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.
Lightweight YOLOv5s-Based Machine Vision System for Real-Time Potato Defect Detection
To solve the drawbacks of traditional potato defect detection, such as heavy manual dependence, low efficiency, strong subjectivity and poor adaptability to complex scenarios, this study proposes a real-time machine vision detection system based on improved lightweight YOLOv5s. In this research, the YOLOv5s model is lightweightly modified, and GhostNet is used to replace the original CSPDarknet backbone network, which reduces model complexity while retaining the core feature extraction capability. The experimental results show that on the self-built dataset covering four types of potato defects including damage, sprouting, scab and dry rot, the Precision, Recall and mean Average Precision (mAP) of the improved YOLOv5s model reach 94.2%, 92.2% and 95.7% respectively, with the model size reduced by 26%. For the real-time detection of potato defects, the BoT-SORT multi-object tracking algorithm is integrated into the YOLOv5 detection framework. Compared with classic multi-object tracking algorithms including SORT, DeepSORT and ByteTrack, BoT-SORT achieves the best overall performance and obtains optimal values in three core evaluation metrics of HOTA, MOTA and IDF1, which are 95.3%, 99.7% and 98.8% correspondingly. Additional experimental verification demonstrates that when the conveyor belt speed is no higher than 62 mm/s, the improved lightweight model maintains a detection accuracy of no less than 94.3% and a tracking accuracy of 100%, and its operating speed fully meets the requirements of real-time detection. The designed system comprehensively balances detection accuracy, lightweight performance and real-time performance, which provides a reliable reference for meeting the practical demands of online real-time potato defect detection.
Lightweight; YOLOv5; potato defects; real-time detection.
Copyright (c) 2026 World Scientific Research Journal https://creativecommons.org/licenses/by-nc/4.0
ECO-TrainNet: An Integrated Framework for Carbon Emission Prediction and Energy-Efficient Control in Urban Rail Transit
Urban rail transit systems are a key component of sustainable urban mobility, yet their increasing operational intensity leads to growing energy consumption and associated carbon emissions. This study proposes ECO-TrainNet, an integrated framework for carbon emission prediction and energy-efficient control in urban rail transit. The framework combines machine learning-based prediction with reinforcement learning-based control in a closed-loop structure. A simulation-based dataset is constructed to capture train dynamics, infrastructure characteristics, and time-varying carbon emission factors. Ensemble learning models, particularly XGBoost, are employed to estimate energy consumption and carbon emissions, while a reinforcement learning agent is trained to optimize acceleration strategies under operational constraints. Experimental results show that the proposed framework achieves high prediction accuracy and enables effective control optimization. Compared with baseline operation, the reinforcement learning-based strategy reduces energy consumption by 11.7% and carbon emissions by 11.0%, while maintaining acceptable schedule deviation. The results demonstrate that integrating prediction and control provides a practical approach for carbon-aware and energy-efficient rail transit operation.
Urban rail transit; Energy consumption; Carbon emission; Machine learning; Reinforcement learning; Optimization.
Influence of Surfactants on Supercritical CO₂ Foaming Polyurethane Materials
Thermoplastic polyurethane elastomer (TPU) foam materials have been widely used in various fields due to their excellent characteristics such as light weight, high elasticity, and non-toxicity. Supercritical CO₂ foaming technology, as an environmentally friendly and efficient physical foaming method, has become the mainstream development direction of polyurethane foaming. However, the traditional process often has problems such as large pores and uneven distribution. Some existing studies have shown that surfactants can improve the foaming performance. This paper systematically analyzes the current development status of polyurethane foaming technology, the advantages and limitations of supercritical CO₂ foaming technology, and focuses on the research progress and results of surfactants on the pore structure of supercritical CO₂ foamed polyurethane materials. It also looks forward to the future prospects and research directions in this field. Relevant studies have shown that surfactants can significantly reduce pore diameter and increase pore density by reducing the surface tension of bubbles and the free energy of gas dispersion, providing an effective technical method for improving the performance of polyurethane foam materials. The related research provides new directions and thoughts for promoting the technological development of the industry.
Surfactants; Supercritical CO₂ foaming; Thermoplastic polyurethane elastomer; Cell structure; Literature Review.
Application of Graphene-based Electrochemical Sensors in The Detection of Pesticide Residues
This study intends to investigate the utilization of graphene-based electrochemical sensors for detecting pesticide residues. By means of literature investigation and experimental examination, the working principle, performance and practical application of graphene electrochemical sensors are comprehensively researched. We find out that due to its special electric and chemical characteristics, graphene can sense pesticide residues with high sensitivity and selectivity. The sensor can quickly and accurately detect the pesticides in the sample, and it also shows great promise for simultaneously detecting multiple types of pesticides. Actual sample testing has shown some initial results. Graphene based electrochemical sensors have a wide range of applications in the field of pesticide residue detection, and they are expected to become an alternative to traditional detection methods, which will provide strong support for food safety and environmental protection.
