Relative wavelet packet entropy (RWPE) computed across nine sensor locations serves as the primary damage-sensitive feature, revealing spatially localized crack signatures that single-channel methods fail to detect. A four-phase experimental framework processes multi-sensor acceleration data through normalization, wavelet transform, and hierarchical nonlinear principal component analysis (h-NLPCA) to classify seven structural states. Experiments demonstrate 93.7% detection accuracy using only 15% labeled samples, outperforming baselines by up to 11.2 percentage points. The results confirm that wavelet-graph fusion provides a robust and data-efficient solution for real-world steel infrastructure inspection.
Characteristics of the Chang 7 Tight Sandstone Reservoir in the Luopangyuan Area, Ordos Basin
This study aims to systematically characterize the Chang 7 tight sandstone reservoir in the Luopangyuan area of the Ordos Basin to provide a geological foundation for tight oil exploration and development. Core samples from three wells were analyzed using an integrated suite of experimental techniques, including conventional thin-section petrography, field-emission scanning electron microscopy (FE-SEM), constant-rate mercury intrusion capillary pressure (MICP) analysis, and micro-computed tomography (micro-CT) scanning. Results indicate that the reservoir lithology is predominantly lithic arkose and feldspathic sandstone, characterized by fine grain size, moderate sorting, and subangular to angular grain roundness. The average porosity is 8.2%, and the average permeability is 0.32×10⁻³μm²—consistent with an ultra-low-permeability reservoir classification. Pore space is dominated by dissolved intergranular pores (37.3%) and feldspar dissolution pores (34.5%). The pore-throat system is typified by fine pores coupled with fine to micro-fine throats. Compaction and cementation are the primary diagenetic processes responsible for reservoir densification, whereas feldspar dissolution plays a critical role in enhancing reservoir quality. Based on comprehensive petrophysical and diagenetic evaluation, the reservoirs are classified into three types: Type IVa, IVb, and V. Effective reservoir intervals account for 54% of the total net sand thickness, with Type IVa being the most prevalent. These findings offer actionable geological insights for tight oil exploration not only in the Luopangyuan area but also in analogous tight sandstone systems.
Study on the Enhanced-Gravity Separation Characteristics of Copper Ore in a Spiral Trapezoidal Pipe
To address the difficulty in separating low-grade, fine-grained and severely slimed copper ores, this study combines the advantages of enhanced-gravity separation technology and continuous separation in a spiral flow channel, and proposes an enhanced-gravity separation method using a spiral trapezoidal pipe. In this method, an enrichment section and a secondary sand-splitting section are arranged successively along the flow direction, forming a gradient separation structure. In the enrichment section, the centrifugal force field generated by the spiral flow channel promotes the preliminary enrichment of Cu particles. In the secondary sand-splitting section, local recirculation vortices and reverse radial flow are induced by abrupt cross-sectional expansion, thereby strengthening the separation of light and heavy particles. Fluent was used to numerically simulate the flow-field distribution, recirculation vortex evolution, particle distribution characteristics, and the influence of structural parameters on the separation performance of the spiral trapezoidal pipe under enhanced-gravity separation. The results show that the stable radial pressure gradient and relatively high tangential velocity in the enrichment section are conducive to the migration of Cu particles toward the outer enrichment wall. In the secondary sand-splitting section, the recirculation vortex first strengthens, then weakens, subsequently decays slowly, and finally disappears rapidly along the flow direction. The recirculation vortex in the front part strips SiO2 particles from the surface layer of the particle bed, while the negative radial-velocity region at the bottom of the middle and rear parts further promotes the lateral entrainment of SiO2 particles from the outer enriched bed toward the inner mainstream region. The particle volume fraction distribution indicates that Cu particles are mainly and stably enriched in the outer bottom region, whereas SiO2 particles gradually migrate toward the newly formed cavity and the tailings side. The structural parameter analysis shows that when the spiral diameter is 260 mm, the pitch is 50 mm, and the boss width of the secondary sand-splitting section is 4 mm, the spiral trapezoidal pipe exhibits better separation performance, with a Cu recovery of 72.35% and an enrichment ratio of 10.51. The results can provide a reference for the enhanced separation of low-grade, fine-grained complex copper ores and the structural design of novel enhanced-gravity separation equipment.
