World Journal of Information Technology (WJIT) is an international peer-reviewed, open access journal.
TRANSMISSION DELAY PREDICTION METHOD FOR OPTOELECTRONIC COMMUNICATION SYSTEMS BASED ON TEMPORAL FEATURE ENHANCEMENT
Transmission delay prediction is essential for low-latency service assurance and intelligent operation in optoelectronic communication systems. In practical transmission environments, delay is not only determined by physical propagation distance, but is also affected by traffic load, bandwidth utilization, queue status, bit error rate, and optical link quality. To improve prediction accuracy under dynamic system conditions, this paper proposes a transmission delay prediction method based on temporal feature enhancement. The proposed method first analyzes the main factors influencing transmission delay and constructs a prediction variable set including historical delay, traffic load, bandwidth utilization, queue length, bit error rate, and received optical power. Then, lag features, sliding-window statistical features, and difference features are introduced to enhance the temporal representation of the original monitoring data. Based on the enhanced feature sequence, an LSTM-based prediction model is developed to learn the nonlinear temporal relationship between system states and future transmission delay. Experimental results show that the proposed method achieves lower prediction errors than ARIMA, SVR, XGBoost, and standard LSTM models. The ablation analysis further confirms that temporal feature enhancement can effectively improve the model's ability to capture historical dependence, short-term fluctuation, and dynamic variation trends.
Copyright (c) 2026 HaoYu Tian https://creativecommons.org/licenses/by/4.0
A DATA-DRIVEN POLICY OPTIMIZATION ANALYSIS FRAMEWORK FOR CYBERCRIME GOVERNANCE BASED ON GNN
In today' s digital age, the rapid growth of information technology has reshaped global economic, social, and cultural landscapes, yet many countries face challenges in coordinating and implementing effective cybersecurity policies. This paper first applies PCA, SEM, GWR, and LSTM to normalize multidimensional indicators of global cybercrime data, cybersecurity policy metrics, and demographic characteristics. It then builds a data-driven policy optimization framework based on graph neural networks (GNN), incorporating spatiotemporal feature modeling, policy effectiveness evaluation, and cross-border collaborative game analysis, along with multi-source data fusion and dynamic prediction. Experimental results demonstrate high prediction accuracy and policy optimization capability, providing a scientific basis for policymakers to formulate targeted cybersecurity strategies and address evolving cyber threats, thereby improving global cybersecurity levels.
Copyright (c) 2026 ZhiFei Xu; PengYu Chen; YanJun Fan https://creativecommons.org/licenses/by/4.0
A RESPONSE SURFACE MODELING METHOD FOR SPAM CLASSIFICATION
Spam identification is an important task in Natural Language Processing. To improve the limited nonlinear representation of traditional linear classifiers, this paper proposes a spam classification model based on Response Surface Methodology. The model constructs a dual response surface on Bag-of-Words features: feature-level quadratic transformations learn nonlinear keyword effects, while a classifier-level quadratic correction adjusts the linear decision output. The proposed structure preserves interpretability through explicit polynomial parameters and improves boundary fitting without relying on large-scale deep models. Experiments on public spam datasets show that the model achieves higher accuracy than Logistic Regression and LSTM, while maintaining much lower computational cost than BERT. The results indicate that response surface modeling provides a lightweight, interpretable, and efficient solution for spam identification. This method is especially suitable for resource-constrained scenarios requiring fast training, low inference cost, and transparent feature-level explanation, and it offers a practical balance between model complexity, classification accuracy, and deployment efficiency.
Non-linear modeling, Feature transformation
Copyright (c) 2026 ShiChen Chen; YouPeng Fan; Yue lv https://creativecommons.org/licenses/by/4.0
DIGITAL-INTELLIGENT PENETRATING SUPERVISION AND DATA ASSET AUDIT RISK EARLY WARNING: AN EMPIRICAL STUDY ON LISTED ELECTRONIC INFORMATION FIRMS IN SICHUAN PROVINCE
The digital economy has elevated data assets to a strategic corporate resource, yet their unique characteristics — non-rivalry, value volatility, and valuation ambiguity — introduce unprecedented audit risks. This study develops a novel theoretical framework integrating Digital-Intelligent Penetrating Supervision (DIPS) theory with audit risk assessment to construct a data asset audit risk early warning system for listed electronic information firms in Sichuan Province, China. Using panel data from 38 listed electronic information companies in Sichuan from 2023 to 2025, we propose a hybrid machine learning model combining XGBoost with SHAP (SHapley Additive exPlanations) interpretability to identify and predict data asset audit risk factors. Our empirical results reveal that (1) data asset valuation uncertainty, regulatory compliance intensity, and internal data governance maturity are the three most significant predictors of audit risk; (2) the proposed XGBoost-SHAP model achieves an AUC of 0.937, outperforming traditional logistic regression (0.781) and random forest (0.892) benchmarks; (3) firms with higher digital supervision maturity exhibit significantly lower data asset audit risk, with a marginal effect of -0.183. This study contributes to the audit literature by operationalizing the DIPS framework in the context of data asset auditing and providing a practically deployable early warning tool. Our findings have direct implications for audit practitioners, regulators, and listed companies navigating the complex landscape of data asset assurance in emerging economies.
