Academic Journal of Science and Technology
Academic Journal of Science and Technology (AJST) is a peer-reviewed journal published by Darcy & Roy Press. The mission of the journal is to provide an academic forum for discussing the issues of science, engineering, and technology across the world. All articles published are rigorously and fast reviewed meeting the Journal Quality standards.
Research on Internal Flow Field Simulation and Structural Optimization of Mesh Belt Dryer
In order to solve the problem that the internal airflow and temperature field distribution of the belt dryer are uneven, resulting in poor drying quality, this paper conducts 3D modeling and flow field simulation analysis of the drying chamber of the three-layer belt dryer based on computational fluid dynamics (CFD). The velocity and temperature fields of the original drying chamber were numerically simulated by using the Realizable k-ε turbulence model and the porous medium model. The results show that in the original structure, the average velocities on the three-layer mesh belts are 0.93 m/s, 1.09 m/s, and 0.88 m/s, with non-uniformity coefficients of 78.36%, 56.95%, and 47.53%, respectively; the average temperatures are 60.18°C, 58.34°C, and 57.51°C, with non-uniformity coefficients of 14.84%, 24.79%, and 15.03%. By adding an air distributor below the air inlet and comparing different perforation sizes, it was found that the optimal optimization effect is achieved when the perforation diameter is 40 mm: the average velocities on the three-layer mesh belts increase by 0.55 m/s, 0.27 m/s, and 0.27 m/s, while the non-uniformity coefficients decrease by 35.85%, 21.09%, and 27.06%; the average temperatures rise by 4.34°C, 5.49°C, and 5.64°C, and the non-uniformity coefficients drop by 9.87%, 14.92%, and 6.67%. This research provides a theoretical basis for the structural optimization of mesh belt dryers.
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First-principles and Experimental Study on Structure and Properties of Fe20Co20Cr20Ni25AlxTi(15-x) High Entropy Alloy Coatings
In this study, based on DFT-based first-principles calculations and the design philosophy of multi-component alloy segmented design, a high-entropy alloy system of Fe20Co20Cr20Ni25AlxTi(15-x) (x = 0, 5, 10, 15) was designed to investigate the synergistic effects of Al and Ti on phase stability, electronic properties, and elastic properties. The designed Fe20Co20Cr20Ni25AlxTi(15-x) high-entropy alloy exhibits good stability; there are no significant differences in the band density among the various high-entropy alloys, and all exhibit metallic properties. The addition of Al and Ti did not compromise the stability of the high-entropy alloy system. Under optimal conditions, defect-free, well-formed high-entropy alloy coatings were successfully prepared. Under optimal conditions, defect-free, well-formed high-entropy alloy coatings were successfully prepared. The basic mechanical properties of the high-entropy alloy coatings were investigated, resulting in a Fe20Co20Cr20Ni25Al5Ti10 high-entropy alloy coating with excellent mechanical properties.
Study on Laser Additive Joining Process of Stainless Steel and Copper Alloy
This study achieved the joining of 316 stainless steel and T3 copper using a laser additive joining method. The process feasibility of using pure 316 stainless steel powder and pure copper powder as filler materials for additive joining was investigated, and the influence of different joining processes on the microstructure evolution and mechanical properties of the joints was analyzed. The research indicates that when pure 316 powder is used as the filler material, with a laser heating power of 1.8 kW on the steel side and 2.2 kW on the copper side, the joint formation is optimal, and the tensile strength of the joint reaches 250.3 MPa. When pure copper powder is used as the joining material, the internal stress within the joint is relatively large, and aging fracture occurs in the transition zone on the copper side.
Research on the Impact Effect of Data Factor Agglomeration Empowering Industrial Chain Resilience
This study examines how data factor agglomeration empowers industrial chain resilience, using the establishment of national big data comprehensive pilot zones as a quasi-natural experiment. Based on panel data of 266 Chinese cities from 2009 to 2022, we apply the multi-period difference-in-differences (DID) and double machine learning (DDML) methods. Results show that data factor agglomeration significantly improves industrial chain resilience, mainly through technological innovation, digital infrastructure construction, and industrial structure rationalization. The positive effects are more pronounced in eastern coastal regions, areas with high market, and developed digital economies. These findings provide empirical support for big data policy optimization and industrial chain upgrading.
