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.
Mycoplasma Pneumonia Diagnosis Algorithm Based on Multimodal Data Fusion
The incidence of Mycoplasma Pneumonia (MP) has been on the rise. Traditional diagnostic methods have limitations, and existing machine - learning - based diagnostic studies also have deficiencies. In this study, a diagnostic system for Mycoplasma Pneumonia based on a multimodal visual hypergraph neural network (Trans - HGNN) was proposed. Firstly, basic processing such as denoising and standardization, as well as optimization using the VAE - GAN algorithm, were carried out on lung CT images and biochemical indicator data. Then, a hypergraph was constructed to fuse multimodal data, the VHNNs architecture was designed, and the Transformer algorithm was applied. Experiments show that the Trans - HGNN model performs best in terms of accuracy, recall rate, and F1 - score, reaching 88.69%, 0.8569, and 0.9229 respectively. The training process has good convergence and strong generalization ability. Although there may be a slight overfitting problem, it still provides reliable technical support for the diagnosis of Mycoplasma Pneumonia and has broad application prospects in the field of medical diagnosis.
Mycoplasma Pneumonia; multimodal data fusion; hypergraph neural network; Transformer algorithm; diagnostic model.
Copyright (c) 2025 World Scientific Research Journal https://creativecommons.org/licenses/by-nc/4.0
Identification of Major Organic Carbon Pollutants and Assessment of Treatment Measures in Chinese Waters Based on Meta Analysis
The issue of organic carbon pollution in China's waters is a matter of grave concern. The primary drivers of this environmental crisis are industrial, agricultural and urbanisation activities, which have resulted in a substantial escalation in the levels of various pollutants, including but not limited to persistent organic pollutants (POPs), perfluorinated compounds (PFCs), micro-plastics (MPs), pharmaceuticals and personal care products (PPCPs), and volatile organic compounds (VOCs). The spatial aggregation of pollution in eastern China poses a significant threat to ecological security and public health. The eastern region has a spatial aggregation of pollution, which poses a significant threat to ecological safety and public health. In order to address the shortcomings of the existing studies on the distribution of pollution and the evaluation of the effectiveness of treatment, this study used meta-analysis to analyse the spatial distribution pattern of organic carbon pollutants in Chinese waters based on the relevant literature from 1980-2024. The study utilised multi-source databases, including the China Knowledge Network (CNKI) and the Web of Science (WOS), in conjunction with Geographic Information System (GIS) technology, to assess the efficacy of treatment methods for organic carbon pollutants in Chinese waters, employing membrane separation and adsorption techniques. Demonstrated by the study findings is the significant presence of POPs and PFCs in eastern China’s regions, especially within the basins of the Yangtze River and Pearl River. Identified as the most effective among various management strategies is membrane separation technology, which shows high removal efficiency-wise. An effective tool for pollution management this technology has proven to be, aiding in the blueuction of contamination levels in these areas. Presented in this study is a useful reference for assessing organic carbon pollution in watersheds, along with its proper management. Data support and practical insights are also provided, assisting in the development of targeted prevention and control measures for water pollution issues. Thus it can be seen—this research offers valuable guidance for evaluating and addressing organic carbon contamination in aquatic systems, facilitating more efficient mitigation efforts overall.
Pollution of Chinese waters, Meta analysis, Organic carbon pollutants, Treatment measures.
Global Temperature Prediction Analysis Based on Random Forest and ARIMA Model
This paper focuses on the study of global temperature change trends. By collecting historical temperature data from multiple sources, it conducts descriptive analysis and uses the Mann - Kendall mutation test to explore the temperature change characteristics. Two models, a random forest regression prediction model and an ARIMA - based time series model, are established to predict future global temperatures. The results show that the global temperature is on an upward trend, and the random forest regression prediction model has a higher fitting degree with the actual data. Additionally, the paper analyzes the spatiotemporal evolution of global temperature, the correlation between natural disasters and global temperature changes, and proposes measures to slow down global warming.
Global temperature change; M - K mutation test; Random forest regression prediction model; ARIMA model; Correlation analysis.