Digital-intelligent penetrating supervision
Copyright (c) 2026 YongHui Li; HaiBo Zhang https://creativecommons.org/licenses/by/4.0
THE CONSTRUCTION OF RISK ASSESSMENT MODEL AND EARLY WARNING MECHANISM FOR CHINA-MYANMAR CROSS-BORDER DATA FLOW
With the deepening of the Digital Belt and Road (DBR) initiative and the rapid development of cross-border digital economy cooperation between China and Myanmar, the scale of cross-border data flow between the two countries has expanded exponentially, covering cross-border e-commerce, digital trade, border smart governance, cross-border financial services and other key fields. However, the significant differences in data governance systems, digital infrastructure construction levels, cybersecurity legal systems, and geopolitical environments between China and Myanmar have led to diversified and dynamic potential risks in cross-border data flow, which seriously restrict the standardized, safe and high-quality development of bilateral digital economic cooperation. To solve the problems of single risk dimension, outdated evaluation methods, lack of dynamic early warning capabilities and insufficient targeted research in existing cross-border data flow risk research, this paper constructs a multi-dimensional risk assessment index system for China-Myanmar cross-border data flow based on the new digital governance synergy theory and risk cascade amplification theory. This paper innovatively adopts the improved entropy weight-TOPSIS method combined with system dynamics (SD) to build a dynamic risk assessment model, and constructs a four-level dynamic early warning mechanism integrating risk identification, real-time assessment, level early warning and emergency response. Based on the latest panel data of China-Myanmar digital cooperation from 2021 to 2025, this paper conducts quantitative empirical analysis on the risk intensity, evolution trend and cascade transmission mechanism of China-Myanmar cross-border data flow. The research results show that the overall risk level of China-Myanmar cross-border data flow presents a fluctuating downward trend from 2021 to 2025, but technical risk and institutional governance risk are the core dominant risks, and geopolitical risk has a significant periodic amplification effect. The model can effectively identify dynamic mutation points of risks, and the early warning mechanism can realize real-time monitoring and hierarchical pre-control of cross-border data flow risks. This research fills the research gap of targeted risk assessment and early warning for China-Myanmar cross-border data flow, provides new theoretical paradigms and technical methods for cross-border data risk governance of neighboring countries along the Belt and Road, and provides empirical support and decision-making reference for the safe and orderly development of bilateral cross-border data cooperation.
PATH SELECTION AND OPTIMIZATION STRATEGY OF CROSS-BORDER DATA FLOW RULE COORDINATION BETWEEN CHINA AND MYANMAR
With the deep integration of digital economy and regional economic cooperation, cross-border data flow has become a core driving force for the upgrading of China-Myanmar economic and trade cooperation, digital infrastructure interconnection, and border industrial integration. As an important node of the Belt and Road Initiative and the China-Indochina Peninsula Economic Corridor, Myanmar has increasingly frequent cross-border data interaction with China in the fields of cross-border e-commerce, digital logistics, border financial services, and public health information sharing. However, significant differences exist between China and Myanmar in data sovereignty governance, data classification supervision, cross-border transmission permission mechanisms, and personal privacy protection systems, resulting in prominent problems such as rule fragmentation, supervision conflicts, data flow barriers, and governance vacuum in bilateral cross-border data flow. These bottlenecks severely restrict the high-quality development of bilateral digital economic cooperation and regional digital integration. This study innovatively constructs a three-dimensional coupling theoretical framework of "institutional rule, technical standard, and governance mechanism" to analyze the current situation, dilemma and internal logic of China-Myanmar cross-border data flow rule coordination. Based on the latest 2025–2026 bilateral digital economy operation data, policy texts and cross-border transaction statistics, this paper sorts out the institutional differences and coordination obstacles of cross-border data rules between the two countries, and explores feasible coordination paths from the dimensions of hierarchical rule docking, technical standard convergence and multi-subject governance collaboration. Furthermore, it proposes targeted optimization strategies in terms of institutional innovation, technical empowerment, supervision coordination and risk prevention, aiming to break bilateral data flow barriers, build a standardized and efficient cross-border data governance system between China and Myanmar, and provide theoretical support and practical reference for digital regional cooperation among South and Southeast Asian countries. This study adopts a new theoretical perspective of multi-dimensional rule coupling, fills the research gap of targeted rule coordination for China-Myanmar cross-border data flow, and enriches the theoretical system of developing country cross-border digital governance.
YOLOV8-KF: A FRAMEWORK FOR PEST AND DISEASE DETECTION AND FRUIT-VEGETABLE RECOGNITION IN COMPLEX SCENARIOS
Crop pests and diseases constitute major constraints affecting crop yield and quality, and pose a threat to global food security. Therefore, in modern agricultural production, accurate and real-time detection of pests and diseases is critical for the timely implementation of prevention and control measures. Although YOLOv8 performs excellently in multi-class pest and disease detection as well as fruit and vegetable recognition, it yields unsatisfactory detection results under scenarios involving leaf occlusion and complex field monitoring.To address these issues, this study develops a framework for pest and disease detection and fruit and vegetable recognition that combines YOLOv8 with Kalman filter post-processing. The Kalman filter is used to predict target positions and smooth detection deviations under occluded conditions, thereby improving the continuity and stability of target tracking.The results show that the proposed model not only maintains high recognition accuracy for small-sized and multi-class pests, diseases and fruit-vegetable targets, but also reduces missed detection caused by occlusion. This method provides an effective solution for real-time dynamic monitoring of crop pests and diseases, fruit and vegetable recognition, and precise prevention and control in complex scenarios.