Genome-wide Identification and Bioinformatics Analysis of the Rice DnaJ Gene Family
This study aims to analyze the structural characteristics of the DnaJ gene family in rice (Oryza sativa L.) and provide support for its molecular regulatory mechanism research. Using whole-genome data from the RAP-DB database, we identified 104 DnaJ family members, which are divided into three subfamilies (OsDjA, OsDjB, and OsDjC) based on phylogenetic relationships and conserved structural features. Most members contain a typical J domain, while some also have specific domains such as zinc finger, TPR, and PDI-related domains. Different subfamilies have highly conserved motif compositions but significant differences in arrangement, and their exon-intron structures also vary notably. The promoter regions of these DnaJ genes are rich in light-responsive, hormone-responsive, and stress-responsive elements, and extensive homologous collinearity between the rice DnaJ gene family and that of Arabidopsis reflects its high evolutionary conservation. This study provides an important theoretical basis and data support for further elucidating the functions of the rice DnaJ family in rice growth, development, and stress responses.
Practice of Human Resource Management Driven by Artificial Intelligence and Employee Response
With the rapid development of artificial intelligence technology, AI driven human resource management (AI-HRM) is profoundly changing the management paradigm of enterprises, but its complex psychological and behavioral impacts on employees still need to be further explored. The aim of this study is to systematically analyze the current application status of AI-HRM in core scenarios such as recruitment, performance, and training, and to construct a comprehensive explanatory model for employees' response to the algorithm. The research content is mainly divided into three dimensions: firstly, the technical path and management limitations of AI in asynchronous interaction, dynamic benchmark evaluation, and workplace skill modeling are analyzed through case studies; Secondly, based on the theories of organizational fairness and cognitive perception, this study explores the psychological processes of employees' perception of fairness, algorithmic anxiety, and organizational alienation in algorithmic decision-making contexts; Finally, key boundary conditions that affect employee response and the organization's communication strategies in responsible AI were identified. Research has found that AI-HRM has significant advantages in activating internal talent markets and enhancing management objectivity, but further optimization and development are still needed in the future.
Artificial Intelligence Driven Human Resource Management (AI-HRM)
Measurement of SiC Epilayer Thickness Using Infrared Interferometry
Accurate measurement of the thickness of silicon carbide (SiC) epitaxial layers is crucial for device performance. Although infrared interferometry offers the advantages of being non-destructive and highly precise, it has limitations in terms of model accuracy and data processing. To address these challenges, this paper establishes an improved measurement methodology. First, a dual-beam interference model is constructed to inversely determine the thickness by correlating interference fringes with wavelength and incident angle. Subsequently, an enhanced computational algorithm is implemented, which includes detrending of experimental spectra and the introduction of the Cauchy dispersion formula to correct for the wavelength-dependent refractive index. The thicknesses calculated based on data at incident angles of 10° and 15° are 7.39 μm and 7.33 μm, respectively, with a cross-validation relative error of only 0.82%. Further optimization using dual-angle joint fitting yields a thickness of 7.43 μm. The results demonstrate that the model exhibits good accuracy and robustness, providing a reliable non-destructive measurement method for SiC epitaxial layer characterization and contributing to quality control in semiconductor processes.
An Efficient Legendre Polynomial Series Approach for Thermoelastic Waves in Functionally Graded Piezoelectric Nano Hollow Cylinder
The Legendre polynomial series approach (LPSA) has the advantages of simple derivation process, high solving efficiency, and small computational scale, making it an efficient method for solving wave mechanics. However, when solving the nonlocal nano hollow cylindrical structure, the nonlocal integral elastic relationship makes it consume extremely much CPU time for integrals containing the reciprocal of radius, Legendre polynomials, absolute values, and exponential function. To overcome this drawback, an efficiently iterative solution method by using the exponential integral function are proposed to solve these difficult to calculate analytically and time-consuming integrals. Then, a mathematical model is established for axial thermoelastic wave propagation and attenuation in functionally graded piezoelectric nano hollow cylinders based on the presented LPSA and modified nonlocal integral elasticity. The coupling effects of nonlocal parameter, circumferential order, piezoelectric effect, and gradient index on wave characteristics are studied. Numerical results show that both the thermal wave and elastic wave are size-dependent. The nonlocal effect decreases the phase velocity of thermal waves and elastic waves. Meanwhile, it increases the attenuation of thermal waves, but decreases the attenuation of elastic waves. For the phase velocity, the nonlocal effect and circumferential order are mutually reinforcing, although the nonlocal effect and piezoelectric effect are mutually inhibiting.