Analysis of Tourism Management Based on Genetic Algorithm and Simulated Annealing Algorithm
Based on genetic algorithm and simulated annealing algorithm, this paper constructs a strategy for sustainable urban tourism management. Firstly, the dynamic relationship between tourist flow, environmental impact and resource consumption is modeled by differential equations, in which the tourist number model realizes flow regulation through seasonal function and fare sensitivity coefficient; secondly, genetic algorithm is introduced for multi-objective optimization to maximize the tourism revenue under the premise of satisfying the capacity constraints of the attractions, and its evolutionary process is evaluated visually through the fitness function; lastly, simulated annealing algorithm is used to deal with complex nonlinear constraints, balancing global search and local optimization through temperature decay mechanism, and realizing synergistic optimization of minimizing environmental impact and rationalizing resource utilization. This study provides a quantifiable decision support framework for smart tourism management.
Tourism Management, Genetic Algorithm, Simulated Annealing Algorithm.
A Review of Research on The Preparation and Application of Porous Geopolymers
Geopolymers are three-dimensional inorganic polymer networks formed through depolymerization-polycondensation reactions of aluminosilicate raw materials under alkaline or acidic activation. Their chemical structure resembles that of natural zeolites but exhibits amorphous or semi-crystalline characteristics, endowing them with excellent properties such as high mechanical strength, thermal stability, corrosion resistance, and low permeability. Unlike conventional ceramics, their synthesis requires no high-temperature calcination and can incorporate industrial byproducts such as fly ash, slag, and metakaolin, offering both environmental sustainability and economic advantages. Porous geopolymer materials can be further developed by introducing controllable pore structure through physical or chemical foaming technology, which not only retains the excellent properties of geopolymer matrix, but also has multifunctional properties due to the presence of pores, and shows broad application prospects in the fields of building thermal insulation, noise control, and environmental treatment. This paper reviews the physical and chemical preparation processes of porous geopolymers, introduces the applications of porous geopolymers in thermal insulation, sound absorption, adsorption, PH adjustment, etc., and discusses the future research direction of porous geopolymers.
Geopolymers; Porosity; Performance effects; Functional applications.
MACA-Net: A Multi-head Attention and Clustering-guided Network for Facial Expression Recognition
Facial expression recognition (FER) in uncontrolled environments poses significantly greater challenges compared to controlled settings. On the one hand, various real-world interferences—such as illumination variations, pose deviations, and occlusions—severely hinder the extraction of robust and discriminative features. On the other hand, field-collected expression datasets often exhibit high intra-class variability and low inter-class separability, further increasing the difficulty of accurate expression classification and degrading overall recognition performance. To address these challenges, this paper proposes a novel FER framework, MACA-Net, which integrates an enhanced loss function with a multi-head attention-based feature extraction strategy. Specifically, a Multi-head Cross Attention (MCHA) module is introduced to capture diverse and complementary local features, thereby enriching the representational capacity of regional expressions. In addition, an Adaptive Feature Clustering Loss (AFC-Loss) is designed to promote intra-class compactness and inter-class dispersion in the learned feature space, effectively improving the model’s discriminative power. Extensive experiments conducted on two challenging FER benchmarks—RAF-DB and FerPlus—demonstrate that MACA-Net achieves recognition accuracies of 89.01% and 89.85%, respectively, outperforming several state-of-the-art methods and validating the effectiveness of the proposed approach.
Facial Expression Recognition; Multi-head Cross Attention; Adaptive Feature Clustering Loss.
Research on Structural Optimization of Longmen Polyurethane Sponge Cutting Machine
Polyurethane sponge has excellent properties and is widely used in industries such as automotive, medical, and furniture. With the development of society, the demand for polyurethane sponge in China has been increasing year by year. However, the efficiency of polyurethane sponge cutting machines in China is currently low and there is no advantage in market competition. Taking the most common and widely used circular blade sponge cutting machine as the research object, it was found that the current circular blade sponge cutting machine has the problem of one-way cutting, which leads to half of the time the machine is idling and low work efficiency. Therefore, based on this issue, this article discusses the current research status and structural working principle of polyurethane sponge cutting machines. Three structural improvement schemes are proposed to address the problem of low work efficiency of circular blade sponge cutting machines for technological innovation. The weight assignment evaluation method is used to evaluate the three structural improvement schemes from multiple perspectives such as feasibility and aesthetics. Finally, it is determined that adding a set of cutting blades in the middle of the cutting machine column to increase work efficiency is the most suitable technical transformation scheme. By optimizing the structure of the polyurethane sponge cutting machine, it is beneficial to improve its working efficiency, enhance its competitive advantage in the market, and provide a theoretical basis for the innovative research and development of polyurethane sponge cutting machines in the future.
