Search Results (25,161)

This research paper focuses on figuring out the impact of trade openness on FDI inflows, which has received relatively less attention in the literature, specifically in the context of ASEAN economies. The ASEAN region, which is relatively more open in terms of both trade openness as well as FDI inflows, is chosen as a sample. Annual data are gathered from “World Development Indicators (WDI)” and “World Governance Indicators (WGI)”. Reported results and findings are based on “Fixed Effect (FE) Modeling”, and the “Generalized Least Square (GLS)” is utilized for the robustness check. The results indicated that trade openness matters significantly for attracting FDI inflows. Similarly, institutional quality has also exerted a positive and significant influence on the inflows of FDI. The disaggregated analysis shows that five aspects of institutional quality, such as rule of law, regulatory quality, control of corruption, voice and accountability, and political instability and absence of violence, have positively and significantly impacted the FDI inflows in the case of selected ASEAN economies. The results demonstrated that exchange rate depreciation is harmful for the inflows of FDI. Moreover, FDI inflows responded positively to market size. Furthermore, the results showed that the impact of natural resources and inflation on FDI inflows is insignificant statistically. The present study suggests that the ASEAN policymakers manage their exchange rate effectively, improve the quality of institutions, and adopt vigorous trade liberalization policies to attract more FDI inflows. Full article

Aim: The aim of this systematic review and meta-analysis was to assess possible histomorphometric differences in new bone formation and in remaining graft particles when hyaluronic acid (HA) was added and mixed with graft materials in bone regeneration. Materials and methods: This review was registered at the International Prospective Register of Systematic Reviews (PROSPERO) of the National Institute of Health Research (registration number CRD42024530030). Electronic research was performed, and involved studies published up to 29 February 2024 using a specific word combination. The primary outcome was to assess possible histomorphometric differences in new bone formation and in remaining graft particles when HA was added and mixed with graft materials in bone regeneration. The search resulted in 138 potential studies. Meta-analyses were performed using the fixed and random effects model to identify significant changes in new bone formation and in the remaining graft particles. Results: After screening procedures, only three randomized controlled trials fulfilled the inclusion criteria and were selected for qualitative and quantitative analysis. The effect size of HA in the new bone formation was not statistically significant at 95% CI (Z = 1.734, p-value = 0.083, 95 % CI -,399; 6516). The effect size of HA in the remaining graft particles was not statistically significant at 95% CI (Z = −1.042, p-value = 0.297, CI -,835; 255). Conclusions: Within the limitations of the present systematic review and meta-analysis, the addition of HA to bone graft did not result in significant changes in bone regeneration procedures in terms of new bone formation and residues, even if the included studies showed encouraging and promising results. Full article

This study investigates the role of social capital within the university context in retaining working students. It specifically examines the effects of university social capital factors—such as teacher–student relationships, peer networks, and support services—on the dropout intentions of working students, emphasizing the mediating role of employability trust. Using a sample of 1902 working students from the Eurostudent VII survey, this study employed factor analysis techniques and structural equation modeling to derive its findings. The results indicated that university social capital significantly reduces dropout intentions among working students. Strong teacher–student relationships, satisfaction with support services, robust peer networks, and high employability trust positively influence this social capital. There is a statistically significant negative association between teacher–student relationships, peer networks, employability trust, and dropout intentions. Furthermore, the findings reveal that without enhancing students’ employability trust, the effectiveness of support services might be limited. These findings not only contribute to the discourse on student retention and the development of university social capital but also provide practical insights for higher education strategies aimed at supporting working students. Full article

Wire Arc Additive Manufacturing (WAAM) represents a disruptive technology in the field of metal additive manufacturing. Understanding the relationship between input factors and layer geometry is crucial for studying the process comprehensively and developing various industrial applications such as slicing software and feedforward controllers. Statistical tools such as clustering and multivariate polynomial regression provide methods for exploring the influence of input factors on the final product. These tools facilitate application development by helping to establish interpretable models that engineers can use to grasp the underlying physical phenomena without resorting to complex physical models. In this study, an experimental campaign was conducted to print steel components using WAAM technology. Advanced statistical methods were employed for mathematical modeling of the process. The results obtained using linear regression, polynomial regression, and a neural network optimized using the Tree-structured Parzen Estimator (TPE) were compared. To enhance performance while maintaining the interpretability of regression models, clusterwise regression was introduced as an alternative modeling technique along with multivariate polynomial regression. The results showed that the proposed approach achieved results comparable to neural network modeling, with a Mean Absolute Error (MAE) of 0.25 mm for layer height and 0.68 mm for layer width compared to 0.23 mm and 0.69 mm with the neural network. Notably, this approach preserves the interpretability of the models; a further discussion on this topic is presented as well. Full article

