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.
Transition Experiences of Adolescent and Young Adult Survivors of Childhood Cancer to Adult Healthcare and Care: A Meta-synthesis of Qualitative Studies
To synthesize qualitative data about the transition experiences of adolescent and young adult (AYA) survivors of childhood cancer (CCS) from pediatric to adult-oriented healthcare and nursing care. This synthesis provides an evidence base for clinicians to design tailored transition programs. Literature searches were conducted in multiple databases (CNKI, Wanfang, VIP, SinoMed, PubMed, Embase, Cochrane Library, Web of Science, CINAHL EBSCO, and Scopus) from the establishment to 26 September 2025. Study quality was evaluated using the Joanna Briggs Institute (JBI) critical appraisal checklist for qualitative research. Findings were synthesized using meta-aggregation. Fifteen studies were included, and 55 discrete findings were extracted. These findings were categorized into nine themes and condensed into four overarching integrative findings: (1) Ambivalent attitudes, simultaneous reliance on pediatric services and anticipation of adult care; (2) Critical facilitators for a successful transition; (3) Challenges encountered during transition; and (4) Perceived benefits of transition. AYA CCS undergo a complicated, emotionally challenging transition marked by numerous obstacles. Medical practitioners should recognize these emotional responses, provide supportive services, and develop targeted interventions. Concurrently, healthcare institutions need to align pediatric and adult resources to ensure continuity of care and offer comprehensive family, psychological, and social support, thereby enabling CCS to transition successfully.
Adolescent; Young Adult; Childhood; Cancer; Meta-synthesis; Qualitative Studies.
Copyright (c) 2026 World Scientific Research Journal https://creativecommons.org/licenses/by-nc/4.0
Prevention, Detection, and Management of Complications Associated with CT-Guided Ablation of Pulmonary Nodules
CT-guided ablation delivers precise positioning, minimal trauma, rapid postoperative recovery and repeatable treatment, so it has become a key clinical strategy for pulmonary nodules. As an invasive intervention, this procedure cannot fully eliminate postoperative complications. This review collects domestic and international research papers published in recent years, summarizes four major complications—pneumothorax, pleural effusion, bleeding and pain, and elaborates each complication’s incidence, clinical manifestations, risk factors, management protocols and preventive strategies. The summaries in this paper offer clinical references to reduce the incidence of post-ablation complications and advance CT-guided pulmonary nodule ablation toward standardized, precise and safer clinical workflows.
CT-guided Ablation, Pulmonary Nodule, Postoperative Complications.
Asymmetric Effects of the COVID-19 Pandemic on Out-migration Scale: A Study Based on the Nonlinear ARDL Model
In view of the impact of COVID-19 on the scale of migration, a nonlinear autoregressive distributed lag model (NARDL) was constructed to analyze the nonlinear asymmetric relationship and dynamic multiplier effect between the number of new positive COVID-19 cases, the number of new discharges, the proportion of quarantine personnel and the scale of migration in Shanghai. The results showed that the model passed the long-term asymmetric test of the long-term asymmetric relationship between the target variables, indicating that the changes of COVID-19 epidemic had an unequal asymmetric effect on the scale of migration. The results of the dynamic multiplier effect show that, in the long run, the increase of new positive cases will significantly change the scale of migration, and the decrease of the number of new discharges may have a significant asymmetric effect on the scale of migration, while the increase of the number of new discharges is difficult to inhibit the scale of migration. Both the rise and fall of the proportion of isolation control personnel have an asymmetric effect on the emigration scale index.
COVID-19; Out-migration Scale; Asymmetric Effects; NARDL Model; Dynamic Multipliers.
A Genetic Algorithm-Based Model for Intelligent Scheduling and Route Optimization of Construction Engineering Inspection Tasks
Construction engineering inspection agencies may suggest that uneven task allocation, underused equipment, and inefficient routes create significant challenges for field inspection work. However, the findings from this study could indicate that intelligent scheduling and route optimization offer key solutions to these important problems. Moreover, the research establishes a multi-objective optimization model based on a genetic algorithm, using inspection tasks, inspectors, equipment, and project locations as its foundational elements. In light of these elements, the model may demonstrate that jointly considering task deadlines, staff competencies, equipment applicability, working time, scheduling cost, and route distance within one analytical framework could yield significant results. Model shows algorithm reduces complexity. Furthermore, the solution procedure could indicate that a hybrid chromosome, a fitness function, a penalty mechanism, and a set of selection, crossover, mutation, and feasibility-repair operations appear to provide critical support for the model. Given that the simulation case demonstrates these results, the findings may suggest that the genetic-algorithm solution reduces total route distance by 26.2%, cuts the estimated total completion time by 22.0%, and lowers comprehensive scheduling cost by 15.8%. Additionally, the significant results could demonstrate that the average utilization rates of personnel and equipment rise to 86.2% and 82.5%, respectively, while the on-time task completion rate increases to 100%. Nevertheless, the evidence may indicate that these key findings appear to confirm the genetic algorithm can handle the construction engineering inspection scheduling problem under multiple tasks, multiple personnel, multiple devices, and multiple locations. Algorithm handles multi-variable scheduling efficiently. Therefore, the study could suggest that the model may provide a quantitative decision-making basis for inspection agencies that appear to need more efficient field task organization. Notwithstanding the limitations of experience-based manual scheduling, the significant evidence may indicate that this approach could demonstrate important reductions in operating costs while raising resource utilization to key levels.
