Advances in Engineering Research : Possibilities and Challenges
Advances in Engineering Research : Possibilities and Challenges ISSN Print: 3079-5192 ISSN Online: 3079-5206
Green Pest and Disease Control Technology Systems and Their Integrated Application in Tea Plantations
Green pest and disease control in tea gardens is a crucial measure for reducing the use of chemical pesticides, ensuring tea quality and safety, and protecting the ecological environment of tea gardens. This paper systematically summarizes key technologies such as agricultural control, biological control, physical and chemical control, plant-based pesticides, and monitoring and early warning systems. It constructs an integrated technical model based on agricultural control, with biological control as the core, physical and chemical control as a supplement, and chemical control as a contingency measure, and proposes recommendations on scientific pesticide use, promotion mechanisms, and policies. Research indicates that the integrated application of green pest management technologies can effectively reduce pesticide usage, enhance tea quality, and improve the ecological environment of tea gardens. This paper serves as a reference for the promotion of green pest management in tea gardens and the sustainable development of the tea industry.
Copyright (c) 2026 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by/4.0/
Research on Static Fracturing Roof Caving Technology for Hard Immediate Roof at the Open-off Cut of 15221 Fully Mechanized Mining Face in Hongliulin Coal Mine
Aiming at the engineering challenge that the hard immediate roof at the open-off cut of the 15221 fully mechanized mining face in Hongliulin Coal Mine is extremely prone to forming a large-area suspended roof and inducing dynamic strata pressure disasters during the initial mining period, this paper conducted an engineering experimental study on the static fracturing roof caving technology for hard immediate roofs. By drilling pre-splitting boreholes in the open-off cut and two gateways of the 15221 working face and injecting expansive cartridges, the roof caving characteristics, working resistance of supports, wind speed, and dust concentration changes before and after fracturing were analyzed. The results show that the static fracturing technology effectively severed the integrity of the hard roof, and the initial weighting step was controlled at 43.5 m. The over-limit ratio of support pressure dropped to 0.5%, effectively avoiding the risk of support crushing, and the average dust concentration in the working face was significantly reduced, achieving safe, efficient, and green roof caving.
Distribution Characteristics of the Overburden Three-Zone Structure in the Goaf of a Y-Type Ventilation Working Face
To elucidate the development law of the overburden three-zone structure in the goaf of a Y-type ventilation working face, the 32010 working face of a coal mine was selected as the engineering background, and the evolutionary characteristics of the overburden three zones were investigated by combining theoretical analysis with numerical simulation. First, the formation mechanism of the overburden three-zone structure was analyzed. Second, empirical formulae were employed to estimate the heights of the caving zone and fractured zone. Finally, a UDEC numerical model was established to simulate the evolution of overburden fractures during face advance. The results indicate that the theoretical calculation gives a caving-zone height of 10.24-14.45 m and a fractured-zone height of 37.86-51.04 m. The numerical simulation shows that when the working face advances to 80 m, the caving-zone height is approximately 14 m and the fractured-zone height is approximately 55 m. The theoretical and numerical results are in good agreement, indicating that the proposed model can effectively capture the development law of the overburden three-zone structure in the goaf of a Y-type ventilation working face.
Effect of Operating Parameters on Rock-Breaking and Hole-Forming Characteristics of High-Pressure Abrasive Water Jet
High-pressure abrasive water jet (AWJ) technology offers a promising alternative to traditional mechanical drilling in coal and geotechnical engineering. This study investigates the rock-breaking and hole-forming characteristics of AWJs on typical sandstone (70 MPa) using a custom-built experimental system. Single-factor experiments were conducted to evaluate the effects of abrasive particle size, jet pressure, and standoff distance. Results indicate that hole depth and diameter peak at an optimal abrasive size of 60 mesh. Increasing jet pressure (20–50 MPa) significantly enhances both penetration depth and hole diameter. Conversely, increasing the standoff distance (30–60 mm) enlarges the hole diameter but reduces the penetration depth. These findings provide a reliable basis for optimizing AWJ drilling parameters in engineering applications.