Since the mid-1980s, there has been little progress in improving survival of patients diagnosed with osteosarcoma. Survival prediction models play a key role in clinical decision-making, guiding healthcare professionals in tailoring treatment strategies based on individual patient risks. The increasing interest of the medical community in using machine learning (ML) for predicting survival has sparked an ongoing debate on the value of ML techniques versus more traditional statistical modelling (SM) approaches. This study investigates the use of SM versus ML methods in predicting overall survival (OS) using osteosarcoma data from the EURAMOS-1 clinical trial (NCT00134030). The well-established Cox proportional hazard model is compared to the extended Cox model that includes time-varying effects, and to the ML methods random survival forests and survival neural networks. The impact of eight variables on OS predictions is explored. Results are compared on different model performance metrics, variable importance, and patient-specific predictions. The article provides comprehensive insights to aid healthcare researchers in evaluating diverse survival prediction models for low-dimensional clinical data. Full article

In this article, we investigate how tertiary humanities students’ perceptions of the study environment, dropout considerations, and background variables, respectively, explain variations in dropout. Based on Tinto’s Institutional Departure Model and a systematic review of the dropout literature, the study environment comprised an academic system, a social system, and teaching. Multivariate statistical analyses in the form of explorative factor analysis and logistic bivariate regressions were used on half-early register and survey data from all humanities students at a Danish university [University of Southern Denmark], matriculated in 2017–2019. This article found that students’ perceptions of their study environment explained between 15.8% and 36.9% of dropout, whereas dropout considerations and background parameters explained only between 0 and 9.1% and between 7.9 and 21.4% of dropout, respectively. We hereby present and discuss the results obtained during different terms. The discussion revolves around the proposed research instrument and the longitudinal research methodology, as well as around what we could learn from this study about being a humanities student and about study environments that could help us increase the number of graduates. Full article

This study presents a numerical investigation into the adhesion strength of micro fibrillar structures, incorporating statistical analysis and the effects of excessive pre–load leading to fibril buckling. Fibrils are modeled as soft cylinders using the Euler–Bernoulli beam theory, with buckling conditions described across three distinct states, each affecting the adhesive properties of the fibrils. Iterative simulations analyze how adhesion strength varies with pre–load, roughness, number of fibrils, and the work of adhesion. Roughness is modeled both in fibril heights and in the texture of a rigid counter surface, following a normal distribution with a single variance parameter. Results indicate that roughness and pre–load significantly influence adhesion strength, with excessive pre–load causing substantial buckling and a dramatic reduction in adhesion. This study also finds that adhesion strength decreases exponentially with increasing roughness, in line with theoretical expectations. The findings highlight the importance of buckling and roughness parameters in determining adhesion strength. This study offers valuable insights into the complex adhesive interactions of fibrillar structures, offering a scalable solution for rapid assessment of adhesion in various rough surface and loading scenarios. Full article

Six-dimensional object pose estimation is a fundamental problem in the field of computer vision. Recently, category-level object pose estimation methods based on 3D-GC have made significant breakthroughs due to advancements in 3D-GC. However, current methods often fail to capture long-range dependencies, which are crucial for modeling complex and occluded object shapes. Additionally, discerning detailed differences between different objects is essential. Some existing methods utilize self-attention mechanisms or Transformer encoder–decoder structures to address the lack of long-range dependencies, but they only focus on first-order information of features, failing to explore more complex information and neglecting detailed differences between objects. In this paper, we propose SAPENet, which follows the 3D-GC architecture but replaces the 3D-GC in the encoder part with HS-layer to extract features and incorporates statistical attention to compute higher-order statistical information. Additionally, three sub-modules are designed for pose regression, point cloud reconstruction, and bounding box voting. The pose regression module also integrates statistical attention to leverage higher-order statistical information for modeling geometric relationships and aiding regression. Experiments demonstrate that our method achieves outstanding performance, attaining an mAP of 49.5 on the 5°2 cm metric, which is 3.4 higher than the baseline model. Our method achieves state-of-the-art (SOTA) performance on the REAL275 dataset. Full article

