Search Results (205)

If intraday price data are unavailable, then using daily returns to construct realized measures of the variances of lower-frequency returns is a natural substitute for using high-frequency returns in this context. Notably, a suitable application of this approach yields realized measures that are unbiased estimators of the unconditional and conditional variances of holding period returns for any investment horizon. I use a long sample of daily S&P 500 index returns to investigate the merits of constructing realized measures in this fashion. First, I conduct a Monte Carlo study using a data generating process that reproduces the key dynamic properties of index returns. The results of the study suggest that using realized measures constructed from daily returns to estimate the conditional and unconditional variances of lower-frequency returns should lead to substantial increases in efficiency. Next, I fit a multiplicative error model to the realized measures for weekly and monthly index returns to obtain out-of-sample forecasts of their conditional variances. Using the forecasts produced by a generalized autoregressive conditional heteroskedasticity model as a benchmark, I find that the forecasts produced by the multiplicative error model always generate lower mean absolute errors. Furthermore, the improvements in forecasting performance are statistically significant in most cases. Full article

Given the high casualty rate on expressways, this study aimed to accurately predict traffic accident rates and the key factors influencing them. Taking an expressway in Southern China as the research object, we constructed a two-layer stacking model integrating neural networks and tree models, based on accident data, traffic flow data, and road segment characteristic data. Six base models were integrated for prediction, and the Shapley Additive exPlanations (SHAP) method was used to analyze influencing factors. Results showed that the proposed model achieved the best performance, with a root mean square error (RMSE) of 11.05 and a mean absolute error (MAE) of 6.12, and its performance was significantly superior to that of other models (p < 0.05). Results from hyperparameter optimization and 5-fold cross-validation indicated that the proposed model had an RMSE of 8.91 ± 2.03, which was better than that of other models. Among all input factors, the proportion of tunnel length to total length and the variance of bridge width had the most significant impact on the expressway traffic accident rate, while the average width of tunnels had the lowest impact. This study realizes accurate prediction using widely available data and clarifies key factor mechanisms, providing support for expressway safety management and early risk warnings. Full article

The objectives were to assess the efficacy of genomic selection in pure line breeding, using a simulated dataset, the significance of several factors, including model updating, selection intensity, early generation (F₂) selection, dominance, and the presence of major-effect genes (QTLs). The simulated genome included 1000 biallelic genes and 49,825 SNPs, distributed on 10 chromosomes of 100 cM. We used genomic selection with partial phenotyping over generations and other scenarios. The efficacy of genomic selection was based on total realized genetic gain and probability of selecting superior pure lines. The results showed that genomic selection with model updating maximized the probability of selecting superior F₈ progeny and provided the highest total genetic gain, comparable to selection based on the true genotypic value. Larger training sets (achieved through model updating) and higher selection intensity were key factors affecting the development of elite pure lines. Dominance did not significantly affect genomic selection efficiency. Including QTLs increased genomic selection efficiency. Direct selection imposed within the F₂ generation was no more effective than selection started in F₃. All selection methods provided a high decrease in the genotypic variance at F₈. The realized genetic gains per cycle were positively correlated with the prediction accuracies. Full article

Meta-DETR is a few-shot target detection algorithm that combines meta-learning and transformer architecture to solve the problem of data sample scarcity. This algorithm uses deformable attention to focus feature learning process more accurately on the target and its surroundings. However, the number of sampling points in the deformable attention is fixed, which limits the effective information involved in feature extraction, resulting in insufficient feature extraction of the target and affecting detection performance. To solve this problem, a Meta-DETR few-shot target detection algorithm based on adaptive sampling deformable attention is proposed. Firstly, the cosine similarity between feature points is calculated by query features that are integrated with support features. Secondly, the number of related features of each feature point is counted by the similarity threshold. Thirdly, the final number of sampling points of the feature map are calculated by using the idea of maximum inter-class variance to achieve adaptive sampling. Finally, adaptive sampling deformable attention is integrated into Meta-DETR to achieve few-shot target detection. From the attention activation map, it can be seen that the deformable attention based on adaptive sampling pays more attention to the target itself. Compared with Meta-DETR, the proposed algorithm improves the detection accuracy of novel classes by 0.9%, 0.7%, 1.4%, and 2.1%, respectively, for shots 1, 2, 3, and 10 in partition 1 on the PASCAL VOC dataset; 3.5%, 0.1%, 5.5%, and 5.7%, respectively, for shots 2, 3, 5, and 10 in partition 2; and 1.9%, 1.0%, 2.1%, and 0.1%, respectively, for shots 2, 3, 5, and 10 in partition 3. Compared with MPF-Net, CRK-Net, and FSCE, the proposed algorithm achieves the best performance and can effectively realize detection under few-shot conditions. In addition, experiments on a self-made infrared dataset further validate the effectiveness of the algorithm proposed in this paper. Full article

