Search Results (2,941)

Objectives: The need for concomitant coronary artery bypass grafting during acute type A aortic dissection repair is common and associated with high mortality. This study aims to characterize the patient cohort, assess outcomes, and evaluate the role of preoperative diagnostics in these high-risk patients. Methods: Patients who underwent concomitant coronary artery bypass and acute type A aortic dissection repair between March 2007 and June 2023 were included. In-hospital survivors and non-survivors were compared. Logistic regression analyses were performed to identify predictors of in-hospital mortality. Preoperative computed tomography scans were independently reviewed by a cardiovascular radiologist to assess potential coronary involvement. The agreement between computed tomography and intraoperative reports of coronary dissection was evaluated using Cohen’s κappa test. Results: The cohort consisted of ninety-eight patients. In-hospital mortality was 26.5% (n = 26). The right coronary artery was the most frequently grafted (57%, n = 56). Elevated preoperative creatine kinase was the only predictor of in-hospital mortality (p = 0.044). Of the 72 available preoperative CT scans, 76% (n = 55) indicated coronary involvement, whereas intraoperative coronary dissection requiring bypass grafting was documented in only 42% (n = 30)). The agreement between computer tomography and intraoperative dissection reports was poor (κappa 0.043 (95% CI, −0.155 to 0.241), p = 0.66). Conclusion: Simultaneous coronary artery bypass during acute type A aortic dissection repair remains associated with high mortality and morbidity. The right coronary artery is most often affected. Coronary bypass is not always linked to coronary dissection, making intraoperative detection challenging. This underscores the importance of preoperative diagnostics, especially computer tomography. Full article

This paper presents a comprehensive analysis of artificial intelligence-powered automated grading systems (AI AGSs) in STEM education, systematically examining their algorithmic foundations, mathematical modeling approaches, and quantitative evaluation methodologies. AI AGSs enhance grading efficiency by providing large-scale, instant feedback and reducing educators’ workloads. Compared to traditional manual grading, these systems improve consistency and scalability, supporting a wide range of assessment types, from programming assignments to open-ended responses. This paper provides a structured taxonomy of AI techniques including logistic regression, decision trees, support vector machines, convolutional neural networks, transformers, and generative models, analyzing their mathematical formulations and performance characteristics. It further examines critical challenges, such as user trust issues, potential biases, and students’ over-reliance on automated feedback, alongside quantitative evaluation using precision, recall, F1-score, and Cohen’s Kappa metrics. The analysis includes feature engineering strategies for diverse educational data types and prompt engineering methodologies for large language models. Lastly, we highlight emerging trends, including explainable AI and multimodal assessment systems, offering educators and researchers a mathematical foundation for understanding and implementing AI AGSs into educational practices. Full article

Background: Industry 4.0 (I4.0) has gained significant attention in recent years, with the term Logistics 4.0 (L4.0) emerging in the logistics industry. However, L4.0 remains vague and lacks a unified definition or classification of related technologies. Existing studies defining L4.0 are mainly conceptual and speculative, rather than grounded in empirical research. To address this gap, this study contributes to defining L4.0 through the sub-area of Warehouse 4.0 (W4.0), focusing on the challenges of adopting I4.0 technologies in warehouses. Methods: Through the I4.0 and L4.0 literature, an initial classification of W4.0 technologies in third-party logistics (3PL) was developed. This was refined using a case study of a global logistics service provider (LSP) in the 3PL industry, through semi-structured interviews with stakeholders. Results: The empirical findings identify new application areas for I4.0 technology in 3PL warehouses, including horizontal and vertical system integration, big data, and cybersecurity, technologies that can enhance 3PL competitiveness. Conclusions: This study offers a structured classification of W4.0 technologies and insights into the application areas of W4.0 in 3PLs. It contributes practical insights into which I4.0 technologies are relevant for the 3PL warehouse industry and their potential application areas. Full article

Background: Accurate route planning is a core challenge in logistics, particularly for small- and medium-sized enterprises that lack access to costly geospatial tools. This study explores whether usable distance matrices and routing outputs can be generated solely from geographic coordinates without relying on full map-based infrastructure. Methods: A dataset of over 5000 Hungarian postal locations was used to evaluate five models: Haversine-based scaling with circuity, linear regression, second- and third-degree polynomial regressions, and a trained artificial neural network. Models were tested on the full dataset, and three example routes representing short, medium, and long distances. Both statistical accuracy and route-level performance were assessed, including a practical optimization task. Results: Statistical models maintained internal consistency, but systematically overestimated longer distances. The ANN model provided significantly better accuracy across all scales and produced routes more consistent with map-based paths. A new evaluation method was introduced to directly compare routing outputs. Conclusions: Practical route planning can be achieved without GIS services. ML-based estimators offer a cost-effective alternative, with potential for further improvement using larger datasets, additional input features, and the integration of travel time prediction. This approach bridges the gap between simplified approximations and commercial routing systems. Full article