Graphene; Electrochemical sensor; Pesticide residue; Detection; Application.
A Study on Provincial-Level Carbon Emission Forecasting Based on Multimodal Data and Spatio-Temporal Neural Networks
Against the backdrop of global warming and the ‘dual carbon’ targets, accurate forecasting of provincial carbon emissions is of great significance for formulating differentiated emission reduction policies and optimising energy structures. This study addresses issues such as the inability of traditional statistical models to capture non-linear characteristics, the limited predictive accuracy of single machine learning models, and the failure of existing deep learning methods to account for the spatial spillover effects of carbon emissions. The study first constructs a provincial multi-modal dataset integrating features such as GDP, industrial output, energy prices and policy intensity, and establishes a spatial adjacency matrix based on geographical proximity; secondly, it proposes a hybrid GCRN-GCNLSTM architecture that accounts for the spatio-temporal dependencies of carbon emissions; finally, model interpretability is enhanced through feature importance analysis and residual diagnostics. Using six major Chinese provinces—Shandong, Guangdong, Jiangsu, Hebei, Zhejiang and Henan—as the empirical subjects, the study conducted a comparative evaluation of ARIMA, LSTM, GCN-LSTM and ensemble models. The results indicate that the LSTM model performed best on the test set, with a coefficient of determination (R²) of 0.8383, a mean absolute error (MAE) of 0.4191 million tonnes, and a root mean square error (RMSE) of 0.5102 million tonnes; the mean absolute percentage error (MAPE) of the ARIMA model was 22.01%, indicating a significant linear component in the carbon emissions time series; The GCN-LSTM model, which incorporates geospatial information, did not outperform the LSTM model. Analysis suggests this may be attributable to the time lag associated with spatial spillover effects, an inappropriate graph structure design, and spatial redundancy in the economic features. The contribution of this study lies in proposing a ‘multimodal features + time series modelling’ forecasting paradigm, systematically evaluating the applicability of various models in provincial carbon emissions forecasting, and providing data support and theoretical grounds for improvements in spatial modelling and the formulation of differentiated emission reduction policies.
Carbon emissions forecasting; multimodal data; graph convolutional recurrent networks; long short-term memory networks; provincial-level analysis; spatio-temporal dependence.
Research Progress on Coupling Disaster-Causing Mechanism of Multiple Factors for Forest Fires in Central Yunnan Region
From the perspectives of regional ecological security and socioeconomic development, central Yunnan is a key distribution area of forest resources in Yunnan Province. Under the combined effects of monsoon climate, mountainous terrain, flammable vegetation and frequent human activities, forest fires have long been characterized by a high incidence and have become an important restrictive factor. Against the background of global climate change, rising regional temperatures and frequent drought events have further increased the randomness of forest fire occurrence and the intensity of their spread. Existing studies have confirmed that forest fires in central Yunnan are not driven by a single factor, but the result of long-term interaction and nonlinear coupling among four types of factors: meteorological conditions, topography, vegetation fuel and human activities. Based on the theoretical framework of multi-factor coupled disaster-causing of forest fires, this paper systematically summarizes the spatiotemporal distribution patterns of forest fires in central Yunnan, integrates and analyzes the internal relationships and synergistic effects of disaster-forming environments, hazard-affected bodies and disaster-causing factors, clarifies the key mechanisms of multi-factor coupling driving the occurrence, development and disaster formation of forest fires, summarizes the progress of core methods such as multi-source data fusion, model simulation and risk identification, points out the deficiencies of current research in the quantification of coupling mechanisms, dynamic early warning and prevention-control transformation, and puts forward prospects for the subsequent establishment of multi-scale coupling analysis, intelligent monitoring and early warning, and accurate prevention and control systems. It is expected to provide a theoretical reference for the effective governance of forest fires and the sustainable protection of forest ecosystems in central Yunnan.
Forest fire; multi-factor coupling; disaster-causing mechanism; spatiotemporal distribution; risk assessment; central Yunnan region.