Sponge cutting machine, double-layer blade structure, label weight, structure optimization.
Research on the Design and Application of Infusion Pump Testing System
The design and research of infusion pump detection systems is crucial because infusion pumps are precise, consistent, continuous pumps that are pumped into the tool's body based on the actual drug concentration and pumping speed adjustment. This helps to improve the quality of treatment provided by essential medical equipment. This paper discusses the infusion pump detection system in detail, clarifies the infusion pump's structure and working principle, elaborates on the hardware and software design program of the detection system, and uses performance testing to confirm the system's accuracy and dependability. This paper offers a theoretical foundation and practical recommendations for the further optimization and broad implementation of an infusion pump detection system, which is crucial for guaranteeing infusion safety and enhancing the standard of medical care.
Infusion pump, Inspection Systems, Medical equipment.
Design and Study of Temperature Compensated Crystal Oscillator Based on Low Phase Noise
In this paper, a temperature-compensated crystal oscillator (TCXO) design scheme based on low phase noise is proposed to address the problems of poor frequency stability and high phase noise of crystal oscillators under temperature change. By analyzing Leeson's phase noise model and the frequency-temperature characteristics of the crystal, the phase noise is reduced by optimizing the structure of the oscillator circuit, choosing a high-Q crystal resonator, selecting low-noise devices, and suppressing the power supply noise; at the same time, combining with the digital and analog hybrid compensation technology, the temperature sensor, polynomial voltage function generator and varactor diode are used to achieve accurate temperature compensation. The experimental results show that the designed TCXO has a phase noise of -160dBc/Hz at 10kHz offset and a temperature stability of ±0.5ppm (-40°C ~85°C), and the long-term reliability is verified by aging test. This study provides theoretical support and practical reference for the design and optimization of high-precision and low-noise crystal oscillators.
Temperature-compensated crystal oscillator; Low phase noise; Circuit design.
Research Progress and Prospects of Temperature Control in Chemical Reactors
Reactors are commonly used reaction vessels for chemical reactions and are widely applied in chemical production. This article introduces the basic structure and working characteristics of chemical reactors, and based on this, proposes the difficult problems in temperature control during the reactor operation process. Subsequently, it elaborates on the working principle of the adaptive fuzzy PID controller and its application scenarios such as temperature alarms and emergency stops. Finally, it further prospects the future research trends of chemical reactor temperature control technology, indicating the direction for future research on reactor temperature control.
Reactor; Temperature Control; Chemical Production; Adaptive Fuzzy PID Control.
Requirement Analysis of Application Development of Blocking Equipment Based on SWOT
With the gradual maturity and development of intelligent technology, the traffic capacity of automobiles, ships and aircrafts has been greatly improved. In modern warfare, the loading capacity and demand for carriers are increasing, and the performance has been greatly improved. Therefore, in order to prevent the hostile carriers from breaking into our administrative office or confidentially, the application of the blocking equipment of various carriers has become a hot research problem. As a special equipment for forcing the carrier to drive, the blocking equipment plays an important role in all kinds of actions. Although the application of blocking equipment has many advantages, how to apply the blocking equipment to the most suitable field, the related demand demonstration research is still few. SWOT is a classic method of strategic objective analysis, which has the characteristics of structure, systematization and clarity. Through SWOT's analysis of blocking equipment, the necessity of its development and the challenges that need to be faced are further clarified.
Blocking equipment, SWOT analysis, Application development.