Background: Hematopoietic cell transplantation (HCT) and chimeric antigen receptor T-cell (CAR-T) therapy have markedly improved survival in pediatric patients with hematological malignancies. However, these treatments cause profound immunosuppression, leading to significant susceptibility to vaccine-preventable diseases (VPDs), including invasive pneumococcal disease and measles. Timely and tailored immunization strategies are crucial to mitigate infectious risks in this vulnerable population. Methods: We conducted a narrative review of the English-language literature from 2000 to 2024, including clinical guidelines, surveys, and original studies, to evaluate immune reconstitution and vaccination practices in pediatric patients undergoing HCT and CAR-T therapy. Literature searches in PubMed, Scopus, and Web of Science used disease-specific, therapy-specific, and pathogen-specific terms. Data synthesis focused on vaccine schedules, immune recovery markers, and adherence challenges. Results: Profound immune deficits post-HCT and CAR-T therapy compromise both innate and adaptive immunity, often necessitating revaccination. Key factors influencing vaccine responses include time since therapy, graft source, immunosuppressive treatments, and chronic graft-versus-host disease. Although inactivated vaccines are generally safe from three to six months post-HCT, live vaccines remain contraindicated until documented immune recovery. CAR-T therapy introduces unique challenges due to prolonged B-cell aplasia and hypogammaglobulinemia, leading to delayed or reduced vaccine responses. Despite established guidelines, real-world adherence to vaccination schedules remains suboptimal, driven by institutional, logistic, and patient-related barriers. Conclusions: Effective vaccination strategies are essential for reducing infectious morbidity in pediatric HCT and CAR-T recipients. Personalized vaccine schedules, immune monitoring, and multidisciplinary coordination are critical to bridging gaps between guidelines and practice, ultimately improving long-term outcomes for immunocompromised children. Full article

Background/Objectives: Multimorbidity, where patients have ≥2 comorbidities, is recognized as a major challenge for health systems worldwide, driving up morbidity and cost. The differences in multimorbidity burden between those with and without type-2 diabetes mellitus (T2DM) in the Veteran population are not well studied. This large retrospective cohort study fills the existing gap. Methods: Using a retrospective cohort of adult Veterans with and without T2DM, we examined 29 comorbidities defined by Elixhauser criteria for 10,499,394 Veterans from 1 January 2008 to 31 December 2009. We then ascertained diabetes status for 10 years of follow-up from 1 January 2010 to 31 December 2019. Multimorbidity status was categorized using the Elixhauser comorbidity index (0, 1, ≥2) and logistic regression was used to estimate the odds ratio (OR) for its association with risk of diabetes, adjusting for covariates. Results: Compared to those with zero comorbidities, the odds of having diabetes were more than doubled (2.53, CI: 2.51–2.54) for those with ≥2 comorbidities. Conclusions: The doubling of the odds of T2DM among those with more than one comorbidity is typical of Veterans with T2DM. In addition, the odds were significantly higher for Hispanics compared to other groups when adjusting for covariates. This calls for more attention to reduce the risk of T2DM through improved management and effective use of treatments informed by disparities that exist in the VHA. Full article

Autonomous navigation for unmanned vehicles in complex, unstructured environments remains challenging, especially in GPS-denied or obstacle-dense scenarios, limiting their practical deployment in logistics, inspection, and emergency response applications. To overcome these limitations, this paper presents a tightly integrated air-ground collaborative system comprising three key components: (1) an aerial perception module employing a YOLOv8-based vision system onboard the UAV to generate real-time global obstacle maps; (2) a low-latency communication module utilizing FAST DDS middleware for reliable air-ground data transmission; and (3) a ground navigation module implementing an A* algorithm for optimal path planning coupled with closed-loop control for precise trajectory execution. The complete system was physically implemented using cost-effective hardware and experimentally validated in cluttered environments. Results demonstrated successful UGV autonomous navigation and obstacle avoidance relying exclusively on UAV-provided environmental data. The proposed framework offers a practical, economical solution for enabling robust UGV operations in challenging real-world conditions, with significant potential for diverse industrial applications. Full article

