The WSRJ is an international peer reviewed, online and print version journal published by Boya Century Publishing. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of science and technology. We bring together Scientists, Field Engineers, Scholars and Students of related fields. WSRJ is a monthly, open access, peer reviewed and fully refereed international journal with minimal processing charge.
Research Progress of Deep Learning-Based Wood Surface Defect Detection
The detection of surface defects in wood is crucial for enhancing product value and manufacturing efficiency. Traditional methods exhibit inherent limitations in both efficiency and robustness, whereas deep learning techniques offer innovative solutions to these challenges. This paper provides a comprehensive review of the research progress in this field, beginning with an overview of the evolution from convolutional neural networks (CNNs) to object detection and image segmentation models, as well as their adaptability in wood defect identification. It then focuses on the role of key optimization strategies, such as attention mechanisms and multi-scale feature fusion, in improving detection performance under complex textures. Finally, this paper highlights future directions, including the integration of multimodal information, the development of self-supervised learning, and the construction of end-to-end systems, all aimed at providing forward-looking technological references for the intelligent upgrading of the wood industry.
Wood surface defects; deep learning; object detection; attention mechanism; multi-scale feature fusion.
Copyright (c) 2025 World Scientific Research Journal https://creativecommons.org/licenses/by-nc/4.0
Design and Implementation of an Intelligent Operation and Maintenance Management System for Near-Zero Energy Buildings
To address the issue of energy consumption and carbon emissions deviating from expectations due to improper operation and maintenance management in near-zero energy buildings, an intelligent operation and maintenance management system based on a B/S architecture was designed and developed. The system integrates active and passive collaborative management technologies to establish a multi-dimensional energy efficiency assessment system covering operational energy consumption, indoor environment, equipment efficiency, and carbon emissions. Based on real-time assessment data and energy consumption baselines, the system generates energy-saving and carbon-reduction strategies. Incorporating user behavior analysis, a closed-loop "assessment-diagnosis-control" management framework is constructed to significantly reduce carbon emissions during building operation. The system frontend uses Vue.js and Three.js to implement 3D building visualization and device status interaction. The backend, based on the Spring Boot framework, utilizes the WebSocket protocol for real-time data communication, enabling real-time diagnosis of building energy efficiency and energy-saving optimization, providing an effective solution for the refined operation and maintenance of near-zero energy buildings.
Near-Zero Energy Building; Operation and Maintenance Management; WebGL; 3D Visualization.
Soil pH is an important factor that influences crop planting due to its effects on mineral availability, growth, and reproduction of crops. This study examined the effects of three soil pH levels 5.5 (acidic), 7.1 (neutral), and 8.1 (basic) on the growth, yield, and fruit quality of Solanum lycopersicum (Brandywine). An experiment of 15 plants was carried out. Plants were grown outdoors for four months with 5 plants per treatment; vegetative traits, fruit production, and quality parameters were evaluated. Overall, the data suggests that acidic soils had the highest number of fruit per plant (13.4 ± 1.02) and superior quality traits, including mass, °Brix, lycopene, and vitamin C, but also showed an increased incidence of blossom-end rot. Neutral soils demonstrated a more balanced vegetative growth and optimal fruit mineral content, while basic soils resulted in taller plants but significantly reduced yield (8.2 ± 0.75 fruits per plant) and lower fruit quality. One-way ANOVA confirmed that there are significant differences among treatments (p < 0.001). These findings indicate that maintaining soil pH slightly acidic to neutral (6.0-6.8) enhances productivity and quality of fruit in tomato farming.
Research on Functional Data Method Based on Air Quality Index of Sichuan Basin
So far, there have been relatively few studies on air quality in the Sichuan Basin. This paper uses the functional data analysis method to study the air quality data of the Sichuan Basin. Firstly, the discrete air quality data of 21 prefecture-level cities in the Sichuan Basin were smoothed using the Fourier basis function to transform them into continuous functional data. Then, the principal component analysis of the smoothed air quality index was conducted. Secondly, the non-negative matrix factorization method is introduced to achieve data dimensionality reduction and retain key information through the linear combination of basis vectors, and to conduct cluster analysis on the air quality data of the Sichuan Basin. The research results show that the fluctuation of the air quality index in the Sichuan Basin has a distinct seasonal pattern. The cumulative variance contribution rate of the first three principal components reached 95.3%, which can explain most of the original data. According to the clustering results, the Sichuan Basin can be divided into three types of regions, namely the core area, the marginal area and the mountainous area in southwest China. These studies provide data support and theoretical basis for the future pollution prevention and control policies in the Sichuan Basin.