The Impact of Adjustments to Time-of-Use Tariff Policies on the Usage Behaviour of New Energy Vehicle Charging Infrastructure
Driven by the strategy for green and low-carbon transition, the development of charging infrastructure for new energy vehicles faces the coupled challenge of fluctuating demand and the optimisation of resource allocation. This paper utilises the time-of-use tariff policy adjustment implemented by the Jibei Power Grid on 1 January 2026 as a natural experiment. Questionnaire surveys were conducted at two time points—before and after the policy announcement—and Structural Equation Modelling (SEM) was employed to empirically analyse the mechanisms through which the policy adjustment influences the charging behaviour of new energy vehicle users. Based on the Technology Acceptance Model (TAM) and demand response theory, the study constructs a conceptual model comprising five latent variables: perceived ease of use, perceived usefulness, policy awareness, subjective norms, and willingness to adjust charging behaviour. A total of 669 valid questionnaires were collected from the two rounds of surveys. Data tests indicated that the data met the normality assumption, with no severe common method bias. Harman’s single-factor variance explained was 29.8%, and all VIF values in the multicollinearity diagnostics were less than 3; furthermore, the model used in this study outperformed competing models. The study found that: (1) The policy adjustment significantly enhanced users’ perceived sensitivity to electricity price signals, with the mean willingness to adjust charging behaviour rising from 3.18 to 4.09, an increase of 28.6%; (2) Policy awareness is the most critical factor influencing the willingness to adjust, with a path coefficient of 0.428 (p < 0.001), and it plays a significant mediating role between perceived usefulness and the willingness to adjust, accounting for 34.9% of the indirect effect; (3) Perceived usefulness (0.347) and subjective norms (0.186) have a significant positive impact on the willingness to adjust, whilst perceived ease of use exhibits a slight negative impact (-0.124); (4) Multi-group analysis indicates that the path coefficients for perceived usefulness and policy awareness significantly increased following the policy announcement; (5) There is a marked disparity between users’ willingness to adjust and the availability of charging facilities during specific time slots: during the winter off-peak period (12:00–15:00), the availability of charging points in office areas was only 31.8%, whilst during the summer peak period (18:00–21:00), the congestion rate of residential charging points reached 67.2%. This paper provides theoretical grounds and empirical support for the refined design of time-of-use pricing policies and the coordinated optimisation of new energy vehicle charging infrastructure.
Time-of-use pricing, New Energy Vehicles, Charging facilities, Structural equation modelling, Resource allocation.
Optimization Design of Wind Environment in Commercial Pedestrian Streets Based on Field Measurement and Numerical Simulation
Taking the core area of Hangzhou Hubin Pedestrian Street as the study subject, this research comprehensively adopts field measurements and CFD numerical simulation to evaluate the existing wind environment and carry out zoned greening layout optimization. The model accuracy is validated as good through field measurements. The existing wind environment exhibits two types of problems: strong wind discomfort zones in the middle section of the main street, and stagnant wind zones on the leeward sides of buildings. Differentiated greening strategies are proposed for seven zones (A-G). After optimization, the green coverage rate of the street area increases from 1.70% to 5.90%, and the proportion of the ideal wind speed range (1.04-5.00 m/s) increases by 9.76 percentage points, among which the proportion of the 1.04-3.40 m/s range increases significantly, while the proportion of the uncomfortable wind speed range (5.01-10.7 m/s) decreases by 9.08 percentage points. The study shows that refined greening layouts based on zoning characteristics can effectively improve the wind environment of commercial pedestrian streets.
Commercial pedestrian street; wind environment; greening layout; pedestrian wind comfort; CFD.
Research Progress on Ship Exhaust Emission Characteristics and After-treatment Technologies
Ship emissions are important anthropogenic pollution sources that affect air quality in port cities and global climate change, mainly including air pollutants such as sulfur oxides, nitrogen oxides and particulate matter, as well as greenhouse gases such as carbon dioxide. The International Maritime Organization (IMO) has established a global emission control system centered on SOₓ and NOₓ limits, promoting the rapid development of ship exhaust after-treatment technologies. This paper systematically analyzes the environmental impacts of main ship pollutants, sorts out IMO emission regulations, focuses on the principles, application effects and economy of mainstream after-treatment technologies such as SCR, EGR and DPF, prospects the technology integration trends and existing challenges, so as to provide a reference for the formulation of ship emission reduction strategies.
Ship emission; IMO regulations; exhaust after-treatment; SCR; EGR; emission control.
Research on Material Pile Volume Measurement Based on Laser 3D Measurement
A high-turnover stockpile storage system requires efficient and high-precision stock inventory capability. In this paper, a volume measurement method based on mean-drift clustering and density interpolation slicing method is proposed to solve the key technical problems such as multiple stockpile separation, point cloud sparsity and hole patching. For the point cloud data of material piles, the density interpolation slicing method is proposed, which detects low density and hole regions by selective frame interpolation, combines with the weighted average method to fill in the missing point cloud, and generates the closed outer contour by the improved contour scanning algorithm, and calculates the total volume layer by layer by accumulating using Shoelace's formula. The experimental results show that the selective frame interpolation method improves the accuracy by over 68% compared with the un-interpolated method, and the efficiency is better than that of 3D global interpolation under the condition of the same accuracy; the outer contour construction method in this paper improves the accuracy by over 80% compared with the traditional Graham algorithm, and the efficiency is better than that of Alpha Shape algorithm under the condition of the same accuracy. This method takes both accuracy and efficiency into account, and provides a reliable solution for the volume measurement of complex storage piles.
Manuscripts; Calculation of material pile volume; Point cloud slicing; Slice selective interpolation; Sliced outer contour construction.