The proliferation of fake news, particularly in sensitive domains like religious texts, necessitates robust authenticity verification methods. This study addresses the growing challenge of authenticating Hadith, where traditional methods relying on the analysis of the chain of narrators (Isnad) and the content (Matn) are increasingly strained by the sheer volume in circulation. To combat this issue, machine learning (ML) and natural language processing (NLP) techniques, specifically through transfer learning, are explored to automate Hadith classification into Genuine and Fake categories. This study utilizes an imbalanced dataset of 8544 Hadiths, with 7008 authentic and 1536 fake Hadiths, to systematically investigate the collective impact of both linguistic and contextual features, particularly the chain of narrators (Isnad), on Hadith authentication. For the first time in this specialized domain, state-of-the-art pre-trained language models (PLMs) such as Multilingual BERT (mBERT), CamelBERT, and AraBERT are evaluated alongside classical algorithms like logistic regression (LR) and support vector machine (SVM) for Hadith authentication. Our best-performing model, AraBERT, achieved a 99.94% F1score when including the chain of narrators, demonstrating the profound effectiveness of contextual elements (Isnad) in significantly improving accuracy, providing novel insights into the indispensable role of computational methods in Hadith authentication and reinforcing traditional scholarly emphasis. This research represents a significant advancement in combating misinformation in this important field. Full article

The strategic development of the low-altitude economy requires efficient urban logistics solutions. The existing Unmanned Aerial Vehicle (UAV) truck delivery system faces severe challenges in dealing with dynamic weather constraints and multi-agent coordination. This article proposes a neural network-based optimisation framework that integrates constrained K-means clustering and a three-stage neural architecture. In this work, a mathematical model for heterogeneous vehicle constraints considering time windows and UAV energy consumption is developed, and it is validated through reference to the Solomon benchmark’s arithmetic examples. Experimental results show that the Truck–Drone Cooperative Traveling Salesman Problem (TDCTSP) model reduces the cost by 21.3% and the delivery time variance by 18.7% compared to the truck-only solution (Truck Traveling Salesman Problem (TTSP)). Improved neural network (INN) algorithms are also superior to the traditional genetic algorithm (GA) and Adaptive Large Neighborhood Search (ALNS) methods in terms of the quality of computed solutions. This research provides an adaptive solution for intelligent low-altitude logistics, which provides a theoretical basis and practical tools for the development of urban air traffic under environmental uncertainty. Full article

The logistics industry saw substantial growth in the second half of the 20th century, and logistics companies play a vital role in today’s modern market. Constant shifts in the market present challenges for logistics firms, which must find the optimal balance between achieved goals and utilized resources. The primary indicator that reflects this relationship is efficiency. Measuring and monitoring efficiency in logistics companies is extremely demanding because the final product is not a tangible item; instead, it often consists of transportation, storage, transloading, and forwarding services that require extensive resources. This paper focuses on measuring and improving efficiency. Numerous approaches and methods for evaluating the efficiency of logistics companies are examined. To measure and enhance efficiency, as well as rank companies based on operational efficiency, a three-phase DEA-fuzzy AHP model has been developed. This model was tested using a real-world example by analyzing the efficiency of ten logistics companies in the Republic of Serbia. The results of the analysis indicate the applicability of this model for measuring and improving the efficiency of logistics companies, as well as for their ranking. Full article

The development of sustainable packaging in the food industry is essential to meet the growing demand for more environmentally friendly practices and to contribute to material circularity and solid waste reduction. In this context, this review explores the main categories of sustainable packaging in the food industry, including recyclable, reusable, biodegradable, and compostable packages, highlighting the materials used, their characteristics, advantages, and limitations. Furthermore, it discusses innovations that combine convenience and safety with lower environmental impact, such as the use of biopolymers, and nanomaterials that extend food preservation, enhance properties, and enable broader application. The adoption of these technologies can reduce dependence on fossil-based plastics and minimize environmental impacts, although challenges remain, such as economic viability, regulatory standardization, and consumer acceptance. Additionally, the review addresses difficulties related to recycling and reverse logistics, emphasizing the need for a joint effort among companies, governments, and consumers to promote a more sustainable food system. Thus, the research highlights the importance of innovation and collaboration in developing viable solutions that reconcile sustainability, food safety, and efficiency in the packaging industry. Full article

Classifying human locomotor activities from wearable sensor data is an important high-level component of control schemes for many wearable robotic exoskeletons. In this study, we evaluated three machine learning models for classifying activity type (walking, running, jumping), speed, and surface incline using input data from body-worn inertial measurement units (IMUs) and e-textile insole pressure sensors. The IMUs were positioned on segments of the lower limb and pelvis during lab-based data collection from 16 healthy participants (11 men, 5 women), who walked and ran on a treadmill at a range of preset speeds and inclines. Logistic Regression (LR), Random Forest (RF), and Light Gradient-Boosting Machine (LGBM) models were trained, tuned, and scored on a validation data set (n = 14), and then evaluated on a test set (n = 2). The LGBM model consistently outperformed the other two, predicting activity and speed well, but not incline. Further analysis showed that LGBM performed equally well with data from a limited number of IMUs, and that speed prediction was challenged by inclusion of abnormally fast walking and slow running trials. Gyroscope data was most important to model performance. Overall, LGBM models show promise for implementing locomotor activity prediction from lower-limb-mounted IMU data recorded at different anatomical locations. Full article

