Academic Journal of Emerging Technologies
Academic Journal of Emerging Technologies ISSN Print: 3104-4417 ISSN Online: 3104-4425 CD ISSN: 3104-4433
Multi-UAV Task Allocation Method for Road Emergency Rescue Based on Improved Multi-Objective Genetic Algorithm
To enhance the operational efficiency of multi-UAV material delivery in emergency rescue scenarios, this paper establishes a mathematical model for multi-UAV task allocation and proposes an improved hybrid multi-objective genetic algorithm, namely the AC-MOGA algorithm. Firstly, the multi-UAV material delivery task allocation problem is elaborated. A mathematical model is constructed with three optimization objectives: minimizing the total flight distance of UAVs, reducing the maximum task completion time, and optimizing the weighted total response time. Secondly, Logistic chaotic mapping and adaptive strategies are introduced to modify the multi-objective genetic algorithm. The improvements are implemented in population initialization, crossover and mutation operations to construct the proposed algorithm for problem solving. Finally, based on six groups of VRPLIB benchmark instances, numerical simulations are carried out on the MATLAB platform to obtain the task allocation schemes derived by the AC-MOGA algorithm. Comparative analyses with a state-of-the-art improved genetic algorithm are conducted on identical benchmark instances. The results verify the effectiveness and feasibility of the AC-MOGA algorithm for solving vehicle routing problems.
Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by/4.0/
Thermo-Mechanical Coupled Analysis of Wellbore Integrity in Perforated Underground Gas Storage Wells During Injection
Aiming at the severe alternating loads and failure risks faced by the perforated wellbore section under the cyclic high-rate injection and production conditions of underground gas storage (UGS), this paper conducts a thermo-mechanical coupled analysis of wellbore integrity during the gas injection process. A 3D fully thermo-mechanical coupled finite element model of the perforated casing-cement sheath-formation system was established, accurately reconstructing the 3D geometric morphology of spiral perforations. Based on the verification of the model's computational accuracy, the sensitivity laws of injection temperature and pressure on the stress and deformation characteristics of the casing and cement sheath were systematically investigated. The results indicate that the injection temperature is the most critical parameter controlling the casing strength. The "cold shock" effect below 80°C causes the local equivalent stress of the P110 casing to exceed its yield limit (reaching 820.5 MPa), simultaneously increasing the risk of cracking and failure in the cement sheath. Furthermore, the elevation of injection pressure intensifies the radial expansion of the casing, pushing it toward the yield limit. Although the local tensile stress of the cement sheath decreases due to the confining pressure effect, its equivalent strain surges dramatically (up to 1.009×10-1), substantially elevating the risk of interfacial micro-annulus formation. This study reveals the damage mechanisms of the perforated wellbore section under multi-field coupling. It is recommended that on-site gas injection operations should co-optimize temperature and pressure parameters to avoid the superposition of extreme low-temperature and high-pressure conditions, providing quantitative theoretical support for the long-term safe operation of UGS and the optimization of gas injection schemes.
Full-Section Support Technology for Mining Roadways in "Three-Soft" Unstable Thick Coal Seams
The main coal seam of Xingcun Coal Mine is a typical "three-soft" unstable coal seam, where mining roadways suffer from severe deformation and cannot remain stable despite repeated repairs and expansions during service. In this study, borehole inspection was employed to detect the development of fractures in the surrounding rock of the mining roadway, and numerical simulation was conducted to compare and analyze the plastic zone distribution and displacement characteristics of the roadway surrounding rock under four support schemes: U-shaped steel support alone; U-shaped steel support combined with roof and sidewall anchor cables; U-shaped steel support combined with full-section anchor cables; and U-shaped steel support combined with full-section anchor cables and grouting. The differences among these support schemes in controlling surrounding rock stability were elucidated. On this basis, a combined support scheme of "U-shaped steel support + full-section anchor cables + grouting" was proposed. The results indicate that the deformation of the roadway surrounding rock and the floor heave were significantly reduced, and the roadway maintained a favorable profile. During the observation period, the convergence between the two sidewalls and between the roof and floor was controlled within 97 mm and 75 mm, respectively, achieving a remarkable surrounding rock control effect. The findings can provide a reference for surrounding rock control in mining roadways under similar geological conditions.