Copyright (c) 2026 HanQiu Shen; QinYi Yi; JunHao Liu; Ling Wu; XiangRun Xiao; Yuan Huang https://creativecommons.org/licenses/by/4.0
CONSTRUCTION AND GEOMETRIC PROPERTIES OF N-QUASI-BEZIER-CURVES WITH MULTIPLE PARAMETERS
By using novel recurrence relation,we define a Quasi-Bernstein-basis of degree n with multi ple parameters, Next, tased on Quasi-Bernstein-basis, constructs n-Quasi-Bézier-curves (n-QB-curves) and systematically investigates their core geometric properties and computational methods. We propose an angle-cutting algorithm specifically designed for QB-curves, deriving the principles governing local shape adjustments and tension control through parameter manipulation. The length quantization method is established to analyze the curve approximation effect, and the proposed n-Quasi-Bézier-curves have better approximation performance than the traditional method.It is proved that the global or local shape modification of the joining curve can be achieved by adjusting the parameters when the polygon is fixed.The research shows that n-Quasi-Bézier-curves can approximate the control polygon without any degree increase, which has the characteristics of high efficiency and good shape control ability, and provides a new method and idea for the curve design in the field of geometric modeling.
Copyright (c) 2026 PeiPei Ji; QingRong Duan https://creativecommons.org/licenses/by/4.0
METHODOLOGY FOR SHELF PRODUCT RECOGNITION AND INTELLIGENT REPLENISHMENT DECISION-MAKING USING YOLOV11
To address the issues of delayed inventory visibility and experience-driven retail shelf management, this paper proposes an intelligent replenishment decision-making framework that integrates computer vision and management science. First, a model for shelf item recognition and quantity counting is constructed using You Only Look Once version 11 (YOLOv11), enabling real-time inventory sensing. On this basis, inventory control theory is incorporated to establish a dynamic safety stock warning mechanism. Combined with exponential smoothing for demand forecasting, the dynamic replenishment quantity is calculated as “Replenishment Quantity = Forecasted Demand – Current Inventory + Safety Stock.” Using a self-built shelf image dataset, this paper compares YOLOv11 models of various scales, among which the m model achieves the optimal balance between computational efficiency and performance, with an average product detection accuracy (mAP50) of 60.2%; Compared to other mainstream detection models (Faster R-CNN, RetinaNet, YOLOv8m), YOLOv11m achieved the highest accuracy with the fewest parameters, validating the reliability of the visual perception module. Theoretical analysis, case simulations, and ablation experiments demonstrate that the proposed dynamic replenishment model can more effectively address demand fluctuations and reduce the risks of stockouts and inventory buildup compared to fixed-threshold strategies and minimum-maximum inventory strategies; furthermore, safety stock and demand forecasting are complementary and indispensable. This study provides a feasible solution and theoretical reference for visual inventory management and scientific replenishment decision-making in smart retail.
Copyright (c) 2026 Xuan Gao https://creativecommons.org/licenses/by/4.0
CHANNEL CAPACITY OPTIMIZATION AND PERFORMANCE ANALYSIS OF FIBER-WIRELESS CONVERGED COMMUNICATION SYSTEMS
With the increasing demand for high data rates, large capacity, and flexible access in 5G and 6G communication networks, fiber-wireless converged communication systems have become an important technical direction for future broadband access and mobile fronthaul networks. This paper investigates channel capacity optimization and performance analysis of fiber-wireless converged communication systems. First, an end-to-end system model consisting of cascaded optical fiber and wireless links is established, and the channel capacity expression is derived based on the equivalent signal-to-noise ratio. Then, the impacts of key factors, including fiber length, wireless transmission distance, transmit power, bandwidth, and modulation format, on system capacity are analyzed. Based on this model, a simplified capacity optimization strategy is proposed by identifying the bottleneck link and adaptively adjusting transmission resources to improve the end-to-end system performance. Simulation results show that, compared with the fixed resource allocation scheme, the proposed adaptive allocation strategy achieves higher channel capacity and effective throughput, while improving the bit error performance to some extent. These results demonstrate that bottleneck-link identification and optimized resource allocation are effective methods for enhancing the performance of fiber-wireless converged communication systems.
Fiber-wireless converged communication systems
DESIGN OF INFRARED OPTICAL COMMUNICATION AND TEMPERATURE TRANSMISSION SYSTEM BASED ON OOK MODULATION
This paper designs an infrared optical communication system based on OOK modulation to realize synchronous transmission of voice signals and digital temperature signals as well as temperature display. The system consists of a transmitter, a relay forwarding node, and a receiver. The transmitter superimposes voice signals and OOK-modulated temperature signals and sends them through an infrared emission tube; the relay node amplifies and forwards the signals; the receiver demodulates and recovers voice and temperature information. The system features low power consumption, with a receiver noise of approximately 0.02 V and a relay power consumption of about 150 mW. Experiments show that the transmission is stable, making it suitable for low-power short-distance scenarios.