E-government represents a global initiative that leverages information and communication technologies (ICTs) to enhance public service delivery and strengthen interactions between governments and citizens. While adoption is critical to realizing the potential benefits of e-government, research from the demand-side perspective remains limited, particularly regarding how individuals engage with these systems, the factors shaping their trust, and the role of media in promoting awareness and uptake. This study examines the influence of media exposure on e-government adoption by assessing its impact on trust, perceived ease of use, satisfaction, relative advantage, complexity, and observability. A quantitative survey was conducted among residents of the Rabat-Salé-Kénitra region, and the proposed model was tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The model demonstrated robust reliability (Cronbach’s alpha = 0.710), and ANOVA results (p < 0.001) confirmed the substantial explanatory power of the independent variables in predicting adoption. The model accounted for 65.7% of the variance in adoption and 67.2% in trust. Media exposure and digitalization exerted strong positive effects on trust, which emerged as the most influential predictor of adoption. Additionally, observability and relative advantage positively influenced adoption, whereas complexity had a negative effect. Notably, 72.86% of respondents expressed an intention to adopt e-government services in the future. These findings underscore the pivotal role of media as a catalyst for digital transformation and offer actionable insights for policymakers aiming to enhance citizen trust and engagement with e-government services. Full article

Agricultural new quality productive forces are an important driving force for the transformation of China’s agricultural economy and the realization of sustainable development. This study proposes a novel channel to verify the negative effects of climate risk on agricultural new quality productive forces based on the empirical evidence of 31 provinces in China from 2012 to 2022. Specifically, baseline regression results indicate that a 10% increase in climate risk leads to a 1.18% decrease in agricultural new quality productive forces. Moreover, mechanism tests indicate that climate risk negatively affects agricultural new quality productive forces mainly through increasing the severity of natural disasters. Heterogeneity analysis indicates that variances in agricultural digital economy levels, government investment in environmental protections, and the depth of agricultural insurance coverage endowments result in substantial discrepancies in the effects of climate risk on agricultural new quality productive forces. Finally, this study finds that the impact of climate risk varies across provinces with different regional locations and geographical conditions. This study provides useful insights for coping with climate risk and promoting the high-quality development of agricultural new quality productive forces. Full article

In this work, a battery active grouping equalization control strategy based on model predictive control (MPC) was proposed, which can promote cell consistency, equalization speed and energy loss during the battery equalization process. The dynamic group equalization topology based on reconfigurable circuits can achieve dynamic grouping. Using a battery state observation estimator and the MPC controller, multiple non-adjacent cells can realize simultaneous equalization in a single equalization process. An algorithm is designed to determine the optimal energy transfer direction and the optimal equalization current. The objective function of this algorithm incorporates weight coefficients that represent the relative importance of equalization time and energy loss. Simulation tests are conducted to evaluate the battery pack state-of-charge (SOC) root mean square, average temperature, and equalization time under various weight coefficients. Compared with two other traditional equalization control strategies, the proposed strategy reduces the equalization time by 43.93%, decreases the battery pack SOC variance by 50.18%, and improves the energy transfer efficiency by 0.59%. Full article