Research Progress and Prospects of Hard Carbon Anode Materials for Sodium-Ion Batteries
Sodium-ion batteries (SIBs) have emerged as a promising large-scale energy storage technology due to the abundant reserves and low cost of sodium resources, and hard carbon (HC) has become the most competitive anode material for SIBs owing to its unique "house-of-cards" microstructure, large interlayer spacing, and excellent electrochemical performance. This review systematically summarizes the latest research progress of hard carbon anode materials for SIBs. First, the microstructure characteristics of hard carbon (graphite-like crystallites, defects, and pore structure) and the mainstream sodium storage mechanisms (adsorption-intercalation, intercalation-pore filling, and adsorption-pore filling) are clarified. Then, the classification, characteristics, advantages, and disadvantages of hard carbon precursors (biomass-derived, synthetic polymer-derived, and fossil fuel-derived) are analyzed, and the core selection principles are summarized. Subsequently, the key optimization strategies for hard carbon performance (structure regulation, heteroatom doping, morphology design, surface modification, electrolyte matching, and pre-sodiation) are elaborated, focusing on their action mechanisms and application effects. In addition, the application of advanced characterization techniques in revealing the sodium storage mechanism of hard carbon is briefly introduced. Finally, the challenges faced by hard carbon in industrialization are discussed, and the future research directions are prospected. This review provides a concise and comprehensive reference for the design, preparation, and industrial application of high-performance hard carbon anodes for SIBs.
Study on the Single-Pile Uplift Bearing Capacity Characteristics of Expanded-Disc Piles Based on FDM Numerical Simulation
Concrete enlarged-head piles, as a novel variable-section pile type, exhibit significantly superior bearing capacity compared to conventional straight-bore piles. To systematically investigate the influence of enlarged-head quantity on vertical uplift bearing capacity, this study employs a combined approach of laboratory model testing and numerical simulation. Finite Difference Mesh (FDM) models were established for straight-bore piles, single-enlarged-head piles, and double-enlarged-head piles to elucidate their mechanical behavior. Results indicate: Computational outcomes from the numerical simulation method demonstrate excellent consistency with model testing, validating the reliability of simulated results. The enlarged-head structure exhibits a markedly significant enhancement effect on pile foundation uplift bearing capacity, with the ultimate bearing capacity of single-head and double-head piles reaching 1.5 times and 2.5 times that of straight-bore piles respectively, highlighting the more pronounced strengthening effect of the double-head configuration. Under ultimate conditions, the shear slip planes of the upper and lower discs interconnect to form a continuous composite failure surface, fully utilizing the shear strength of both inter-disc and disc-top soils. In summary, the finite difference numerical model accurately replicates the complex interaction mechanisms between pile foundations and soil, providing reliable technical support for studying pile-soil interaction principles and quantifying the soil reinforcement zone around enlarged-disc piles.
Experimental Study on Influencing Factors of Fatigue Performance of Larch Plywood Beam
To investigate the fatigue performance of larch plywood beams under cyclic loading, this study systematically examined the effects of three key factors—loading frequency, loading waveform, and span-to-height ratio—on fatigue life, failure modes, and stiffness degradation through static and fatigue tests. Three plywood beams were first subjected to static tests, yielding a maximum load capacity of approximately 88 kN, with failure primarily occurring due to fiber fracture in the tension zone. Subsequent fatigue tests were conducted under varying loading frequencies (1 Hz, 3 Hz, 5 Hz), waveforms (sine, triangular, square), and span-to-height ratios (12,15,18). Results demonstrated that higher loading frequencies extended fatigue life, though the gain effect diminished beyond 3 Hz. The loading waveform significantly influenced fatigue life, with square wave loads causing maximum damage at equivalent stress levels compared to triangular wave loads. Increased span-to-height ratios reduced fatigue life, while smaller ratios exhibited more pronounced shear stress-dependent failure. A fatigue damage evolution model based on stiffness degradation theory was established, showing good agreement with experimental predictions. This research provides theoretical foundations for fatigue design of timber-structured bridges.