functionally graded piezoelectric material
Risk Assessment of Landslide Disasters Induced by Extreme Precipitation in the Western Henan Region
Against the backdrop of frequent extreme meteorological events caused by global climate change, the mountains of Western Henan, with their complex geological structures and surface incisions, have become sensitive areas where landslide disasters threaten the safety barrier of the middle and upper reaches of the Yellow River. This study, from an emergency management perspective, integrates multi-source raster data and machine learning algorithms to set different return period scenarios for extreme precipitation. A four-dimensional risk assessment framework—comprising hazard, exposure, vulnerability, and disaster prevention capability—was constructed. Empirical analysis shows that landslide risk in Western Henan exhibits a significant spatial distribution pattern: high in the northwest and low in the southeast. As extreme precipitation increases, the risk of landslide disasters also rises. Lingbao City and Luoning County have formed significant high-exposure risk areas due to the high concentration of population and economy. Conversely, the weak disaster prevention resources in grassroots areas such as Lushi and Luanchuan are accompanied by high vulnerability. The study further finds that establishing a cross-departmental collaborative dynamic early warning mechanism and setting differentiated rainfall trigger thresholds can effectively guide emergency resources to transition from comprehensive prevention to precision control.
Analysis of Pore Structure and Fractal Characteristics of Coal Samples in Pingdingshan Mining Area Based on N2/CO2 Adsorption
In order to reveal the multi-scale pore structure and fractal characteristics of coal samples in Pingdingshan mining area, typical medium metamorphic coal samples were selected to carry out low-temperature N2 adsorption and low-temperature CO2 adsorption experiments. Combined with FHH fractal model and Sierpinski fractal model, the pore volume, specific surface area, pore size distribution and fractal dimension of coal samples were analyzed. The results show that the coal samples in the study area have the characteristics of medium-low ash, low volatile matter and high fixed carbon. The macerals are mainly vitrinite, followed by inertinite. The results of low temperature N2 adsorption show that the pore volume in the 2-300 nm pores of coal samples is mainly contributed by micropores and mesopores, among which mesopores have more obvious control effect on reservoir space. The specific surface area is mainly controlled by 2-10 nm micropores. The adsorption-desorption isotherm is type IV and superimposed type II. The hysteresis loop reflects the development of composite pore structures such as ink bottle pores, slit pores and fissure pores in the coal body. The results of CO2 adsorption show that the ultramicropores of coal samples are relatively developed, and the pore size is mainly concentrated in 0.40-0.80 nm, of which about 0.52 nm pores are dominant, which is the main space for gas adsorption and occurrence. The fractal analysis shows that the fractal dimension D1 of N2 adsorption is 2.28-2.58, D2 is 2.55-2.67, and most samples D2 > D1, indicating that the pore complexity of coal samples is mainly controlled by the heterogeneity of three-dimensional pore network. The fractal dimension Dm of supermicropores for CO2 adsorption is 2.32-2.38, indicating that the structure of supermicropores is relatively simple and the connectivity is weak. According to the research, the gas occurrence of coal samples in Pingdingshan mining area is mainly controlled by the adsorption space of ultra-micropores, while the gas diffusion and migration are restricted by the complexity of micropore-mesopore pore network.
Evolution Mechanism of Fluid Flow Regimes under the Dual Constraints of Fracture Geometric Roughness and Aperture Heterogeneity
This study investigates the nonlinear flow behavior in rough fractures under the combined influence of surface roughness and aperture heterogeneity using the lattice Boltzmann numerical simulation method. Surface roughness is decomposed into the local roughness effect, hydraulic tortuosity effect, and surface tortuosity effect. In addition, an aperture roughness effect induced by aperture heterogeneity is introduced. From a mechanistic perspective, the influences of these four effects on fluid flow characteristics within fractures are systematically analyzed. The results show that the Forchheimer equation can adequately describe nonlinear fluid flow in rough fractures, and its nonlinear coefficient can be determined by quantitatively characterizing fracture geometry. Based on the fourfold effects of fracture seepage, the fracture geometric structure is quantitatively described, and the transition mechanism between linear and nonlinear flow under the influence of rough fracture geometry is clarified.
lattice Boltzmann numerical simulation method
Preparation and Properties of Hollow TiO2 Nanospheres
To address the issues of narrow photoresponse range, rapid carrier recombination, and difficulty in recovery associated with TiO2 photocatalytic materials, this study employed a morphology control strategy to fabricate hollow spherical TiO2 structures. By optimizing the microstructure of the hollow spheres, the aim was to broaden the light absorption range, increase the specific surface area, and enhance carrier separation. The photocatalytic degradation performance of the prepared material toward Rhodamine B aqueous solution-a typical organic pollutant-was experimentally evaluated, and its enhancement mechanism was explored. This work provides a feasible approach for designing efficient and easily recoverable TiO2 photocatalysts.
