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.
Research on Improvement of LED Lamp Production Line Based on FlexSim
Addressing the core issues of low production line balance rate, severe idle human resources, and limited production capacity in a certain LED lamp assembly line, this study comprehensively employs the stopwatch time study method, the Westinghouse Rating System, and production line balancing theory to systematically diagnose the current state of the production line. Through work measurement, it identifies that the OP8 lamp board soldering station and the OP18 packaging station constitute a bimodal bottleneck. The original line's bottleneck cycle time reaches 43.089 seconds, the balance rate is only 49.65%, and the smoothness index is as high as 23.81, indicating severe "cycle time cliffs" and resource waste. Based on this, this paper proposes two differentiated improvement strategies: the efficiency-driven scheme (Scheme I) applies the ECRS principle to merge 18 workstations into 12. With no additional investment, it increases the balance rate to 74.48%, increases per capita output by over 50%, and achieves staff reduction and efficiency improvement. The capacity-expansion scheme (Scheme II), based on the Theory of Constraints, implements parallelization transformation for the bottleneck processes, compressing the cycle time to 21.545 seconds, raising the balance rate to 77.72%, reducing the smoothness index to 5.25, and doubling the theoretical single-shift capacity to 1,336 units. Finally, the FlexSim simulation software is used to dynamically verify the improvement schemes. The error between the simulated output and the theoretical calculation is controlled within 1%, confirming the effectiveness of the model and the robustness of the optimization schemes. This study indicates that enterprises can flexibly choose improvement paths based on their strategic phases of cost priority or scale priority, providing a reliable decision-making basis for lean production management.
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Adaptive Window Diffusion Decoding for Long-Context Language Generation Under Memory Constraints
Masked diffusion language models (MDLMs) are attractive for long-form generation because they denoise all positions with bidirectional context, but this advantage also creates a severe inference-time memory bottleneck: each denoising step requires dense attention over the active sequence. Recent sliding-window and speculative wrappers have shown that the bottleneck can be reduced without retraining the base model. However, fixed window sizes remain inefficient under heterogeneous documents, where some regions require broad context and others can be decoded safely with smaller memory footprints. This paper proposes Adaptive Window Diffusion Decoding (AWDD), a memory-budgeted inference framework that adjusts window size, overlap commitment, and context-summary capacity according to observable uncertainty in the partially masked sequence. AWDD uses a lightweight dependency-pressure score, a bounded summary buffer, and confidence-aware overlap commitment to preserve long-range terms while keeping peak memory below a fixed budget. We derive the memory and computational bounds of the scheduler and provide a reproducible CPU benchmark rather than unsupported large-model claims. On a controlled long-range dependency benchmark and a real-text sanity set extracted from the uploaded SW-SpeedDLM article, AWDD improves long-range entity reconstruction over fixed-window baselines while using substantially less peak memory than a large fixed window. These results support adaptive windowing as a practical direction for memory-constrained diffusion decoding and provide code, raw results, and document-level figures for replication.
WeatherPrompt-Fusion: Prompt-Guided Multi-Modal Perception for Autonomous Driving in Adverse Weather
Reliable autonomous driving perception remains difficult in adverse weather because camera appearance, LiDAR point density, and radar responses degrade in different and condition-dependent ways. Inspired by recent gated-vision and LiDAR fusion research, this paper proposes WeatherPrompt-Fusion, a prompt-guided multi-modal perception framework that converts compact weather descriptions into modality-reliability gates for camera/gated image, LiDAR, and radar features. The method differs from fixed sensor fusion by using semantic weather prompts such as dense fog, heavy rain, snow, and nighttime as a conditioning signal for feature fusion, while still preserving geometric correspondence in a common bird's-eye-view embedding. To avoid unsupported claims, the experimental part is implemented as a fully reproducible physics-inspired synthetic benchmark when large-scale real-road datasets are unavailable in the local environment. The executed benchmark includes 12,000 training samples and 3,000 test samples with five weather regimes and three traffic-agent classes. WeatherPrompt-Fusion obtains a macro mAP of 0.752, improving over fixed average fusion (0.673), single-modality LiDAR (0.621), radar (0.604), and camera-only perception (0.504). Under fog, the proposed model reaches 0.744 mAP versus 0.651 for fixed fusion and 0.702 for naive concatenation. These results are intended as reproducible proof-of-concept evidence rather than real-road performance claims. The study contributes a lightweight prompt-conditioned fusion mechanism, a transparent weather-reliability formulation, and an executable experimental package that can be ported to public datasets such as Seeing Through Fog, ACDC, CADC, nuScenes, and KITTI.
