Search Results (374)

Civil aviation safety remains a critical concern globally, with continuous efforts aimed at reducing accidents and fatalities. This paper focuses on the comprehensive evaluation of civil aviation safety in the Slovak Republic over the past several years, with the main objective of identifying prevailing trends and key risk factors. A comprehensive analysis of 155 accidents and incidents was conducted based on selected operational parameters. Logistic regression was applied to identify potential causal factors influencing various levels of injury severity in aviation accidents. Moreover, the prediction model can also be used to predict the probability of specific injury severity for accidents with given parameter values. The results indicate a clear declining trend in the annual number of aviation safety events; however, the fatality rate has stagnated or slightly increased in recent years. Human error, particularly mistakes and intentional violations of procedures, was identified as the dominant causal factor across all sectors of civil aviation, including flight operations, airport management, maintenance, and air navigation services. Despite technological advancements and regulatory improvements, human-related failures persist as a major safety challenge. The findings highlight the critical need for targeted strategies to mitigate human error and enhance overall aviation safety in the Slovak Republic. Full article

The effective design of logistics platform business models is an important means for platform-type logistics enterprises to gain a competitive advantage. This study employs RRS Logistics as a case study to clarify the dynamic environmental mechanisms of logistics platform business models from the perspective of value co-creation and build a novel structural framework for logistics platform business models with community at their core. The research findings are as follows: First, guided by the idea of “value positioning–value co–creation–value support–value maintenance–value capture”, the conceptual framework of business models is redefined. The key steps in designing logistics platform business models, which can provide guidance and assistance for different logistics platforms, are proposed. Second, the design process for logistics platform business models should be dynamically adjusted in real time according to changes and environmental uncertainty. Third, in the process of transitioning to an ecological platform, logistics platforms’ ecosystem service clusters and ecosystem envelope are key factors in achieving a win–win scenario for all the stakeholders in the community. The case studies show that in logistics platform business model design, methods and key steps based on value co-creation could enhance the core competitiveness of logistics platforms. Full article

Background: The identification of pathogenic variants in the Breast Cancer Genes 1 and 2 (BRCA1/2) is a critical predictive biomarker for poly (ADP-ribose) polymerase inhibitor (PARPi) therapy in epithelial ovarian cancer (EOC). The aim of this study is to define real-world rates and determinants of germline and somatic BRCA1/2 testing and subsequent PARPi utilisation in Australia using a national clinical quality registry. Methods: This multi-centre cohort study analysed data from 1503 women with non-mucinous EOC diagnosed between May 2017 and July 2022, captured by the Australian National Gynae-Oncology Registry (NGOR). We evaluated rates of germline and somatic testing and PARPi use, using multivariate logistic regression to identify associated clinical and demographic factors. Results: Overall germline and somatic testing rates were 68% and 32%, respectively. For the high-grade serous ovarian cancer (HGSOC) cohort, rates were higher, at 78% and 39%, respectively. Germline testing was significantly less likely for women aged >80 years (OR 0.49), those in regional areas (OR 0.61), and those receiving single-modality treatment. Somatic testing uptake increased significantly following public reimbursement for PARPi (p = 0.004). Among eligible women with a newly diagnosed BRCA pathogenic variant and advanced disease (n = 110), 52% commenced first-line maintenance PARPi. Conclusions: This national study offers valuable insights into Australian ovarian cancer care, highlighting opportunities to enhance testing equity for older women (aged >80) and regional patients. Furthermore, it identifies the translation of a positive test into PARPi therapy as a complex area that warrants further collaborative investigation to optimise patient outcomes. Full article