A Fault-Constrained Partitioned 3D Modeling Method for Coal Seams in a Working Face
Traditional coal seam geological models often fail to represent the complex spatial distribution of coal seams in a working face, particularly in fault-affected areas where structural discontinuities tend to reduce model continuity and prediction accuracy. To address this issue, this study develops a fault-constrained partitioned 3D modeling workflow by integrating data quality inspection, spatial partitioning, local surface reconstruction, and model fusion. Gas-drainage borehole records are first checked through roof-floor elevation comparison and geometric consistency screening. Fault control points are then used to delineate partition boundaries, and coal seam points are assigned to different modeling units using a ray-casting-based point-in-polygon procedure. Guided by the partitioning logic and the density-evaluation idea described in the workflow, local surfaces are reconstructed using alternative interpolation strategies, including inverse distance weighting (IDW), ordinary kriging, and radial basis function (RBF) interpolation, together with regular-grid and TIN-based mesh generation. The resulting partition-level models are fused into a unified 3D representation of the working face. The workflow clarifies the spatial relationship between faults and coal seams and provides a practical technical route for transparent underground geology and safe mining in structurally complex areas.
Analysis of Unlocking Process of Concave-convex Bodies in Wenchuan Earthquake Sequence Based on B-value Tomography
B-value mapping seems to have an abnormally low value before the main earthquake. When this gridding technique is applied to Wenchuan earthquake, Lushan earthquake and Jiuzhaigou earthquake of the Wenchuan earthquake sequence of Longmenshan fault, the characteristics of low B-value before the earthquake are not found. On the contrary, the anomalies of high B-value above 20km before Lushan earthquake and Jiuzhaigou earthquake are obvious in the shallow crust. Although Wenchuan earthquake had a strong shadow on Jiuzhaigou earthquake, there was an obvious area where B value increased between the two faults. After Jiuzhaigou earthquake, the area increased to 0.78. Combined with the Lushan earthquake, there is a funnel-shaped high-value area below 25km depth. The reasonable explanation is that the unlocking of deep stress and shallow seismic activity jointly promoted the Jiuzhaigou earthquake.
Optimal Configuration Strategy of Hybrid Energy Storage System to Stabilize Wind Power Fluctuations
Regarding the issue of optimal configuration for hybrid energy storage to independently stabilize wind power fluctuations, this paper proposes a hybrid energy storage optimization method considering the life-cycle economics of supercapacitors and lithium batteries. First, at the upper level of power allocation, an Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise combined with superposition reconstruction is used to decompose the wind power into a grid-connected component that meets fluctuation standards and a component requiring smoothing by hybrid energy storage. At the lower level of power allocation, a frequency division parameter is introduced to further decompose and reconstruct the smoothing component into low-frequency and high-frequency power demands. Furthermore, a zonal power control strategy based on state of charge recovery is proposed to adjust the allocated power for supercapacitors and lithium batteries. Finally, considering constraints such as charge–discharge power and capacity of the hybrid energy storage, an optimization model for hybrid energy storage configuration is established with the objective of minimizing the annualized cost over the full life cycle. The model is solved using an improved particle swarm optimization algorithm. Case study results demonstrate that the proposed method can effectively enhance the economic performance of the hybrid energy storage system in smoothing wind power fluctuations.
A Review of the Research Status and Development Trends of the Physical and Mechanical Properties and Instability Mechanisms of Moraine
This paper focuses on moraine deposits, a typical heterogeneous accumulation with wide gradation in high-altitude regions. The grain-size characteristics, structural features, damage evolution under freeze–thaw and water effects, and the research progress on slope instability are systematically reviewed. Particular attention is given to the application of methods such as sieving and image recognition, large-scale direct shear and triaxial tests, microstructural observation, and numerical simulation in revealing the mechanical behavior and failure mechanisms of moraine deposits. Based on existing studies, a unified framework of “material structure characteristics–strength degradation–slope response” is proposed to summarize the established understandings, remaining controversies, and engineering implications. The results indicate that moraine deposits generally exhibit wide gradation, poor sorting, strong structural characteristics, and pronounced water sensitivity. Freeze–thaw cycles, rainfall infiltration, and seismic disturbances jointly control the progressive instability process through particle rearrangement, pore expansion, fine particle migration, and interface weakening. Current studies still face several limitations, including difficulties in preserving in situ structure, significant scale effects in testing widely graded materials, and insufficient integration of multi-field coupling and multi-scale analysis. Future research should emphasize the characterization of intact moraine structure, experimental investigation of freeze–thaw–seepage–stress coupling, and the development of multi-scale numerical models, so as to support hazard prevention and control in high-altitude transportation corridors and major engineering projects in glacial regions.