Prediction of Development Height of Water-Conducting Fracture Zone in Pingmei No.12 Mine
The determination of the height of the water-conducting fracture zone is of great significance for water disaster prevention and control and efficient coal mining. To study the development law of the water-conducting fracture zone in multi-coal seam mining, taking the development height of the water-conducting fracture zone generated after the mining of the 31040 working face in Pingmei No.12 Mine as the research object, theoretical analysis, numerical simulation, and borehole peeping method were adopted to explore the height of the water-conducting frac-ture zone generated in the 31040 working face. The results show that the height of the water-conducting fracture zone in the 31040 working face is 59m.
Research on Common Road Damage Problems in Road Engineering Applications
As a core component of transportation infrastructure, the service status of road engineering has a direct impact on traffic safety and operational efficiency. This paper focuses on the frequent damage problems in road engineering applications. Through literature research and data analysis, it systematically sorts out three major types of common diseases: structural damage, functional damage, and auxiliary facility damage. The research indicates that overloaded traffic, construc-tion quality defects, environmental degradation, and delayed maintenance are the primary factors contributing to these issues. Among them, the incidence of dis-eases such as cracks, ruts, and uneven settlement accounts for more than 80%. For every 5% increase in the proportion of heavy-duty vehicles, the service life of the pavement can be shortened by 12%-18%. Damage problems not only cause annual economic losses of hundreds of billions of yuan but also directly or indirectly induce 17.3% of road traffic accidents. By analyzing the formation mecha-nisms and influence laws of various types of damage, this paper provides theoretical support for optimizing road engineering design, construction control, and the formulation of maintenance strategies, which is of great practical significance for extending road service life and enhancing infrastructure resilience.
Improved YOLOv11-Based Object Detection Algorithm for Autonomous Driving in Low-Light Conditions
To address the severe challenges of blurred object features and strong background interference in low-light nighttime environments, which often lead to false detections and missed detections in autonomous driving object detection, this paper proposes an improved YOLOv11-based object detection algorithm for autonomous driving in low-light conditions (IEWS-YOLO). First, an inverted residual attention mechanism called the iEMA module is designed and inserted into the backbone network to enhance detection performance in low-light environments. Second, the standard convolutions in the backbone network are replaced with wavelet transform convolution layers (WTConv), improving global feature representation efficiency and noise suppression capability. Finally, the traditional CIoU loss function is replaced with the ShapeIoU loss function to optimize bounding box regression accuracy, making predicted box locations more precise and enhancing object localization. Experimental results show that the IEWS-YOLO model achieves significant improvements in various performance metrics compared to the traditional YOLOv11n model. On the ExDark standard low-light dataset, IEWS-YOLO improves mAP@0.5 by 9.6% and mAP@0.5:0.95 by 6.7% compared to YOLOv11n. In conclusion, the IEWS-YOLO model can perform object detection for autonomous driving in low-light environments more accurately.
Research on the Influence of Magnetron Sputtering parameters on the Structure and Properties of ITO Thin Films
This study systematically investigates the influence of key magnetron sputtering parameters—working pressure, sputtering power, and nitrogen doping—on the growth, microstructure, and functional properties of indium tin oxide (ITO) thin films. Through a designed experimental matrix, the deposition kinetics, crystallographic structure, chemical bonding states, and electrical resistivity were characterized to establish clear process-structure-property relationships. The results reveal that increasing the working pressure enhances oxygen vacancy concentration, which dominates the reduction in electrical resistivity despite a degradation in crystallinity. Higher sputtering power improves film crystallinity and dopant activation, synergistically optimizing carrier mobility and concentration. Nitrogen incorporation introduces complex doping effects, initially passivating oxygen vacancies to lower resistivity at low partial pressure but forming carrier-trapping nitride phases at higher levels, leading to increased resistivity and amorphization. This work provides a mechanistic understanding of parameter-controlled defect engineering and microstructure evolution in ITO films, offering practical guidance for tailoring their optoelectronic performance for advanced transparent electrode applications.