Construction engineering inspection; Intelligent scheduling; Route optimization; Genetic algorithm; Resource allocation.
Review on the Instability Mechanism of Tunnel Center Diaphragm Under Blasting Action
Tunnel blasting is a complex and critical process in engineering geology and tunnel construction. The middle partition support structure and temporary support structure during the tunnel construction period are prone to vibration instability, which poses a great risk of damage to the tunnel. Therefore, investigating the mechanism of instability of intermediate partitions under explosive loads is of great significance for enhancing the safety and economy of tunnel projects. This paper provides a comprehensive overview of the characteristics of tunnel blasting loads, analyzes in detail the changes in the stress state of intermediate partitions under blasting actions, and summarizes the current research methods and achievements on the instability mechanism of intermediate partitions, including numerical simulation, field monitoring, and theoretical analysis. It aims to provide references for a deeper understanding of the instability issues of intermediate partitions and for improving the safety of tunnel construction.
Tunnel Engineering, Intermediate Partition Damage, Blasting Engineering, Local instability.
Design and Analysis of a Photocatalytic Microbial Fuel Cell System Using a C-TiO₂ Anode for the Synergistic Treatment of Composite Pollution in Mine Water and Power Generation
Mine drainage is a typical type of wastewater generated throughout mining activities, which commonly contains persistent organic contaminants represented by bisphenol A, as well as heavy metal ions including iron, manganese and zinc. Improper discharge and treatment of such wastewater will lead to severe ecological environmental pollution and massive waste of available water resources. Although traditional wastewater treatment techniques are capable of eliminating partial pollutants from mine drainage, they are restricted by inherent defects such as high energy consumption, expensive operating cost and low resource recycling efficiency. Microbial fuel cells (MFCs) can realize in-situ electricity generation relying on microbial biodegradation of organic pollutants, achieving synchronous pollutant removal and energy recovery. Nevertheless, the practical application of conventional MFCs is still hindered by unsatisfactory power output, mainly stemming from low interfacial electron transfer efficiency and inferior long-term system stability. Targeting these key technical limitations, this work constructs a novel double-layer eight-chamber MFC system integrated with TiO₂ photocatalysis technology. The double-layer eight-chamber structural design effectively enlarges the overall reaction space of the reactor and accelerates the electron transfer rate inside the system. Moreover, the introduced TiO₂ photocatalysis module further improves the degradation efficiency of refractory organic compounds and the removal capacity of coexisting heavy metals. The systematic analysis on reactor structure configuration, internal working mechanism and pollutant removal pathways demonstrates that the integrated hybrid system can efficiently purify mine drainage polluted jointly by bisphenol A and heavy metals, while achieving stable bioelectricity generation. This research provides a novel feasible technical solution for harmless treatment and resource reuse of mine drainage, as well as theoretical and technical support for the construction of green and low-carbon mining ecosystems.
Microbial fuel cell, titanium dioxide photocatalysis, mine water, bisphenol A, and heavy metals.
Self-Management Status and Influencing Factors Among Patients with Stroke
This study aimed to investigate the current status of self-management among patients with stroke in a tertiary hospital in Shaanxi Province and to explore the influencing factors of their self-management behaviours. This study included 104 stroke patients who were diagnosed and treated between April and October 2025. Data were collected using a general information questionnaire, the Stroke Self-Management Behaviour Scale, and the Stroke Self-Efficacy Scale, and statistical analyses were performed to identify the factors influencing self-management behaviours. The results showed that the overall level of self-management among the stroke patients was moderate to high, with a mean self-management score of 168.12 ± 18.56. Specifically, the scores of each dimension were as follows: disease management (40.86 ± 6.65), medication safety management (18.46 ± 1.90), diet management (23.64 ± 2.78), daily life management (27.90 ± 3.84), emotional management (17.64 ± 2.78), social function and interpersonal management (19.47 ± 3.17), and rehabilitation exercise management (20.13 ± 5.48). Univariate analysis revealed that age, education level, current employment status, recurrence risk classification, and self-efficacy had significant effects on self-management behaviours (P < 0.05). Further multiple linear regression analysis confirmed that these five factors (age, education level, current employment status, recurrence risk classification, and self-efficacy) were independent influencing factors of self-management behaviours among stroke patients. In conclusion, the self-management level of stroke patients in this study was moderate to relatively high; healthcare professionals should take the relevant influencing factors into consideration and implement individualized intervention strategies to improve the patients’ self-management abilities and further enhance their quality of life.