Study on Surface Micro-Texture of Ti2AlNb-based Alloy Cladding Layer by Ultrasonic and Temperature Field Assisted Milling
Ti2AlNb-based alloy is difficult to machine due to its high thermal deformation resistance, significant work hardening and poor surface processing quality. In this paper, Ti2AlNb alloy cladding layer was prepared on Ti-6Al-4V substrate by laser cladding technology. Combined with ultrasonic vibration and temperature field assisted milling composite processing technology, the influence of ultrasonic amplitude and preheating temperature on the surface texture morphology of the alloy was investigated. The results show that ultrasonic vibration can change the material removal method, change the machined surface from ordinary parallel tool marks to regular periodic grid micro-texture, and significantly improve the surface texture regularity. Moderate preheating can improve the cutting stability through the thermal softening effect of the material, and effectively suppress the processing defects such as ploughing and tearing. As the preheating temperature increases from 30℃ to 200℃, the surface roughness continues to decrease, and the texture quality is gradually improved. Too high preheating temperature (300℃) will lead to excessive softening, sticking and thermal damage of the material, resulting in surface texture distortion and significant increase in roughness. Within the range of experimental parameters, 200℃ preheating coupled with 2μm ultrasonic amplitude can obtain the optimal surface morphology and processing quality.
Improved YOLOv11n-Based Algorithm for Wood Surface Defect Detection with Edge-Guided and Adaptive Fusion Modules
Wood surface defect detection is a critical process in industrial quality control. This manuscript proposes WLA-YOLO, an estimated YOLOv11n improvement that integrates an Edge-Guided P2 Branch, Grain-aware Local-Context Attention, and Adaptive Fusion FPN. The method is designed to improve fine crack localization, suppress wood-grain interference, and balance multi-scale feature fusion.t WLA-YOLO reach 75.1% mAP@0.5 and 39.8% mAP@0.5:0.95 on an 7-class wood surface defect dataset.
RSD-YOLO: An Improved YOLOv11n-Based Algorithm for Railway Surface Defect Detection
Railway surface defect detection is a critical component of automated track maintenance and safety assurance. To improve the detection capability of lightweight YOLOv11n on rail surface images, this paper proposes RSD-YOLO, a conservative structural enhancement that adds a P2 high-resolution detection branch, replaces selected high-level C3k2 blocks with C2f modules, and introduces P2-SPPF context enhancement. The resulting detector changes the original three-scale P3/P4/P5 output to a four-scale P2/P3/P4/P5 output. Experiments on a four-class rail surface defect dataset show that RSD-YOLO achieves 0.7966 mAP@0.5 and 0.5616 mAP@0.5:0.95 on the test set, improving YOLOv11n by 8.05 and 7.24 percentage points, respectively.
Multimodal Autonomous Navigation by Fusing Visual and Tactile Perception for Deformable Obstacle Traversal
Autonomous mobile robots predominantly rely on visual perception for obstacle avoidance, which inherently treats all detected obstacles as rigid and impenetrable. However, in real-world environments, many obstacles such as curtains, vegetation, and flexible partitions are deformable and can be safely traversed with appropriate force control, yet visual appearance alone rarely provides reliable compliance information. This paper proposes a multimodal navigation framework that fuses exteroceptive visual sensing with proprioceptive tactile perception to assess the passability of ambiguous obstacles. A global visual planner generates an initial path, while a novel tactile-driven local passability classifier determines whether a frontal obstacle is rigid or soft. A custom CNN–LSTM network processes tactile time-series signals from a dedicated probing arm to output a haptic passability score. When a soft obstacle is identified, the navigation system activates an admittance controller to compliantly push through; otherwise, the obstacle is added to the costmap for re-planning. Simulations and real-robot experiments in environments containing curtains and artificial foliage demonstrate that the proposed visual–tactile fusion method reduces traveled distance by 22.3% and mission time by 18.7% compared to a pure vision-based detour approach, while maintaining a 100% hard-collision avoidance rate.
Support of The Return Airway In The Lower Slice of A Thick Coal Seam
To address the support challenges of lower-seam gate roads under conditions of thick coal seam slicing mining and village-subjacent coal extraction, this study investigates the 52204 tailgate at WulanSetai Coal Mine. The gate road is situated 6 m inward from the solid coal boundary of the upper-seam 52113 goaf. Based on Protodyakonov’s pressure arch theory, a mechanical source analysis model for the tailgate was established, and support effectiveness was evaluated using FLAC3D numerical simulation. Results indicate that due to the gate road’s position beneath the goaf, I-beam steel sets with a spacing of 450 mm are recommended. Additionally, to counteract the lateral load exerted by the boundary solid coal on the gate road ribs, supplementary rock bolts were installed on the pillar-side rib. These bolts are positioned at heights of 1.8 m, 2.7 m, and 3.3 m above the floor, thereby significantly enhancing the overall stability of the gate road.