Air quality; Functional principal component analysis; Non-negative matrix factorization; Cluster analysis.
Research on the Alignment Method for Multi-Segment Long Shaft Systems
Aiming at the problems of frequent measurement, difficult adjustment and error accumulation in conventional alignment of a ship's multi-segment long shaft system due to many moving parts and dense nodes, this paper proposes an integrated scheme of "tooling constraint-stepwise alignment-laser detection-overall re-inspection" by analyzing technical principles, designing special tooling and optimizing process flow, combined with the onshore experiment scenario. The scheme restricts the movement allowance of self-aligning roller bearings and drum gear couplings through positioning tooling, and achieves high-precision measurement with a laser aligner. Finally, the radial deviation of the shaft system is controlled within ≤0.2mm and the axial deflection within ≤0.25mm/m. Moreover, the shaft system shows excellent precision stability after 4 hours of no-load operation, providing engineering reference for the alignment of similar shaft systems.
Multi-segment long shaft system; Drum gear coupling; Self-aligning roller bearing; Laser alignment technology.
Instabilities of Plasma in Magnetic Constraint Controlled Nuclear Fusion Devices
This paper investigates the plasma instability issues in magnetic confinement nuclear fusion devices and explores their impact on fusion reaction efficiency and control methods. The research aims to analyze the physical mechanisms of macroscopic and microscopic scale plasma instabilities and conclude suppression strategies. Through theoretical models, numerical simulations, and data-driven methods, a systematic study was conducted on various instabilities such as resistive wall mode, tearing mode, and edge localized mode, as well as their interactions. The research results indicate that by optimizing the magnetic field structure, employing resonant magnetic perturbation (RMP) technology, and using supersonic molecular beam injection (SMBI), instabilities can be effectively suppressed, and plasma confinement performance can be improved. Additionally, the research on high-parameter steady-state operation and the interaction between fast particles and instabilities provides important basis for the commercial operation of future fusion devices. The conclusion of this paper emphasizes the crucial role of plasma instability control in achieving controllable nuclear fusion and looks forward to the future development direction of related technologies.
Magnetic confinement nuclear fusion; Plasma instability; macroscopic plasma perturbation; Magnetohydrodynamics; Microscopic-scale plasma perturbations.
Development and Application of a Smartphone-Based App for Screening Myopia and Astigmatism in Elementary School Students
This study aimed to develop a smartphone-based application for visual acuity and astigmatism screening among elementary school students and to investigate the prevalence and progression patterns of these refractive problems across different grades and genders. The application was developed using the Uni-App framework, integrating standardized logarithmic visual acuity charts and astigmatism screening diagrams. A total of 904 students from grades 1 to 6 were recruited. Self-administered tests were performed under standardized conditions. Data on visual acuity and astigmatism were collected over six months and analyzed by grade and gender. The overall prevalence of myopia was 56.1% (507/904). Myopia prevalence increased markedly with grade, from 37.4% in Grade 1 to 81.1% in Grade 6. A significant gender disparity was observed in Grade 6, where 91.9% of girls and 70.8% of boys had myopia. Astigmatism prevalence also increased with grade, from 29.8% to 57.5%, largely driven by its comorbidity with myopia. In summary, the Eye-Health application proved to be an effective tool for large-scale, school-based vision screening. The findings highlight a critically high and grade-dependent prevalence of refractive errors among Chinese elementary school students, with girls being particularly vulnerable in higher grades. The findings of this study provide evidence-based references for families, schools, and health departments to formulate targeted prevention and control strategies, thereby facilitating early warning and tailored interventions.
Elementary school students; Myopia; Astigmatism; Smartphone-based App.