Spatiotemporal Dynamics of Cropland Non-agriculturalization in Zhengzhou City Using Deep Learning-Based Interpretation
Traditional monitoring of cropland non-agriculturalization heavily relies on land use/cover change data, which is constrained by long update cycles and the difficulty of accurately isolating the unidirectional conversion from cropland to non-agricultural uses. To address these limitations, this study utilizes a semantic change detection network with GaoFen-2 (GF-2) high-resolution time-series remote sensing imagery of Zhengzhou City from 2021 to 2025 as the primary data source. Integrating a land-use transfer matrix, grid cell analysis, spatial autocorrelation analysis, and the Geographic Detector model, we systematically analyze the spatiotemporal evolution dynamics and driving mechanisms of cropland non-agriculturalization in the region. The results demonstrate that: (1) Temporally, the intensity of cropland conversion in Zhengzhou City exhibits an unbalanced fluctuating pattern characterized by "initial intensity, subsequent stabilization, and localized rebound," with construction land being the absolute dominant destination of cropland outflow, resulting in a cumulative converted area exceeding 80 km² over the five-year period. (2) Spatially, cropland non-agriculturalization exhibits a distinct gradient pattern described as "commencing in the central urban core, highly aggregating in near suburbs, and dispersedly distributing with low intensity in far suburbs." Hot spot zones are heavily clustered in the near-suburb plains (e.g., Zhongmu County and Xinzheng City), whereas cold spot zones remain long-term stable in the western and southern hilly and mountainous regions (e.g., Dengfeng and Xinmi). The Global Moran's I indices are consistently positive and statistically significant, revealing pronounced spatial polarization and clustering features. (3) Regarding the driving mechanisms, cropland non-agriculturalization in Zhengzhou City is generally characterized by "transportation location dominance, industrial economic support, and topographical constraints."
Design of Separation Method for Floccules in Condensate Oil from Tight Gas Reservoirs
Floccule contamination in condensate oil is a widespread and severe problem. Conventional separation processes fail to achieve thorough separation and cannot meet industrial requirements. To address this issue, this paper designs a separation scheme tailored for floccules in condensate oil, thereby solving the critical problems of substandard condensate oil quality and frequent product returns. By investigating and comparing various separation schemes, this study determines the use of a decanter centrifuge as the separation equipment and completes the design of the separation scheme and device. Through principle experiments, the influences of material properties and rotating speed on centrifugal separation performance are analyzed. The flow field of the centrifuge is investigated using the finite element method. This research not only improves the separation efficiency of condensate oil and oil product quality but also holds great significance for enhancing the economic benefits of oil and gas fields.
An Automated Annotation and Lightweight Detection Framework for Rice Leaf Disease Based on Color Space Analysis and YOLOv11
Accurate and timely detection of rice leaf diseases is essential for precision agriculture and food security. However, existing object detection approaches heavily rely on labor-intensive manual bounding box annotations, which limits their scalability and practical deployment. In this paper, we propose an automated annotation and detection framework that combines color space analysis-based lesion localization with the lightweight YOLOv11n model for rice leaf disease detection. Our automated annotation method leverages disease-specific HSV color space characteristics and morphological processing to generate bounding box labels without human intervention, achieving a 98.6% successful annotation rate across 5,932 images. The annotated dataset is then used to train YOLOv11n, which achieves a mean Average Precision (mAP@0.5) of 93.36% and mAP@0.5:0.95 of 77.53% on the test set, with only 2.58M parameters and 1.0ms inference time per image. Experimental results on four rice disease categories (Bacterial Blight, Blast, Brown Spot, andTungro) demonstrate that the proposed framework provides an effective and efficient solution for rice disease detection with minimal annotation cost, making it suitable for real-time deployment on edge devices in agricultural settings.
Literature Review on the Construction of the Kill Chain of the Informationized and Intelligentized Combat System
Currently, modern technologies centered on artificial intelligence are driving the evolution of warfare from informatization to intelligentization at an accelerated pace. The construction of informationized and intelligentized combat system kill chains has become a hot topic in military research in recent years. This paper adopts a multi-perspective approach, integrating various research methods to analyze and summarize existing literature on kill chain studies. It focuses on aspects such as conceptual essence, winning mechanisms, design philosophies, model construction, effectiveness evaluation, scenario applications, and research on foreign militaries. Furthermore, by examining the current status of kill chain research and application among the world's major military powers and considering the demands of military struggle preparations, the paper looks ahead to future research directions.
Research on Bearing Mechanism and Key Construction Technologies of Bag-Encased Cast-in-Place Pile Foundations for Overhead Transmission Lines in Hard Rock and Highly Corrosive Regions
Overhead collector line projects for renewable energy frequently encounter special geological conditions where hard rock strata coexist with highly corrosive environments. Traditional foundation solutions exhibit significant limitations in construction efficiency, anti-corrosion durability, and economic viability. This paper systematically presents the technological principles of bag-encased cast-in-place pile foundations, revealing the bearing capacity and durability mechanisms arising from the synergistic integration of physical isolation anti-corrosion and efficient hole-forming and grouting. By analyzing the hydraulic expansion principle of the "water (slurry) injection and slurry discharge method," the key controlling factors for uniform expansion and tight adherence of the anti-corrosion bag against the borehole wall are elucidated. Based on engineering measurement data, pile bearing performance was verified through self-balanced load tests and low-strain integrity testing, with the ultimate vertical compressive capacity of single piles satisfying design requirements and all tested piles classified as Class I. Comparative economic analysis demonstrates that, compared with large open-cut foundations with anti-corrosion coating, rock-anchored pile foundations, and driven steel pipe pile solutions, the comprehensive cost of the proposed method is reduced by approximately 25%–39%, 35%–44%, and 72%, respectively, while the earthwork volume per foundation is reduced by over 90%. The research results indicate that bag-encased cast-in-place pile foundation technology effectively resolves the dual technical challenges of rapid hole-forming in hard rock strata and foundation anti-corrosion in highly corrosive environments, offering significant economic and environmental benefits. This technology provides a reliable technical solution for transmission line foundation engineering under similar geological conditions.