Uncertainty-Aware Evidential Adversarial Defense for Cloud Intrusion Detection under GAN-Based Evasion Attacks
Cloud intrusion detection systems (IDSs) increasingly depend on deep neural classifiers, which are vulnerable to adversarial evasion attacks—including realistic, GAN-crafted traffic—that flip malicious flows to benign with near-imperceptible, functionally consistent perturbations. Adversarial training (AT) hardens the decision boundary but yields overconfident point predictions: a hardened detector still cannot signal when an input is suspicious, and Bayesian or ensemble uncertainty estimators that could do so require many forward passes, which is impractical at cloud line rate. We propose UA-EAD, an uncertainty-aware evidential adversarial defense that (i) equips the detector with an evidential (Dirichlet) head yielding calibrated predictive uncertainty in a single forward pass, (ii) trains it with an uncertainty-weighted adversarial objective plus a consistency regularizer that concentrates robustness on the most uncertain, near-boundary flows, and (iii) uses the resulting uncertainty for selective prediction, abstaining on inputs it cannot confidently classify. On NSL-KDD under FGSM, BIM, PGD, C&W, and a black-box WGAN-GP transfer attack, UA-EAD matches the strongest AT baselines in robust accuracy (97.4%) while attaining the highest clean accuracy (98.1%). Its single-pass evidential uncertainty equals maximum-softmax-probability and surpasses Monte-Carlo Dropout for adversarial detection at 21x lower inference cost, and yields the best adversarial-detection AUROC among robust models. Selective prediction lifts accuracy on 17 novel (out-of-distribution) attack types from 76.9% to 87.0% at 70% coverage. We further report an empirical robustness–detectability trade-off that clarifies why hardening alone is insufficient for trustworthy cloud IDS.
PotentialNet: Explainable Employee Potential Prediction via Multi-View Representation Learning
Employee potential assessment is a high-impact human resource analytics problem, but it differs from promotion prediction because potential is prospective, multi-dimensional, and rarely available as a clean public label. This paper proposes PotentialNet, an explainable multi-view representation learning framework that separates employee attributes into profile, performance, growth, and behavior views. Dedicated view encoders learn view-specific representations, a contrastive alignment objective encourages agreement across complementary views of the same employee, and an attention fusion module produces both a high-potential probability and a view-level explanation. Because public data with verified potential labels was not available, we construct a fully reproducible semi-synthetic HR benchmark rather than inventing real employee data. Experiments compare PotentialNet with logistic regression, random forest, gradient boosting, multilayer perceptron, and LightGBM. Results show that PotentialNet is competitive with strong tabular baselines while providing interpretable view-level explanations and counterfactual action audits. The study is deliberately conservative: it avoids claims of real-world deployment validity and releases all code, generated data, figures, and raw results to support transparent reproduction.
Research on Stress Distribution Characteristics in the Wheel-Seat Zone of railway wheelset
The wheel-seat zone is a high-risk area for fretting fatigue and wheel-axle loosening failure. During service, it simultaneously bears multi-field coupling effects including mechanical loads, rotational centrifugal force, emergency braking thermal loads, and track impact loads. The dynamic fluctuation of contact stress directly determines the operational safety of the running gear. Taking the EA4T hollow power axle as the research object, this paper adopts a fully coupled thermal-mechanical algorithm to carry out independent single-field simulations and multi-field superimposed combined-condition simulations respectively: 1) Simulate the entire emergency braking process at 200/300/400/500 km/h and the wheel-axle temperature conduction and contact stress transfer laws during the parking cooling stage; 2) Quantify the weakening effect of centrifugal force at different rotational speeds from 200 to 500 km/h on the interference fit interface stress, and analyze the disturbance of track defect impact on the contact field combined with line-measured impact load spectra; 3) According to four typical coupled conditions—static load, high-speed centrifugal load, single emergency braking, and multiple consecutive braking—systematically reveal the axial and circumferential stress and micro-slip distribution characteristics in the wheel-seat zone under multi-physical field superposition. The results show that the wheel-seat stress exhibits a "high at both ends, low in the middle" distribution along the axial direction, with significant stress gradients at the contact edges. Both centrifugal force and braking temperature rise reduce the average interface contact stress, with the thermal accumulation of consecutive braking having the most significant weakening effect. Impact loads only locally amplify the edge stress peaks without altering the overall distribution pattern. Under the four coupled conditions, the fatigue strength of the wheel-seat zone meets UIC standards, but the fatigue safety margin at the inner edge is relatively low. This research can provide simulation-based references for the selection of interference fit amounts in EMU wheel-axle press-fitting, operational control of braking conditions, and axle fatigue life extension.