Copyright (c) 2026 ShunYu Li https://creativecommons.org/licenses/by/4.0
ACCESSIBILITY EVALUATION OF URBAN LOW-ALTITUDE LOGISTICS NETWORKS BASED ON MULTI-SOURCE SPATIOTEMPORAL DATA
With the rapid development of the low-altitude economy, urban low-altitude logistics has become a promising solution for improving delivery efficiency and expanding logistics services. Compared with conventional ground-based logistics, it shows strong potential in reducing traffic congestion, improving timeliness, and enhancing service capacity in special scenarios. However, existing studies have mainly focused on route planning, facility location, and scheduling optimization, while systematic evaluations of service capacity at the urban scale remain limited. Taking Shenzhen as a case study, this research integrates multi-source spatiotemporal data on spatial environment, logistics demand, operational constraints, and network supply to construct an urban low-altitude logistics network and assess its accessibility in terms of spatial coverage and travel-time performance. The results reveal a clear core–periphery pattern: central districts such as Nanshan, Futian, and Luohu show higher coverage and shorter travel times, while peripheral districts such as Pingshan, Dapeng, and Yantian exhibit lower accessibility. Scenario analysis further shows that peak periods and operational restrictions reduce network coverage and increase travel time, especially in peripheral areas. The study provides an accessibility evaluation framework that supports network optimization and differentiated governance.
Copyright (c) 2026 JianRui Sun https://creativecommons.org/licenses/by/4.0
AN INTERNET OF THINGS (IOT) MANHOLE COVER STATUS MONITORING SYSTEM BASED ON CH32
According to 2024 data from the Ministry of Housing and Urban-Rural Development, China has more than 200 million urban manhole covers. The traditional manual inspection approach suffers from delayed response, high missed-detection rates, and elevated operation and maintenance costs. The resulting damage and loss of manhole covers have caused over 2,000 traffic accidents and hundreds of billions of yuan in direct economic losses. To address these issues, this study designs an IoT manhole cover status monitoring system based on the RISC-V core CH32L103C8T6. The system adopts multi-sensor fusion technology to preprocess parameters such as manhole cover displacement and inclination, verifies data validity using GPS positioning status, enables high-speed data transmission via the ADP-L610 communication module, and realizes real-time tracking of manhole cover status through 3D visualization on the cloud platform. This system effectively resolves pain points in traditional operation and maintenance, achieves precise and real-time monitoring of manhole covers, and provides an efficient and feasible industrial-grade solution for the management of urban smart infrastructure, thereby reducing related safety accidents and economic losses.
Copyright (c) 2026 WenXiu Liu; ZuoHai Jiang; PeiHang Su; Tao Liu; XiLin Xiao https://creativecommons.org/licenses/by/4.0
GENERAL EDUCATION COURSES OF DIGITAL ARCHITECTURE AND URBAN COGNITION IN ARCHITECTURE UNIVERSITIES BASED ON KNOWLEDGE GRAPHS
The rapid transformation of architectural practice by digital modeling, urban data, artificial intelligence, geographic information systems, building information modeling, and spatial cognition research has created a new educational challenge for architecture universities: how to cultivate digitally literate, spatially sensitive, and critically reflective students beyond the boundaries of specialized design studios. This paper proposes a knowledge-graph-based framework for the construction of a general education course titled “Digital Architecture and Urban Cognition.” Unlike conventional tool-oriented digital courses, the proposed course treats digital architecture as an interdisciplinary knowledge system connecting architectural form, urban perception, data structures, environmental behavior, cultural context, and ethical judgment. Drawing on learning science, knowledge graph theory, architectural pedagogy, BIM standards, semantic city modeling, and urban cognition studies, the paper develops a curriculum model composed of three interrelated graphs: a domain knowledge graph, a learning-path graph, and an assessment-evidence graph. The study argues that knowledge graphs can help general education courses overcome three persistent problems: fragmented knowledge organization, separation between technical skills and humanistic understanding, and weak visibility of students’ cognitive development. The proposed framework includes curriculum objectives, ontology construction, teaching modules, learning activities, assessment indicators, and implementation strategies. It concludes that knowledge-graph-based general education can support architecture universities in forming a new pedagogical ecology in which students learn not only to use digital tools, but also to understand cities as complex, perceivable, computable, and culturally meaningful environments.
Copyright (c) 2026 Lei Zhang; JieQing Xu; Xin Huang https://creativecommons.org/licenses/by/4.0
SENTIMENT ANALYSIS MODEL BASED ON ATTENTION MECHANISM MULTIMODAL FUSION AND SPATIO-TEMPORAL GRAPH NEURAL NETWORK
As a key task in the field of artificial intelligence, the application of sentiment analysis has expanded from a single text modality to cover multi-modal information such as vision and hearing. However, the existing multi-modal sentiment analysis methods often ignore the time sequence and structure of the dynamic interaction between modalities, and fail to fully model the evolution of emotional States in the time dimension. Therefore, this study proposes a sentiment analysis model based on multi-modal fusion of attention mechanism and spatio-temporal neural network. The model first extracts the high-dimensional features of text, visual and auditory modalities through a dedicated encoder, and then designs a hierarchical attention fusion mechanism to adaptively weight the contributions of different modalities at the feature level and decision level. In order to capture the dynamic evolution of emotional expression, the spatio-temporal graph neural network module is introduced into the model, and the fusion features of each time step are regarded as graph nodes, and the dynamic edges are constructed based on the correlation and temporal continuity between modalities, so as to model the spatio-temporal dependence of cross-modal interaction. Experiments are conducted on three open multimodal emotion datasets, including CMU-MOSEI, IEMOCAP and MOSI. The results show that the proposed model significantly outperforms the baseline method in both sentiment classification and sentiment regression tasks. On the CMU-MOSEI data set, the accuracy of the model reaches 88. 7% in the binary classification and 53. 2% in the seven-class classification, both of which reach the current advanced level. The ablation experiment further verifies the effectiveness of the hierarchical attention mechanism and the spatiotemporal neural network module. This study provides a new framework for multimodal sentiment analysis that can simultaneously model the complex interaction and temporal dynamics between modalities, which has theoretical significance for understanding the complex mechanism of human emotional expression, and provides technical support for the development of more accurate emotional intelligence applications.