FAS-YOLO: An Improved Algorithm for Small Target Detection of Unmanned Aerial Vehicles
Small target detection from the perspective of unmanned aerial vehicles (UAVs) faces the challenges of low accuracy, high missed detection rate, and high false detection rate. To address these issues, this paper proposes an improved small target detection model based on YOLOv8, named FAS-YOLO (Feature Attention Small Object Detection-YOLO). Firstly, the model replaces the traditional pooling operation by introducing the FPSharedConv module, which can effectively extract fine-grained features and retain the detailed information of small targets. Secondly, based on the improvement of PAFPN, the smallObjectEnhancePyramid feature pyramid structure is proposed: without adding the P2 detection layer, through the fusion of the SPDConv convolution of the P2 feature layer and CSP-OmniKernel, the feature representation ability of small targets is effectively enhanced. In addition, the AFGCAttention mechanism is introduced after the FPSharedConv to further improve the model's attention to key small targets. Finally, the loss function is improved based on WIoUv3, and the detection and positioning accuracy is improved by using a more reasonable aspect ratio measurement. The experimental results show that the precision, recall, mAP50, and mAP50-95 of the improved model on the VisDrone2019 dataset are increased by 9.6%, 8.6%, 10.9%, and 7%, respectively. FAS-YOLO significantly improves the performance of small target detection and provides a new solution for efficient target detection in UAV scenarios.
Small target detection, YOLOv8, Feature Pyramid, FPSharedConv, Attention mechanism, AFGCAttention, WIoUv3.
MBD-YOLO: Steel Defect Detection Model Combining Multi-scale Features and Routing Attention
With the growing demand for high-quality steel, accurate and efficient surface defect detection is becoming increasingly important. Traditional methods often face challenges such as missed detection, inaccurate localization, and poor performance in identifying small defects. This study introduces MBD-YOLO, an enhanced defect detection model based on the YOLOv8 framework. The model incorporates a novel attention module, Bivo, in the feature fusion stage, which captures both global and local features while enabling effective cross-scale information exchange. This significantly improved the accuracy and performance of small-object detection in complex backgrounds. Second, a new DRM detection head was designed. Through its multi-context enhancement and its dual-branch pooling combined with upsampling, the global and local context information is enhanced, and the detection ability of small target, low contrast and direction sensitive defects is significantly improved. The backbone integrates MobileViT, combining CNN and Vision Transformer architectures to improve feature extraction for small targets. Experiments on the NEU-det dataset show that MBD-YOLO achieves an mAP@0.5 of 85.7%, outperforming mainstream models. A large number of experiments not only confirm the combined effectiveness of the proposed modules, provide excellent performance in various defect categories, but also verify the generalization of the model. MBD-YOLO provides a robust and high-precision solution for steel defect detection that meets the needs of modern industrial applications.
Deep learning; steel defect detection; YOLO; attention mechanism; Small object detection.
Effects of Aging on The Bioavailability of Cu and Cd Immobilized by Biochars with Varying Endogenous Heavy Metal Concentrations
Biochar is widely used because of its good passivation effect on soil heavy metals; however, the biochar itself may contain heavy metals due to the difference in raw materials, and whether the passivation effect of biochar with high endogenous heavy metals is altered after aging, and whether there is a potential environmental risk should be a concern. In this study, three types of biochar (BB, MB, and HB) with varying endogenous Cu and Cd levels, derived from Pennisetum giganteum grown in background, moderately contaminated, and heavily polluted areas, were used as the study objects. The effects of aging on the bioavailability of Cu and Cd in contaminated soil, when subjected to alternating dry-wet and freeze-thaw conditions, were analyzed using the CaCl2 extraction and the thin-film diffusive gradient (DGT) extraction method. We examined the effects on the bioefficacy of Cu and Cd in contaminated soil by using the BCR sequential extraction method. To analyze the changes in the morphological distribution of soil Cu and Cd. The results showed that the three types of biochar were alkaline, and the contents of Cu in HB and Cd in MB were the highest, being 3.72 and 3.29 times higher, respectively, than those of BB. After 30 days of pre-cultivation, a dosage of 2% to 10% biochar increased the soil pH by 0.32 to 4.50, respectively. All of them significantly reduced soil Cu and Cd bioefficacy, among which a 10% dosage of the three kinds of biochar reduced soil CaCl2 extraction of Cu and Cd by 98.2%~99.1% and 86.9%~97.0%, respectively, and DGT extraction of Cu and Cd by 80.0%~89.9% and 81.7%~99.0%, respectively. Compared with the control treatment, the addition of biochar significantly reduced the soil acid-soluble Cu content and promoted its transformation to oxidizable, reducible and residual Cu; however, the 5% and 10% MB treatments significantly increased the soil acid-soluble Cd content, which has a substantial potential environmental risk, and thus high endogenous pollutant biochar needs to be paid extra attention to when applied in large quantities. In addition, alternating dry-wet and freeze-thaw aging decreased the CaCl2- and DGT-extracted soil Cu and Cd contents in each treatment group, which further promoted the transformation of soil Cu and Cd from active to inactive states. Overall, the stabilizing effects of the three types of biochar at the exact dosage were HB > MB > BB for soil Cu, and BB > HB > MB for soil Cd. The results of this study are of great significance for the development of biochar application standards and for evaluating the stability of biochar in long-term passivation and remediation.