Digital Transformation, Innovation Ecosystem Coordination, And Urban Low-carbon Innovation: Evidence from China
Climate challenges, exemplified by global warming, necessitate deeper low-carbon innovation in urban areas, wherein digital transformation emerges as a pivotal enabler. In this context, this study reveals how digitalization intrinsically shapes urban low-carbon innovation from the perspective of innovation ecosystem coordination. It constructs a theoretical framework with innovation ecosystem coordination as the mediating variable and environmental regulatory intensity and public environmental concern as the moderating variables. Empirical analysis of panel data covering 273 Chinese cities (2011–2021) substantiates the hypotheses. Results demonstrate that digitalization significantly fosters urban low-carbon innovation, with ecosystem coordination fully mediating this relationship. Environmental regulation attenuates digitalization's positive effect on innovation ecosystem coordination, which is more robust in cities with higher public environmental concerns; however, innovation ecosystem coordination contributes less to low-carbon innovation in cities with strict environmental regulations or high public environmental concerns. These findings furnish novel empirical support for digitalization's catalytic role in low-carbon innovation while illuminating the underlying nexus between digital transformation and urban sustainability based on the innovation ecosystem perspective, which is important for harmonizing digitalization and decarbonization trajectories in China.
Digital transformation, innovation ecosystem coordination, low-carbon innovation, environmental regulation, public environmental concern.
Research Progress on Traditional Chinese Medicine Syndrome Characteristics and Syndrome Differentiation-Based Treatment of Tic Disorders in Children
Tic Disorders (TD) significantly affects the physical and mental health as well as the social adaptability of the affected children. In recent years, the effectiveness of Traditional Chinese Medicine(TCM) in treating tic disorder has been widely verified in clinical practice. However, the standardization and normalization of the syndrome classification remain key bottlenecks restricting the deepening of clinical research. This article systematically reviews the origin of TCM disease names for childhood TD, the understanding of its etiology and pathogenesis, and the existing syndrome classification system. It also sorts out the empirical research data on the distribution of syndromes based on cluster analysis, focuses on discussing the connotation characteristics and identification key points of the existing core syndromes, and outlines the practical strategies and research progress of syndrome-based treatment from the perspectives of harmonizing the yin and yang of the internal organs, clinical evidence-based research of formulas and prescriptions, and the application of acupuncture.
Tic Disorders; Traditional Chinese Medicine Syndrome Characteristics; Syndrome Differentiation-Based Treatment.
Synaptic Plasticity of Memristors Based on ZnO Thin Films Fabricated by PLD
Cu/ZnO/Cu memristive devices with planar-structure were successfully fabricated using pulsed laser deposition (PLD) technology by regulating substrate temperature. At a substrate temperature of 500 °C, ZnO thin film of the device exhibits smooth and uniform surface morphology demonstrated by systematic characterization. Such memristive devices show excellent dynamic resistive switching behavior under electrical pulse stimulation, with their resistance states capable of being precisely modulated by the polarity, amplitude, and duration of electrical pulses. Leveraging this tunable resistive switching property, the devices successfully emulate various biological synaptic behaviors including excitatory postsynaptic current and paired-pulse facilitation, demonstrating promising potential for applications in neuromorphic computing.
Memristor; Synaptic Plasticity; Resistance Switching; ZnO; PLD.
A Review of the Application of Low-Carbon Concepts in Civil Engineering Structural Design
Against the background of global warming and China's "Dual Carbon" strategy, the low-carbon transformation of the civil engineering industry has become imperative. As a critical stage in the whole life cycle of engineering projects, structural design serves as an important approach to achieving carbon emission reduction. This paper systematically reviews the connotation of the low-carbon concept and its integration paths with structural design, summarizes key technologies including low-carbon building materials, innovative structural systems, and efficient energy utilization, and analyzes their application effects based on typical engineering cases. In view of existing challenges in technology, economy, policy and public awareness, countermeasures are proposed for the future intelligent, standardized and industrialized development. The results show that integrating low-carbon concepts into structural design can effectively reduce life-cycle carbon emissions, which is of great significance for promoting the green and sustainable development of civil engineering and supporting the achievement of the "Dual Carbon" goals.
Low-carbon Concept, Civil Engineering, Structural Design, Life Cycle.
Research on Agricultural Machinery Operation Path Planning for Irregular Fields in Hilly Areas
Aiming at the problems of complex shape of irregular fields, frequent turning of agricultural machinery operation and low operation efficiency in hilly and mountainous areas, a path planning method of agricultural machinery operation based on minimum turning times was proposed. Firstly, the field boundary model is constructed by using Gauss-Kruger projection and grid method, and the irregular fields are divided into convex polygons, concave polygons and complex polygons according to the theory of computer graphics. Secondly, the field classification is realized by concave and convex judgment and polygon subdivision, and the safe operation area is constructed on this basis. Then, the simulated annealing algorithm is used to search the best operation direction, the operation angle is determined with the minimum number of turns as the optimization goal, and the reciprocating straight path is generated based on the active side table method. Finally, combined with the constraints such as the minimum turning radius of agricultural machinery, the turning decision model is constructed to realize the complete operation path planning. The simulation results of typical hilly fields show that the proposed method can achieve more than 90 % operation coverage in different types of irregular fields and effectively reduce the number of turns, which has good applicability and practical value.