Current data trading systems only support plaintext or unaudited private transactions. To overcome these, we present a confidential batch payment scheme with integrated auditing for enhanced data trading security. We use Castagnos–Laguillaumie (CL) homomorphic encryption and batch zero-knowledge proofs to construct the scheme. The scheme reduces decryption complexity and ciphertext length while enabling malicious model operations. In addition, it supports efficient batch payments to multiple recipients and includes features for payment statistic analysis and auditing. Experimental results indicate that the system efficiently handles encryption, decryption, and auditing tasks, completing each operation in an average of 0.89, 1.55, and 1.55 milliseconds, respectively. Full article

Background: Hyperhomocysteinemia can increase the risk of cardiovascular disease (CVD), cancer, and neurological disorders; however, hypohomocysteinemia is generally not considered harmful. This study aimed to evaluate the relationship between all levels of homocysteine, both low and high homocysteine levels, and the risk of all-cause and cause-specific mortality in adult Korean men. Methods: Adult Korean men (n = 221,356) were categorized into quintiles based on their homocysteine levels. The primary endpoints were all-cause, CVD, cancer, and dementia mortality. Hazard ratios were calculated using Cox proportional hazards models, and the dose–response relationship between homocysteine levels and mortality risk was further explored using restricted cubic spline models. Results: Compared with the reference category (Q2, 8.8–9.9 µmol/L), there was a significant increase in all-cause mortality associated with both low and high levels after multivariable adjustment (P_interaction = 0.002). Additionally, in spline regression, a U-shaped association between homocysteine levels and all-cause and CVD mortality was observed (inflection point = 9.1 µmol/L). This association was not observed in the vitamin supplementation subgroup. Conclusion: Among Korean adult men, both low and high homocysteine levels increased the risk of all-cause and CVD mortality, indicating a U-shaped relationship. However, this relationship was not statistically significant with vitamin supplementation, suggesting a potential protective role for vitamins. Full article

Understanding the impact of variables involved in soluble-extracellular polymeric substance (S-EPS) production processes is crucial for reducing production costs and enhancing sustainability. Response surface methodology (RSM) provides essential tools that assist in developing predicted interactions among process variables for both industrial and non-industrial applications. The present study offers a simple and systematic demonstration of RSM capabilities, focusing on maximizing efficiency and minimizing production costs of S-EPS produced by Cyanocohniella rudolphia. RSM was employed to (1) design the production setup; (2) fit the collected data into a second-order polynomial model; (3) statistically evaluate the model’s validity and the significance of the involved variables; and (4) identify and optimize production variables to enhance output and reduce costs. Focused on four key variables, each at three levels, RSM designed 25 distinct S-EPS production conditions, each with three replicates. Statistical analysis identified the most significant variables affecting S-EPS production as the culture medium/wet biomass ratio, production days, and nitrogen concentration. The model’s validation demonstrated a strong correlation between the predicted and experimental values, with S-EPS production ranging from 70.46 to 228.65 mg/L and a maximum variation of 11.6%. This study demonstrates the effectiveness of RSM in optimizing S-EPS production, with the developed model showing a strong correlation between the variables and the response. The RSM model offers a promising approach for the bioprocessing industry, enhancing productivity and efficiency, minimizing costs, and leading to sustainable, cost-effective practices. Full article

The construction industry is known to present various stress-inducing conditions for its workforce, especially for workers under different employment arrangements. This research aims to investigate the influence of employment status (permanent and temporary workers) on the perception of well-being at work (WBW). The study also aims to assess whether variables such as satisfaction, commitment, and involvement are statistically significant in evaluating construction workers’ WBW. The research was conducted at various construction sites located in the Brazilian Amazon. A total of 376 responses were obtained using the Work Well-being Inventory (IBET-13) questionnaire. A multiple linear regression model was constructed to understand the relationship between self-perceived well-being (dependent variable) by employees and employment status, satisfaction, commitment, and involvement at work (independent variables). The results suggest that the employment arrangement does not significantly impact the evaluation of well-being, indicating that other factors may mediate the relationship between WBW and mental health, as well as contribute to explaining this result, such as current legislation, occupational characteristics, and unique aspects of the Brazilian reality. The research findings can contribute to the development of strategies that promote a more sustainable and healthy construction environment for workers. Full article