One-Click Quotation System and Method for Online Customization of Door Furniture
Aiming at the problems of complex wiring, significant voltage drop, uneven brightness and insufficient intelligent adjustment of low-voltage LED lamps in customized home scenes, this paper proposes a customized home lighting adaptive adjustment management method based on artificial intelligence and big data, and designs a complete implementation system. The system takes the user 's house decoration map and the lamp belt installation parameters as input, constructs the lighting demand analysis model and the lamp belt wiring optimization model, and completes the lighting strength area determination, converter number optimization and installation position planning through the artificial intelligence algorithm. Under the premise of significantly reducing the influence of voltage drop, the number of converters used is minimized. In addition, the system is also equipped with an environment-aware sensor, and a periodic closed-loop brightness adjustment algorithm is used to achieve adaptive matching of light brightness and illumination in different scenarios. The simulation results show that the proposed method can improve the brightness uniformity of the lamp belt by 28.7 %, reduce the number of converter configurations by 32.4 % on average, and the adaptive adjustment response time is less than 0.6 s. It can effectively solve the pain points of low-voltage lamp belt engineering application and improve the intelligence and practicability of customized home lighting system.
Application and Progress of Nanotechnology in Oil and Gas Fields
Nanotechnology, which emerged as a novel scientific and technological field in the 1990s, has gradually permeated the petroleum industry by virtue of its unique size and surface effects, as well as its excellent optical, electrical, thermal, and magnetic properties. It offers a new technical pathway to address development challenges in onshore oilfields, such as high water cut, strong heterogeneity, and low waterflooding recovery factor. This article reviews the origin and evolution of nanotechnology and summarizes relevant research findings and field application cases across six key areas: downhole nanosensors, drilling engineering, enhanced oil recovery, oil displacement operations, secondary development of mature oilfields, and reservoir nanorobots. The study indicates that nanomaterials and nanodevices exhibit distinct advantages in improving drilling fluid performance, reducing fluid loss, plugging high-permeability layers, altering reservoir wettability, lowering oil–water interfacial tension, and reducing injection pressure to enhance water injectivity. The research and field testing of reservoir nanorobots have achieved milestone-level progress, opening new possibilities for fine reservoir characterization and residual oil recovery. At the same time, this article identifies the technical challenges currently facing nanotechnology in petroleum industry applications and discusses future development directions, providing a reference for the large-scale deployment of nanotechnology in the petroleum sector.
Optimization of Asphalt Mixture Mix Proportion Based on Bayesian Optimization Algorithm Combined with Marshall Test
This study presents an optimization framework that integrates physical testing with intelligent algorithms. With the amount of asphalt and the passing rates of key sieves aggregate gradation as design variables, and Marshall stability, flow value, void ratio, mineral aggregate void ratio, and asphalt saturation as optimization goals, a high-performance prediction model constructed. First, an initial data set is obtained through a small number of carefully designed Marshall tests (such as orthogonal tests, Latin hypercube sampling). Then, a Gaussian model is constructed to replace the complex “variables-performance” black box function. Finally, the Bayesian optimization algorithm is used, with the expected improvement as the acquisition function, to iter recommend the “most potential” mixture ratio for the next round of Marshall test verification until convergence. The results show that this method can quickly locate the Pareto optimal combination frontier that all technical specifications under the premise of significantly reducing the number of tests (expected to reduce by 40%-60%), and can quantify the contribution of each variable to the index. This study provides a new paradigm of data-driven and intelligent efficient for asphalt mixture design, which has important reference value for promoting the intelligent design of road engineering materials.
Foundational Study on Magnetic-Chain-Based Magnetic Flux Leakage Detection for High-Throughput Screening of Under-Magnetized Small Permanent Magnets
Small permanent magnets are indispensable functional components in smart electronic products, where the stability of magnetization directly influences actuation, positioning, sensing, acoustic output, and overall system reliability. In industrial production, however, the final magnetic performance of a small magnet is sensitive to raw-material fluctuations, local field nonuniformity, fixture tolerance, and process disturbance during magnetization. The dominant failure mode is insufficient magnetization intensity, which creates under-magnetized or “NG” parts. Existing commercial inspection equipment usually adopts a discrete strategy in which each magnet is measured independently. Although this approach offers high accuracy, its serial nature limits throughput and is increasingly incompatible with large-scale manufacturing. To support high-speed screening, this work develops a foundational study of magnetic-chain-based magnetic flux leakage (MFL) detection using Hall sensing and finite-element analysis. Rather than treating each magnet as an isolated inspection target, the method organizes multiple magnets into a chain and evaluates whether the leakage-field distribution along the chain can preserve sufficient contrast for reliable NG identification. A three-dimensional Ansys Maxwell model is established using NdFe36 permanent magnets, with a single under-magnetized magnet embedded at the center of the chain. The study first constructs the batch-detection framework, then investigates the signal morphology obtained along vertical and horizontal sensing directions, and finally introduces direction-dependent threshold metrics for quantitative discrimination. For magnet lengths of 10, 8, and 6 mm, the vertical-direction thresholds are 40.35, 37.95, and 31.71, respectively, whereas the horizontal-direction thresholds are 27.99, 29.35, and 28.35. The results show that each magnet produces a quasi-N-shaped leakage signature in the vertical direction and a quasi-parabolic signature in the horizontal direction. More importantly, the vertical arrangement consistently provides stronger NG contrast and clearer waveform separation. This work therefore solves a key early-stage problem for magnetic-chain inspection: how to preserve both throughput and signal distinguishability in a batch-screening configuration. The study provides a physically interpretable detection metric, identifies the more favorable sensing direction, and offers direct guidance for subsequent equipment design, algorithm development, and extension toward more complex magnetic-chain inspection scenarios.