Hilly area, Irregular plots, Agricultural machinery operation, Path planning, Turning number optimization.
Research on Lightweight Wound Recognition Algorithm and System for UAV in Accident Emergency Treatment
Aiming at the problems of slow manual rescue response for emergency hemostasis at accident scenes, single emergency function of emergency UAVs, insufficient lightweight degree of airborne wound recognition algorithms, and poor hemostatic effect of traditional hemostatic materials, this paper takes YOLOv5s as the basic model. Through channel pruning, INT8 quantization and knowledge distillation, a lightweight wound recognition algorithm suitable for edge deployment on UAVs is developed. Integrating temperature-sensitive self-expanding nanofiber hemostatic materials and an umbrella-type precise delivery mechanism, a fully automatic emergency UAV system is constructed. Experimental results show that the optimized algorithm model is only 89.2 KB in size, with a wound recognition accuracy of 93.6% in complex scenes and an inference speed of 34.2 fps, which meets the requirements of real-time airborne deployment. The hemostatic material expands completely within 28 s at 37 ℃ with an expansion ratio of 2.4 times, and the in vitro coagulation time is 76 s, improving hemostasis efficiency by 72% compared with traditional gauze. In a level 4 wind environment, the system delivery error is controlled at 2.1 cm, and the total hemostasis time is 110 s. The system realizes integrated operations of automatic wound recognition, precise delivery and rapid hemostasis, which can improve emergency efficiency at accident scenes and provide technical support for emergency medical rescue.
Accident site first aid; UAV; Lightweight wound recognition; YOLOv5s; Self-expanding nanofibers; Precise delivery.
Research Status and Trends of Maize Planters for Hilly and Mountainous Areas
Hilly and mountainous areas are important maize-producing regions in China. However, due to complex terrain, limited economic conditions, and a lack of suitable machinery, the mechanization level of maize sowing in these regions has long lagged behind that of plain areas, becoming a key bottleneck restricting industrial development. Focusing on maize seeders suitable for hilly and mountainous areas, this paper begins with the types of seed metering devices, summarizing mechanical types such as fluted roller, cell wheel, spoon wheel, and turntable structures, as well as typical representatives of pneumatic seed metering devices, and analyzes their working principles and applicable characteristics. On this basis, relevant research progress both domestively and internationally is reviewed: foreign countries started earlier and have continuously advanced in areas such as precision seeding, pneumatic metering, structural optimization, and electric drive; since the 1980s, China has developed various small-scale seeders—including hand-held, pedal-operated, self-propelled, and electromagnetic vibration types—for small plots and complex terrain, achieving certain progress in adaptability. However, from the perspective of practical application, current small-scale maize seeders still commonly suffer from insufficient reliability, poor adaptability, and weak versatility, with a notable shortage of specialized models suitable for hilly and mountainous areas. Considering actual agricultural production needs and technological development trends, future improvements in such seeders should focus on multi-function integration, intelligent control, and environmental sustainability to better accommodate the planting conditions and mechanization development requirements of hilly and mountainous regions.
Hilly and mountainous areas, maize planter, mechanization.
Development of a Regional Photovoltaic Baseline Dataset for Qinghai Province
To support the operational needs for detailed assessment of solar resources and PV power forecasting in high-altitude regions of Qinghai Province, this study conducted the standardization and development of a PV dataset, along with end-to-end quality control, based on 15-minute interval measured data from June 2021 to June 2022 at four typical PV power plants in Qinghai Province. Missing values were imputed using physical threshold constraints and linear/co-periodic mean interpolation. A combined outlier detection model utilizing DBSCAN density clustering and random forest regression was constructed to automatically identify and remove invalid values, abnormal spikes, and physically implausible data. The results show that after quality control, the valid data rate for total radiation increased from less than 30% to 87.5%, with a correlation coefficient of 0.92 relative to meteorological parameters and a root mean square error of 48.7 W·m⁻². The data quality meets the requirements for solar resource assessment and power forecasting.
Photovoltaic data, Data processing, Quality control, DBSCAN, Isolation Forest.
Research Status and Trend of Vision and Laser in Agricultural Machinery Navigation
With the aging of the population and the development of urban and rural integration, the shortage of agricultural productivity restricts the development of agricultural economy. The automatic navigation technology of agricultural machinery can effectively reduce the input of manpower in agricultural production, which is the key direction of agricultural machinery automation research. In the agricultural environment with lush vegetation, the signal interference is more serious, and the visual and laser sensors are less affected in this case, which is widely used in the research of agricultural machinery navigation. This paper mainly summarizes the methods of vision and laser navigation information acquisition in agricultural machinery navigation, analyzes the advantages and disadvantages of different types of sensors, and points out the research status of multi-source data fusion methods. Finally, the problems and development trends of vision and laser technology in agricultural machinery navigation are proposed.