Overview of Coalbed Methane Mining and Envi-ronmental Impacts
Coalbed methane extraction holds significant importance in the energy sector, yet its extraction process and potential environmental impacts cannot be overlooked. This paper reviews the current state of coalbed methane extraction technology and its environmental implications. Through analysis of coalbed methane formation, extraction techniques, resource distribution, and environmental effects, it summarizes the challenges facing coalbed methane extraction today and future development trends. Subsequently, it discusses potential environmental issues arising from CBM extraction, such as increased geological hazard risks and groundwater contamination. To address these concerns, the paper outlines common environmental protection measures and technical approaches, including water resource management and methane emission control. Subsequently, the environmental impacts of coalbed methane extraction are explored in depth, ex-amining both the consistency and discrepancies among different studies. Finally, future research directions are proposed, including improvements in environmental monitoring technologies, refinement of environmental management policies, and the development of novel eco-friendly technologies.
Numerical Solving Strategy for High-Energy Laser Thermal Management Based on the Nonlinear Heat Conduction Equation
High-energy laser systems are fundamental to modern advanced manufacturing and defense engineering, where effective thermal management is the key bottleneck limiting beam quality and operational longevity. This paper presents a robust numerical framework for simulating transient temperature fields in laser gain media, governed by a nonlinear parabolic partial differential equation (PDE). We first establish a comprehensive thermophysical model incorporating conductive heat transfer, convective boundary cooling, and crucially, the temperature-dependent thermal conductivity of the solid-state crystal (e.g., YAG or Sapphire). This results in a nonlinear heat conduction equation. For its numerical solution, an implicit Finite Difference Method (FDM) scheme is implemented, ensuring unconditional stability for the stiff problem arising from intense internal heat generation. A predictor-corrector iteration is designed to handle the nonlinearity introduced by the variable conductivity. Applied to a simplified cylindrical laser rod geometry, the model successfully simulates the evolution of temperature and thermal stress under pulsed operation. The numerical solution reveals a significant thermal lensing effect, with a maximum temperature rise of 152.3 K and a corresponding focal power of 0.85 m⁻¹. Comparative analysis demonstrates that neglecting the nonlinearity (assuming constant conductivity) leads to a 12.7% underestimation of the peak temperature and a mischaracterization of the stress profile. This work provides a reliable and adaptable computational tool for the design and optimization of thermal management systems in high-power laser engineering, highlighting the critical role of applied mathematics in tackling complex multiphysics challenges.
Research on Bolt-Cable Reinforcement Support Technology for Gob-Side Entry Retaining in "Three-Soft" Coal Seams
This paper focuses on the application of the gob-side entry retaining technology under complex surrounding rock conditions in "Three-Soft" coal seams. Numerical analysis was conducted using FLAC to study the ground pressure behavior before and after gob-side entry retention in such seams. The research shows that when applying gob-side entry retaining technology under complex roof conditions in "Three-Soft" coal seams, roof subsidence is difficult to control. By utilizing surrounding rock control technology and combined bolt-cable reinforcement support technology, the bearing capacity of the surrounding rock is fully mobilized. This enables the bolts, cables, and surrounding rock to jointly support the roof, thereby reducing excessive subsidence of the retained entry roof. Under reasonable roadway support conditions, the gob-side entry retaining technology proves to be effective and significantly improves mining efficiency.
Combined bolt-cable reinforcement support technology
Study on vibration isolation effect of different vibration isolation structures
This study investigates the effectiveness of open, EPS-filled, and sand-filled trenches in isolating ground vibrations using the finite element numerical sim-ulation method. A three-dimensional finite element model was established to analyze the attenuation of vibration acceleration induced by different barrier types. The isolation performance was evaluated based on the amplitude attenua-tion coefficient and the average amplitude attenuation coefficient. The results indicate that the vibration isolation effect of all three trench types decreases approximately linearly with increasing distance from the vibration source, with better performance observed in the near field compared to the far field. Under identical conditions, the open trench provides the best overall vibration isola-tion, followed by the EPS-filled trench, while the sand-filled trench performs the least effectively. The corresponding average amplitude attenuation coefficients are 0.823, 0.686, and 0.626, respectively. Further analysis reveals that filling material reduces the barrier's isolation efficiency, particularly in the far field, where vibration attenuation remains significant but the relative isolation effect is diminished.