In today’s evolving society, meaningful development cannot be fully realized without acknowledging the vital role of the electronics sector, especially as it functions within informal markets. These markets have become more than just centers of commerce; they serve as informal learning grounds where many young people acquire entrepreneurial skills, develop resilience, and find alternatives to social vices. For many, informal entrepreneurship is not just an option but a means of survival and self-empowerment. Despite their growing relevance, the link between the entrepreneurial abilities nurtured in these informal markets and actual business performance has not been adequately examined. This study, therefore, aimed to explore how informal electronics entrepreneurs in a developing economy navigate their environment, overcome challenges, and create wealth through vision, innovation, and calculated risk-taking. Anchored in institutional theory, the research employed a qualitative approach, using cluster, purposive, and simple random sampling to select participants from key informal business units. Interviews were conducted, transcribed, and analyzed using QSR NVivo 12, allowing for deep insight into the lived experiences of the entrepreneurs. Findings revealed that 78% of participants emphasized practical suggestions that aid informal business survival, such as customer-driven innovations, adaptive strategies, and avoiding confrontations with regulatory agencies. Key attributes such as foresight, adaptability, and risk management accounted for 66% of the variance in corporate success. Strategic and innovative approaches are enabling informal firms to endure and prosper, since 61% of respondents associated these competencies with organizational success. The new BSP framework, which integrates institutional and contingency theories, illustrates how informal enterprises endure by conforming to or opposing institutional pressures and adjusting to environmental changes. The results indicate that, when properly understood and supported, the informal electronics sector may develop sustainably. This study demonstrates that informal entrepreneurship is influenced by formal regulations, informal norms, and local enforcement mechanisms, therefore enhancing institutional theory and elucidating business behavior in developing nations. The Business Survival Paradigm [BSP] illustrates how informal enterprises navigate institutional obstacles to endure. It advocates for policies that integrate the official and informal sectors while fostering sustainable development. The paper advocates for ongoing market research to assist informal firms in remaining up-to-date. It implores authorities to acknowledge the innovative potential of the informal sector and to provide supportive frameworks for sustainable growth and formal transition where feasible. Full article

This paper addresses the problem of constructing optimal equity portfolios under volatile market conditions by minimizing realized volatility—an alternative risk quantifier that more accurately captures short-term market fluctuations than traditional variance-based approaches. This issue is particularly relevant for investors seeking robust risk management strategies in dynamic and uncertain environments. We propose a mathematical optimization framework that determines portfolio weights by minimizing realized volatility, subject to expected return constraints. The model is empirically validated using historical data from stocks listed in the Stock Exchange of Thailand 50 (SET50) index. Through a comparative analysis of realized volatility and variance-based optimization across multiple portfolio sizes and return levels, we find that portfolios constructed using realized volatility consistently achieve higher Sharpe ratios, indicating superior risk-adjusted performance. We further introduce an efficiency metric based on the Euclidean distance between optimal portfolio weight vectors to evaluate the stability of allocations under extended investment horizons. The findings underscore the practical advantages of realized volatility in portfolio construction, offering enhanced responsiveness to market dynamics and improved performance outcomes. The novelty of this study lies in integrating realized volatility into a constrained portfolio optimization model and empirically demonstrating its superiority, thereby extending traditional mean-variance methods in both scope and effectiveness. Full article