Vision, Lidar, Data fusion, Agricultural machinery navigation.
Research Progress and Prospects of Detection Technology for Tri-Rare Elements in Coal from the Lianghuai Coalfield
"Triple-rare" minerals (referring to rare elements, scattered elements, and rare earth elements) are key strategic resources supporting the new generation of information technology, new energy, and high-end equipment manufacturing, and their safe supply has risen to the national strategic height. Coal-bearing associated tripleple-rare elements, as important alternative resources to traditional minerals, show great development potential worldwide. As a core energy base in East China, the Lianghuai Coalfield contains abundant lithium, gallium, germanium, and rare earth element resources in its deep coal-bearing strata. However, the superposition of multi-stage tectonic movements and magmatic hydrothermal fluids has resulted in high ash and high sulfur content of coal and extremely complex occurrence states of tripleple-rare elements, posing severe challenges to analytical and detection technologies. This paper systematically combs the research progress of tripleple-rare elements in coal at home and abroad, and focuses on analyzing the core bottlenecks faced by detection technologies under the special geological background of the Lianghuai Coalfield. The research shows that the international community has established a genetic classification system for rare earth elements in coal and a general detection method based on low-ash and low-sulfur coal, but there is a significant mismatch in geological background. Domestically, preliminary achievements have been made in the geochemical characteristics and enrichment rules of tripleple-rare elements in the Lianghuai Coalfield, but the detection system still has shortcomings such as insufficient pertinence, lack of special reference materials, and weak regional research foundation. Finally, this paper discusses the development trend of detection technology for tripleple-rare elements in coal, and proposes that establishing a targeted digestion system based on element occurrence states, constructing a quality control network adapted to the regional geological background, and improving the resource availability evaluation standards are the key research directions in the future, which can provide technical support for the precise exploration and high-value utilization of tripleple-rare resources in the Lianghuai Coalfield.
Geochemistry; Triple-rare Elements (Rare Elements, Scattered Elements, Rare Earth Elements); Lianghuai Coalfield.
Sensory-Guided Characterization of Key Aroma Compounds in Five Five-Grain Strong-Flavor Baijiu Samples
Five-grain strong-flavor Baijiu is characterized by its intense pit aroma and mellow, sweet, and clean taste, and it plays a pivotal role in the Chinese Baijiu market. Its production is based on key processes including solid-state fermentation in mud pits, continuous fermented grain supplementation, mixed steaming and distillation, and the use of medium- to high-temperature Daqu. In terms of aroma composition, ethyl hexanoate is the predominant compound and, together with ethyl lactate, ethyl acetate, ethyl butyrate, and other esters, forms a well-balanced and complex flavor system. In this study, the five-grain strong-flavor Baijiu samples were produced using sorghum as the primary raw material, supplemented with rice, glutinous rice, wheat, and corn through multigrain co-fermentation. Different sorghum varieties can influence microbial metabolism and flavor formation due to variations in physicochemical properties such as starch, protein, and tannin content. Therefore, a sensomics-based approach was employed in this study. Under the guidance of national-level Baijiu experts, an olfactory panel was selected and systematically trained. By integrating quantitative descriptive analysis (QDA), gas chromatography–olfactometry–mass spectrometry (GC-O-MS), and aroma extract dilution analysis (AEDA), the aroma profiles and key aroma compounds of five-grain strong-flavor Baijiu produced from five different sorghum varieties were comprehensively characterized.
Five-grain strong-flavor Baijiu; Sorghum varieties; Sensory evaluation; Aroma profile; Key aroma compounds.
Advances in Chlorpromazine Detection Technologies: A Review
Chlorpromazine (CPZ) is a representative phenothiazine antipsychotic that needs to be analytically assessed reliably in clinionmental fate and transformation research. Conventional techniques such as high-performance liquid chromatcal therapeutic monitoring of drugs, residue analysis of animal-derived foods, forensic toxicology, and envirography, gas chromatography and enzyme-linked immunosorbent assay have traditionally been employed to analyze CPZ but remain limited in ultra-trace detection, scalability to complex matrices, swift on site screening, and multiplexed multi-analyte analysis. Over the past few years, chromatographic-mass spectrometry-based methodologies, spectroscopic and nano-optical assays, electrochemical sensors, new immunoassays and biosensors have grown at a fast pace offering new lines of analysis of the CPZ quantitation in plasma, oral fluid, tissue and animal samples, food, and environmental samples. The current review presents the advances in CPZ detection technologies in the last few years focusing on selected articles published between 2015 and 2025 on the chromatographic and chromatography-mass spectrometry methods, rapid testing approaches, and up-and-coming sensing technologies. Also considered are sample preparation, method optimization, technical comparison, applications, and future trends. On the whole, chromatography-mass spectrometry remains the most popular platform in confirmatory analysis and high-precision quantification, whereas fast optical, electrochemical, and immunochemical techniques are also promising in field screening, microsample analysis, and point-of-care testing.