Research on Optimization of Roadway Support in Metal Mines Based on Engineering Safety
Aiming at the problem of roadway support stability under complex geological conditions in deep metal mines, this study takes Ma cheng Iron Mine, one of the largest underground iron mines under construction in China, as the research object. It systematically analyzes the failure mechanism of the roadway support system during multi-level mining and proposes an optimization scheme based on the collaborative support theory. A hierarchical bolt-mesh-shotcrete + anchor cable + steel arch frame collaborative support system is established: low-prestress resin bolts (anchoring force 180kN) and high-strength steel mesh (mesh density 100×100mm) are introduced, anchor cable parameters are optimized (Φ21.8mm, prestress 250kN), and a dynamic support adjustment mechanism is constructed. The research results provide theoretical basis and technical support for the safe and efficient mining of deep metal mines.
Self-healing test of concrete mixed with permeable crystalline materials
Concrete is a core component of infrastructure. Owing to its low tensile strength and exposure to complex service environments, it is prone to cracking, which seriously impairs the durability and safety of structures. Traditional repair techniques suffer from drawbacks such as high cost and low efficiency, while the self-healing technology of concrete slabs provides a new approach by simulating biological self-healing mechanisms to achieve autonomous crack repair.
An Elliptical Envelope-Based Method for Casing Corrosion Diagnosis
Casing corrosion leading to wall thinning or perforation poses a major threat to wellbore integrity, highlighting the urgent need for accurate corrosion diagnosis technology. To address this, this study proposes a casing corrosion diagnosis method based on the EllipticEnvelope anomaly detection algorithm. Utilizing well logging data, the method establishes a robust covariance estimation model via the EllipticEnvelope algorithm to effectively identify corrosion-related anomalies. Taking the data from Well XX in the Ordos Basin as an example, the proposed method achieved a detection accuracy of 92.3%. Compared to the Isolation Forest and threshold methods, this represents an improvement of 12.42% and 31.48%, respectively. The results demonstrate that the method can identify casing corrosion more effectively, providing a reliable technical means for downhole condition assessment.
Copyright (c) 2025 by author(s) and Erytis Publishing Limited. https://creativecommons.org/licenses/by/4.0/
The potential power of ecological value visualization for the sustainable development to industrialized cities by combing ArcGIS and GeoSOS-FLUS: A case study in Jingmen
Land-use change has an impact on the value of ecosystem services. To uncover the relationship that has guiding significance for achieving ecological protection and coordinated socio-economic development in industrialized City. Based on the interpretation results of land use data from 2000 to 2020, the land use transfer matrix was established to analyze the characteristics of land-use changes in Jingmen. The ecosystem service values (ESV) was estimated using the equivalent factor method, in order to illustrating the spatiotemporal characteristics of land use change and ESV, ArcGIS software and the natural break-point method were employed. Results showed that：(1) From 2000 to 2020, the amount of cultivated land, forest land and grass land decreased following the construction land increased. The conversion of cultivated land to construction land is the most important way of land-use transfer. (2) The change of ESV showed a slow upward trend to a gradual downward trend. Among the single ecosystem services, regulation services held the main position. The spatial distribution of ESV did present the scattered distribution in low, medium, and high ecological value service area, and high value areas mainly distributed in areas of river and lake. (3) The simulating results of the land use pattern by GeoSOS-FLUS model showed the ecological service system rises 9.85% compared to 2020.