Design and Implementation of an FPGA-Based Data Acquisition System for Accelerator Synchronous Power Supply
In synchrotrons, the quality of beam acceleration depends on precise control of the composite magnetic field, which requires multiple sets of magnet power supplies to achieve accurate synchronization. Therefore, multi-channel synchronous acquisition of magnet power supply signals is a key step in evaluating their actual synchronization performance. However, the existing power supply acquisition system of CSRm suffers from issues such as complex structure and large volume. To address the synchronization requirements and system upgrade needs of the acquisition system, a synchronous multi-channel data acquisition system scheme is designed. The system consists of an AD acquisition module and a logic processing module. The AD acquisition module performs synchronous acquisition of multiple sets of power supply signals. The logic processing module, based on the FPGA of the ZYNQ platform, handles data transmission and filtering; the ARM side runs an embedded Ubuntu system and, together with the driver, achieves functional control and data transmission of the acquisition system. Finally, module functions and indicators of the acquisition system are tested. The test results show that the acquisition frequency remains stable above 9999 Hz, and the time difference between two channels is stable around 1 μs, meeting the accelerator channel requirement of less than 5 μs delay, thus verifying the feasibility of the system.
LSFFNet: Large-kernel Small-span Feature Fusion Network
High-resolution optical remote sensing images are crucial for improving ground object interpretation and supporting precise earth observation. However, existing mainstream super-resolution methods struggle to adapt to the inherent characteristics of optical remote sensing images, such as large scale variation, weak texture details and complex imaging degradation processes. These methods commonly suffer from blurred high-frequency details, high computational complexity and difficulty in lightweight deployment for practical applications. To address these limitations, this paper proposes a lightweight Large-kernel Small-span Feature Fusion Network (LSFFNet) for optical remote sensing image super-resolution reconstruction, targeting practical scenarios with limited computing resources. A Large-kernel Small-span Feature Extraction Block (LSBlock) is designed in the proposed model. By adopting a small number of large-sized depthwise separable convolutions, multi-scale contextual information can be captured with extremely low parameter overhead. Meanwhile, an Attention Multi-level Feature Fusion Block (AFFBlock) is constructed. Integrating channel and spatial dual attention mechanisms, it enables adaptive selective fusion of multi-layer features and mitigates feature information loss effectively. Experimental results on multiple remote sensing datasets demonstrate that compared with state-of-the-art methods, the proposed LSFFNet achieves comparable or even better quantitative performance with fewer parameters and lower computational cost, striking a favorable balance between reconstruction quality and inference efficiency. Quantitative evaluations show that our method outperforms several existing mainstream and lightweight models in terms of PSNR and SSIM. Qualitative visual comparisons further verify its superior capability in edge restoration, texture preservation and artifact suppression. The designed LSFFNet also provides a valuable reference for performance optimization of lightweight super-resolution models in remote sensing tasks.
Research on Real-time Data Processing System Based on Augmented Reality (AR) Technology
Big data analytics plays a crucial role in modern enterprise decision-making. To meet the demands of efficient data processing and accurate prediction, a novel big data analytics and prediction system has been successfully developed. The system integrates multiple data sources and applies advanced machine learning algorithms for predictive analysis. Functional testing shows that the system achieves 98.6% of its functional points, and performance evaluation results indicate optimal performance balance with 3000 concurrent users. User experience research reveals that 89% of users are satisfied. Security testing confirms that the system can effectively defend against common network attacks. This system provides enterprises with a reliable data analysis tool, expected to significantly improve decision quality.
Identification of Hazard-Source Indicators for Coal Spontaneous Combustion in Deep Mine Goafs
To improve the proactive prevention and control of coal spontaneous-combustion disasters in the goafs of deep coal mines, this study identifies the factors associated with hazard-source recognition for coal spontaneous combustion in the goaf of a coal mine in Hebi, China. The mine is characterized by coal-seam explosibility and spontaneous-combustion proneness, large quantities of residual coal under fully mechanized top-coal caving, a complex air-leakage field, and concealed fire sources that are difficult to detect in a timely manner. On the basis of systems engineering theory and accident-causation theory, literature analysis, field investigation, and expert evaluation were integrated to conduct hierarchical identification of hazard sources related to goaf coal spontaneous combustion. A risk-evaluation indicator system was then established from four dimensions: human factors, equipment factors, environmental factors, and management factors. The results indicate that goaf coal spontaneous combustion is not governed by a single factor; rather, it is a dynamic disaster process driven by the coupled effects of residual-coal oxidation and heat accumulation, air leakage and oxygen supply, production organization, equipment reliability, and management execution. Among the identified indicators, the accuracy of early hazard recognition, reliability of standby power supply and emergency equipment, matching degree between longwall face advance rate and the spontaneous-combustion period, and closed-loop rate of hazard investigation and rectification exert critical influences on risk evolution. The findings provide theoretical support for precise prevention and control of goaf coal spontaneous-combustion risk in the studied mine and offer a reference for fire-prevention and fire-extinguishing management in similar deep, fully mechanized top-coal-caving mines.