Chlorpromazine; chromatography-mass spectrometry; electrochemical sensing; immunoassay; rapid detection; simultaneous multi-analyte detection.
RANSAC-based Point Cloud Calibration Algorithm Accounting for Point Quantity and Distribution
To tackle the challenge of inadequate accuracy in UAV-LiDAR point cloud data, this research introduces a RANSAC calibration algorithm that takes into account both the quantity and spatial distribution of points. Simulation experiments were carried out across four experimental sites to ascertain the optimal point placement strategy under diverse terrain conditions. Initially, control points were established within the experimental zones, and both control point data and point cloud data were collected. Subsequently, calibration strategies incorporating different point counts and distributions were devised. Ultimately, the precision of each calibration approach was evaluated. The experimental findings reveal that: (1) calibration accuracy decreases as terrain complexity increases within the experimental areas; (2) a greater number of points correlates with higher calibration accuracy; (3) given an equal number of points, a diamond-shaped point distribution yields the highest calibration accuracy.
Mechanical Resonance Suppression of Servo System Based on Resonant Frequency Online Identification
This paper analyzes the mechanical resonance of permanent magnet AC servo systems and identifies the main factors that affect resonance. Further use fast Fourier transform for frequency online identification of the resonant system, and finally use a notch filter to filter the resonant frequency of the system. The impact of the notch filter on the system was analyzed and the parameters of the filter were determined, ultimately achieving the digitization of the notch filter. By analyzing the mechanical resonance system and identifying the resonance frequency online, the mechanical resonance of the permanent magnet AC servo system itself has been effectively suppressed. The experimental results have verified the effectiveness of the vibration suppression control algorithm.
Mechanical resonance; Fast Fourier transform; Online identification; Notch filter.
Meteorological Drought Prediction in the Loess Plateau Based on Three Machine Learning Models
Drought, a severe natural hazard, exerts profound impacts on both ecological systems and agricultural productivity. Consequently, advancing drought prediction methodologies is paramount for optimizing regional water resource management and mitigating associated risks. This research centers on the Loess Plateau, leveraging monthly precipitation (P) and potential evapotranspiration (PET) datasets spanning 1960 to 2022 to compute the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) at temporal resolutions of 1-, 3-, 6-, and 12-months. The predictive efficacy of three computational models—Convolutional Long Short-Term Memory (CNN-LSTM), Random Forest (RF), and Support Vector Machine (SVM)—was systematically evaluated across semi-humid, semi-arid, and arid climatic zones. Empirical results elucidate three key findings: (1) CNN-LSTM outperformed its counterparts across all regions and time scales; RF demonstrated superior accuracy relative to SVM at 3- and 6-month lags, whereas SVM yielded stronger predictive power at the 12-month scale. Overall, the performance hierarchy prioritizes CNN-LSTM > RF > SVM for short-to-medium term forecasts, while CNN-LSTM > SVM > RF holds for long-term projections. (2) Model predictive accuracy achieved higher magnitudes when employing SPEI as the predictor compared to SPI within semi-arid and semi-humid domains. Conversely, in arid regions at extended time scales, SPI-12 outperformed SPEI-12, indicating that precipitation-dominated trends exhibit greater relevance for long-term drought forecasting in water-scarce areas. (3) Incorporating SPEI into the predictive framework conferred significantly enhanced performance in semi-arid and arid zones relative to semi-humid regions, further underscoring SPEI’s utility in water-limited landscapes. Collectively, this study elucidates the interrelationships among model architectures, drought indices, and regional climatic characteristics, thereby establishing a scientific basis for developing regional drought early-warning systems and decision-support tools. These outcomes carry practical implications for optimizing agricultural management strategies and regulating water resources.
Loess Plateau; drought prediction; SVM; RF; CNN-LSTM; SPI; SPEI.
Research on the Performance of SiC Particle Reinforced Aluminum Matrix Composite Brake Discs
To meet the performance requirements of wear resistance, energy saving and consumption reduction during operation, as well as speed and noise reduction for rail transit brake discs, a SiCp/A356 composite material with a SiC particle volume fraction of 20% was prepared by liquid stirring casting method using SiC particles as reinforcement and A356 aluminum alloy as matrix. Firstly, experiments were conducted on the thermal cycling characteristics, crack propagation, and fracture performance of the high-speed train brake disc made of this material. Secondly, a brake disc model was established using HYPERMESH software. Then, finite element simulation analysis of the temperature and stress fields under emergency braking conditions at 200km/h was conducted using ANSYS software to study the fracture failure mechanism. Compared with traditional steel brake discs, the results show that the maximum temperature during the braking process of SiCp/A356 reinforced aluminum based composite brake discs is reduced by 29.6% compared to ordinary steel materials; The amplitude of stress changes is small, which can effectively reduce thermal damage to the surface of the brake disc; The fatigue life is increased by about 20% compared to ordinary steel materials, exhibiting excellent mechanical and fatigue properties.