Safety Risk Identification and Prevention Strategies of Intelligent Mining in Coal Mines
A set of intelligent isolation drive and safety guarantee system based on dynamic multi-view is constructed to realize accurate reconstruction of coal mine space. In the coal mine operation area, multiple point cloud cameras are deployed, and advanced human body recognition technology is used to carry out accurate three-dimensional reconstruction of operators according to the location of each camera, and the specific location information corresponding to the three-dimensional model is returned in real time. Once a person is detected in a predefined danger area, the system immediately alerts and provides accurate positioning support for downhole smart equipment to assist in its operations. The application of this system can greatly improve the efficiency of coal mine operation safety control and protect the life safety of coal miners in an all-round way.
Three-dimensional model; point cloud cameras; human body recognition technology; coal mine operation safety.
Research on the Mechanical Properties of Economical UHPC and Application in UHPC-NC Slabs
In this study, an economic ultra-high performance concrete (UHPC) fabrication process is proposed and the mechanical properties are investigated. In order to better promote it in engineering projects, finite element analysis is conducted on ultra-high performance concrete-normal concrete (UHPC-NC) composite slabs and a normal concrete (NC) slab. By comparing UHPC-NC composite slabs with NC slabs of the same size, the flexural properties are derived and guidance is given for the further optimization of UHPC-NC floor slabs, so that the ultra-high-performance concrete can be used reasonably and efficiently for the promotion of engineering applications.
Ultra-high performance concrete; Mechanical properties; UHPC-NC composite slabs; Flexural properties.
Research on Complex Power Flow Calculation in Power Systems Using P-Q Decomposition Method Based on POWERWORLD Software
According to the three basic requirements of power flow calculation, and following the development of this field, this paper uses the large scale power system visualization software POWERWORLD SIMULATOR, developed by the University of Illinois in the United States to establish the international general power system model, using P-Q decomposition method to calculate the power flow under various operation modes, and the current carrying capacity and voltage level of the line are analyzed. This design focuses on determining the operating state of the whole system, such as voltage (amplitude and phase angle), power distribution and power loss on each bus. This design simulates the power flow of three-machine and nine-node power system. By simulating different running states, the power flow is calculated and compared with the result of MATLAB, and the correctness is verified. Then the different fault schemes are simulated, and the stability calculation is carried out, and the limit cutting time of the fault is calculated. The simulation results can reflect the power distribution intuitively and visually, guarantee the power system safer, steady and economic performance, and raise the economic benefit.
Power system; Power flow calculation; P-Q decomposition method; Power distribution; Visual simulation.
Links Dark Matter-driven Dynamics to Star Formation Triggers Via Hi Line Diagnostics, Bridging Cosmology and Stellar Physics
This study investigates the role of dark matter-driven dynamics in the initiation of star formation using HI line diagnostics to bridge cosmology and stellar physics. From large-scale HI surveys (e.g., ALFALFA, HIPASS) and dark matter dynamics simulations, we investigate the connection between HI line properties (intensity, width, spatial distribution) and star formation indicators (star formation rates, H-alpha emission). They outline significant HI intensity and star-forming rate correlations and the ways in which dark matter halos determine the kinematics and distribution of the HI gas, particularly for star-producing regions. Dark matter-induced gravity is assumed to drive the HI gas toward areas that create more massive stars, further building upon the theory in an attempt to better understand the indirect contribution of dark matter toward star and galaxy formation. Subsequent research will focus on the dark matter-molecular gas relation and higher-resolution HI observations to better resolve the role of dark matter in the dynamical evolution of galaxies.
Dark Matter; Star Formation; HI Line Diagnostics; Galaxy Evolution; Gravitational Dynamics; Star Formation Rates.
Field Studies of PMV and Its Several Extended Models in Real Buildings: A Literature Review
The Predicted Mean Vote (PMV) is widely acknowledged as the quintessential model for assessing thermal comfort in indoor environments on a global scale. Its empirical validation has sparked debates regarding the applicability and dependability of the model within real-world building contexts. This paper initially presents the fundamental principles of the PMV model, encompassing its mathematical formulation, influential variables, and criteria for thermal comfort assessment. Subsequently, a comprehensive classification and review of the PMV model is conducted with respect to office buildings, residential spaces, educational institutions, healthcare facilities, fitness centers, religious structures, and commercial complexes. Finally, detailed descriptions of several extended PMV models (ePMV, aPMV, and nPMV) are provided. This paper aims to assist architectural designers and HVAC system operators in navigating the intricate and diverse landscape of standards related to thermal environments. The ultimate goal is to enhance indoor environmental quality and energy efficiency.