Currently, the viscoelastic properties of conventional asphalt cement need to be improved to meet the increasing demands caused by larger traffic loads, increased stress, and changing environmental conditions. Thus, using modifiers is suggested. Furthermore, the Sustainable Development Goals (SDGs) promote using waste materials and new technologies in asphalt pavement technology. The present study aims to fill this gap by investigating the use of pulverized oil palm industry clinker (POPIC) as an asphalt–cement modifier to improve the fatigue life of bituminous concrete using an innovative prediction approach. Thus, this study proposes an approach that integrates statistically based machine learning approaches and investigates the effects of applied stress and temperature on the fatigue life of POPIC-modified bituminous concrete. POPIC-modified bituminous concrete (POPIC-MBC) is produced from a standard Marshall mix. The interactions between POPIC concentration, stress, and temperature were optimized using response surface methodology (RSM), resulting in 7.5% POPIC, 11.7 °C, and 0.2 MPa as the optimum parameters for fatigue life. To improve the prediction accuracy and robustness of the results, RSM and ANN models were used and analyzed using MATLAB and JMP Pro, respectively. The performance of the developed model was assessed using the coefficient of determination (R2), root mean square error (RMSE), and mean relative error (MRE). The study found that using RSM, MATLAB, and JMP Pro resulted in a comprehensive analysis. MATLAB achieved an R² value of 0.9844, RMSE of 3.094, and MRE of 312.427, and JMP Pro achieved an R² value of 0.998, RMSE of 1.245, and MRE of 126.243, demonstrating higher prediction accuracy and superior performance than RSM, which had an R² value of 0.979, RMSE of 3.757, and MRE of 357.846. Further validation with parity, Taylor, and violin plots demonstrates that both models have good prediction accuracy, with the JMP Pro ANN model outperforming in terms of accuracy and alignment. This demonstrates the machine learning approach’s efficiency in analyzing the fatigue life of POPIC-MBC, revealing it to be a useful tool for future research and practical applications. Furthermore, the study reveals that the innovative approach adopted and POPIC modifier, obtained from biomass waste, meets zero-waste and circular bioeconomy goals, contributing to the UN’s SDGs 9, 11, 12, and 13. Full article

The construction sector is notorious for its high rate of fatalities globally. Previous research has established that near-miss incidents act as precursors to accidents. This study aims to identify research gaps in the literature on near-miss events in construction and to define potential directions for future research. The Scopus database serves as the knowledge source for this study. To identify publications on near-miss events, the search field “Article Title, Abstract, Keywords” was utilized with the keywords “construction” and “near miss”. The main research themes were defined based on keyword mapping performed using VOSviewer. Selected publications were assessed for their alignment with the defined research theme. A statistical analysis of the publications and the co-occurrence of keywords was conducted. The authors of the identified publications primarily used statistical analyses, artificial intelligence, employee monitoring, tracking systems, and building information modelling in their research. The conclusions from the literature review indicate a need for further research focused on developing effective predictive models for workplace accidents based on knowledge of near-miss events. This will contribute to a better understanding of the mechanisms leading to accidents and their prevention, ultimately resulting in a significant reduction in accidents in the construction sector. Full article

The rudder surfaces and lifting surfaces of a missile are utilized to acquire aerodynamic forces and moments, adjust the missile’s attitude, and achieve precise strike missions. However, the harsh flying conditions of missiles make the rudder surfaces and lifting surfaces susceptible to faults. In practical scenarios, there is often a scarcity of fault data, and sometimes, it is even difficult to obtain such data. Currently, data-driven fault detection and localization methods heavily rely on fault data, posing challenges for their applicability. To address this issue, this paper proposes an HBOS (Histogram-Based Outlier Score) online fault-detection method based on statistical distribution. This method generates a fault-detection model by fitting the probability distribution of normal data and incorporates an adaptive threshold to achieve real-time fault detection. Furthermore, this paper abstracts the interrelationships between the missile’s flight states and the propagation mechanism of faults into a hierarchical directed graph model. By utilizing bilateral adaptive thresholds, it captures the first fault features of each sub-node and determines the fault propagation effectiveness of each layer node based on the compatibility path principle, thus establishing a fault inference and localization model. The results of semi-physical simulation experiments demonstrate that the proposed algorithm is independent of fault data and exhibits high real-time performance. In multiple sets of simulated tests with randomly parameterized deviations, the fault-detection accuracy exceeds 98% with a false-alarm rate of no more than 0.31%. The fault-localization algorithm achieves an accuracy rate of no less than 97.91%. Full article