Red-Burnt Clay and Early Chinese Architectural Technology: Archaeological Significance and Scientific Approaches to Firing Temperature Reconstruction
Red-burnt clay remains (hongshaotu) are clayey architectural and occupation deposits that were reddened, hardened, and mineralogically altered by intentional firing or incidental heating. These materials document the transition from unfired earth-and-timber construction to fired ceramic architecture in Neolithic and early Bronze Age China, yet their archaeological value has been limited by reliance on macroscopic description for temperature reconstruction. This narrative review examines published cases — including Guanmiaoshan red-burnt house remains (~600 °C), Lingjiatan red ceramic blocks (>950 °C), Sujiacun burnt soil deposits (~500–600 °C), and Qiaocun composite roof tiles — and compares archaeometric methods for estimating firing temperature, heating temperature, and thermal history. The methods reviewed include magnetic susceptibility and thermal demagnetization, colorimetry, X-ray diffraction, petrography, Fourier-transform infrared spectroscopy, differential thermal analysis, thermal expansion, and physical property testing. Each method responds to different parts of the heating process and operates within a limited temperature range; no single technique covers the full spectrum from low-temperature occupation deposits to high-fired ceramic building materials. The review proposes a multi-method workflow that begins with field screening and contextual classification, matches analytical methods to the likely temperature range, and returns temperature evidence to questions of building practice, site formation, production organization, and the diffusion of firing techniques. Future work should build regional calibration databases from local raw materials, standardize the reporting of temperature estimates, and connect thermal data with architectural and technological history.
Research on the Influence Characteristics of Pitting Damage on the Stress of Downhole Tubular Columns
Production gas well tubing is long-term exposed to harsh corrosive environments, and pitting damage has become one of the main forms of tubing failure, seriously threatening the safe production of oil and gas fields. In response to the failure problems of tubing surface multi-pitting damage and under the action of complex loads in actual working conditions, this paper adopts a method combining finite element numerical simulation and fracture mechanics theory analysis to systematically study the influence of pitting damage on the stress distribution, load-bearing characteristics and failure mechanism of downhole tubing. The research shows that the depth of a single pitting pit is significantly positively correlated with stress concentration, while the hole diameter is negatively correlated with equivalent stress when the depth remains unchanged. For multi-point overlapping damage, the smaller the axial distance between the two holes, the more significant the stress concentration, while the radial distance has a smaller impact on the equivalent stress. In addition, under various loading conditions such as tension, internal pressure and external pressure, the increase in the number of pits will significantly intensify the stress concentration phenomenon and greatly reduce the ultimate load-bearing capacity of the tubing. Theoretical analysis further reveals that pitting damage leads to tubing failure through inducing crack initiation and propagation as well as weakening the effective load-bearing cross-section, ultimately resulting in tensile fracture and collapse. The results of this study provide important theoretical basis for the corrosion protection, remaining life assessment and structural safety design of gas well tubing.
A Study on Improving the Rapid Assembly of Prefabricated Container-Type Substations in Cold, High-Altitude Regions
With the development of China’s power industry, the government has been vigorously promoting the sector since the start of the 15th Five-Year Plan. There has been a significant increase in demand for prefabricated modular substations, which can shorten the construction and installation cycle of substation projects. The Nyingchi Construction Power Supply Package I project is located in Milin City at an altitude of 3,027 meters. Due to its high-altitude setting, the project faces harsh conditions such as extremely cold weather, underdeveloped infrastructure, and difficult transportation, which have posed numerous challenges to the construction of the substation. This paper primarily explores the rapid construction of prefabricated container-type substations in high-altitude areas of Tibet. Focusing on prefabricated container-type modular construction, the study analyzes the advantages of this approach in high-altitude regions, reduces on-site fabrication work, and facilitates modular manufacturing and installation. This method is environmentally friendly in the Tibetan region, minimizes the need for post-construction environmental and water restoration, and thus provides a reference for subsequent prefabricated container installation and application projects.