Copyright (c) 2026 AiQun Zhu; Jin Lu https://creativecommons.org/licenses/by/4.0
CONTAINER DAMAGE DETECTION BASED ON DEEP RESIDUAL NETWORKS AND REAL-TIME OBJECT DETECTION ALGORITHMS
Container damage detection is a critical component for enhancing automation and transport safety in modern smart ports. To address practical challenges such as complex background interference in port environments, a wide range of damage scales, and uneven data distribution, this study constructs a damage detection framework based on the deep residual network ResNet50 and the lightweight detection algorithm YOLOv8. The study first employs a Super-Resolution Generative Adversarial Network (SRGAN) to enhance image resolution, thereby improving the distinguishability of fine cracks and dents. By introducing a cross-entropy loss function and a cosine-annealed learning rate strategy, the model achieves robust learning for three major damage types: dents, perforations, and corrosion. Experimental results demonstrate that the ResNet50 model excels at feature extraction in complex backgrounds, achieving a classification accuracy of 76.99% on the validation set, with particularly outstanding performance in identifying dent-type damage with distinct features. Meanwhile, the YOLOv8 model, which incorporates an attention mechanism, exhibits significant advantages in inference speed and multi-object localization, with an average accuracy of approximately 0.85, effectively meeting real-time monitoring requirements. This study confirms the efficiency and practical value of deep learning technology in handling container image recognition tasks characterized by high dynamics and diverse operating conditions.
Copyright (c) 2026 XingYu Zhao https://creativecommons.org/licenses/by/4.0
PROBING THE COGNITIVE APPRAISAL STRUCTURE OF EMOTION REPRESENTATIONS IN LARGE LANGUAGE MODELS (LLMS): A FULLY AUTOMATED GEOMETRIC ANALYSIS BASED ON CHINESE–ENGLISH BILINGUAL CORPORA
The emotional capabilities of large language models (LLMs) have been extensively validated, yet the organizational principles and cognitive regularities underlying their internal emotion representations remain poorly understood. This study introduces Smith and Ellsworth’s (1985) cognitive appraisal theory and designs an automated experimental pipeline in which five mainstream LLMs rate Chinese and English emotional texts along six appraisal dimensions. Combining representational similarity analysis, principal component analysis, and unsupervised clustering, we systematically probed the geometric structure of the LLMs’ emotion space. The results show that the appraisal structures of all LLMs are significantly aligned with human templates; however, under the English condition, the alignment did not surpass a purely semantic baseline, whereas under the Chinese condition the LLMs’ appraisals significantly outperform the semantic baseline. The LLMs’ appraisal space exhibits a systematic “responsibility shift,” in which the responsibility dimension is disproportionately amplified on higher-order principal axes. Chinese alignment is significantly higher than English alignment for all models, and clustering structures were driven more by linguistic properties than by model architecture differences. These findings reveal that LLMs form emotion-cognitive structures that exhibit both commonalities and language-specific particularities, providing a psychology-theory-driven quantitative framework for explainable artificial intelligence (AI).
Copyright (c) 2026 HuaYing Liu; LiJie Luo https://creativecommons.org/licenses/by/4.0
HIGH-PRECISION SEISMIC SOURCE LOCALIZATION ALGORITHMS BASED ON NONLINEAR OPTIMIZATION AND 3D DEPTH LAYOUT
Addressing the core challenge in seismic monitoring—where the accuracy of seismic source localization is affected by geological medium heterogeneity and geometric constraints of the monitoring network—this study constructs a progressive modeling framework that integrates geometric analysis, nonlinear optimization, and statistical sampling. The study first establishes an algebraic analytical model based on the principle of spherical intersection and, through iterative least-squares methods, reduces the sum of squared positioning residuals in an ideal scenario to 0.02 units. To address the ±0.2% ranging errors encountered in practical engineering applications, the Levenberg–Marquardt algorithm and Monte Carlo sampling are introduced to quantify the anisotropy of the error distribution and reveal the sensitivity of vertical positioning accuracy to station geometric constraints. To further enhance system robustness, the study utilized the Shapiro-Wilk test to remove outliers and employed multi-source data fusion via high-density station network redundancy constraints, significantly reducing vertical positioning errors. Finally, numerical simulations validated the scientific validity of the three-dimensional depth layout of monitoring stations, demonstrating that multi-depth configurations can reduce the average positioning error by 42.84%. The “theoretical modeling—error analysis—engineering optimization” technical chain established in this study provides critical technical support for the development of earthquake early warning systems and three-dimensional monitoring networks.
Three-dimensional station network layout
Copyright (c) 2026 PengShuo Zhao https://creativecommons.org/licenses/by/4.0
THE SPATIO-TEMPORAL EVOLUTION OF OLYMPIC MEDALS BASED ON MULTILEVEL REGRESSION ANALYSIS AND MULTIDIMENSIONAL FEATURE COUPLING
Olympic medal performance is a measure of a country’s competitive sport achievements but also largely shaped by various socioeconomic aspects. This study attempts to comprehensively investigate the spatiotemporal development of Olympic performance and to build a highly accurate hierarchical forecasting model. Firstly, using data from the 2008-2024 Olympic Games (five editions) after data cleaning and feature engineering it can be observed an extremely positive correlation between size of the games and total number of medals with correlation coefficient. The exploratory analysis suggests that GDP is the main socio-economic factor determining a nation’s medal competitiveness and its relationship with the number of medals has significant structural differences. It can be seen that for high-income countries, the conversion efficiency of economic resources into competitive advantages is best. This research devised separate forecast systems for nations on varying levels of competition; it used a share-smoothing prediction method on top-performing countries so grasp long-term trend; added up weight least-squares shares regression plus reliability shrinkage system for mid-ranking countries; and innovatively developed a two-step prediction framework integrating logistic regression and random forests models for those nations without any previous medal record. Forecast results suggest at the 2028 Los Angeles Olympics USA will get 150 medals because they are the host nation while China UK and France which are all powerhouses will stay in the lead positions. With the coupling of multiple features, this paper gives scientific evidence for people to know about the change in Olympic performance.