Field studies; Predicted Mean Vote(PMV); thermal comfort; extended PMV models.
Dynamic Assessment of Geological Hazard Risk of Double-Track High-Risk Slope of Chongqing-Hunan Expressway
This study focuses on the dynamic assessment of geological hazard risk of the Hetaoping landslide on the left side of Beacon K51+960--K52+060 of Bapeng Road 3 in the Banan-Pengshui section. Through a variety of means such as Utilizing various methods, including field geological mapping, in-situ testing, and indoor geotechnical tests, we conducted a comprehensive investigation of the slope’s topography, stratum lithology, geological structure, and hydrogeological conditions. Based on the monitoring data, we dynamically analyzed the slope’s stability under different working conditions using both the limit equilibrium method and numerical simulation techniques. We evaluated the likelihood and impact range of geological disasters, such as landslides and collapses. The results indicate that the slope is generally stable under natural conditions; however, under extreme circumstances, such as heavy rainfall and earthquakes, the local stability coefficient of the slope decreases, posing a risk of instability that could lead to small-scale landslides and collapses. This dynamic assessment provides a crucial foundation for the scientific prevention and engineering decision-making regarding the slope, aiding in the reduction of geological disaster risks and ensuring the safety of surrounding residents and the smooth progression of construction projects.
Hetaoping landslide, dynamic analysis, dynamic assessment, data monitoring.
The Functional Characterization of CCD Genes in Loquat
Carotenoid cleavage dioxygenase (CCD) is a pivotal enzyme that is widely distributed in plants, catalyzing the cleavage of carotenoids to produce a variety of apocarotenoid volatile compounds. However, the functional characterization of the CCD gene in loquat (Eriobotrya japonica) has not been thoroughly investigated. In this study, we compared 3-oxo-ionol glycosides in loquat and speculated that a highly expressed CCD enzyme in the leaves is responsible for carotenoid cleavage. We employed bioinformatics approaches to analyze the CCD gene family in loquat, constructing a phylogenetic tree and an expression heatmap. Based on subfamily classification and expression patterns, we inferred the potential functions of these genes. Three genes with high expression levels were identified, and their expression patterns correlated with the accumulation of carotenoid cleavage derivatives in loquat leaves. To validate their functions, we cloned these genes and successfully obtained two of them. And we confirmed that Eja10G000940.1 exhibits cleavage activity on zeaxanthin. This study lays a foundation for further analysis of the function of CCD genes in loquat.
Loquat; CCD; expression pattern; function; zeaxanthin.
Overseas Technology Protection Issues and Countermeasures of Chinese Infrastructure Enterprises under the “Belt and Road” Initiative
With the in-depth promotion of the “Belt and Road” initiative, Chinese infrastructure enterprises have ushered in an important opportunity for internationalization, but the problem of overseas technology protection is becoming more and more prominent, which has become an important challenge affecting the core competitiveness of the enterprises and national interests. By analyzing the current situation of intellectual property disputes faced by Chinese infrastructure enterprises in countries along the Belt and Road Initiative, this paper reveals the major problems in overseas technology protection, including illegal imitation of patents, over-exploitation of technology by partners, preemptive application of patents for new technologies, and the risk of cross-border data transmission, etc. The study further explores the causes of these problems and the impacts on the core competitiveness and national interests of enterprises. The study further explores the causes of these problems, such as the lack of overseas patent layout, the low cost of breach of contract, the imperfection of the enterprise's intellectual property risk control system, and the lack of data security monitoring system. Aiming at these problems, this paper puts forward countermeasures and suggestions such as strengthening overseas patent layout, improving default cost, and constructing enterprise intellectual property rights prevention and control system and data security prevention and control system, which are aimed at providing theoretical support and practical guidance for China's infrastructure enterprises to “go global”, and helping China's infrastructure technology to realize sustainable development on a global scale.
“Belt and Road”; Infrastructure Enterprises; Overseas Technology Protection; Intellectual Property Disputes; Patent Layout; Data Security; International Cooperation.