Biochar; endogenous heavy metals; aging; passivation.
Price Prediction and Analysis of Price Influencing Factors for Second-hand Car Sales in AutoTrader Based on XGBoost Algorithm
The global used car market continues to expand, reaching a scale of 1.6 trillion US dollars in 2023. In 2024, China's transaction volume reached 19.61 million units, setting a new high. However, information asymmetry, sharp price fluctuations, and subjective assessment severely constrain market efficiency. To solve the pricing problem, this study, based on a large amount of data from the AutoTrader platform in the UK, builds an XGBoost high-precision price prediction model, integrates multiple vehicle attributes and market characteristics, and achieves low-error residual value estimation. At the same time, random forest feature analysis is used to quantify the contribution of key factors, revealing the hierarchical influence structure, providing intelligent and data-driven pricing decision support for all parties involved in the transaction, and promoting the market to transform towards transparency and efficiency.
Used car evaluation; XGBoost algorithm; Random forest; Used Car Valuation System.
Research on the Path of Supply Chain Collaborative Innovation Empowered by Digital Technology - A Case Study of Wenzhou's Pump and Valve Industry
In the era of digital economy, supply chain collaborative innovation has become a key path for the transformation and upgrading of the manufacturing industry. This study takes Wenzhou's pump and valve industry as the research object, adopts a combination of literature research and empirical investigation methods, and systematically analyzes the pain points existing in the supply chain of the traditional pump and valve industry, such as low efficiency, weak collaboration, and insufficient digitalization level. Based on the new generation of information technologies including the Internet of Things (IoT) and big data, a supply chain collaborative innovation model is proposed. Survey data shows that 87.5% of enterprises face the problem of low supply chain collaboration efficiency, and 62.5% of respondents believe that the existing business processes are difficult to meet the needs of digital transformation. The study puts forward a technical path centered on "digital platform + data-driven decision-making". Through strategies such as building a supply chain collaboration platform, optimizing business processes, and strengthening resource aggregation, it promotes the transformation of the pump and valve industry's supply chain towards networking and intelligence, and provides a theoretical framework and practical reference for the collaborative innovation of the supply chain in the traditional manufacturing industry.
Supply Chain Collaboration; Digital Technology Integration; Wenzhou Pump and Valve Industry.
Research and Application of the Testing Technology for Impacting Screw Drilling Tools in Coal Mine Roadways
With the popularization and application of underground directional drilling technology in hard rock strata of coal mines, the performance test of impact screw drilling tools is of key significance for the optimization of drilling tools. Based on the working principle of the cam impact hammer-spring energy storage impact mechanism, this paper designs a test platform including a mechanical loading system, a hydraulic circulation system, and a high-speed data acquisition system, realizing the synchronous collection of parameters such as torque (1480 N·m), impact force (5-9 kN), and rotational speed. Field applications show that this testing technology can guide the service life of drilling tools to increase by more than 2 times, providing data support for the optimization of hard rock directional drilling technology.
Coal mine; impact screw drilling tool; dynamic testing; hard rock breaking; directional drilling.