Stability analysis of a landslide based on FLAC 3D
This paper focuses on the problem of the 1# and 2# aqueducts in the sliding section of the Huaipai Yellow River water diversion project, which were damaged due to the reactivation of the ancient landslide body. A stability analysis of the initial state of the slope was conducted. The Midas GTS NX and FLAC3D joint modeling method was adopted: the geological CAD drawings were sorted out, the surface and solid models were established through Midas, and after cutting the sliding body, it was imported into FLAC3D. Based on the Mohr-Coulomb criterion, boundary constraints and gravity loads were applied for numerical simulation. The results showed that the maximum horizontal displacement was 3.9 cm (in the middle-upper part) and the maximum vertical displacement was 4.95 cm (in the middle-upper part of the sliding body); the surface was mainly under tensile stress, and the absolute value of the bottom compressive stress increased with depth; the maximum shear strain increment was small; the slope stability coefficient was 1.48. The conclusion indicates that the slope as a whole was in a stable state in the initial state.
Research on Real time Acquisition System Based on Binocular Stereoscopic Vision
This paper presents the design and implementation of a real-time 3D acquisition system utilizing binocular stereoscopic vision. The primary objective is to develop a robust and efficient pipeline capable of capturing, processing, and reconstructing three-dimensional geometry of dynamic scenes with low latency. The core methodology integrates synchronized image capture from a calibrated stereo camera pair, followed by real-time stereo rectification and a dense stereo matching algorithm optimized for speed. The system successfully achieves real-time performance, generating dense depth maps at a frame rate sufficient for interactive applications. Experimental results demonstrate the system's accuracy in reconstructing static objects and its capability to track depth variations in moderately dynamic environments. The conclusion highlights that the implemented system provides a practical and effective solution for real-time 3D perception, establishing a reliable foundation for applications in robotics guidance, quality inspection, and augmented reality where immediate spatial feedback is critical.
Multifunctional Intelligent Water Curtain Air Cabinet
This project aims to develop an intelligent model-specific water curtain system, providing a compact, cost-effective, and high-performance comprehensive solution for model enthusiasts and small studios to address issues such as formaldehyde pollution, particulate diffusion, temperature and humidity, and noise during painting operations. The system integrates formaldehyde sensors, temperature and humidity sensors, and a display screen, and uses intelligent control algorithms to link formaldehyde concentration with fan speed, ensuring air quality remains within safe limits. Additionally, the system features real-time data display, alarm notifications, and remote control capabilities, enhancing user experience. Experimental results demonstrate that the system meets expected targets in purification efficiency, energy consumption control, and operational convenience. In the future, with further advancements in intelligent technology, this system is expected to play a greater role in model making and air purification, providing users with a healthier and more environmentally friendly working environment.
Architectural Choice in Inverse Design of THz Metamaterials: Local Priors vs. Global Attention under Data Scarcity
The inverse design of terahertz (THz) electromagnetically induced transparency (EIT) metamaterials is complicated by high-dimensional parameter spaces and complex spectral mappings. While deep learning has emerged as a potent tool for addressing these issues, the suitability of distinct neural architectures under data-constrained conditions remains underexplored. Focusing on EIT spectra characterized by long-range frequency dependencies, this study compares the inverse design performance of the Fully Connected Residual Network (FC-ResNet), which leverages local inductive biases, against the Transformer architecture, which relies on global self-attention mechanisms. We evaluated these models using a comprehensive dataset of 20,476 samples and a restricted dataset of only 500 samples. The results demonstrate that in data-rich environments, both architectures achieve exceptional accuracy (R² > 0.99), indicating that architectural differences do not constitute a bottleneck when data is abundant. However, in small-sample regimes—simulating scenarios with scarce experimental data—the FC-ResNet exhibits significantly superior generalization capabilities compared to the Transformer. Our findings suggest that while Transformers offer global modeling potential, they are prone to overfitting when data is limited; conversely, the structural priors of the FC-ResNet provide essential regularization in such contexts. This work offers empirical guidance for selecting architectures in the intelligent design of metamaterials where data availability is a limiting factor
Spatio-Temporal Simulation and Change Analysis of Land Use on the Qinghai-Tibet Plateau Under Future Scenarios