Lightweight Design and Manufacturing of High-Pressure Die-Cast Magnesium Alloy Components for Automotive Applications: A Comprehensive Review
The imperative for vehicle mass reduction, driven by increasingly stringent greenhouse gas emission regulations and the rapid proliferation of electric vehicles, has positioned magnesium (Mg) alloys as compelling candidates for structural automotive applications. With a density of merely 1.74 g/cm3 — approximately two-thirds that of aluminium and one-quarter that of steel — Mg alloys offer an unparalleled weight advantage among engineering metals. High-pressure die casting (HPDC) remains the dominant manufacturing route for automotive Mg components, accounting for approximately 90% of total production volume. This review systematically synthesizes findings from peer-reviewed publications to establish a comprehensive knowledge framework encompassing alloy systems, vehicular applications, and original equipment manufacturer (OEM) development practices. In the domain of alloy selection, the Mg-Al-Zn system (principally AZ91) provides superior castability and strength for non-safety-critical components, whereas the Mg-Al-Mn system (AM50/AM60) delivers the requisite balance of strength and ductility for crash-relevant structures such as seat frames and instrument panel beams. Recent advances in Mg-Al-Sn (AT72) and Mg-Al-Zn-Mn alloy families demonstrate promising avenues for tailored strength–ductility combinations. With respect to vehicular deployment, Mg die castings have been successfully implemented across body systems (door inners, front-end carriers, closure panels), achieving mass reductions of 40–50%, and chassis systems (wheels, steering columns, subframes). A detailed case study of the Mg alloy seat frame (MASF) forward-development programme at Changan Automobile illustrates the complete design–simulation–process–verification chain, yielding a 9.88 kg seat frame representing a 24.6% mass reduction with process yields exceeding 90%. The challenges and future prospects for expanded automotive Mg adoption are critically assessed.
The Impact of ESG Practices on Corporate Performance: An Empirical Analysis
The impact of ESG performance on corporate financial outcomes was examined using panel data from A-share listed companies in China spanning 2014-2024, with 2020 excluded due to exceptional circumstances. A mediation model was employed to explore the underlying mechanisms. The results indicated that strong ESG performance positively affected financial performance, with financing constraints and agency costs acting as partial mediators. Additionally, the effect of ESG performance was found to be more pronounced among low-pollution firms and small- and medium-sized enterprises. These findings provided important insights for strengthening ESG integration and advancing corporate sustainability.
Comparison of Performance and Life of Threaded Holes under Different Forming Processes
This paper systematically compares the differences in microstructure, mechanical properties, and fatigue life of non-magnetic drill collar threaded holes fabricated by two forming processes: cold extrusion and machining. The research primarily involved material characterization through microscopic observation, hardness testing, and mechanical property analysis. Fatigue tests were conducted to obtain life data under various stress levels and to construct S-N curves, with particular attention to the inflection points and fatigue limits. High-frequency three-point bending fatigue tests were performed on full-scale bolt specimens produced by both machining and cold extrusion, under a stress ratio of R=0.1 and frequencies ranging from 10 to 30 Hz. The tests employed rectangular cross-section specimens with a span of 160 mm, subjected to sinusoidal wave loading using an Instron electro-hydraulic testing machine, while crack initiation at the threaded holes was closely monitored. Based on the experimental data, the fatigue life, scatter, and allowable design stress of the two processes were compared. The experimental parameters included thread forming method (cold extrusion/machining), microhardness, surface roughness, and tensile strength. The results indicate that the fatigue performance of the cold extrusion process is significantly superior to that of the machining process. At a stress amplitude σa= 345.6 MPa, the fatigue life of the extruded specimens was approximately 4.1 times that of the machined specimens, with a notably lower scatter band factor (1.42 vs. 8.69), indicating superior performance stability. Fitting using the Basquin equation yielded a fatigue strength coefficient σ'f = 1132.6 MPa and exponent b = -0.1097 for the machining process, compared to σ'f = 3033 MPa and b =-0.1777 for the extrusion process. At a design life of 10⁵ cycles, the allowable stress amplitudes for the machined and extruded threads were 270 MPa and 300 MPa, respectively, corresponding to actual safety factors of 1.28 and 1.15. This suggests that fatigue failure in these cases is dominated by local plastic strain. The extrusion process effectively reduces the fatigue notch factor by introducing surface compressive residual stress and improving the material state around the notch, thereby extending fatigue life. In summary, the cold extrusion process offers significant advantages in high-cycle fatigue applications. It is therefore recommended for use in the design of critical load-bearing components, and that stress concentration effects and process-induced performance variations be fully accounted for in fatigue life assessment to enhance structural reliability and service life.
Research on Intelligent Coding Optimization and Speech Denoising & Enhancement Methods for Audio Processing
To address the trade-off between audio quality and file size as well as the demand for enhancing noisy speech intelligibility, this paper proposes a full-process audio processing scheme integrating intelligent analysis, adaptive coding optimization, speech denoising and quantitative evaluation. First, 34 music and speech samples with diverse coding parameters are used to construct the Quality-Size Index (QSI), and combined with a random forest regression model, bit rate and compression algorithm are identified as the core factors affecting coding performance. Second, a random forest classifier based on audio time-frequency features achieves 100% accuracy in music-speech classification, enabling adaptive coding optimization via matching optimal parameters for different audio types. Finally, an optimized spectral subtraction-Wiener filtering cascade denoising algorithm is designed for non-reference noisy speech. Experimental results show that the adaptive coding scheme effectively balances audio quality and file size; the proposed denoising algorithm improves the SNR of two speech samples by 6.22 dB and 5.51 dB respectively, significantly reduces spectral flatness, eliminates musical noise and enhances speech intelligibility. This scheme provides an intelligent technical solution for audio coding and speech denoising with high practical application value.