Smartphone Battery Life Prediction Based on Multi-Module Power Decomposition and Second-Order Thevenin Thermo-Electro Coupled Model
This paper proposes a continuous-time forecasting method that integrates power consumption decomposition with battery state evolution for predicting smartphone battery life. The method unifies the modeling of power-consuming modules—such as the screen, CPU, network, GPS, and background activities—and combines them with a second-order Thevenin thermoelectric coupling model to describe the dynamic changes in SOC, voltage, polarization, and temperature. In the parameter estimation phase, least squares and weighted least squares estimation are introduced to improve the stability of the model fit. In the prediction phase, the remaining discharge time is calculated using the SOC decay relationship, and prediction reliability is evaluated through uncertainty propagation and sensitivity analysis. The results indicate that this model can reliably characterize battery discharge trends under various load conditions and demonstrates good transferability and practical value.
Second-Order Thevenin Equivalent Circuit
Battery State Prediction for Mobile Devices Based on Coupled CETM and ALE-SOH Models
This paper proposes a two-layer coupled model for mobile device battery capacity prediction, integrating the CETM electrothermal model with the ALE-SOH aging model to achieve a coordinated estimation of SOC, temperature, and battery degradation status. The inner layer uses state-space estimation to track real-time load changes, while the outer layer adjusts capacity decay and internal resistance growth based on cycle count and temperature, thereby balancing short-term prediction with long-term degradation characterization. Validated using data from the Samsung Galaxy S8, the model achieved an SOC prediction MAE of 4.231%, an RMSE of 5.673%, and an R² of 0.952, with an average absolute error of 0.592°C for temperature. The results indicate that this model is applicable to various scenarios, including web browsing, navigation, 5G data transmission, and gaming, demonstrating good prediction accuracy, stability, and practical value for widespread application.
A Comprehensive Review of Remote Sensing Image Change Detection Methods
Remote sensing image change detection aims to identify spatiotemporal changes in land cover, land use, and ground-object structures by comparing remote sensing images acquired over the same area at different times. It is an important research topic at the intersection of remote sensing image processing, geographic information science, and computer vision. With the rapid accumulation of high-resolution, multi-source, and multi-temporal remote sensing data, change detection has been widely applied to urban expansion monitoring, land-use surveys, disaster damage assessment, and ecological environmental protection. This review focuses on remote sensing image change detection. It summarizes the general workflow, outlines the principles and limitations of traditional methods, and systematically reviews deep learning-based change detection methods. In addition, mainstream public datasets and commonly used evaluation metrics are summarized. The core challenges faced by current research are analyzed from the perspectives of data quality, data resources, and methodological design, including image registration errors, pseudo-change interference, and the high cost of annotation. Finally, future research directions are discussed to provide references for method development, model optimization, and practical applications in remote sensing image change detection.
Research on Key Technologies of Intelligent Steer-By-Wire System
With the deep integration of new energy vehicles and intelligent network technology, the steer-by-wire system, as the core execution unit of the steer-by-wire chassis1, completely breaks the structural shackles of traditional mechanical steering and realizes the electronic, precise and intelligent transmission and execution of steering instructions2. Based on the Chinese latest national standard of GB 17675-2025 'Basic Requirements for Automobile Steering System'3, this paper systematically expounds the structural composition, working principle and core key technologies of the steer-by-wire system, deeply compares and analyzes the performance differences between the steer-by-wire system and the traditional steering system, analyzes the functional safety redundancy design and fault handling mechanism of the system, verifies the technical advantages with the measured data of the industry, sorts out the current technical application pain points and industrial development trends, and provides theoretical reference and technical support for the technical iteration, engineering application and standardized landing of the steer-by-wire system.
AI-Empowered Intelligent Instrumentation: From Automatic Meter Reading to Predictive Maintenance
The integration of artificial intelligence (AI) into instrumentation and measurement systems is reshaping industrial monitoring, control, and maintenance practices. This article provides a comprehensive overview of AI-empowered intelligent instrumentation, with a focus on three representative application paradigms: automatic meter reading, fault diagnosis for predictive maintenance, and sensor calibration with drift compensation. We review recent advances in deep learning-based object detection for analog and digital meters, highlighting frameworks such as improved YOLO and Fast R-CNN that achieve accuracy exceeding 98% while reducing measurement time by up to 85%. In the domain of prognostics and health management, we examine how convolutional neural networks with time-frequency transformations enable near-perfect fault classification in rotating machinery. Additionally, we discuss AI-driven calibration methods using neural networks and Gaussian process regression, which not only improve accuracy but also provide rigorous uncertainty quantification compatible with international measurement standards. Despite these successes, challenges remain regarding data scarcity, model interpretability, uncertainty quantification, and real-time edge deployment. We conclude by advocating hybrid approaches that combine data-driven AI with conventional model-driven techniques to achieve both high performance and trustworthiness. This review serves as a practical reference for researchers and engineers seeking to adopt AI solutions in instrumentation applications.