Research on the Application of 3D Printing Technology in Surgical Clinical Training and Rare Disease Treatment
3D printing technology is penetrating into every corner of the medical field, serving as an innovative driving force for surgical clinical training and rare disease treatment. With the help of 3D printing technology, it is possible to intuitively understand the anatomical structure of the relevant parts of patients, conduct surgical simulation training, and assist in formulating personalized treatment plans. This article reviews the application of 3D printing technology in surgical clinical training and rare disease treatment, and discusses its development limitations and future directions.
3D Printing Technology; Simulation Model; Surgical Clinical Training; Rare Disease Treatment.
Kinematic Analysis of xMate ER7 Pro Robotic Arm Based on MATLAB
For the xMate ER7 Pro robot with seven degrees of freedom redundant manipulator of ROKAE, the standard D-H method is used to establish its link coordinate system and derive the forward and inverse kinematic equations. Based on the Robotics Toolbox toolbox in the MATLAB platform, the robot model is built in a simulation environment, and the forward and inverse kinematic equations are simulated and calculated respectively. The correctness of the kinematic equations is verified to achieve smooth movement of the manipulator.
Redundant manipulator; kinematics; MATLAB.
Based on LAMP and Channel Pruning and Knowledge Distillation Model Algorithm
To solve the problem of limited computing power of mobile devices for tea picking robots, this study proposed a hybrid compression algorithm based on LAMP pruning and channel pruning, and combined with knowledge distillation technology to optimize the performance of the lightweight model. The LAMP-adaptive normalization strategy of LAMP pruning was used to balance the proportion of pruning at different levels to avoid the over-pruning problem of traditional amplitude pruning. The channel pruning is further introduced to eliminate redundant convolution kernels, reducing the computational effort by 37.3% and the parameter count by 20.3%. The Mimic loss and linear attenuation distillation strategies were proposed, and the reasoning speed was increased by 19.7% at the pruning rate 1.7 through weighted multi-layer feature difference and dynamic adjustment of loss weight. Experiments show that the proposed method reduces the average delay to 8.26ms while maintaining the accuracy of the model, which is significantly better than the traditional pruning methods such as L1 and group_taylor, and provides an efficient solution for real-time tea recognition on edge devices.
Model pruning, Distillation of knowledge, Lightweight model.
Improved Tea Recognition Algorithm Based on RT-DETR
An improved RT-DETR model is proposed to solve the challenges of complex texture details, diverse target scales and high computational efficiency in tea recognition tasks. The convolutional gated linear unit (CGLU) is introduced to achieve dynamic weight adjustment, which enhances the robustness to complex background and variable target. OmniKernel module is designed to integrate multi-direction and multi-scale convolution kernel to improve the modeling ability of tea texture directivity and scale diversity. Combined with frequency-domain feature enhancement module (FSAM), the global context and local detail features are modeled jointly to suppress the interference of background noise. A feature segmentation and fusion module (SPDConv) is proposed to optimize the global consistency of features through space segmentation and channel fusion. Experiments show that the improved model, under the synergistic effect of enhanced input features and multi-scale feature pyramid network, significantly improves the accuracy of tea recognition and adaptability to complex scenes, while maintaining high computational efficiency, providing a high-precision and robust solution for tea detection tasks.
RT-DETR model, Tea identification, Object detection.
AI Big Model Enabling High Quality Development Logic, Mechanism and Path of Sports Industry
With the arrival of the “Artificial Intelligence +” era, the application of generative AI big model technology in the sports industry has become more and more widespread, injecting new vitality into the high-quality development of the industry. This study aims to explore the development logic, mechanism and path of the sports industry in the context of the “AI+” era, and analyzes the significant potential of AI big model technology in improving decision-making efficiency, optimizing resource allocation and enhancing user experience. Through literature review, case analysis and other methods, the technical path, industrial application path and policy guidance path of generative AI big model in sports industry are elaborated in detail. At the same time, this paper also reveals potential obstacles such as technical challenges and social and ethical issues, and puts forward reasonable (targeted) countermeasure suggestions. Finally, this paper looks forward to the future application of AI big model technology in the sports industry and points out the future research direction, with a view to providing theoretical guidance and practical reference for the intelligent transformation and sustainable development of the sports industry.