Characteristics and Analysis of Volatile Organic Compounds Emission from Petrochemical Facility
Petrochemical enterprises are major emission sources of volatile organic compounds (VOCs), which significantly impact ambient air quality. This paper systematically summarizes the research progress on the current status, characteristics, and potential environmental and health impacts of VOCs emissions from China's petrochemical industry. Based on the results of atmospheric pollution analysis in the regions where petrochemical enterprises are located, and in line with the actual needs for improving local air quality, this study takes the environmental impacts of different VOCs emission sources in petrochemical enterprises as the evaluation objects. It identifies critical challenges and gaps in VOCs management and proposes targeted strategies for future control and mitigation.
Volatile organic compounds; Petrochemical facility; Emission source; Air pollution.
Modal Analysis and Optimization of Electric Vehicle Chassis Structure
The interior noise of electric vehicles (EVs) at low velocities is predominantly attributed to structural vibrations. This study proposes a systematic optimization of the EV chassis structure through modal analysis, integrated with spectral characteristics of interior noise. Initially, finite element models of critical chassis components were developed and validated via experimental modal testing. Subsequent the modal frequency and vibration mode of each component were obtained by finite element analysis. By correlating the frequency spectrum of interior noise with the chassis modal frequencies, the rear subframe and strengthening plates were identified as priority optimization targets. Targeted structural reinforcements were implemented to elevate the natural frequencies of these components, thereby achieving effective frequency avoidance and mitigating structural vibrations. The proposed methodology demonstrates a data-driven approach to enhance NVH (Noise, Vibration, and Harshness) performance of the vehicle.
Electric Vehicle, Chassis structure, Modal Analysis, Optimization.
Cost Estimation Method for Missile Equipment in the Development Stage Based on Linear Regression - Random Forest Method
This paper addresses the nonlinearity and small sample size characteristics of cost prediction in the missile equipment development stage. By using the linear regression method, 11 cost-related indicators were selected from 22 indicators. A cost estimation model based on the linear regression - random forest method was constructed using 20 sets of historical data from a certain missile development project. The estimation results of the established model were compared and analyzed with those of the neural network model. The root mean square error (RMSE) of the established model was 67.31% lower than that of the neural network model, and the mean absolute error (MAE) of the established model was 63.47% lower than that of the neural network model. This verified the superiority of the linear regression - random forest method in complex nonlinear prediction.
Linear regression; Random forest model; Performance metrics; Cost estimation.
Design and Implementation of Real time Detection System for Indoor Air Quality under Multiple Environmental Temperature Conditions
This study aims to design and implement a real-time monitoring system for in car air quality suitable for multiple environmental temperature conditions, in order to improve the level of air quality monitoring in driving environments. the basic principles of air quality detection and the impact of different environmental temperatures on the characteristics of air pollutants in vehicles were analyzed, and the hardware selection and design considerations of the system were determined. On this basis, an analysis method for the impact of temperature on the accuracy of air quality sensors was proposed, and algorithms for data acquisition, real-time processing, temperature compensation, and multi-sensor data fusion were designed. The hardware implementation of the system includes sensor modules, data processing modules, and communication modules, while the software architecture covers real-time data processing and communication protocol design. The accuracy and stability of the system were verified through experiments under multiple environmental temperature conditions. The experimental results show that the system can effectively monitor the air quality inside the vehicle and accurately compensate for the concentration of pollutants under different temperature conditions, with good application prospects. This study provides a technical foundation and reference for further optimization and promotion of the in car air quality monitoring system.
Air quality inside the car; Real time detection; Multiple environmental temperatures; Temperature compensation; Multi sensor data fusion.
A Review of Patent Technologies on Smart Diapers
Driven by both global population aging and the rise of intelligent parenting concepts, smart diapers have become a critical entry point for personal health monitoring. Their core technologies directly influence caregiving efficiency and user well-being. This paper conducts a comprehensive review of global patents related to smart diapers and innovatively analyzes the progress in three major technological pathways: sensing technology innovations, energy and structural design, and intelligent systems for health management. Current technological development is undergoing a transformation from single-function wetness alerts to multidimensional physiological monitoring systems. With breakthroughs in sensing technologies and system architectures, product performance and commercialization feasibility have been significantly enhanced. The paper identifies core directions for future technological breakthroughs and provides new insights and references for R&D and industrial development in relevant fields.