Research on a Lightweight Vision-based Monitoring Method for Rigid Guide Rail Joint Defects in Coal Mine Shafts
Prolonged mechanical impacts on mine shaft rigid guide rails inevitably induce joint gaps and misalignments. However, existing visual inspection methods struggle to strike an optimal balance among detection accuracy, inference speed, and edge-deployability. To address these challenges, we propose YOLOv10n-LBC, a highly efficient, integrated model tailored for defect detection, gap measurement, and rail edge extraction on resource-constrained edge devices. Specifically, the network incorporates a Lightweight Ghost Convolutional CSP (LGC-CSP) module to compress the architecture, a Cross-Scale Decoupled (CSD) head to enhance multi-scale feature fusion, and a BiFormer attention mechanism to compensate for feature degradation caused by the lightweight design. Following detection, joint gap widths are quantified using a pixel-to-physical ratio methodology, while misalignments are identified via depth estimation-based edge extraction. Extensive experiments on a custom 6,100-image dataset demonstrate that YOLOv10n-LBC achieves a Precision of 98.6%, a Recall of 97.1%, and an mAP@0.5:0.95 of 72.2%. Despite its robust performance, the model maintains a highly compact footprint with only 1.57 M parameters and a 3.4 MB weight file. Real-world mine field tests validate the system's stability, maintaining maximum gap measurement errors below 1 mm, thereby offering a reliable solution for intelligent underground shaft inspection.
Key Technologies and Applications of Geoscience Big Data
Geoscience big data refers to massive, multi-source, heterogeneous, and spatiotemporally associated datasets generated in earth science research and engineering practice. It covers geological, geographical, remote sensing, meteorological, hydrological, environmental, and resource-related data. With the development of remote sensing observation, GIS, Internet of Things monitoring, cloud computing, and artificial intelligence, geoscience data are increasing continuously in scale, type, and update frequency. Conventional stand-alone data processing methods have gradually become insufficient in terms of storage capacity, computing efficiency, spatial query, and integrated analysis. Based on a course report on geoscience big data, this paper reorganizes the concepts, technical framework, implementation process, and application scenario of geoscience big data in the form of an academic paper. First, the data sources, basic characteristics, and processing requirements of geoscience big data are analyzed. Then, a technical framework for geoscience data processing is constructed around cloud servers, Linux operating environments, Hadoop/MapReduce, MongoDB, PostgreSQL/PostGIS, and Python batch processing. On this basis, practical operations, including server connection, file management, MapReduce-based column statistics, and Java program compilation and execution, are summarized. The study indicates that geoscience big data technologies can improve the organization, storage, computation, and analysis efficiency of multi-source geoscience data and provide technical support for typical applications such as WebGIS-based disaster monitoring and early warning. However, further application still needs to address issues such as inconsistent data standards, insufficient data quality control, complex system integration, and data security protection.
Recent Advances in Stability Analysis State Estimation for Time-Delay Neural Networks
This paper reviews recent advances in stability analysis and state estimation for time-delay neural networks (TDNNs). TDNNs are widely applied in various fields, but inherent time delays degrade their performance and induce instability, making the two topics core research focuses. We systematically summarize mainstream stability analysis methods, focusing on the Lyapunov-Krasovskii (L-K) functional method, its improved construction forms, and auxiliary tools like integral and reciprocally convex inequalities. We also synthesize state estimation research progress, emphasizing the core role of estimation error system stability. Finally, we point out existing key problems and prospect future trends, providing a reference for subsequent research.
Application of Pile Foundations in Subsided Areas with Mining-Induced Void Formation: A Comprehensive Review
Ground subsidence due to mining activities presents significant challenges for foundation design in affected areas. The formation of voids beneath the surface creates unstable soil conditions, necessitating robust solutions for supporting structures. This paper reviews the application of pile foundations in subsided mining regions, emphasizing their effectiveness in transferring loads to more stable soil layers beneath the voids. We analyze various pile types, installation methods, and design considerations specific to mining-induced subsidence, supported by case studies from regions such as Australia and China. The review highlights the need for advanced geotechnical analysis, including soil-structure interaction modeling and real-time monitoring during construction, to ensure the safety and durability of pile foundations in subsided areas.