Stroke; Self-management; Self-efficacy; Influencing factors.
Research Progress in Bone Defect Repair Based on Piezoelectric and Natural Polymeric Materials
Natural polymer materials have attracted considerable attention in bone tissue engineering and oral and maxillofacial bone defect repair because their structures are highly similar to the extracellular matrix (ECM) in the human body. However, single natural polymer materials usually have several limitations related to osteogenesis, including insufficient mechanical properties, difficulty in controlling the in vivo degradation rate, and limited three-dimensional structural stability.In recent years, researchers have combined piezoelectric materials with natural polymers to mimic the microelectrical signals generated by bone tissue under mechanical stimulation. Piezoelectric ceramics, such as barium titanate, potassium sodium niobate, and zinc oxide, as well as piezoelectric polymers such as poly-L-lactic acid, can generate microelectrical signals under mechanical force. These signals can promote osteoblast proliferation and differentiation, enhance mineral deposition in bone tissue, and ultimately contribute to bone defect repair.This review focuses on the current research progress of piezoelectric materials, natural polymer materials, and bone defect repair. It summarizes the characteristics, biological mechanisms, and application advances of different materials. This review aims to promote further research on natural polymer-based piezoelectric composites for bone defect repair and improve their translational potential for future clinical applications.
Bone defect; piezoelectric material; natural polymer; silk fibroin; chitosan; bone tissue engineering.
Human Neural Progenitor Cells Incorporate into Functional Network in Mouse Hippocampus
Human neural progenitor cell (NPC) transplantation into mouse brain has been widely used as an in vivo human-relevant model system. The integration of differentiated human cells or tissue with local circuit has also been intensively investigated. However, as part of the central nervous system which governs the behaviors of living organisms, evidence on whether xenografted neurons are responsive to mouse behavior in intended ways is still poor. In this work, we transplanted iPSC-derived NPCs (iNPCs) into the hilus region of mouse hippocampus and monitored their survival, migration, differentiation and integration in different host mouse lines for different timespan. We found that iNPCs survived for up to two months in C57BL6 mice and for more than six months in Rag2 KO immunodeficient mice. To assess functional integration, we performed contextual fear conditioning with Rag2 KO xenograft mice and examined endogenous Fos protein expression. Dentate gyrus neurons are largely silent and sparsely activated during contextual information learning. We found that fear conditioning training induces Fos gene expression in iNPC-differentiated neurons 6 months after transplantation. The result suggests that behavior-responsive functional integration of human xenografted neurons in mouse hippocampus can be formed after long-time integration.
iNPCs; hippocampus xenotransplantation; functional integration.
Effect of Brackish Water Irrigation on Cotton Growth and Yield Based on Multi-method Comprehensive Evaluation
In order to explore the effects of different concentrations of brackish water irrigation on the growth and yield of cotton, clarify the appropriate threshold of brackish water irrigation for cotton, and alleviate the shortage of fresh water resources and the problem of brackish water utilization in cotton areas, five brackish water irrigation gradients were set up in this study. The agronomic traits such as plant height, leaf area, number of fruit branches and yield components such as single boll weight, number of bolls per plant and seed cotton yield were systematically measured. Combined with three evaluation methods of entropy weight-TOPSIS, grey correlation analysis and principal component analysis, the growth and production performance of cotton under different brackish water treatments were comprehensively quantified in multiple dimensions. The results showed that the salt stress of brackish water had a significant gradient inhibitory effect on the vegetative growth and reproductive growth of cotton. With the increase of irrigation water salinity, the plant height, leaf area and fruit branch number of cotton showed a continuous downward trend. Among them, leaf area was the most sensitive to salt stress, which was the core dominant index affecting the comprehensive growth of cotton. Compared with the clear water control, the photosynthetic organ formation and reproductive growth of cotton treated with high concentration of brackish water were inhibited, and the single boll weight and the number of bolls per plant were significantly reduced, resulting in a significant reduction in seed cotton yield. The yield of T4 treatment was 24.19 % lower than that of CK. The order of the three evaluation models was highly consistent, which was CK > T1 > T2 > T3 > T4. The traits and yield of cotton treated with low salt T1 only decreased slightly, and the yield reduction was only 3.17 %, which could maintain a high production level. In summary, mild brackish water irrigation has little effect on cotton production and can be popularized and applied in saline water-deficient cotton areas, while medium and high concentrations of brackish water stress will significantly inhibit cotton growth and reduce production capacity, which should be strictly avoided in production. This study can provide theoretical basis and technical support for the safe and efficient utilization of brackish water resources in cotton fields and the stable and high-yield cultivation of cotton.
Brackish water irrigation; salt stress; agronomic traits; yield; comprehensive evaluation.
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.
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.