Adaptive Multispectral Perception and Distributed Trajectory Optimization for Low Altitude Economic UAV Swarms Operating in Complex Mountainous Agricultural Ecosystems of Yunnan
The emergence of low altitude economic ecosystems presents transformative potential for precision agriculture in topographically challenging regions. This study introduces an integrated framework designated Adaptive Multispectral Edge Fusion Trajectory Optimization for low altitude unmanned aerial vehicle swarms deployed in the mountainous tea plantations of Yunnan Province. The proposed architecture unifies an adaptive middleware stack for resilient inter vehicle communication, a multispectral machine vision network with cross channel spectral attention, and a distributed model predictive control trajectory planner that explicitly encodes orographic constraints, wind drift, and energy budgets. A digital twin enabled hardware in the loop simulation environment calibrated with field data from Pu'er and Dali operational sites serves as the primary validation platform. Comparative assessments against four baseline perception pipelines, three classical trajectory planners, and two conventional middleware stacks demonstrate that the proposed system achieves a mean average precision of 0.893 for tea plant stress detection across five spectral bands, reduces trajectory length by 18.4 percent in high complexity gorges, and maintains sub 35 millisecond end to end latency under 30 percent packet loss conditions. These findings illustrate that coupling adaptive software architectures with multispectral machine vision and distributed planning significantly enhances operational safety, chemical use efficiency, and mission endurance in rugged mountainous agricultural environments. Notwithstanding these advances, prolonged adverse weather interactions and full beyond visual line of sight regulatory compliance require further validation.
Collaborative Design Strategies for Electronic and Electrical Architectures in the Intelligent Driving Domain
The evolution of intelligent driving technology toward higher levels imposes new demands on electronic and electrical architectures, requiring high computing power, high real-time performance, and high functional safety levels. Traditional development models face challenges such as a disconnect between architecture and functionality, inefficient cross-team collaboration, and lengthy validation cycles. This paper focuses on the intelligent driving domain and establishes a three-tier collaborative design strategy system comprising the application layer, service layer, and hardware layer. Through layered decoupling, it achieves separation of software and hardware as well as standardized interaction between domains; through model-driven approaches, it supports cross-team parallel development; and through digital twins, it advances verification and enhances scenario coverage. A development project for a Level 2+ highway pilot assist function at a certain automaker demonstrates that this framework can shorten the development cycle by 25%, improve validation efficiency by 70%, and achieve a product safety score of 94%, providing a reusable methodological foundation for the development of intelligent driving electronic and electrical architectures.
Method for Extracting Tree Diameter at Breast Height Based on Vehicle-Mounted LiDAR
Addressing the issues of low fitting efficiency and precision in the process of fitting tree diameter at breast height (DBH) using vehicle-mounted LiDAR point clouds, this paper proposes an energy function model based on least squares roughness constraint weighting to compensate for fitting accuracy and improve the extraction method of tree DBH parameters. Through fitting and comparative analysis of tree DBH using the energy function on roads in the experimental area, the accuracy of DBH extraction has been significantly improved, with the mean absolute error (MAE) of road tree DBH being 1.70 cm and the root mean square error (RMSE) being 2.60 cm. The results indicate that working on only one side of the tree, namely the side visible from the road, is still feasible for approximating different DBH structural parameters using precise fitting.
Concept For Using Fpga-Based Computer Vision In Smart-Home IoT Paradigm
The smart home IoT sector continues to grow every year, with an ever-increasing number of new solutions emerging that incorporate computer vision technologies using CNN-based machine learning approaches. For the most part, CNN computations are performed either on a cloud server—which can increase computation time to 100 milliseconds and leave the data vulnerable to uncontrolled cyberattacks—or on edge devices powered by expensive GPUs or low-performance CPUs. This article describes a concept for an FPGA-based device built on the ZYNQ platform, which, in theory, could resolve these issues. The proposed concept is capable of capturing video feed using an OV5640 camera, processing it, and performing object detection using a CNN model. The system can manage and control most types of IoT devices using the Home Assistant software, installed as a Docker container on a custom-built Linux distribution. As part of this paper, an experiment was conducted to compare the inference times of an FPGA-based and a CPU-based CNN accelerator on YOLOv3 model. According to the results of the experiment, the FPGA-based solution performed best. This result could serve as a foundation for future work on integrating FPGAs into the IoT paradigm.