Smart diapers; sensing technology; reusable design; self-powered systems; health management.
Technical Status and Development Trend of Medical Dynamic Multi-leaf Collimator
Medical multi-leaf collimator technology, as one of the core technologies in radiotherapy, effectively enhances the accuracy and safety of treatment by precisely controlling the shape of the radiation beam. This article first reviews the overall situation of cancer treatment and the important position of radiotherapy. Then, it deeply analyzes the basic principles, structural composition, working mechanism of DMLC (Dynamic Multi-Leaf Collimator) technology and its specific applications in clinical radiotherapy. On this basis, it further analyzes the development overview of DMLC technology at home and abroad, covering major manufacturers, technical parameters, market changes and the progress of localization. Finally, the article discusses the current challenges faced by DMLC technology and looks forward to its future development trends and research directions.
Multi-leaf collimator, Radiotherapy, Leakage rate, Conformality.
Research Status on the Helical Conveying Characteristics of Agricultural Materials Based on the Discrete Element Method
The efficient conveying and processing of agricultural materials are core links in the development of modern agricultural mechanization. As a key technology for studying the dynamics of granular systems at the mesoscopic scale, the Discrete Element Method (DEM) provides an innovative pathway to reveal the mechanisms of agricultural material processing and conveying. This paper systematically reviews the research progress of the discrete element method in the field of agricultural material processing and helical conveying, focusing on the construction of multi-layer structural models, contact parameter calibration methods, process simulation applications, and performance optimization of screw conveyors. The discrete element method can accurately simulate the motion trajectories, force characteristics, and equipment wear patterns of agricultural materials during helical conveying, significantly improving conveying efficiency and optimizing energy consumption. Current research still faces challenges such as multi-physical field coupling modeling and dynamic calibration of complex working condition parameters. In the future, it is necessary to strengthen the deep integration of the discrete element method with machine learning and experimental verification to provide a more solid theoretical foundation for the intelligent design of agricultural equipment.
Screw conveyor; Particle; Agricultural materials; Discrete Element Method (DEM).
YOLO-Based Ship Detection in Remote Sensing: A Practical Perspective
Ship detection in remote sensing imagery is essential for maritime surveillance, port operations, and coastal safety. This study presents a practical evaluation of YOLO-based models tailored for real-time deployment in civilian maritime environments. The experiments utilize a curated version of the ShipRSImageNet_V1 dataset, from which all military vessels were removed to focus exclusively on civilian ships. This refinement ensures the model's applicability to non-military monitoring tasks and enhances its relevance for port and commercial vessel management. To support scalable deployment, the proposed framework integrates unmanned aerial vehicles (UAVs) for data acquisition with a collaborative edge-cloud inference strategy. Lightweight models operate onboard UAVs, while centralized GPU resources handle complex computations, enabling efficient and responsive maritime monitoring. This architecture aligns with emerging trends in low-altitude economy and supports rapid decision-making in dynamic coastal scenarios. Comparative results demonstrate that YOLOv8n, YOLOv11n, and YOLOv12n achieve a favorable balance between detection accuracy and computational efficiency. Future research will focus on improving model robustness, expanding domain-specific datasets, and incorporating multi-modal data to enhance performance in diverse operational conditions.
Ship Detection; Remote Sensing Imagery; YOLO Object Detection; Civilian Maritime Monitoring.
A Multi-View Feature-Based Retrieval Method for On-Machine Measurement Model Reuse
In the design of on-machine measurement (OMM) schemes for complex parts, the involvement of diverse geometric features, physical environments, and hardware/software systems makes the process intricate and inefficient. To address the high dependency on manual experience and the low efficiency in current OMM scheme design, this paper proposes a multi-view 3D model retrieval method aimed at facilitating the reuse of existing measurement solutions. Specifically, multiple view images of the same model are processed using a multi-view convolutional neural network (ResNet-18) to extract view-specific features. A self-attention mechanism is introduced to enhance the network’s focus on informative views and critical features. These features are then fused into a global feature descriptor through a long short-term memory (LSTM) network. Finally, similarity between the query model and models in the feature database is computed, and retrieval is performed by ranking similarity scores in descending order. Based on the retrieved models, corresponding measurement schemes can be adjusted and reused efficiently. Experimental results demonstrate that the proposed method outperforms the MVRNN approach in terms of Precision and Recall, and improves the mean Average Precision (mAP) by 5%. The method effectively enhances the efficiency of OMM scheme design and shows strong potential for practical engineering applications.