Analysis on Common Manufacturing Defects and Related Issues of Lattice Structures in 3D Printing
Lattice structures have been used to construct some of the light-weight, high-surface-area and mechanically-adaptable parts of additive manufacturing. However, the complex shape of these parts is also more prone to manufacturing defects such as porosity, lack of fusion, dimensional deviation, strut waviness, surface roughness and poor interlayer bonding. This paper introduces the main kinds of defects in 3D-printed lattice structures and analyzes the problems they cause to the mechanical properties, fatigue life and design reliability of such structures. Many of the lattice failures have been caused by the combined effect of geometry, process parameters and material behaviour during printing, not by design problems alone. According to the latest research on defect characterization, numerical modelling and machine learning for defect reduction have been applied to pinpointing frequent production problems and their expanded influence on quality control and structural performance. Therefore, to obtain defect-free lattice structures, integrated control at all stages of design, process supervision and post-processing is needed, and a single solution is insufficient.
Environmental Applications of Advanced Superwetting Membranes for Oil-Water Separation
The prevalence of oil pollution is steadily escalating due to the growing reliance on oil in human daily routines and industrial operations, with global consumption exceeding 100 million barrels per day. Membrane-based separation of oil-water emulsions has demonstrated critical performance characteristics essential for industrial applications, including affordability, environmental sustainability, simplicity of operation, and energy efficiency. The membranes show remarkable durability, maintaining over 98.5% separation effectiveness after 40 repeated operations. Investigating Membranes with distinctive super-wettability properties for filtration and various practical uses has become a highly compelling area of research in recent years. These super-wetting membranes are not just limited to Oil-water filtration but have a wide range of potential applications, making them a versatile solution to many environmental challenges. This review synthesizes recent advancements in membrane technology, highlighting key innovations and emerging trends, focusing on its exceptional super-wettability qualities and enhanced characteristics for separating oil and water. In addition, this article focuses on the wetting behavior of these versatile membranes and summarizes various surface-wetting categories, including superhydrophilic (θ < 10°), underwater superoleophobic (θoil > 150°), and superhydrophobic-superoleophilic (θwater > 150°, θoil < 10°) membranes. It also analyses the possible difficulties and barriers associated with these membranes. This research summarizes the advancements in creating versatile super-wetting membranes for separating oil and water. It ends by proposing prospective areas for additional enhancement and alteration, instilling a sense of intrigue about the potential applications of these membranes.
Rural Management and Value Co-creation: Rural Areas of Pakistan and the Digital Age
This review investigates the application of digital technologies in rural Pakistan, including a co-creation of value and sustainable development. The article discusses the situation in rural management today, drawing attention to the important role of IoT, wireless communication technologies, and digital platforms in changing agriculture, healthcare, education, and economic activities. It recognizes the barriers of rural communities such as lack of infrastructure, digital literacy and the resistance of culture, which is a barrier to the uptake of these technologies. The review carries success stories of digital and renewable energy projects that have empowered the rural communities, enhancing their resource, information and market accessibility. It further talks about how smart agriculture, financial inclusion, and e-governance can be utilized to build a more inclusive, sustainable rural future. The paper ends with recommendations to overcome the obstacles to digital integration and highlights why policy interventions, infrastructure investment, and partnerships between the government and the business sector are required to achieve inclusive digital transformation in rural Pakistan.
The Application of Recycled Aggregate Concrete in Sustainable Civil Engineering
As the global construction industry accelerates its transformation toward low-carbon and circular development, the demand for eco-friendly and renewable construction materials has risen sharply. Recycled aggregate concrete (RAC), which is manufactured by replacing natural coarse and fine aggregates with crushed construction and demolition (C&D) waste, has become one of the most promising sustainable alternatives to conventional ordinary concrete. This paper systematically explores the fundamental material characteristics, structural mechanical performance, practical engineering application scenarios, and comprehensive environmental benefits of recycled aggregate concrete. Meanwhile, it comprehensively analyzes the core technical bottlenecks restricting its large-scale promotion, including unstable material quality, decreased mechanical strength, and poor durability performance caused by residual adhered mortar on recycled aggregates. This study systematically summarizes three mainstream performance enhancement technical systems: optimized concrete mix proportion design, physical and chemical modification treatment of recycled aggregates, and innovative advanced curing and processing technologies. Typical engineering case studies are supplemented to elaborate on the practical application effects of RAC in building structural components, road pavement engineering, and non-load-bearing precast concrete members. Furthermore, the industrial development barriers such as imperfect unified industry standards, insufficient construction experience, and low market recognition are discussed in depth, and targeted suggestions for future academic research and engineering popularization are proposed. The research results of this paper clarify the application potential and development value of RAC, aiming to provide theoretical support and technical reference for the large-scale application of recycled aggregate concrete and further promote the high-quality sustainable development of modern civil engineering.