A Review of the Current Research Status and Intelligent Development of Small-Diameter Natural Gas Pipeline Inspection Robots
For small-diameter natural gas pipelines widely distributed in gathering and transportation networks, the confined space, frequent bends, and complex branch configurations pose significant challenges to routine inspection and safe operation and maintenance. This paper presents a comprehensive review of the current state of research on inspection robots for small-diameter pipelines and examines the unique constraints imposed by ultra-narrow environments on robotic structural design and intelligent perception. First, through a comparative analysis of mainstream locomotion mechanisms-including wheeled, tracked, and helical drives-it is identified that adaptive structures featuring active diameter-adjustment capabilities and modular design are essential for enhancing a robot’s flexibility and compliance when navigating bends. Second, the integrated application of detection techniques such as magnetic flux leakage, ultrasonic testing, and visual inspection in small-diameter environments is discussed, with particular emphasis on the advantages and limitations of machine vision in real-time monitoring. Finally, addressing the limitations of embedded computational resources, this study explores the development trends of pipeline inspection technologies toward lightweight, autonomous, and intelligent systems, in conjunction with lightweight deep learning algorithms. The findings indicate that constructing intelligent systems with multimodal sensing capabilities and edge computing performance is a key pathway to achieving proactive, full-lifecycle operation and maintenance of small-diameter pipelines.
Dye-Sensitized Metal-Organic Frameworks (MOFs): Design, Mechanisms, and Application Advances
Metal-organic frameworks (MOFs) have emerged as ideal carriers in photocatalysis owing to their high specific surface area, precisely tunable pore channels, and adjustable electronic structures. However, most MOFs suffer from weak visible-light response and facile recombination of photogenerated charge carriers. The dye-sensitization strategy, which constructs dye-MOF heterojunctions, can effectively broaden the light absorption range of materials and enhance interfacial charge separation and transport, representing an important pathway to overcome the limitations of traditional MOF and inorganic semiconductor-sensitized systems. This review summarizes the characteristics of dye-sensitized MOFs; elaborates on the mechanisms of electron injection, energy transfer, interfacial interactions, and multi-component synergistic catalysis in heterojunction construction; and surveys the research progress of these materials in photocatalysis
Study on the formation of rock mass cracks based on the interval charge structure with constant blasting energy
In order to solve the problem of energy concentration of traditional continuous charge and poor quality of pre-splitting surface in deep hole pre-splitting blasting, this study aims to explore the efficient and uniform utilization of explosion energy by optimizing the structure of axial interval charge under the premise of constant total blasting energy, so as to form a continuous and flat pre-splitting surface. Under the constraint of constant total charge and total charge length, the method of combining theoretical analysis with numerical simulation is adopted. The three-stage fracture mechanism of interval charge is explained from the mechanical theory, and the double-hole pre-splitting blasting model is established based on LS-DYNA software. Three kinds of interval charge schemes of three sections, four sections and five sections are designed systematically, and the blasting effects are compared. The numerical simulation results show that the number of charge segments plays a key role in regulating the energy distribution and stress field morphology under the condition of constant total energy. Through comparison, it is found that the four-stage charge scheme achieves the best balance between energy concentration and dispersion, and can form a continuous, uniform and lasting tensile stress field, which is most conducive to the straight penetration of the pre-splitting surface. The three-stage and five-stage schemes have the defects of uneven distribution and insufficient driving force, respectively.
Study on Support Optimization of Roadway Surrounding Rock under Mining-induced Influence
Coal resources are still dominant in national resources, and in the process of mining coal, the increasing depth of burial is a situation that must be faced at present, so the problem of roadway rock control is unavoidable. This paper comprehensively adopts the method of combining theoretical analysis and numerical simulation to design the support scheme for the transport roadway of 30101 working face in Yang gui coal industry, and analyzes it using FLAC3D numerical simulation software to verify the feasibility of the support design. It is concluded that after the support is carried out, the transportation tunnel can ensure that the deformation of surrounding rock can be controlled within a reasonable range, and the stability of the roadway is significantly improved, and the safe and rapid advance of the mining operation is ensured at the same time.