Copyright (c) 2026 YuHang Xiao https://creativecommons.org/licenses/by/4.0
PREDICTING OLYMPIC MEDALS: UNVEILING THE INFLUENCES
This study focuses on the prediction of Olympic medal data and the quantitative analysis of its influencing factors, aiming to address the complexity and nonlinearity in performance data. The research primarily employs the Autoregressive Integrated Moving Average (ARIMA) model to forecast time-series trends in medal counts, capturing the inherent periodicity of medal acquisition. Furthermore, by integrating linear regression with the Gradient Boosting Tree (GBT) model, the study quantifies correlations among event structures, medal attainment, and the host effect. The primary innovation lies in establishing a multi-level comprehensive model that overcomes the limitations of traditional methods in handling time-series dependence. The research findings provide quantitative support for predicting future Olympic trends, offer a research reference for quantifying the host country's advantages, and provide a new perspective for sports policy-making and related academic research.
Copyright (c) 2026 ChaoYin Liu; JinLing Chen; Jin Lu https://creativecommons.org/licenses/by/4.0
THE ASSOCIATION ANALYSIS AND TIMING OPTIMIZATION OF NIPT DETECTION INDICATORS BASED ON GENERALIZED ADDITIVE MODELS AND CLUSTERING DECISION-MAKING
Addressing the challenge of insufficient accuracy in non-invasive prenatal testing (NIPT) caused by traditional empirical grouping and a uniform testing timeline, this study focuses on the dynamic evolution mechanism of Y-chromosome concentration in male fetuses and the scientific optimization of the clinical intervention window. The study first utilized the Generalized Additive Model (GAM) to overcome the limitations of linear assumptions, providing an in-depth analysis of the nonlinear driving effects of gestational age, BMI, and their interaction terms on Y-chromosome concentration. Empirical results indicate that concentration exhibits significant three-stage fluctuations with gestational age, and BMI demonstrates a strong negative inhibitory effect beyond the critical threshold of 28. Subsequently, the study applied the K-means clustering algorithm combined with the elbow rule to achieve scientific stratification of the pregnant population, dividing it into two core subgroups: normal-to-high BMI and high BMI. Building on this, a logistic regression model was constructed and coupled with a comprehensive risk function incorporating weights for testing failure and diagnostic delay, thereby establishing a testing paradigm aimed at minimizing risk. The study confirmed that the optimal testing time point for both groups is 12 weeks of gestation, effectively balancing detection accuracy and clinical timeliness. Residual diagnosis and risk validation demonstrated that this modeling system possesses high statistical robustness, providing mathematical support for improving the quality of decision-making in precision prenatal screening.
Copyright (c) 2026 YiHan Ma https://creativecommons.org/licenses/by/4.0
GLOBAL MAJOR STOCK INDEX VOLATILITY PREDICTION AND RISK ASSESSMENT BASED ON LSTM-ATTENTION
Volatility is a core indicator for risk assessment and investment decisions in financial markets, and accurate prediction of stock index volatility is crucial for risk management. Traditional time series methods struggle to capture the nonlinear characteristics and long-term dependencies of financial data, while single deep learning models have limited ability to capture key time-series information, making it difficult to meet the needs of global stock index volatility prediction. This paper takes major global stock indices such as the S&P 500 and the Shanghai Composite Index as research objects, and constructs a stock index volatility prediction model based on LSTM-Attention. By collecting stock index trading data from the past ten years, the model undergoes cleaning, feature engineering, and standardization preprocessing. A three-layer LSTM stacked structure is designed and an Attention mechanism is integrated to achieve dynamic weighting of key time-series information. The model is validated using indicators such as MSE and MAE combined with a rolling window. Comparative experiments are conducted with pure LSTM and CNN-LSTM models, and a multi-dimensional risk assessment system is constructed based on the prediction results. Experimental results show that the MAE and MSE of the LSTM-Attention model are reduced by more than 12% and 20% respectively compared with the pure LSTM model. It exhibits better predictive stability and adaptability under extreme market conditions. In actual investment cases, the Sharpe ratio of portfolios guided by this model is significantly improved, and the maximum drawdown is effectively reduced. The LSTM-Attention model constructed in this paper provides a more accurate technical method for predicting global stock index volatility. Its accompanying risk assessment system and investment strategy suggestions can provide investors with scientific quantitative references for optimizing asset allocation and regulatory agencies for conducting market risk warnings, promoting the refined development of financial market risk management.