Experimental Study of Droplet Impact on Superhydrophobic Surface at Different Speeds
The formation of ice caused by droplets hitting the cold surface may adversely affect the operation of various mechanical devices, causing a series of safety hazards. The surface modification with superhydrophobic properties effectively reduces the contact time and the heat exchange between the contact droplets and the cold surface, promotes the rapid shedding of droplets, and reduces the damage caused by the formation of ice layer. However, the research on the dynamics and heat transfer process of the effect of droplets on low-temperature superhydrophobic surfaces is still limited. The dynamic process of droplets with different Weber numbers impacting on different normal temperature surfaces was studied by using the prepared sample surface F1 ~ F5 and the visual droplet impact test bench. The experimental results show that the droplet spreading stage shows different dynamic characteristics with the change of droplet velocity and surface microstructure spacing. The microstructure spacing has little effect on the dynamic state of the impact droplet rebound with low Weber number, and mainly affects the dynamic state of the impact droplet rebound with high Weber number. The droplet spreading factor increases first and then decreases with the impact time, and is not affected by the surface microstructure spacing at lower Weber numbers. With the increase of Weber number and surface microstructure spacing, the variation curve of droplet spreading factor in the retraction stage will be different. The maximum spreading factor of the droplet is directly affected by the Weber number, and there is a certain fitting relationship with the Weber number: max=We1/4. The increase of surface microstructure spacing under high Weber number will make the change trend of max deviate from the fitting curve. Under the same surface, the droplet contact time Tc basically decreases with the increase of Weber number, while under the same Weber number, the change of microstructure spacing will cause the change of wetting state, so the droplet contact time basically decreases first and then increases. Through energy analysis and statistics of the first rebound height of droplets, it is shown that with the change of Weber number and surface microstructure spacing, the change of droplet rebound height is opposite to the change trend of contact time. However, when Weber number is greater than a certain value, the change trend of droplet rebound height is no longer corresponding to the change trend of droplet contact time, and the rebound height is generally reduced.
Evaluation of Coal Reservoir Fracturability Based on Mechanical Stratigraphy
The difficulty of coal reservoir stimulation is a key bottleneck restricting coalbed methane (CBM) development. Scientific and accurate evaluation of coal reservoir fracturability is essential for achieving commercial CBM production. Given the complexity and uncertainty of coal reservoir geological conditions, the concept of mechanical stratigraphy was introduced in this study. Seven quantitative indicators were selected to establish a fracturability evaluation system, including the geological strength index (GSI), in-situ stress difference coefficient, critical strain energy release rate, brittleness index, fracture pressure, mosaic index, and water inrush coefficient. The analytic hierarchy process (AHP) was adopted to determine the weight of each indicator. Membership functions and membership degrees were defined according to relevant industry standards, and an evaluation model together with a grading set was constructed. Reservoir fracturability was divided into three grades: Class I reservoirs with strong fracturability (index ≥ 0.8), Class II reservoirs with moderate fracturability (0.6–0.8), and Class III reservoirs with weak fracturability (≤ 0.6). A case study was carried out in a block on the eastern margin of the Ordos Basin, and the evaluation results were verified by actual well gas production performance. The results show that coal reservoir fracturability presents a vertical distribution pattern from top to bottom: Class I, Class II, Class III, Class II. Class I engineering sweet spots are widely developed in gentle slope zones and structural platforms where the formation dip angle is less than 3°, with weak Yanshanian–Himalayan tectonic deformation. In such areas, Class I mechanical layers are thick and laterally continuous, the coal reservoir structure remains relatively intact, the in-situ stress difference coefficient is low, and the fracture pressure is relatively high. These structural locations are favorable for the formation and deployment of CBM engineering sweet spots. The coal reservoir fracturability evaluation system based on mechanical stratigraphy exhibits good practicability and reliability. It can provide a scientific basis for CBM favorable area optimization and hydraulic fracturing design.