Heterogeneous Integration System Design for Domain Controller Chips in Autonomous Vehicles: Mechanical-Electronic-Computing Co-Optimization
Autonomous vehicles (AVs) demand increasingly sophisticated domain controllers (DCs) capable of processing immense sensor data and executing complex algorithms in real-time. Traditional monolithic designs are proving insufficient, necessitating the adoption of heterogeneous integration (HI) to combine diverse semiconductor dies and components within a single package. This research addresses the critical challenge of co-optimizing HI system design for AV DCs across mechanical, electronic, and computing domains, a holistic approach currently lacking in siloed design methodologies. The study investigates the trade-offs inherent in various HI architectures, including 2.5D, 3D stacking, and chiplet-based designs, to determine their suitability for AV DC applications. Novel methodologies are developed for concurrently optimizing mechanical aspects, such as thermal dissipation and structural integrity, with electronic design considerations, including power delivery networks and signal integrity. Furthermore, the research explores how AV-specific computing workloads influence HI requirements, informing the development of a unified co-design framework. This framework integrates mechanical, electronic, and computing considerations from the initial design stages, enabling proactive mitigation of interdependencies and maximization of system-level benefits. Simulation-based validation demonstrates significant improvements in performance, power efficiency, and thermal management compared to non-co-optimized designs. The findings provide crucial design guidelines and a deeper understanding of the interplay between mechanical robustness, electronic integrity, and computing capabilities for next-generation AV DCs.
Generation and 3D Regulation of Optical Needle Beams via Caustic Engineering
This paper summarizes the generation and regulation of optical needle beams based on caustic engineering and angular-spectrum control. An optical needle beam can be regarded as a specially truncated Bessel-type beam with a narrow full width at half maximum, an extended depth of focus, and a strong non-diffracting core. Compared with traditional needle-beam generation methods that rely on bulky axicons, binary optical elements, or high-numerical-aperture systems, the reported approach uses a programmable phase modulation strategy to produce and regulate the beam more flexibly. The method first interprets the central focus of a Bessel beam as a degenerate caustic point, then suppresses peripheral rings to form a highly localized needle-like main lobe. By establishing the relation between the modulated angular spectrum and the desired on-axis intensity distribution, the axial intensity can be made nearly uniform, linearly increasing, or linearly decreasing over a designed propagation interval. The source material also extends the caustic-point concept from a single focal point to a sequence of focal points, providing a route for trajectory control in space. The study indicates that caustic-based optical needle beams are promising for particle manipulation, light-sheet microscopy, deep imaging, laser manufacturing, and other photonic applications requiring compact and reconfigurable beam shaping.
Design and Implementation of an Embedded UAV Visual Detection System for Agricultural Inspection
To address the problems of low efficiency in manual pest identification, complex field environments, and other challenges in agricultural inspection scenarios, this paper designs and implements a low-cost embedded UAV visual inspection system. Based on a quadrotor UAV platform, the system integrates a flight control system, an onboard camera, and an embedded computing unit to achieve automatic image ac-quisition, edge-side processing, and result output. In the target detection module, an improved Global Context Perception YOLO(GCP-YOLO) model is introduced to enhance the recognition ability for pest targets under complex backgrounds. Exper-imental results show that the proposed system achieves good performance in both detection accuracy and real-time processing, and verifies its engineering feasibility in low-cost agricultural inspection applications.
Molecular dynamics simulation analysis of hydrogen adsorption
The adsorption and dissolution behaviour of hydrogen on metal surfaces is a key fundamental scientific issue for understanding the mechanisms of hydrogen-induced damage and the hydrogen embrittlement sensitivity of materials. In this study, based on first-principles principles and utilising molecular dynamics simulations, we systematically investigated the adsorption and dissolution behaviour of hydrogen molecules on the surface of α-Fe. The Embedded Atomic Model (EAM) was employed to accurately describe the interactions between α-Fe and H atoms. By constructing a surface model incorporating a vacuum layer and periodic boundary conditions, the dissociation, adsorption and dissolution of hydrogen molecules into the subsurface layer on an iron surface were simulated under various operating conditions. The results indicate that, at a given temperature, the adsorption of hydrogen on the metal surface is positively correlated with pressure; when the pressure increases from 5 MPa to 50 MPa, the hydrogen adsorption on the metal surface increases by a factor of 1.3. This provides a theoretical basis for a deeper understanding of the initial processes of hydrogen-induced damage and for the design of hydrogen embrittlement-resistant materials.