The construction industry faces persistent challenges, including low productivity, high waste generation, and resistance to technological innovation. Off-site modular construction, supported by Building Information Modeling (BIM), emerges as a promising strategy to address these issues and advance sustainability goals. This study aims to evaluate the practical impacts of industrialized off-site construction in the Brazilian context, focusing on cost, execution time, structural weight, and architectural–logistical constraints. The novelty lies in applying the methodology to a high standard, mixed-use multifamily building, an atypical scenario for modular construction in Brazil, and employing a MultiCriteria Decision Analysis (MCDA) to integrate results. A detailed case study is developed comparing conventional and off-site construction approaches using BIM-assisted analyses for weight reduction, cost estimates, and schedule optimization. The results show an 89% reduction in structural weight, a 6% decrease in overall costs, and a 40% reduction in project duration when adopting fully off-site solutions. The integration of results was performed through the Weighted Scoring Method (WSM), a form of MCDA chosen for its transparency and adaptability to case studies. While this study defined weights and scores, the framework allows the future incorporation of stakeholder input. Challenges identified include the need for early design integration, transport limitations, and site-specific constraints. By quantifying benefits and limitations, this study contributes to expanding the understanding of off-site modular adaptability of construction projects beyond low-cost housing, demonstrating its potential for diverse projects and advancing its implementation in emerging markets. Beyond technical and economic outcomes, the study also frames off-site modular construction within the three pillars of sustainability. Environmentally, it reduces structural weight, resource consumption, and on-site waste; economically, it improves cost efficiency and project delivery times; and socially, it offers potential benefits such as safer working conditions, reduced urban disruption, and faster provision of community-oriented buildings. These dimensions highlight its broader contribution to sustainable development in Brazil. Full article

Geohazard investigation in volcanic fields is essential for understanding and mitigating risks associated with volcanic activity. Volcanic vents are often concealed by processes such as faulting, subsidence, or uplift, which complicates their detection and hampers hazard assessment. To address this challenge, we developed a predictive framework that integrates high-resolution gravity data with multiple machine learning algorithms. Logistic Regression, Gradient Boosting Machine (GBM), Decision Tree, Support Vector Machine (SVM), and Random Forest models were applied to analyze the gravitational characteristics of known volcanic vents and predict the likelihood of undiscovered vents at other locations. The problem was formulated as a binary classification task, and model performance was assessed using accuracy, precision, recall, F1-score, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The Random Forest algorithm yielded optimal outcomes: 95% classification accuracy, AUC-ROC score of 0.99, 75% geographic correspondence between real and modeled vent sites, and a 95% certainty degree. Spatial density analysis showed that the distribution patterns of predicted and actual vents are highly similar, underscoring the model’s reliability in identifying vent-prone areas. The proposed method offers a valuable tool for geoscientists and disaster management authorities to improve volcanic hazard evaluation and implement effective mitigation strategies. These results represent a significant step forward in our ability to model volcanic dynamics and enhance predictive capabilities for volcanic hazard assessment. Full article

The construction sector is a major contributor to global waste output, with construction and demolition waste (CDW) producing substantial environmental, economic, and logistical challenges. Traditional methods for handling waste in developing countries have failed to implement sustainability concepts successfully, resulting in inefficient resource consumption and increasing landfill reliance. This study develops an Analytic Hierarchy Process (AHP) framework to integrate circular economy (CE) principles into construction waste management (CWM). The framework evaluates four criteria under economic, environmental, social, and technological categorization and applies expert-based pairwise comparisons to prioritize alternative strategies. To ensure reliability, the results were further validated through sensitivity analysis and cross-validation using complementary MCDM methods, including the TOPSIS, WSM, and WPM. The research attempted to determine the most successful waste management approach by examining critical economic, social, technical, and environmental issues in the setting of Sri Lanka as a case study. A hierarchical model was built, and expert views were gathered using pairwise comparisons to assess the relative importance of each criterion. The results showed that environmental considerations had the greatest relative importance (41.6%), followed by economic (38.4%), technical (12.6%), and social aspects (7.4%). On-site waste segregation appeared as the most suitable method owing to its immediate contribution to sustainability, while off-site treatment, prefabrication, modular construction, and waste-to-energy conversion followed. The research underlines the significance of organized decision-making in waste management and advises incorporating real-time data analytics and artificial intelligence to boost adaptable and sustainable construction practices. Full article