Reward Modeling from Human Feedback Improves Controllability in Large Generative Models
Reward modeling from human feedback has emerged as a pivotal technique for directing the behavioral tendencies of large generative models toward outputs that reflect human intentions, value alignment, and task-specific constraints. This paper examines the mechanisms through which reward modeling, embedded within reinforcement learning from human feedback (RLHF) frameworks, enables systematic and fine-grained controllability in large language models (LLMs) and other large-scale generative architectures. We present a multi-stage experimental pipeline encompassing preference dataset construction, reward model training across multiple capacity levels, and policy optimization via proximal policy optimization (PPO) to investigate how reward signal fidelity influences downstream generation controllability. Evaluation dimensions include instruction-following fidelity, toxicity suppression, and stylistic consistency, assessed through both automated metrics and human preference ratings. Results demonstrate that RLHF-trained models substantially outperform supervised fine-tuning (SFT) baselines across all evaluation dimensions, with reward model capacity and preference data diversity emerging as primary determinants of controllability generalization. These findings yield practical guidance for constructing robust alignment pipelines in safety-sensitive and user-facing generative AI deployments.
reinforcement learning from human feedback
Predicting Supply Shocks Before They Propagate Using Temporal Graph Reasoning
Supply shocks in modern production networks rarely originate from isolated events; they propagate upstream and downstream through densely interconnected supplier-customer relationships, often manifesting far from their point of origin before conventional monitoring systems detect any signal. This paper proposes a temporal graph reasoning framework for predicting supply shocks before they complete their propagation cascade. By modeling supplier-customer networks as dynamic directed graphs with time-stamped transaction edges, the proposed architecture integrates diffusion-based spatial aggregation with recurrent temporal encoding to capture the evolving topology of inter-firm dependencies. A graph convolutional network (GCN) backbone encodes node-level structural features, while a long short-term memory (LSTM) layer learns the sequential dynamics of nodal activity. The model is evaluated against static graph baselines and non-graph sequential models on a multi-industry transaction dataset spanning 36 months. Results demonstrate that the proposed approach achieves a mean absolute error (MAE) reduction of 18.3% over the best competing baseline and exhibits substantially earlier detection capability, identifying disruption precursors an average of 11.3 days before shock propagation reaches tier-1 suppliers. These findings suggest that temporal graph reasoning constitutes a promising direction for proactive supply chain risk management under conditions of structural uncertainty.
Profitability Evaluation and Commercial Development Path of Natural Gas Power Generation Projects in Sichuan-Chongqing Region
Against the dual strategic backgrounds of the “dual carbon” goal and the construction of the Chengdu-Chongqing Economic Circle, natural gas power generation, as a clean and efficient peak-shaving power source, plays a vital role in the energy transition and power supply security in the Sichuan-Chongqing region. Taking the natural gas power generation industry in Sichuan-Chongqing as the research object, this paper systematically sorts out the regional natural gas resource endowment, operation models and development status of gas power projects, deeply analyzes the practical dilemmas of gas power projects in gas source procurement, electricity price mechanisms, cost control and market competition, measures and judges the profit prospects of the projects, and identifies business opportunities and potential risks from the dimensions of policy environment, resource advantages, market demand and technological iteration. The research shows that:(1)Gas power projects in Sichuan-Chongqing are currently generally faced with problems such as insufficient gas source guarantee, excessively high fuel cost ratio, inadequate implementation of two-part electricity price, and low profitability.(2)Under the conditions of improved electricity price mechanism, improved unit efficiency and integrated development of gas power and new energy, projects can achieve low-margin operation, and peak-shaving gas power and cogeneration in industrial parks have priority development value.(3)Short-term commercial opportunities are better than long-term ones, Chongqing market has higher potential than Sichuan, and gas power projects should transform into peak-shaving reserve, multi-energy complementation and integrated energy service providers.
A Mathematical Investigation of Hallucination in Large Language Models
This paper investigates the phenomenon of 'hallucinations' in large language models through a mathematical lens, analyzing their origins (including inadequate data and bias) and proposing three mitigation strategies: optimizing the reward function in reinforcement learning from human feedback (RLHF), employing low-probability tokens to enhance decoding strategies, and implementing uncertainty-based detection methods (such as SelfCheck-GPT). The study seeks to improve the precision and dependability of model results.
Dual-Motor Synchronous Control Method for Mining Electric Locomotives Based on Multi-Strategy Improved Cross-Coupling
To address the speed synchronization error in the dual permanent magnet synchronous motor (PMSM) control system for mining electric locomotives, this paper proposes an improved synchronous control method based on multi-strategy optimized cross-coupling control. The synchronization error characteristics are analyzed, and the dual-motor system model is established under the cross-coupling structure. A double exponential decay prescribed performance function is designed to achieve fast error convergence combined with Lyapunov stability analysis. Meanwhile, a disturbance observer is constructed to estimate and compensate for the lumped disturbance, thus improving control accuracy. Furthermore, a non-singular integral terminal sliding mode controller is designed to satisfy error constraints and enhance system stability. Experimental results demonstrate that compared with traditional cross-coupling control, the proposed method shortens the settling time by 25% and restricts the overshoot within 20 r/min under no-load conditions. Under sudden load impact, the speed recovery time is reduced by 28%, and the synchronization error is significantly reduced.