Research on the Comprehensive Forest Fire Monitoring System in Central Yunnan Based on Satellite Monitoring Hotspot Data and Smoke Collection Devices
Central Yunnan, as a major urban agglomeration in Yunnan Province, is characterized by the interplay of complex topographic conditions, highly flammable vegetation, and frequent human activities, making it a high-incidence area for forest fires and a challenging zone for prevention and control. Traditional monitoring methods that rely solely on satellite remote sensing or ground-based observations are constrained by insufficient spatiotemporal resolution and limited coverage, making it difficult to achieve early and accurate forest fire warning. In recent years, multi-source monitoring strategies integrating satellite hotspot data and ground-based sensor networks have emerged as a key pathway to enhance forest fire early warning capabilities. This paper systematically reviews the research progress of the integrated forest fire monitoring system in Central Yunnan, focusing on the spatiotemporal distribution patterns and trend prediction methods derived from satellite hotspot data such as MODIS and VIIRS, revealing the spatiotemporal coupling characteristics of forest fires—high incidence in spring and winter, and clustering around coniferous forest belts and road networks. Meanwhile, it elaborates on a self-developed smoke and dust collection device suitable for the complex plateau environment, which integrates laser scattering sensing, LoRa spread-spectrum communication, and solar-powered long-duration power supply modules, enabling real-time collection and long-distance transmission of multiple parameters including PM2.5, temperature, and humidity. On this basis, a three-level early warning model based on the random forest algorithm and an LSTM trend prediction model are constructed. Through multi-source data fusion and spatiotemporal alignment, the early warning response time is shortened to within one hour, and the fire detection efficiency in pilot areas has increased by 60%. The study points out that future efforts should further optimize data acquisition frequency, improve model generalization under extreme climatic conditions, and expand device deployment density to achieve precise and dynamic control of fire risks in Central Yunnan and even the broader southwestern forest regions.
The Application of Internet of Things Technology in the Field of Industrial Robots
This study aims to explore the application of Internet of Things technology in the field of industrial robots and the changes it brings. The system has sorted out the definition, characteristics, and key technologies of the Internet of Things, including sensor technology, cloud computing, and big data analysis. Subsequently, the development history of industrial robots and their current applications in intelligent manufacturing, warehousing, and logistics were reviewed. The application architecture of IoT technology in industrial robots was analyzed in detail, and the specific applications and effects of IoT technology were elaborated through three typical cases: intelligent manufacturing production lines, intelligent warehousing systems, and intelligent logistics systems. The significant advantages of IoT technology in improving production efficiency, optimizing resource allocation, and enhancing system intelligence were revealed. At the same time, the challenges faced in the application of technology, such as standardization, data security, system integration, and cost control, were pointed out, and corresponding countermeasures and suggestions were proposed, such as strengthening the formulation of technical standards and enhancing data security protection. This paper summarizes the application value of Internet of Things technology in the field of industrial robots, and looks forward to the future development trend, including artificial intelligence fusion, edge computing applications and other technological innovation directions, as well as the application prospects in emerging industries. This study provides theoretical support and practical guidance for the in-depth application of IoT technology in the field of industrial robots.
Preparation and Performance Study of Ultra-lightweight In-situ Self-foaming Composite Insulation Cores
To achieve the lightweighting of pillar composite insulators, this study employs an in-situ self-foaming molding process to prepare expandable microsphere/epoxy composite insulation core materials. Using DU658 expandable microspheres as fillers and E-51 epoxy resin as the matrix, the core density was controlled at 0.35 g/cm3. Four types of specimens with resin mass fractions of 55%, 60%, 65%, and 70% were prepared. The effects of resin content on microscopic morphology (SEM), chemical structure (FT-IR), interfacial properties (dye penetration), thermal properties (DSC, TG, DMA), and power frequency breakdown strength were systematically investigated. Results indicate that resin content influences cell size and distribution by regulating the expansion space of microspheres and the matrix constraint. The 55% resin group exhibited the smallest cells with a concentrated distribution; the 65% group had the largest cell size and highest dispersion; the 70% group's cell distribution tended toward concentration again due to enhanced matrix encapsulation. FTIR testing confirmed that only physical bonding exists between the microspheres and the resin, with no chemical reaction. All specimens passed the dye penetration test, showing tight interfacial bonding. Regarding thermal properties, DSC results showed that the glass transition temperature increased from 85 ℃ to 109 ℃ with increasing resin content. TG analysis indicated that the initial decomposition temperature (T5%) of all specimens was above 192 ℃, making them suitable for long-term service below 200 ℃. DMA results showed the 60% resin group had the highest initial storage modulus (1250 MPa), satisfying rigidity requirements. Breakdown performance tests revealed a downward trend in power frequency breakdown strength as resin content increased; however, the breakdown strength of all specimens exceeded 30 kV/cm, meeting electrical standards, with the 55% resin group performing best. This study provides an experimental basis for the formulation and process optimization of lightweight insulation cores for UHV/EHV insulators.