Research on Curvature Adaptive Control Method for ABB Robot Blade Polishing Residuals
To address challenges in traditional blade polishing such as low error control precision, irrational path planning, and poor parameter matching of belt pulleys, error control methods were investigated using ABB industrial robots as execution platforms and belt pulleys as polishing tools, combined with curvature adaptive algorithms. By analyzing blade surface curvature characteristics, a correlation model linking polishing errors to parameters and surface curvature was established. A curvature-adaptive error control algorithm was designed to dynamically adjust polishing parameters across different curvature zones. Simulation environments were built using Robot Studio platform to validate error control through both simulation and experimental testing. Results demonstrate that: In high-curvature zones, polishing errors reach 0.012mm at 1500r/min belt speed and 80N polishing force; In medium-curvature zones, errors minimize to 0.009mm at 1700r/min belt speed and 70N force; In low-curvature zones, errors decrease to 0.007mm at 2000r/min belt speed and 60N force. The curvature-adaptive control strategy achieves an average error of 0.011mm and a maximum error of 0.021mm, both remaining within the target error range of 0.1mm. Compared to traditional fixed-parameter methods, this approach improves allowance control precision by 61.1%. These findings provide theoretical support and technical references for automated high-precision blade polishing.
Research and Practice on Meteorological Data Security Supervision Technology Based on Tianqing·Qinghai
To address the challenges of data circulation security supervision in the meteorological big data environment and enhance the security protection capabilities of meteorological data usage in Qinghai Province, this paper, based on the "Tianqing·Qinghai" meteorological big data cloud platform and the "Tianjing·Qinghai" comprehensive business monitoring system, adopts three core methods: data structure processing, data denoising, and data security usage auditing. It integrates advanced technologies such as distributed collection control, parallel computing, and big data storage to construct a meteorological data security supervision system. The research focuses on three dimensions: the development of meteorological data security supervision technology, analysis of usage situations, and security risk management. It establishes the judgment rules and disposal processes for six types of data security risk events, achieving real-time monitoring of account access behaviors, precise control of key accounts, and collaborative handling of risk events. Practical application shows that this system can effectively reduce data security risks, improve supervision efficiency, and provide a replicable technical solution and practical reference for data security governance in provincial meteorological departments.
Study on the Flow Field Characteristics of a Gas–Liquid–Solid Three-Phase Combined Impeller Agitator Based on CFD
To address the issues of mixing efficiency and impeller configuration in the gas–liquid–solid three-phase system during CO₂ mineralization and sequestration using coal-based solid waste, a numerical investigation of the internal flow characteristics in a three-phase stirred reactor was carried out using computational fluid dynamics (CFD). A reactor model was established using Fluent simulation software. The multiple reference frame (MRF) method was adopted to construct the stirring flow-field model, and the Navier–Stokes equations were employed as the governing equations. The standard k–ε turbulence model was applied to solve the fluid flow in the stirred tank. Under a stirring speed of 100 r/min, the flow-field characteristics of different impeller combination schemes were simulated and analyzed. By examining the axial, radial, and tangential velocity distributions at specific cross-sections of the reactor, and combining velocity contours with the distributions of solid volume fraction and gas volume fraction, the flow characteristics and mixing performance of different impeller combinations were comparatively studied. The results show that different impeller combinations have significant effects on the internal flow-field structure and multiphase distribution in the reactor. Among the investigated configurations, the combination of an upper double-helical ribbon impeller and a lower pitched-blade turbine impeller can generate a more pronounced axial circulation flow, promoting the suspension of solid particles and the dispersion of gas bubbles. This configuration results in a more uniform distribution of the three phases within the reactor and improves the overall mixing efficiency of the system. The findings provide a theoretical reference for the structural optimization of three-phase stirred reactors and the design of impeller combinations in the CO₂ mineralization and sequestration process using coal-based solid waste.
Double helical ribbon and three-pitched blade turbine combined impeller
Hardware-in-the-Loop Simulation of Pressure Measurement Modules in While-Drilling Systems
To address the issues of high cost and low efficiency in traditional testing of pressure measurement modules while drilling—which rely on physical sensors and simulated high-temperature environments—a testing system based on hardware-in-the-loop simulation technology is proposed to achieve efficient and low-cost performance verification of the measurement module. The system consists of a PC host computer, a real-time simulator (PXIe platform), and the measurement module. Simulation models of pressure and temperature sensors are built using Simulink and deployed to the real-time simulator via VeriStand software to emulate voltage and resistance signals consistent with those of actual sensors. A host computer interface is developed using C# to enable parameter configuration, data acquisition, and error analysis. The system validates the effectiveness of the temperature drift compensation algorithm through comparative experiments with and without correction. Test results show that the system can effectively simulate downhole sensor outputs, and after correction, the pressure measurement error is controlled within ±108 psi, significantly suppressing the influence of temperature on measurement accuracy. This verifies the feasibility and superiority of the hardware-in-the-loop simulation method in testing measurement-while-drilling modules.