Ai big model, sports industry, decision support, intelligence, policy path.
Recent Advances in the Structure, Properties, and Applications of Lead-Free Tin-Based Perovskites in Optoelectronic Devices
In recent years, metal halide perovskites (MHPs) have emerged as a research hotspot in optoelectronic devices due to their exceptional optoelectronic properties. However, the environmental toxicity of commonly used lead halide perovskites (LHPs) has prompted researchers to explore alternative lead-free perovskite materials. Among these, tin halide perovskites (THPs), which exhibit structural and performance similarities to LHPs, have become the most significant focus in the study of lead-free perovskites. This review comprehensively summarizes the types, structural characteristics, defect mechanisms, and recent research progress of lead-free tin-based perovskites in optoelectronic devices. First, the typical tin-based perovskite material systems, including FASnX₃, MASnX₃, and CsSnX₃, are introduced. Subsequently, their optoelectronic properties, defect types, and environmental stability issues are discussed, with a particular emphasis on their applications and optimization strategies in perovskites solar cells (PSCs), photodetectors (PDs), and light-emitting diodes (LEDs). These strategies include defect passivation, stabilization through additives, interface engineering and energy level modulation of hole transport layers (HTLs) and electron transport layers (ETLs), as well as structural optimization and research on low-dimensional nanostructured perovskites. Finally, the current challenges faced by tin-based perovskites are analyzed, and their future development directions toward highly efficient and stable optoelectronic devices are prospected.
Lead-free tin-Based perovskites; Environmental stability; Solar cells; Photodetectors; Light-emitting diodes.
Response Relationship Between River Benthic Fauna Community Structure and Environmental Factors in Qinyuan County
Benthic animals are an important part of aquatic ecosystem, and their distribution and population structure are deeply affected by environmental factors. As an important aquatic ecosystem, the diversity and abundance of benthic animals in rivers are regulated by a variety of environmental factors. Qinyuan County is a typical inland river area, and the changes of river ecological environment may have important effects on the distribution pattern of benthic animals. In order to reveal the effect of environmental factors on the population distribution of benthic fauna in Qinyuan County, the relationship between environmental factors and the characteristics of benthic fauna community was discussed by canonical correlation analysis. The results showed that river width (P=0.008) and flow velocity (P=0.042) were significant environmental factors, which had important effects on the distribution of benthic fauna. This study reveals the key role of water environmental factors, especially river width and velocity, in the distribution of river benthic communities in Qinyuan County, and provides scientific basis for further understanding of the structure and function of aquatic ecosystems in this area. At the same time, the research results also provide a reference for regional ecological protection and water resources management, especially in waters ecological restoration and biodiversity conservation.
Macrobenthic invertebrates; Canonical correlation analysis; Qinyuan county.
Cargo Volume Prediction Based on K-means Clustering and Back Propagation Neural Network
Volume forecasting for sorting centers in e-commerce logistics networks is important for optimizing resource scheduling and transportation efficiency. In this paper, a joint prediction method incorporating K-means clustering algorithm and Back Propagation neural network (BP neural network) is proposed for 57 sorting centers after route adjustment. Firstly, based on the historical transportation data, we construct the logistics network topology map and identify the key hub nodes, and then divide the sorting centers into 3 classes by K-means algorithm to reveal the characteristics of their cargo volume distribution. Then, combined with the information of future transportation route changes, the training and prediction sets of daily and hourly cargo volume are constructed, and the BP neural network model is used for multi-scale prediction. Finally, Analysis of Variance (ANOVA) further shows that the clustering results are statistically significant, indicating that there are significant differences in the shipment volumes of different categories of sorting centers. In addition, in order to test the accuracy of the BP neural network, this paper conducted mean square error and regression analysis to verify the reliability of the prediction results. The model provides a scientific basis for personnel scheduling and transportation optimization in sorting centers, and has high robustness and practical value.
Volume forecasting; K-means clustering algorithm; Back Propagation neural network; analysis of Variance; Regression analysis.