Study on Multi-Product System of Supercritical Water Gasification of Paper Mill Black Liquor
To address the issues of high pollution and low resource utilization efficiency in papermaking black liquor treatment, this study developed a novel polygeneration system based on Aspen Plus software. The system achieves coordinated production of hydrogen, thermal energy, and electricity at a treatment capacity of 1 ton/hour through supercritical water gasification (SCWG), Allam cycle, combustion furnace, and cold energy utilization. Gasification characteristics and product composition were evaluated, with simulation models validated against published experimental data. Key operational parameters including SCWG temperature, pressure, and black liquor concentration were systematically investigated for their impacts on gas generation properties and system performance. Results demonstrate that increasing SCWG temperature significantly enhances hydrogen yield and molar fraction but markedly reduces methane production. SCWG pressure within the 23-28 MPa range shows negligible effect on gas product distribution. Elevated black liquor concentration suppresses hydrogen generation while promoting methane and carbon monoxide production. The proposed SCWG-Allam cycle coupling system efficiently converts papermaking black liquor into clean energy with high efficiency and near-zero carbon emissions. This study provides feasible technical solutions and theoretical foundations for black liquor resource utilization and optimized design of low-carbon polygeneration systems.
Local Differentially Private Data Stream Mean Estimation
With the rapid development of mobile IoT and smart devices, time-series data generated in applications such as health monitoring, traffic management, environmental sensing, and smart homes are continuously collected and uploaded for state monitoring, trend analysis, and intelligent decision-making. Due to their continuous generation, strong temporal correlation, and long-term accumulation, such data may still reveal sensitive information, including behavioral patterns, health conditions, and lifestyle preferences, even without explicit identifiers. Local Differential Privacy (LDP) offers an effective user-side privacy protection paradigm by perturbing raw data before transmission. However, in continuous data stream publishing scenarios, existing LDP methods for mean estimation still suffer from two major limitations: frequent perturbation uploads lead to cumulative noise and degraded estimation stability, while fixed privacy budget allocation cannot adapt well to dynamic data changes, resulting in an unsatisfactory privacy–utility trade-off. To address these challenges, this paper proposes Local Differentially Private Data Stream Mean Estimation (LDP-SM) for continuous data publishing. On the user side, a Kalman prediction-based triggering mechanism is introduced to adaptively switch between prediction-based and perturbation-based uploads, thereby reducing unnecessary perturbations and privacy budget consumption. When perturbation is required, a dynamic privacy budget allocation strategy combining PID feedback and sliding-window constraints is applied to improve budget utilization and maintain continuity in long-term release. On the server side, the reported data are aggregated in real time to produce more accurate and stable mean estimation results. Experimental results show that the proposed method effectively improves the accuracy and stability of mean estimation under LDP constraints.
Reservoir Characteristics and Controlling Factors of the Benxi Formation in the Eastern Yanchuan Block
With the continuous advancement of oil and gas exploration in China, the recoverable reserves of conventional oil and gas fields have entered a stage of high depletion, and unconventional oil and gas resources have gradually become important targets for exploration and development. Among them, tight sandstone gas has attracted considerable attention due to its large resource potential [1,2]. However, compared with conventional reservoirs, tight sandstone reservoirs are generally characterized by low porosity and permeability, narrow pore-throat systems, dense lithology, and complex diagenetic processes, which increase the difficulty of reservoir evaluation and productivity prediction. The Benxi Formation in the eastern Yanchuan Block of the Ordos Basin mainly develops tight sandstone reservoirs and represents an important gas-bearing interval in the Upper Paleozoic. In this study, the tight sandstone reservoirs of the Benxi Formation in the eastern Yanchuan Block of the Ordos Basin were selected as the research object. Based on previous studies, core observation and casting thin-section analysis were conducted to investigate the petrological characteristics, pore types, and diagenetic features of the reservoirs, as well as their influence on reservoir physical properties. The results indicate that multiple pore types are developed in the tight sandstone reservoirs of the Benxi Formation, with complex pore structures. Reservoir quality is mainly controlled by sedimentation and diagenesis. This study clarifies the evolutionary characteristics of tight sandstone reservoirs in the Benxi Formation and provides a theoretical basis for predicting favorable tight gas“sweet spots”in the study area.