Copyright (c) 2026 GuanXiao Zhang https://creativecommons.org/licenses/by/4.0
AN AUTOMATED SOFTWARE DEFECT PREDICTION MODEL BASED ON OPEN-SOURCE CODE AND META-LEARNING
Software defect prediction is essential for improving software quality, yet traditional models often struggle with cross-project generalization and require extensive manual tuning. To address these challenges, this paper proposes an automated defect prediction approach based on open-source data and meta-learning. The method constructs project-level meta-features from multiple open-source datasets and builds a meta-dataset by associating these features with the best-performing models. A meta-learner is then trained to automatically recommend suitable prediction models for new projects. Experiments on PROMISE datasets demonstrate that the proposed approach consistently outperforms traditional machine learning and cross-project methods in terms of AUC and F1-score, while significantly enhancing cross-project generalization. Statistical tests confirm the significance of these improvements, and analysis of model selection shows high consistency with optimal choices. Overall, the proposed method effectively reduces manual intervention and provides a robust, automated solution for software defect prediction.
Copyright (c) 2026 JingXuan Guo; ShiYu Jiao https://creativecommons.org/licenses/by/4.0
OPTIMIZING THE TIMING OF NIPT BASED ON MULTI-FACTOR REGRESSION AND DECISION ANALYTICS
Noninvasive prenatal testing (NIPT) is widely used for screening common fetal aneuploidies, but its reliability is strongly affected by fetal fraction and is often reduced in pregnant women with elevated body mass index (BMI). This study develops an integrated statistical and optimization framework to generate practical recommendations on testing timing under heterogeneous maternal characteristics and sequencing quality conditions. Using real-world NIPT records from male fetal samples, Y chromosome concentration was adopted as a proxy for fetal signal adequacy, and qualification was defined by the empirical threshold of Y ≥ 4%. First, multivariate regression and weighted least squares were used to quantify the joint effects of gestational age and BMI on Y chromosome concentration and to derive a closed-form qualification probability. Second, BMI-stratified logistic models were constructed to estimate the earliest gestational week required to achieve a target qualification probability, such as 90%, and to reveal systematic timing delays associated with increasing BMI. Third, a risk-based optimization strategy was applied to generate a smooth BMI-dependent timing trajectory by explicitly balancing non-qualification risk against delay cost, and robustness was assessed through perturbation-based resampling. Finally, a multi-factor logistic generalized linear model incorporating maternal age, weight, unique aligned reads, and GC content was established, and joint optimization of BMI boundaries and testing times was used to eliminate infeasible extreme-BMI subgroups and improve group-level fairness. The proposed framework translates observational associations into deployable timing recommendations and provides quantitative support for individualized NIPT timing decisions.
Copyright (c) 2026 Shuo Bai; Lei Cheng https://creativecommons.org/licenses/by/4.0
INTELLIGENT FALL DETECTION SYSTEM FOR DRONES IN AGRICULTURAL FIELD SCENARIOS
Aiming at the high fall risk of elderly farmers during field operations and the lack of suitable intelligent monitoring technology for complex agricultural scenarios, this paper proposes an intelligent UAV-based fall detection and alarm system dedicated to farmland environments. We constructed a dedicated fall posture dataset covering diverse field vegetation, lighting conditions, and clothing styles, and expanded the sample size to 3,360 images through rich data augmentation. A fall detection model based on the YOLOv8 algorithm is designed to realize real-time and accurate positioning of fall events. Once a fall is detected, the system automatically triggers an on-board buzzer alarm and sends a timely notification email to emergency contacts via the SMTP protocol. Experimental results demonstrate that the proposed model achieves 92.6% mAP@50 and 92.3% precision, with an average system response time of 16 seconds and email notification completed within 40 seconds. The system maintains favorable robustness and generalization ability under complex conditions such as low illumination, and can effectively adapt to fall detection tasks in actual farmland scenes. It reduces the delay of manual monitoring and expands the coverage of safety protection, providing a practical and reliable technical approach for safety monitoring and emergency assistance in smart agriculture.
Copyright (c) 2026 SiQi Wang; JiaLei Guo; KeYi Lv; JiaXin Liang https://creativecommons.org/licenses/by/4.0
RESEARCH PROGRESS ON DECENTRALIZED FINANCE
Decentralized Finance (DeFi) has emerged as one of the most transformative applications of blockchain technology, constructing a financial ecosystem that operates without traditional intermediaries through smart contracts and distributed protocols. This paper systematically reviews the research progress of DeFi in terms of technical architecture, core protocols, governance models, and application scenarios, with particular focus on analyzing key domains including lending platforms, decentralized exchanges, stablecoin mechanisms, and asset tokenization. Research indicates that although DeFi demonstrates revolutionary potential in enhancing financial inclusion and transaction efficiency, it still faces significant challenges in smart contract security, regulatory compliance, and systemic risk prevention. This paper further explores future research directions including the integration of DeFi with traditional finance, technological innovation, and real-world asset integration, providing reference for scholars, policymakers, and industry participants to understand this rapidly evolving field.
Copyright (c) 2026 ChaoFu Qin; YanLing Liu https://creativecommons.org/licenses/by/4.0
A DYNAMIC TRUST EVALUATION METHOD FOR BOUNDARIES OF NEW POWER SYSTEMS BASED ON MULTI-MODEL PARALLEL ANALYSIS
Aiming at the architectural characteristics of numerous nodes and fragmented access in new power systems, as well as the technical pain points of traditional boundary trust evaluation, such as difficulty in capturing short-term anomalies, identifying medium risks, perceiving complex threats, and poor model collaboration, a dynamic trust evaluation method for power system boundaries based on multi-model parallel analysis is proposed. This method constructs a four-level architecture consisting of data collection and preprocessing, multi-model parallel analysis, model collaborative fusion, and trust level output. It collects multi-dimensional boundary data through distributed probes and performs standardization processing, realizes full-complexity risk identification relying on three parallel layers of statistical, machine learning, and deep learning models, completes the fusion of multi-model results combined with a dynamic weight adjustment strategy, and finally maps to four-level grayscale trust levels with targeted grayscale control strategies. Experiments and applications show that this method controls the trust evaluation delay within seconds, realizing real-time, accurate, and grayscale evaluation of the trust status of boundary entities, and provides reliable technical support for boundary security protection of new power systems.