Study on the Preparation Process of Poly (L-lactic acid)-icariin Drug-loaded Microspheres
The o/w type emulsification-solvent evaporation method was utilised to prepare the drug-carrying microspheres, with dichloromethane designated as the solvent phase, poly-L lactic acid as the carrier material, Icariin as the encapsulated drug, and Tween-80 as the emulsifier. The effects of ultrasonic time, ultrasonic power, volume ratio of internal and external phases, and dosage of poly-L lactic acid on the particle size, encapsulation rate, and drug loading rate of drug-carrying microspheres were explored during the preparation process of drug-carrying microspheres. The morphology of the drug-carrying microspheres was characterised by scanning electron microscopy, and the particle size and polydispersity index of the microspheres were characterised by nano-laser particle sizer. The optimal preparation of the drug-carrying microspheres was obtained as follows: The optimal parameters for the preparation of the drug-carrying microspheres were identified as follows: a Tween-80 concentration of 10 mg/mL, a poly(L-lactic acid) concentration of 25 mg/mL, an oil-to-water phase volume ratio of 1:2, an ultrasonic time of 15 min, and an ultrasonic power of 180 W.The drug-carrying microspheres of poly(L-lactic acid) were produced with a smaller particle size and better dispersion, and the encapsulation rate was 88.65%. The present study has the potential to furnish technical and parametric assistance for the fabrication of poly(L-lactic acid) drug-loaded slow-release microspheres.
o/w emulsification-solvent evaporation method; poly(L-lactic acid); Icariin; drug-loaded microspheres; encapsulation rate; drug-loading rate.
The Correlation Analysis of Short-circuit Current and Magnetization of Ferromagnetic Metals
This paper investigates the effect of short-circuit current on non-magnetic steel pipes to explain how different materials utilize the remanence method in fire investigations to analyze the variation laws of their own remanence. This provides data support and theoretical basis for determining the cause and location of fire incidents. The results show that under the influence of short-circuit current, the non-magnetic steel pipe becomes magnetized, and the extent of magnetization depends on the magnitude of the short-circuit current. The steel pipe will not be magnetized infinitely under different short-circuit currents; instead, the remanence value will reach a This paper investigates the effect of short-circuit current on non-magnetic steel pipes to explain how different materials utilize the remanence method in fire investigations to analyze the variation laws of their own remanence. This provides data support and theoretical basis for determining the cause and location of fire incidents. The results show that under the influence of short-circuit current, the non-magnetic steel pipe becomes magnetized, and the extent of magnetization depends on the magnitude of the short-circuit current. The steel pipe will not be magnetized infinitely under different short-circuit currents; instead, the remanence value will reach a certain range and then stabilize without further increase. For every 20A increase in short-circuit current, the stable remanence range of the steel pipe increases by 4-8 Gs. Finally, the regression equation between current (x, in A) and remanence value (y, in Gs) is given as , with a coefficient of determination of 0.9997, indicating a high level of reliability in the results. certain range and then stabilize without further increase. For every 20A increase in short-circuit current, the stable remanence range of the steel pipe increases by 4-8 Gs. Finally, the regression equation between current (x, in A) and remanence value (y, in Gs) is given as , with a coefficient of determination of 0.9997, indicating a high level of reliability in the results.
Short-circuit current, fire investigation; remanence value, regression analysis.
Based on Convolutional Neural Network Study of CT Images of Ovarian Cystadenoma
Cystadenoma of the ovary is a disease that occurs in the ovary. In order to realise automatic segmentation of ovarian CT images, an improved U-net model based lesion segmentation method of ovarian cystadenoma CT images was proposed. VGG16 was used as the encoder to further simplify the structure of U-net model, and combined with CT image features for data enhancement. An ovarian CT image dataset was constructed based on clinical diagnosis data, and the model was trained and evaluated. The model achieved an Intersection over Union (IoU) of 88.85% on the test set, and an Area Under the Curve (AUC) of 99.72%, indicating the feasibility and accuracy of the improved U-net model for the segmentation of ovarian cystadenoma lesions. In comparison with the original U-net model, the proposed method can reduce the size of the model without compromising the accuracy of segmentation, and is more suitable for auxiliary clinical diagnosis.
Medical image segmentation, U-net, Deep learning, ovarian cystadenoma.