On-machine measurement; Model retrieval; Multi-view; Neural networks; Reuse technology.
Design and Optimization of Bionic Pressing Roller
Aiming at the high resistance and "soil accumulation" phenomenon existing in the operation of the traditional potato harvester's pressing roller, this study, based on the non-smooth surface characteristics of the grub and combined with bionics principles to extract the structural features of its back convex hull, designed a bionic pressing roller. The surface morphology of the grubs was obtained by a 3D contour scanner. A roller model with a diameter of 110mm and a width of 580mm was constructed, and the finite element model of the pressing roller - soil was established by using ABAQUS software (the Drucker-Prager constitutive model was adopted for the soil). The simulation results show that the bionic pressure roller effectively eliminates the soil accumulation phenomenon and significantly reduces the working resistance compared with the traditional cylindrical roller. Further through single-factor tests and Box-Behnken response surface optimization, the optimal parameter combination was determined to be a convex hull height of 15mm, an operating speed of 0.72m/s, and a suppression load of 320N. At this time, the traction resistance was 228.64N, verifying the superiority of the bionic structure in drag reduction and surface leveling.
Bionic pressing roller; Finite element simulation; Grubs; Response surface experiment.
Energy Consumption Prediction of Air-conditioning Based on SARIMA-GARCH Model
Energy consumption prediction plays an important role in building design & retrofit, energy management system, but the nonlinear, dynamic and complex air-conditioning energy consumption data make it difficult. We employ SARIMA (Seasonal Autoregressive Integrated Moving Average) and GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models respectively to extract the level and fluctuating information according to the trend, seasonality and random fluctuation of time series of energy consumption based on the real-time operation of air-conditioning in an office building, which are integrated to obtain a combined model of SARIMA(0,1,1)(1,1,1)9-GARCH(1,2). The MAPE of SARIMA(0,1,1)(1,1,1)9-GARCH(1,2) model is 4.46%.
Air-conditioning energy consumption prediction; SARIMA-GARCH model; Seasonal time series; Heteroscedasticity.
Machine Vision in Flexible Cutting of Irregular Leather Workpieces
With the increasing demand for high-quality and customized leather products in industries such as automotive, fashion, and furniture, traditional manual cutting methods struggle to meet precision and efficiency requirements. This paper proposes a machine vision-based flexible cutting system for irregular leather workpieces, integrating image processing, contour recognition, and path planning to optimize material utilization and cutting accuracy. A multi-stage algorithm combining edge detection, adaptive thresholding, and deep learning-based defect detection is employed to enhance cutting precision. Experimental results demonstrate that the proposed system achieves a cutting accuracy of ±0.3 mm and reduces material waste by 15% compared to conventional methods. The system's adaptability to varying leather textures and defects makes it suitable for industrial applications requiring high flexibility and automation.
Machine vision, leather cutting, irregular workpieces, flexible manufacturing, defect detection, path planning.
Study on the Carrier Function and Skin Permeability of Nanoemulsion in Sunscreen and Skin Care Products
In this study, nanoemulsion was prepared by high-pressure homogenization method, and its carrier mechanism, skin penetration law and safety in sunscreen products were systematically evaluated, which provided theoretical and practical basis for the design of high-efficiency and low-irritation sunscreen formula. The experimental results show that the nanoemulsion (particle size 150±20 nm) has excellent sun protection performance, SPF value is 50±3, which is about 67% higher than that of traditional emulsion, and the critical wavelength is 382 nm, which meets the broad-spectrum protection standards. At the same time, it showed excellent light stability and water resistance. The residual rate of Avobenzone reached 92% after illumination, and the SPF retention rate exceeded 85% after 40 minutes of water bath. The particle size of nanoemulsion significantly affects the skin penetration behavior, in which the 200 nm emulsion has the highest retention in the stratum corneum (98.7%) and the dermal penetration is only 0.2%. In addition, nanoemulsion showed good biocompatibility, the survival rate of HaCaT cells was still higher than 90% after 48 hours, and no irritation or allergic reaction was found in EpiDerm™ model and human patch test, indicating that it is suitable for special people such as sensitive skin and children.