As a sensitive response zone to global change and a critical ecological barrier, the Qinghai-Tibet Plateau's land use and land cover change (LUCC) profoundly impacts regional ecological security and sustainable development. To systematically analyze its evolution patterns and project future trends, this study integrated three phases of remote sensing data from 2000 to 2020. coupled with multi-source drivers including topography, climate, vegetation, and socioeconomic factors. Employing the PLUS (Patch-generating Land Use Simulation) model, we simulated and revealed the spatiotemporal differentiation and transformation mechanisms of LUCC on the Tibetan Plateau over the past two decades. We further projected land use patterns for 2060 under three scenarios: Natural Development (ND), Water Resource Protection (WP), and Sustainable Development (SD). Results indicate: ①Between 2000 and 2020, the land use structure of the Qinghai-Tibet Plateau generally maintained a pattern of "grassland as the dominant type, with unutilized land as the secondary type." Grassland area increased from 132.26×10⁴ km² to 134.73×10⁴ km², and its proportion rose from 51.37% to 52.33%. Unutilized land decreased from 101.57×10⁴ km² to 98.57×10⁴ km², with its proportion declining from 39.45% to 38.28%; Forest land, water bodies, and constructed land all showed slight increases, while cultivated land decreased marginally. Overall changes were limited in magnitude but exhibited clear trends. ②Significant spatial differentiation in LUCC emerged, revealing a gradient pattern: concentrated forest land in the southeast, extensive grassland in the central region, and dominant unutilized land in the northwest. Constructed land expanded slowly along river valleys and transportation corridors in a "point-axis" pattern. ③Multi-scenario simulations indicate that by 2060, the SD scenario demonstrates the most optimal comprehensive performance in ecological conservation and construction control; the WP scenario significantly promotes the expansion of forest and water areas; while the ND scenario may delay ecological improvement processes. The land use evolution mechanisms clarified under these policy scenarios provide scientific basis for optimizing territorial space and advancing ecological conservation and restoration on the Qinghai-Tibet Plateau.
Accuracy and Challenges of Machine Learning in Predicting Carbon Emissions from Coal-Fired Power Plants: An Analysis Based on Load Variations
This study explores the application of machine learning (ML) techniques in predicting carbon emissions from coal-fired power plants, with a focus on the impact of load variations. The purpose of this research is to evaluate the accuracy and challenges associated with ML models when predicting carbon emissions in response to dynamic load changes, a critical factor for emission forecasting in power generation. The study employs a combination of time-series forecasting models, including regression techniques and advanced deep learning algorithms, such as Long Short-Term Memory (LSTM) networks, to analyze historical load and emission data from a selected coal-fired plant. The results show that while ML models can offer significant improvements in prediction accuracy compared to traditional methods, the accuracy decreases during periods of rapid load fluctuation, with prediction errors increasing by 10-15% under high load variations. Furthermore, model explainability and the integration of real-time data pose considerable challenges. The study highlights the importance of robust feature engineering, including the integration of fuel composition and environmental factors, as well as the need for real-time data processing to enhance model performance. In conclusion, the research demonstrates that machine learning can be a pow-erful tool for carbon emission prediction in coal-fired power plants, but it also faces limitations in terms of accuracy under fluctuating load conditions. To overcome these challenges, future work should focus on improving model ro-bustness through hybrid approaches that combine ML techniques with physical modeling and real-time data analytics. This would enable more accurate and reliable carbon emissions forecasting, helping to optimize emissions reduction strategies and support regulatory compliance in the power sector.
Research on System Optimization and Decision-Making for Building Industrialization Chain Focused on Carbon and Waste Reduction
Aiming at the "dual carbon" goals, this paper treats the industrial chain of building industrialization as a complex system and focuses on the synergistic reduction of carbon and waste. By analyzing the key contradictions within the system, a multi-dimensional indicator system encompassing policy drivers, technical capabilities, and environmental performance is constructed. An evolutionary game model between the government and core enterprises is then established to explore their strategic interaction mechanisms. Simulation results show that under current parameters, the system stabilizes in a "regulatory dilemma" state of "strict government supervision and traditional enterprise practices," revealing that excessively high transformation costs are the key bottleneck suppressing market-driven dynamics. Sensitivity analysis further identifies a priority sequence for breaking the dilemma: the primary task is to significantly reduce transformation costs, followed by establishing synergistic incentives combining subsidies and long-term benefits, while punitive mechanisms primarily serve as a safety net. Based on this, the study proposes a collaborative decision-making system from a system optimization perspective, prioritizing cost reduction, with incentives as the core and supervision as the safeguard, providing theoretical foundations and pathways for advancing the green and low-carbon transformation of the building industrialization industrial chain.