Thermal Characterization and Simulation of the Iron Ore Sintering Process
Accurately identifying the state of the sintering process and stably controlling the sintering endpoint are fundamental challenges that cannot be bypassed in the iron ore sintering process .In this study, by analyzing the temperature and concentration of flue gas emitted during the four stages of preheating, combustion, sintering, and cooling in the sintering process, optimizing the parameters of the Gaussian emission distribution using forward-backward and Baum-Welch algorithms, and dynamically tracking the transition path of the sintering thermal state using a Viterbi decoding strategy, a numerical characterization method for sintering states based on a Gaussian HMM model is proposed. The computational results indicate that the model demonstrates high discrimination capability across the preheating, sintering, and cooling stages, with F1 scores all exceeding 0.98. The model achieves a calculation accuracy of up to 96.63% for sintering thermal states, providing reliable support for the accurate identification of thermal states during the sintering process.
EARSeg3D: An Automated Segmentation Method for Ear Canal Impressions
Hearing impairment is a significant global public health issue. Hearing aids, as critical devices for improving the auditory experience of individuals with hearing loss, require personalized customization that decisively affects wearing comfort and acoustic performance. Traditional ear canal impression fabrication relies on manual procedures, which are time-consuming, cause patient discomfort, and introduce subjective errors. Although deep learning has achieved remarkable progress in medical image analysis, its application in the core steps of hearing aid customization—namely, automated semantic understanding and three-dimensional structural analysis of ear canal impressions—remains limited. To address this research gap, this paper proposes EARSeg3D, an end-to-end semantic segmentation method for ear canal impression point clouds based on PointNet++. Targeting the complex geometric characteristics of ear canal morphology, the method introduces a Local Context Fusion Module (LCFM) built upon PointNet++’s hierarchical feature learning framework. By encoding relative spatial positional relationships and feature gradients among points within local neighborhoods, LCFM enhances the network’s ability to perceive the intricate geometric topology of the ear canal. Following a six-class semantic labeling standard, this study partitions ear canal impression point clouds into six semantic regions: ear canal tip, middle ear canal, ear canal base, redundant material, concha cavity, and concha cymba. Ablation study results on a self-constructed dataset show that with the LCFM module, the model achieves a mean Intersection over Union (mIoU) of 92.31% and an overall accuracy (OA) of 83.36%, which are 1.18 and 1.07 percentage points higher than the baseline model, respectively. Visualization results further demonstrate that the method effectively mitigates jagged segmentation boundaries, achieving smoother and more precise segmentation in curved regions of the ear canal and at the junctions between different anatomical structures.
Current Status and Prospects of the Acid Leaching Process for Zinc from Zinc-Containing Steel Metallurgical Dust
The steel industry is a cornerstone of the national economy. The entire metallurgical process generates vast quantities of dust, accounting for approximately 8% to 12% of crude steel output, with annual domestic production exceeding 100 million tonnes. As this dust is rich in valuable metals such as zinc and iron, whilst also containing harmful heavy metals such as lead and cadmium, it presents both resource value and environmental risks. Due to their high selectivity, low energy consumption and high product purity, hydrometallurgical processing technologies have become a research focus for the resource recovery of zinc-containing dust. This paper provides a systematic review of the progress in processes for the wet extraction of zinc from steel metallurgical dust. It first outlines the sources and physicochemical properties of zinc-containing dust, then introduces research developments in acid leaching for zinc extraction, and finally compares and analyses the technical characteristics and limitations of different acid leaching processes for zinc extraction, whilst offering an outlook on future technological development directions.
Scenario-based Simulation of Land Use/Cover Change and Carbon Storage Evaluation in Zhangye City Using the PLUS-InVEST Model
Carbon storage (CS) and its cycling processes in terrestrial ecosystems constitute a fundamental component of the global carbon cycle. Ecologically fragile regions, characterized by low ecosystem stability and high sensitivity to external disturbances, play a critical role in understanding regional and global carbon dynamics. Land Use/Land Cover Change (LUCC), as a major manifestation of human activities, is widely recognized as a key driver influencing CS and its spatiotemporal variations. However, for a typical ecologically fragile area such as Zhangye City in Gansu Province, there remains a lack of multi-scenario quantitative assessments regarding the impacts of LUCC on CS. In this study, the InVEST model and the PLUS model were integrated to analyze the evolution of land use patterns and CS in Zhangye City from 1990 to 2020. Furthermore, land use configurations and corresponding CS distributions under four ecological protection scenarios for 2030 were simulated. The results indicate that: (1) from 1990 to 2020, grassland and unused land decreased by 0.69% and 1.35%, respectively, while cultivated land expanded by 1.82% (approximately 698.86 km2), reflecting a notable shift in land use structure; (2) LUCC contributed to a net increase of 0.49×107 t in total CS, exhibiting a spatial pattern of gradual increase from north to south; (3) by 2030, compared with the natural development scenario, CS under low-, medium-, and high-level ecological protection scenarios increased to 38.05×107 t, 38.06×107 t (38.05546×107 t), and 38.06×107 t (38.05544×107 t), respectively, with the medium-level ecological protection scenario yielding the most optimal land use configuration. By coupling the InVEST and PLUS models, this study effectively characterizes the impacts of land use change on CS, and provides methodological support for land use optimization and CS assessment in ecologically fragile regions. Based on these findings, it is recommended to strengthen the protection of ecological land, appropriately regulate the expansion of built-up areas, and promote a coordinated balance between economic development and ecological conservation, thereby contributing to the achievement of carbon peaking and carbon neutrality goals.