permanent magnet synchronous motor (PMSM)
Design and Experimental Research on the Orientation Planting Device of Electric Strawberry Transplanting Machine
To address the agronomic requirements of greenhouse strawberry cultivation involving high-ridge planting, narrow row spacing, close plant spacing, and directional planting, this study developed a deep-planting directional transplanting device featuring a small cavity opening. The device consists of a servo motor-driven screw transmission mechanism for deep planting and a cam-swing-based directional clamping seedling-release mechanism, effectively solving the challenges of achieving sufficient planting depth and maintaining proper orientation during strawberry plug seedling transplantation. Through Box-Behnken experimental design, maintaining average cavity length of 139.3 mm within the qualified range. Furthermore, the optimized configuration achieved a minimal seedling deflection angle of 0.31°, meeting stringent directional planting standards. Field validation tests confirmed the device's superior performance, with planting qualification rates exceeding 90.56% and directional accuracy reaching 91.67%, thereby verifying the scientific validity and practical effectiveness of this electric strawberry transplanter's directional planting system design.
Deflection angle of strawberry seedling pot
Exploring Structure–Function Coupled Brain Network Construction for Brain Disease-Aided Identification
Brain disorders are often associated with coordinated abnormalities in brain structure, functional interactions, and cross-regional connectivity patterns rather than isolated changes in single regions. Conventional single-modality brain network analysis usually captures only one side of these alterations, either from the perspective of anatomical organization or from the perspective of statistical functional coupling, and therefore may not fully characterize the complexity of disease-related network disruption. With the rapid development of multimodal neuroimaging techniques, especially structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and functional magnetic resonance imaging (fMRI), increasing attention has been paid to how structural information and functional information can be jointly organized into a unified brain network representation. This paper discusses structure–function coupled brain network construction for brain disease-aided identification. First, the complementary roles of structural connectivity, functional connectivity, and morphology-related association information are analyzed. Second, several representative coupling paradigms are summarized, including structure-prior-constrained construction, graph-level cooperative fusion, and coupling strategies for time-varying functional connectivity. Their respective characteristics are compared in terms of representational completeness, topological stability, and interpretability. Finally, major open issues are discussed, including modality heterogeneity, bias in connectivity estimation, the reliability of dynamic coupling, and the biological interpretability of coupled graphs. The purpose of this paper is to examine multimodal brain network representation from the perspective of coupled graph construction rather than classification model design, and to provide a focused methodological reference for future studies on brain disease-aided identification.
Electric Field-Enhanced Oil Recovery Performance in Shale Inorganic Pores during CO2 Injection: A Molecular-Scale Study
To solve the problems of strong crude oil adsorption and low displacement efficiency of conventional CO2 in shale inorganic pores, and to reveal the micro-mechanism of electric field-enhanced CO2 displacement of shale oil, this paper takes the widely developed quartz inorganic nano-pores in shale as the research object. A quartz-crude oil-CO2 micro-simulation system was constructed by adopting the method of molecular dynamics simulation combined with theoretical analysis, to systematically study the shale oil displacement effect and micro-interaction characteristics before and after applying electric field. By comparing the relative concentration distribution of crude oil molecules, the radial distribution function (RDF) of CO2 and crude oil, the wall-oil interaction energy, and the evolution law of molecular motion under the conditions of no electric field (without electric field) and with electric field (with electric field), the influence of electric field on the adsorption-desorption behavior of crude oil and CO2 displacement efficiency was quantitatively analyzed, and the molecular change characteristics at different time nodes (0.2 ns, 1 ns, 2 ns, 4 ns) were explored. The results show that the application of an external electric field can significantly weaken the adsorption between crude oil molecules and the quartz wall, reduce the interaction energy, promote the miscibility between CO2 and crude oil, and effectively strip the adsorbed crude oil in the pores; after applying the electric field, the displacement efficiency of quartz inorganic pores increases from 35.92% to 51.96%, with a difference of 9.79%. This paper clarifies the intrinsic mechanism of electric field-enhanced CO2 oil displacement at the molecular scale, improves the micro-theoretical system of electric field-assisted CO2 development of shale oil, and provides theoretical basis and technical support for the optimization of electric field synergistic displacement process parameters and on-site technology research and development in shale oil reservoirs.