Background/Objectives: The consumption of opioids is a public health problem that significantly affects quality of life. In Spain, 7585 people are enrolled in the Methadone Maintenance Programme (MMP), which is an effective intervention with a low adherence rate. In this study, factors associated with the quality of life of MMP users, especially perceived social support and treatment adherence, were analysed. We hypothesised that low levels of adherence and social support would be associated with poorer quality of life. Methods: This was a cross-sectional observational study with an analytical approach. Quality of life (WHOQoL-BREF), perceived social support (DUKE-UNC-11), and treatment adherence (MMAS-8) among MMP users were studied, and data on sociodemographic and clinical characteristics were collected through ad hoc questionnaires and a review of electronic medical records. Linear and logistic regression models were used. Results: A total of 70 individuals were included in this study. The mean age was 56.9 years, and 83% of the participants were male. The perceived quality of life was low in the four domains evaluated (range of 47.4–48.2). A total of 38.57% of the participants had low perceived social support. Treatment adherence was low or moderate in 77.1% of the participants. Greater perceived social support was associated with better quality of life in all domains (p < 0.05). Quality of social life was negatively associated with the use of nonbenzodiazepine neuroleptics and HIV status. Treatment adherence was lower in insulin therapy users. Conclusions: Social support is a key determinant of the quality of life of MMP users. Health policies should promote social support networks as a strategy to improve the well-being of this population. Full article

Unexpected machine failures in industrial environments lead to high maintenance costs, unplanned downtime, and safety risks. This study proposes a proactive predictive model using a hybrid of eXtreme Gradient Boosting (XGBoost) and Neural Networks (NN) to forecast machine failures. A synthetic dataset capturing recent breakdown history and time since last failure was used to simulate industrial scenarios. To address class imbalance, SMOTE and class weighting were applied, alongside a focal loss function to emphasize difficult-to-classify failures. The XGBoost model was tuned via GridSearchCV, while the NN model utilized ReLU-activated hidden layers with dropout. Evaluation using stratified 5-fold cross-validation showed that the NN achieved an F1-score of 0.7199 and a recall of 0.9545 for the minority class. XGBoost attained a higher PR AUC of 0.7126 and a more balanced precision–recall trade-off. Sample predictions demonstrated strong recall (100%) for failures, but also a high false positive rate, with most prediction probabilities clustered between 0.50–0.55. Additional benchmarking against Logistic Regression, Random Forest, and SVM further confirmed the superiority of the proposed hybrid model. Model interpretability was enhanced using SHAP and LIME, confirming that recent breakdowns and time since last failure were key predictors. While the model effectively detects failures, further improvements in feature engineering and threshold tuning are recommended to reduce false alarms and boost decision confidence. Full article

This study examined the integraftion of airborne Light Detection and Ranging (LiDAR), Simultaneous Localization and Mapping (SLAM)-based handheld LiDAR, and iPhone LiDAR to inspect forest road networks following forest operations. The goal is to overcome the challenges posed by dense canopy cover and ensure accurate and efficient data collection and mapping. Airborne data were collected using the DJI Matrice 300 RTK UAV equipped with a Zenmuse L2 LiDAR sensor, which achieved a high point density of 285 points/m² at an altitude of 80 m. Ground-level data were collected using the BLK2GO handheld laser scanner (HPLS) with SLAM methods (LiDAR SLAM, Visual SLAM, Inertial Measurement Unit) and the iPhone 13 Pro Max LiDAR. Data processing included generating DEMs, DSMs, and True Digital Orthophotos (TDOMs) via DJI Terra, LiDAR360 V8, and Cyclone REGISTER 360 PLUS, with additional processing and merging using CloudCompare V2 and ArcGIS Pro 3.4.0. The pairwise comparison analysis between ALS data and each alternative method revealed notable differences in elevation, highlighting discrepancies between methods. ALS + iPhone demonstrated the smallest deviation from ALS (MAE = 0.011, RMSE = 0.011, RE = 0.003%) and HPLS the larger deviation from ALS (MAE = 0.507, RMSE = 0.542, RE = 0.123%). The findings highlight the potential of fusing point clouds from diverse platforms to enhance forest road mapping accuracy. However, the selection of technology should consider trade-offs among accuracy, cost, and operational constraints. Mobile LiDAR solutions, particularly the iPhone, offer promising low-cost alternatives for certain applications. Future research should explore real-time fusion workflows and strategies to improve the cost-effectiveness and scalability of multisensor approaches for forest road monitoring. Full article