This study systematically analyzed the fruit traits of four sources and 117 families of Juglans mandshurica Maxim. in Jilin Province. By measuring key traits such as fruit phenotype and nut phenotype, the relationship between fruit characteristics and environmental adaptability was explored, leading to the selection of superior materials with high oil content potential. The study used fruit from J. mandshurica of 117 families (random sampling) across four provenances as experimental materials and measured 13 fruit phenotypic traits, including fruit length and fruit width. Finally, principal component analysis and genetic variation parameters were conducted. The results of the variance analysis (ANOVA) indicated that except for the nut roundness index, all other traits exhibited highly significant differences among provenances and families (p < 0.01). The range of genetic and phenotypic variation coefficients for the various traits was 7.47–23.23% and 8.76–29.59%. The family heritability ranged from 0.968 to 0.988. Correlation analysis among fruit traits revealed a non-significant correlation between fruit width and seed yield, fruit type index and nut weight, kernel weight and kernel yield, as well as nut longitudinal diameter and kernel yield. However, significant correlations were observed among all other traits. The Pearson correlation analysis between fruit traits and environmental factors revealed a significant negative correlation between longitude and seed yield. Cluster analysis results, based on the Euclidean distance method, showed that materials from four provenances were categorized into three groups at a genetic distance of 5. Principal Component Analysis (PCA) revealed that the cumulative contribution rate of four principal components reached 87.00%. PCI demonstrated the highest contribution rate and included traits such as fruit length, nut longitudinal diameter, nut transverse diameter, nut side diameter, three-diameter mean, and nut weight. One elite provenance and five elite families were preliminarily selected. The realized gain for the selected provenance fruit traits was higher for fruit weight and kernel weight, with values of 2.41% and 3.67%, respectively. For the selected families, the genetic gain was highest for kernel yield and kernel weight, with values of 16.51% and 26.66%, respectively. The findings will provide insights into breeding strategies to enhance walnut oil yield. The identified traits may be used to guide breeding programs for developing high-oil-content varieties; However, further validation studies are required to confirm these traits and their applicability in large-scale breeding efforts. Full article

This research aims to develop and validate an organizational maturity framework (OM framework) to assess an organization’s maturity and improve the operational performance of the EQM. The study adopts a multi-methods approach. Qualitative data are sourced from 18 respondents and analyzed through thematic analysis. The analysis reveals that pollution control and energy efficiency are the primary EQM concerns. The maturity assessment occurs through data from one or multiple sources, with the most preferred models being the five-phase models. Finally, maturation has diverse effects on EQM, which mirrors continuous improvement expectations. The quantitative study involved 212 respondents drawn from PBOs across the country. The data were analyzed through SEM, culminating in hypothesis testing. Three of the eight hypotheses were supported, including H4: Legal requirements have a statistically significant impact on PBO maturity (β = −0.150, p = 0.015); H5: Sustainability has a positive statistically significant impact on PBO maturity (β = 0.169, p = 0.045); and H1: the level of maturity determines efficiency in EQM (β = 0.066, p = 0.050). The rest of the variables have an inverse relationship or effects that are not statistically significant. The assessment of weightings for the determinants of PBO maturity culminates in the realization that the variables whose hypothesized relationships were confirmed received moderate priority. These findings explain why the determinants of PBO maturity only explain 8.8% of the variance in maturity, while the entire model explains only 3% of the EQM efficiency. The findings culminate in the validity of the operational instructions for improvement in the task specificity of PBO maturity for EQM performance and an improvement in the conceptualization of EQM efficiency among the PBOs. Full article

Realized downward semi-variance (RDS) has been realized as a key indicator to measure the downside risk of asset prices, and the accurate prediction of RDS can effectively guide traders’ investment behavior and avoid the impact of market fluctuations caused by price declines. In this paper, the RDS rolling prediction performance of the traditional econometric model, machine learning model, and deep learning model is discussed in combination with various relevant influencing factors, and the sensitivity analysis is further carried out with the rolling window length, prediction length, and a variety of evaluation methods. In addition, due to the characteristics of RDS, such as aggregation and jumping, this paper further discusses the robustness of the model under the impact of external events, the influence of emotional factors on the prediction accuracy of the model, and the results and analysis of the hybrid model. The empirical results show that (1) when the rolling window is set to 20, the overall prediction effect of the model in this paper is the best. Taking the Transformer model under SSE as an example, compared with the prediction results under the rolling window length of 5, 10, and 30, the RMSE improvement ratio reaches 24.69%, 15.90%, and 43.60%, respectively. (2) The multivariable Transformer model shows a better forecasting effect. Compared with traditional econometric, machine learning, and deep learning models, the average increase percentage of RMSE, MAE, MAPE, SMAPE, MBE, and SD indicators is 52.23%, 20.03%, 62.33%, 60.33%, 37.57%, and 18.70%, respectively. (3) In multi-step prediction scenarios, the DM test statistic of the Transformer model is significantly positive, and the prediction accuracy of the Transformer model remains stable as the number of prediction steps increases. (4) Under the impact of external events of COVID-19, the Transformer model has stability, and the addition of emotional factors can effectively improve the prediction accuracy. In addition, the model’s prediction performance and generalization ability can be further improved by stacked prediction models. An in-depth study of RDS forecasting is of great value to capture the characteristics of downside risks, enrich the financial risk measurement system, and better evaluate potential losses. Full article