Multivariate Data Fusion and Health Analysis of Coal Processing Facilities
This paper presents a comprehensive health diagnosis and predictive maintenance management system based on multi - source data fusion. Firstly, the research constructs a data acquisition framework covering multi - source sensors such as vibration, temperature, and pressure. Wavelet transform and empirical mode decomposition are employed for signal processing, and multi - dimensional features in the time domain, frequency domain, and time - frequency domain are extracted. Subsequently, the local preserving projection algorithm is introduced to achieve effective dimensionality reduction and visualization of high - dimensional features. At the diagnostic level, a fuzzy clustering - fuzzy integral fusion diagnostic algorithm based on fuzzy measure theory is innovatively proposed to address the challenge of composite fault identification. At the predictive level, by constructing a degradation feature evaluation system encompassing correlation, monotonicity, and robustness, the self - organizing map network is used to fuse multiple features into a comprehensive health index, and the long - short - term memory network model is utilized to accurately predict the evolution trend of the equipment's health status. Finally, based on the health status assessment and remaining useful life prediction, a full - life - cycle predictive maintenance strategy is formulated, and an intelligent maintenance decision - support system is designed.
Data-Driven Inverse Design of THz Metamaterials: A Deep Learning Approach for Small-Data Regimes
Inverse design based on deep learning offers a revolutionary paradigm for ac-celerating the development of novel terahertz (THz) metamaterials. However, its application is often constrained by the high cost of acquiring large-scale sim-ulation datasets. This work focuses on the high-accuracy inverse design of THz electromagnetically induced transparency (EIT) metamaterials under small da-taset conditions, systematically evaluating the effectiveness of different deep learning architectures. To achieve precise inversion from a target spectrum to its physical structure, we constructed a representative dataset through parametric electromagnetic simu-lation. On this foundation, we built, trained, and compared three neural network models: a Multi-Layer Perceptron (MLP) as the baseline, a residual fully con-nected network (FC-ResNet) for enhanced deep network training, and a one-dimensional convolutional neural network (1D-CNN) designed for sequen-tial data. The results reveal a key finding: for this inverse design task, the FC-ResNet demonstrated superior predictive performance, achieving a coefficient of de-termination (R²) of 0.9794 on an independent test set. This significantly out-performed the baseline MLP (0.9438) and surprisingly surpassed the theoreti-cally more suitable 1D-CNN. Further analysis suggests that for the EIT inverse problem investigated here, the prediction relies more on the global morphology and correlation features of the spectrum than on localized characteristics. The FC-ResNet, with its deep architecture and effective residual learning mecha-nism, successfully captured this complex, non-local mapping relationship. The core contribution of this work is demonstrating that for complex physical prob-lems, a deep, general-purpose model with sufficient expressive power and stable trainability can outperform a more specialized architecture that is prone to in-formation loss. This finding provides a crucial guideline for model selection in the intelligent design of physical devices, particularly in resource-constrained scenarios
Retraction: Research Progress on Novel Water-Soluble Lubricant Additives
At request of the authors, this article has been retracted by Erytis Publishing Limited in light of clear evidence that the results and conclusions are no longer valid. We thank the authors for notifying us so that the publication record can be amended accordingly. Retraction published: November 10, 2025.
Research on Welding Methods of Monel Alloy
Monel 400 alloy boasts excellent mechanical and processing properties, along with good corrosion resistance in high-temperature acid-base and strongly alkaline media. It is widely applied in high-pressure gas supply systems across aerospace, shipbuilding, chemical, pharmaceutical, and other industries. This article primarily analyzes and studies the welding issues of Monel 400 alloy pipelines, as well as the determination of welding process parameters, aiming to improve the welding process of Monel 400 alloy and ensure welding quality.