Nano-emulsion; carrier function; skin permeability; sunscreen and skin care products.
Evaluation of Wetland Ecosystem Services Value in the Minjiang Estuary Wetland National Nature Reserve
Wetlands, known as the "kidneys of the Earth," are crucial for their ecological services but are often overexploited due to inadequate protection and scientific assessment. The Minjiang Estuary Wetland National Nature Reserve, a significant ecological barrier, lacks a systematic and monetary evaluation of its ecological service values. This study addresses this gap by developing an assessment model based on relevant standards and guidelines, using multiple methods to ensure scientific accuracy and applicability. The assessment reveals that the wetland's total ecosystem service value is approximately 4.6835 billion yuan. Specifically, the value of ecological regulation services is the highest at 2.548 billion yuan (54.4%), primarily from soil conservation and air purification. The value of product supply services is 2.0816 billion yuan (44.44%), mainly from fishery and freshwater resources. The value of cultural and spiritual services is 54.05168 million yuan (1.16%). These findings provide a theoretical foundation and quantitative basis for evaluating the ecological service values of the Minjiang Estuary Wetland, enhance awareness of ecological protection, inform policy-making for wetland conservation, and promote a balance between economic and ecological benefits.
Coastal Wetland, Ecosystem Services, Value Assessment.
Ship Service Speed Estimation Based on the Backpropagation Neural Network
To enhance the accuracy of ship service speed estimation, a model based on a Backpropagation (BP) Neural Network was developed. This model was trained using data from 53 bulk carriers built over the past 30 years, and the corresponding model parameters were derived. A reference regression formula was then generated by re-fitting the training data to evaluate the BP Neural Network's estimation performance. Comparative analysis of the results demonstrates that both the BP Neural Network model and the reference regression formula significantly outperform the original regression formula in terms of estimation accuracy. Furthermore, residual analysis indicates that the BP Neural Network model offers greater estimation stability than the reference regression approach, making it well-suited for practical applications in ship service speed prediction.
Service Speed; Estimation; Regression formula; BP Neural Network.
Approximate Concepts Analysis Based on {1,0,-1}-valued Formal Contexts
With the rapid growth of data volume and the increasing complexity of data analysis, traditional formal concept analysis (FCA) models have become inadequate for addressing the diverse and intricate requirements of big data environments. In response to these challenges, this paper introduce a novel approximate concept model grounded in three-way concepts and incorporating the notion of {1,0,-1}-valued formal contexts. We formally introduce the foundational definitions of the proposed model, thoroughly examine its essential properties and the partial order relations among concepts, and explore its enhanced capacity for representing and reasoning about uncertain or {1,0,-1}-valued information. Furthermore, a comprehensive comparison with existing concept models is provided to highlight the advantages and distinguishing features of the proposed approach. Finally, we demonstrate the potential applications of this model in data analysis and knowledge representation, illustrating its value for addressing the complexities inherent in modern data-driven tasks.
Formal concept analysis, Concept lattice, Approximate concepts.
Recent Developments in Lorentz Microscopy
Lorentz transmission electron microscopy (LTEM) has gradually evolved into a quantitative technique for analyzing the magnetization state of samples. This review presents latest advances of imaging techniques, including using spherical aberration correction to enhance spatial resolution, the application of Fresnel-variant 4D LTEM to improve temporal resolution, and the development of novel in situ sample holders. Discussion on vector field electron tomography (VFET), which enables three-dimensional reconstruction of magnetization information is also included. In addition, it provides a brief overview of how these advancements have been applied in recent material studies.
LTEM, VFET, MBIR, In situ sample holder, Spherical aberration correction, AI.