Non-contact Anti-collision Technology Based on Electromagnetic Repulsion and Levitation Theory
With the development of the automotive industry and road conditions, cars have become essential for daily life and travel. However, the number of traffic accidents is rising rapidly, and rear - end collisions are common, posing a threat to safety. Anti - collision technology is crucial for road traffic, but existing ones can't reduce losses or prevent accidents. This paper focuses on non-contact anti-collision technology based on electromagnetic repulsion and levitation, using relevant conversion technology. Permanent magnet devices like electromagnetic coils are placed around the vehicle. When a collision is about to happen, the magnetic levitation mode is activated; when a collision occurs, emergency levitation braking is triggered to reduce losses and achieve levitation or repulsion. By analyzing previous collision problems, a vehicle-road cooperative collision avoidance method is proposed in combination with the active collision avoidance system. Simulation designs of rear - end collision, active collision avoidance, and magnetic levitation are completed using MATLAB.It's concluded that this technology has high future development value.
A PCB Micro-Defect Detection Method Based on Mult-Scale Feature Enhancement and Background Suppression
To address the issues of strong background texture interference, small defect scales, and the tendency for feature loss in the detection of defects on Printed Circuit Boards (PCBs) within complex industrial environments—which lead to high miss rates and false positive rates in existing detection algorithms—this paper proposes a PCB defect detection method based on multi-scale feature enhancement and background suppression. An end-to-end detection network is introduced. First, a multi-scale contextual feature enhancement module is designed. By constructing a parallel atrous convolutional pyramid with different dilation rates, it captures global context rich in semantic information. This is combined with shallow, high-resolution feature maps to fully preserve the edge and texture details of defects, addressing the information loss caused by down-sampling in deep networks. Second, a background decoupling and suppression attention mechanism is introduced. This mechanism separates foreground responses from background noise in the feature maps, generating a discriminative attention weight map. It adaptively enhances the response values in defect regions while suppressing interfering responses from complex circuit traces and copper foil backgrounds. Finally, an improved localization loss function is incorporated to optimize the regression accuracy of the predicted bounding boxes. Experimental results on the public PCB defect dataset PKU-Market-PCB demonstrate that the proposed method achieves a mean Average Precision (mAP50) of 96.8%, which is a 4.3 percentage point improvement over the mainstream algorithm YOLOv8n (92.5%). Particularly for defects such as nicks and pinholes, the recall rate is improved by more than 5.8%. Furthermore, the model achieves a detection speed of 85 frames per second, and its parameter count is reduced by 22% compared to the baseline, meeting the real-time requirements of industrial production lines. The method proposed in this paper effectively suppresses complex background interference and enhances the feature representation for multi-scale defects, significantly improving detection accuracy while maintaining detection speed, demonstrating good robustness and industrial application value.
Uncovering Influencing Mechanisms and Practical Strategies for Teaching Effectiveness of College Mathematics General Education Courses: Evidence from SEM-fsQCA Hybrid Analysis
As a core foundational course in higher education, college mathematics general education courses play a crucial role in fostering students' logical thinking and supporting their subsequent professional learning. However, current teaching practices face challenges such as insufficient student engagement and significant disparities in learning outcomes. Traditional evaluation methods struggle to reveal implicit correlations among multiple variables, making it difficult to provide targeted optimization strategies. This study adopted a mixed‑method approach combining Structural Equation Modeling (SEM) and fuzzy‑set Qualitative Comparative Analysis (fsQCA) to evaluate and optimize the teaching effectiveness of college mathematics general education courses at University A. A total of 572 valid samples were collected from freshmen of Grade 2025 using a structured questionnaire. SEM results indicated that teaching methods had the strongest direct effect on learning outcomes (standardized coefficient = 0.362, p < 0.001). Meanwhile, teachers’ literacy and teaching resources exerted indirect effects on learning outcomes through teaching methods, with mediating effect ratios of 81.8% and 55.7%, respectively. For three groups of majors with different mathematical needs (high, moderate, and low), fsQCA identified three distinct configurational paths leading to high learning outcomes: (1) For majors with high mathematical needs, the optimal path was “teaching resources + high school mathematics foundation + teaching methods”; (2) For majors with moderate mathematical needs, the effective path was “teaching content + teaching methods + teachers’ literacy”; (3) For majors with low mathematical needs, the key path was “teachers’ literacy + teaching methods”. This study addresses the limitations of single quantitative or qualitative research by integrating SEM and fsQCA. The proposed optimization strategies, including hierarchical teacher training, diversified collaborative teaching teams, and targeted student support, provide practical references for improving the teaching quality of college mathematics general education courses and realizing personalized teaching in similar universities.
College Mathematics General Education Courses
Fuzzy-Set Qualitative Comparative Analysis (fsQCA)