Privacy Protection Range Query Protocol for Multidimensional Data in Two-Tiered Wireless Sensor Networks
Range query is the main query method in two-tiered wireless sensor networks. In the process of query operation, privacy protection technology is needed to ensure the security and integrity of private data. Therefore, it is of great significance to study the range query problem with privacy protection capability in two-tiered wireless sensor networks. This paper analyzes the privacy security of multidimensional data and the communication energy consumption of range query, proposes an improved cross 0-1 encoding technology to optimize the comparison factor and realize data comparison in ciphertext state. An improved multidimensional encrypted constraint chain is proposed to verify the authenticity and integrity of the query results of each dimension. In the experimental part, the protocol in this paper was implemented using the development board of Cortex-M4 core, the development board of Cortex-A9 core and PC, and the security and effectiveness of the protocol were verified. Comparing the communication energy consumption of the proposed protocol with the Optimized HMAC Protocol in two aspects, the experimental results show that in the same experimental environment, the communication energy consumption of the proposed protocol is lower than that of the optimized HMAC protocol, indicating better performance.
Research on an Intelligent Early Warning Model for Coal Spontaneous Combustion Based on Stacking Ensemble Learning
To address the challenge of accurately predicting the spontaneous combustion temperature of water-immersed and air-dried coal, and to enhance the reliability of early warning systems for coal spontaneous combustion hazards, this paper constructs a coal spontaneous combustion temperature prediction model based on Stacking-based ensemble learning. Based on multi-stage indicator data derived from water-immersed and air-dried coal samples—obtained through programmed-temperature coal spontaneous combustion experiments—Grey Relational Analysis was employed to identify CO, CO₂, C₂H₄, C₂H₆, and the alkane-to-alkene ratio (C₂H₄/C₂H₆) as the core early-warning indicators. Data preprocessing was subsequently completed through normalization and linear interpolation. A two-layer ensemble prediction model was then constructed by selecting Random Forest (RF), Support Vector Machines with Radial Basis Function kernels (RBF-SVM), and Extreme Gradient Boosting (XGBoost) as the base learners for a Stacking framework, with Gradient Boosting Decision Trees serving as the meta-learner; this ensemble model was subsequently validated through comparison against each of the individual models. The results indicate that the RF model exhibits optimal generalization capability, achieving a coefficient of determination (R²) of 0.9027 on the test set. The Stacking ensemble model demonstrates excellent fitting performance on the training set, with a Mean Squared Error (MSE) of 0.3234 and an R² of 0.99; on the test set, it yields an R² of 0.9019 and a Root Mean Squared Error (RMSE) of 23.9473. Its generalization performance is on par with the RF model and superior to the XGBoost model, effectively integrating the strengths of the individual base learners to achieve a dual balance between fitting accuracy and generalization capability. Conversely, the SVM model demonstrates poor adaptability within the specific data context of this study and fails to meet the requirements for coal temperature prediction. The Stacking ensemble learning model constructed in this paper is capable of accurately predicting the spontaneous combustion temperature of water-immersed and air-dried coal, thereby providing reliable technical support for the early warning of spontaneous combustion hazards in the goafs of shallow-buried, closely spaced coal seams.
Indoor cleaning robots are with the profound connection of artificial intelligence and robotics, transforming automated tools into smart cleaning assistants. The paper offers a systematic discussion of the technological advances made in two fundamental capabilities of indoor cleaning robots: garbage recognition and handling, and navigation control. The paper is good in the field of navigation outlines the technological development between inertial navigation and SLAM-based path in real-time planning to a Vision-Language-Action (VLA) model-based multi-modal navigation, computation of the benefits, objectives, and difficulties of each strategy. It recognizes such intelligent navigation that merges learning via semantic understanding and reinforcement learning as one of the major future directions. Regarding garbage surveying, the paper discusses vision-based (e.g., YOLO, ViT) and multi-modal sensor fusion solid and liquid waste recognition and classification technologies and potential application of manipulation akin to humans. The review concludes that existing studies continue to have loopholes in the profound assimilation of cross-modal data and system-level execution. Future research will be on smart navigation, multi-mode garbage handling, and delicate anthropomorphic control, finally to achieve extremely autonomous cleaning robot systems based on a closed (perception-decision-action) loop.