Performance Optimization of Intelligent Sensor Monitoring System for Industrial Mechanical Equipment Based on Improved Algorithm
With the rapid development of industrial Internet of Things (IIoT) and intelligent manufacturing technology, intelligent sensor monitoring systems have been widely applied in the condition monitoring and fault early warning of industrial mechanical equipment. However, traditional monitoring systems suffer from prominent problems such as low data processing efficiency, high transmission delay, poor anti-interference ability and low fault recognition accuracy in complex industrial working conditions, which seriously restrict the real-time performance and stability of equipment state monitoring. To solve the above defects, this paper proposes an improved hybrid optimization algorithm based on variational mode decomposition (VMD) and adaptive weighted ensemble learning, and applies it to the performance optimization of industrial mechanical equipment intelligent sensor monitoring system. Firstly, the system overall architecture is optimized, and a hierarchical data processing framework of edge acquisition, real-time processing and cloud analysis is constructed to reduce data transmission pressure. Secondly, aiming at the noise interference and redundant data of sensor vibration, temperature and current signals in industrial environments, an improved VMD algorithm with adaptive penalty factor is designed to realize efficient denoising and feature extraction of monitoring data. Finally, an adaptive weighted ensemble fault detection model is established to optimize the data analysis and state recognition performance of the monitoring system. Experimental results show that compared with the traditional system, the improved monitoring system reduces the data processing delay by 41.6%, improves the signal denoising signal-to-noise ratio (SNR) by 3.8 dB, and increases the equipment fault recognition accuracy to 96.2%. The optimized system has better real-time performance, stability and detection accuracy, which can effectively meet the high-precision and real-time monitoring requirements of industrial mechanical equipment under complex working conditions.
Close Packing Theory of Binary System of Fly Ash-Cement with Different Fineness
The high packing density of particle mixtures is an important factor in achieving ultra-high performance in cement-based composites. In this study, the effect of fly ash with different fineness on the dense packing performance of cement-based composite cementitious material mixing systems are investigated. The results found out that when the particle size of fly ash is significantly smaller than that of cement particles (such as D50=1.0μm, 3.0μm), it can effectively fill the voids between cement particles and reduce the void ratio; when the particle size is similar to that of cement (such as D50=12.0μm), there is no filling effect, and even the packing effect may be deteriorated. At the same time, the smaller the particle size, the better the filling effect. In specific, at the optimal dosage, fly ash with D50=1.0μm can achieve a lower porosity than fly ash with D50=3.0μm, and the overall effect of reducing porosity is more significant.
Research on Key Technologies for Converted-Wave Seismic Data Processing
Compared with the processing technology of P-wave seismic data, there are still many challenges in the processing of converted-wave seismic data: The initial arrival of the conversion wave cannot be effectively and accurately picked up, and due to the difficulty in establishing an accurate near-surface model of the transverse wave, the calculation of the static correction amount of the conversion wave is difficult, The conventional conversion wave velocity analysis method relies on the establishment of the longitudinal wave velocity model, which is inefficient, Meanwhile, its accuracy is doubly affected by the longitudinal wave velocity and the transverse wave velocity. The conversion wave velocity has multiple solutions, which affects the imaging effect of seismic data.in the light of of the problems above, This paper proposes the multi-information fusion conversion wave static correction technology, the integrated Offset velocity modeling, and the OVT domain (Offset Vector Tile)VTI(Vertical Transverse Isotropy) prestack time offset technology, Through the processing of actual data: The static correction quantity calculated by variable coefficient static correction has higher accuracy than that calculated by constant coefficient static correction, Regularization processing in the OVT domain can effectively reduce the acquisition footprints of the conversion wave data and restore the missing areas of the data, making the amplitude distribution more uniform, Integrated velocity modeling can achieve the interactive update of the four parameters of the conversion wave, effectively improving the processing efficiency and accuracy of the pre-stack time offset of the conversion wave.
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.
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.