Copyright (c) 2026 Yang Cao; Yang Su; Peng Zhou; XianHan She; Qian Liu; KaiMin Zheng; WeiJie Qiu https://creativecommons.org/licenses/by/4.0
MEASURING DIGITAL GREEN INNOVATION IN ENTERPRISES: A LIGHTWEIGHT TEXT MINING APPROACH BASED ON ANNUAL REPORTS
Digital green innovation has become a critical pathway for Chinese enterprises to achieve sustainable development amid the convergence of the digital economy and green transition strategies. However, existing studies predominantly rely on structured indicators such as patents and financial data, which may fail to capture the strategic and contextual dimensions of firms’ innovation activities reflected in corporate disclosures. To address this gap, this study analyzes the annual reports of 1,285 Chinese listed manufacturing firms using web scraping and text mining techniques. Based on recent literature, a multidimensional indicator system containing 11 digital green innovation keywords is constructed to measure firms’ innovation activities. Empirical results reveal significant heterogeneity in keyword distribution across firms, with “artificial intelligence” and “Internet” appearing most frequently. Further analysis shows that heavily polluting enterprises exhibit significantly higher levels of green innovation, suggesting strong policy and regulatory incentives. By providing a scalable, text-based measurement framework, this study not only enriches the methodological toolkit for assessing digital green innovation, but also offers new empirical evidence on how firms disclose and prioritize innovation under environmental and digital pressures, thereby generating important implications for both policymakers and enterprise managers.
Copyright (c) 2026 QiWen Wei; ZiJie Xu https://creativecommons.org/licenses/by/4.0
A SOFT VOTING MECHANISM-BASED RANDOM FOREST MULTI-MODEL ENSEMBLE APPROACH FOR EMPLOYMENT STATUS PREDICTION
Employment constitutes the cornerstone of people's livelihoods and the foundation of development. Presently, structural employment contradictions remain prominent in China, rendering the achievement of high-quality, full employment the foremost objective of current socio-economic development. Conventional single-model forecasting approaches exhibit limitations when handling high-dimensional, complex employment data, including insufficient generalisation capabilities and restricted capture of feature interactions. To address this, this study constructs a multi-model fusion employment status prediction model based on random forests, thereby enhancing predictive accuracy and stability. The study utilised a dataset comprising 4,980 employment samples, encompassing 57 raw features. Through data cleansing, Pearson correlation analysis, and feature extraction methods based on XGBoost and AUC cross-validation, 12 key feature variables were ultimately selected. Building upon this foundation, a soft voting ensemble strategy was employed to fuse three major prediction models—random forest, SVM, and LSTM—into a novel ensemble learning model. The predictive performance and stability of both the traditional trio of models and the new ensemble model were evaluated. Results indicate: The ensemble model achieved an accuracy of 85.49%, with recall and F1 scores of 97.34% and 93.21% respectively, outperforming each individual model. It effectively synergises the strengths of multiple models, enhancing adaptability to complex employment data and improving prediction robustness. This research provides a scientifically sound and effective ensemble learning approach for employment status prediction, offering practical value in supporting government precision policy-making and optimising employment services.
Copyright (c) 2026 Can Luo; Zhen Liang https://creativecommons.org/licenses/by/4.0
A COLLABORATIVE DECISION-MAKING FRAMEWORK FOR INFLUENCE MAXIMIZATION BASED ON GRAPH CONTRASTIVE LEARNING AND DEEP REINFORCEMENT LEARNING
To address the limitations of traditional influence maximization methods in complex social networks, including insufficient feature representation, strong dependence on predefined diffusion models, and limited capability for collaborative seed selection, this paper proposes a collaborative decision-making framework based on graph contrastive learning and deep reinforcement learning. First, the social network is modeled as a directed weighted graph, and a mathematical formulation of influence propagation and seed selection is established under the Independent Cascade(IC)model. Then, a graph contrastive learning strategy is introduced to conduct self-supervised pre-training over network topology, edge weights, and node attributes. By leveraging multi-view graph augmentation and node-level contrastive objectives, the framework learns high-quality node representations that better characterize diffusion potential. On this basis, the influence maximization task is reformulated as a sequential decision-making process, and a Double DQN-based seed selection mechanism is designed to iteratively select seed nodes. Marginal influence gain is adopted as the immediate reward to enable dynamic collaborative optimization of the seed set. Experiments conducted on six real-world social network datasets, including Petster-hamster, Tv-show, Politician, Advogato, Public, and Epinions, demonstrate that the proposed method consistently outperforms Random, PageRank, gIM, S2V-DQN, and ToupleGDD under different seed budgets. The results verify that combining the representation advantages of graph contrastive learning with the sequential decision-making capability of deep reinforcement learning can effectively improve influence maximization performance in complex networks.
Copyright (c) 2026 JiaWei Kang https://creativecommons.org/licenses/by/4.0