In high-orbit space missions, the significant attenuation of Global Navigation Satellite System (GNSS) signals due to long transmission distances and complex environmental interferences has led to a drastic degradation in the accuracy of traditional positioning models, which has attracted great attention in recent years. Although multi-system GNSS fusion positioning can alleviate the problem of insufficient satellite visibility, the existing methods are difficult to effectively cope with the challenges of multi-source noise coupling and inter-system error differences unique to high orbit. In this paper, we propose an adaptive GNSS positioning optimization framework for a high-orbit environment, which improves the orbiting reliability under complex signal conditions through dynamic weight allocation and a multi-system cooperative strategy. Different from the traditional weighting model, this method innovatively constructs a two-layer optimization mechanism: (1) Based on BP neural network, it evaluates the noise characteristics of pseudo-range observations in real time and realizes the adaptive suppression of receiver thermal noise, ionospheric delay, etc.; (2) it introduces Helmert variance component estimation to optimize the weighting ratio of GPS, GLONASS, BeiDou, and Galileo and reduces the impact of signal heterogeneity on the positioning solution of the multi-system. Simulation results show that the new method reduces the root-mean-square error of positioning by 32.8% compared with the traditional algorithm to 97.72 m in typical high-orbit scenarios and significantly improves the accuracy loss caused by the defective satellite geometrical configurations under multi-system synergy. Full article

Although the mechanized harvesting rate of maize in China has exceeded 90%, there are still shortcomings in the mechanized harvesting of silage maize. Some areas still rely on manual harvesting, which is not only inefficient but also requires more labor. Therefore, it is extremely important to realize the mechanized harvesting of silo maize. The aim of this paper is to improve the harvesting efficiency of silo maize, ensure the quality of the silage and reduce the loss of nutrients. Aiming at the problems of wide cutting width, difficult access, low operating efficiency, and uneven straw feeding in the process of corn silage harvesting in terraced fields in hilly and mountainous areas. This study creatively designed a single-disk corn silage harvester. The optimal Latin hypercube method and MATLAB R2021 software are used to analyze the influence of various factors on the evaluation index. The ternary quadratic regression prediction model was constructed by using Isight 5.6 software, and the accuracy of the model was verified by variance analysis and field experiments. In addition, the main program was optimized by writing the program of the SMPSO algorithm. The optimal combination of working parameters was determined: the working speed was 1.00 m/s, the cutter rotation speed was 1085.89 rpm, and the drum rotation speed was 30 m/s. At that time, the machine productivity was 38 t·h⁻¹, the average standard grass length rate was 82.15%, and the stubble qualification rate was 91.95%. After two field trials, the results showed that all indicators met the national standards and industry standards, which confirmed the efficiency and practicality of this design. Full article

Projects are exposed to risks that may hinder their success regarding cost, schedule, and quality/content. After identifying these risks, the project manager must select a subset for mitigation, constrained by a limited risk response budget. The problem lies in the uncertainties surrounding risk realization, their impact on the project’s parameters, and the outcomes of the risk response plan. This paper proposes a method for allocating the risk–response budget to mitigate project risks. The method begins with a Monte Carlo simulation to assess each risk’s impact and residual impact post-mitigation. These simulation results are the input for mathematical programming calculations, determining the optimal budget allocation among the risks based on various objective functions (e.g., maximizing expected net savings or minimizing variance). Each objective function can yield a different optimal budget allocation, so the final step involves weighing all results to make a conclusive decision. A case study illustrates the proposed method. Full article