Objective: This study aimed to identify plasma exosomal microRNAs (miRNAs) associated with weight loss and type 2 diabetes (T2D) remission following low-calorie diet (LCD) intervention. Methods: A 6-month dietary intervention targeting T2D remission was conducted among individuals with T2D. Participants underwent a 3-month intensive weight loss phase consuming LCD (815–835 kcal/day) and a 3-month weight maintenance phase (N = 32). Sixteen participants were randomly selected for characterization of plasma-derived exosomal miRNA profiles at baseline, 3 months, and 6 months using small RNA sequencing. Linear mixed-effects models were used to identify differentially expressed exosomal miRNAs between responders and non-responders. Pathway enrichment analyses were conducted using target mRNAs of differentially expressed miRNAs. Logistic regression models assessed the predictive value of differentially expressed miRNAs for T2D remission. Results: Among the 16 participants, 6 achieved weight loss ≥10% and 12 achieved T2D remission. Eighteen exosomal miRNAs, including miR-92b-3p, miR-495-3p, and miR-452b-5p, were significantly associated with T2D remission and weight loss. Pathway analyses revealed enrichment in PI3K-Akt pathway, FoxO signaling pathway, and insulin receptor binding. The addition of individual miRNAs including miR-15b-3p, miR-26a-5p, and miR-3913-5p to base model improved the area under the curve values by 0.02–0.08 at 3 months and by 0.02–0.06 at 6 months for T2D remission. Conclusions: This study identified exosomal miRNAs associated with T2D remission and weight loss following LCD intervention. Several exosomal miRNAs might serve as valuable predictors of T2D remission in response to LCD intervention. Full article

Background and Objectives: Glucose instability has been established to be related to postoperative morbidity and mortality in liver transplantation. To date, the impact of maintaining optimal blood glucose (BG) levels on the incidence of acute kidney injury (AKI) following liver transplantation (LT) remains unclear. This study aimed to determine the impact of optimal BG level after reperfusion (REP BG) on the incidence of AKI after living donor LT (LDLT). Materials and Methods: This study retrospectively reviewed 3331 patients who underwent LDLT between January 2008 and December 2019. Patients were divided into optimal (110 mg/dL < BG < 180 mg/dL) and non-optimal (BG < 110 mg/dL or >180 mg/dL) REP BG groups. Multivariable logistic regression analysis was performed to assess factors associated with AKI. Propensity score matching (PSM) was used to compare the incidence of AKI, AKI severity, and progression to chronic kidney disease (CKD) between the groups. Results: The incidence of AKI was 66.7%. After PSM, patients in the optimal REP BG group showed a lower incidence of AKI (66.5% vs. 70.6%, p = 0.032). Multivariable logistic regression analysis showed that the non-optimal REP BG group was independently associated with a higher risk of AKI (odds ratio [OR], 1.21; 95% confidence interval [CI], 1.02–1.45; p = 0.037) compared to the optimal group. Similarly, the risks of severe AKI (OR, 1.32; 95% CI, 1.11–1.58; p = 0.002) and progression to CKD (OR, 1.19; 95% CI, 1.01–1.41; p = 0.039) were significantly higher in the non-optimal group after PSM. Conclusions: Maintenance of an optimal REP BG was associated with a significantly lower incidence of AKI and a reduced risk of progression to CKD within 1 year after LDLT. Full article

Terahertz (THz) communications and simultaneous wireless information and power transfer (SWIPT) hold the potential to energize battery-less Internet-of-Things (IoT) devices while enabling multi-gigabit data transmission. However, severe path loss, blockages, and rectifier nonlinearity significantly hinder both throughput and harvested energy. Additionally, high-power THz beams pose safety concerns by potentially exceeding specific absorption rate (SAR) limits. We propose a sensing-adaptive power-focusing (APF) framework in which a reconfigurable intelligent surface (RIS) embeds low-rate THz sensors. Real-time backscatter measurements construct a spatial map used for the joint optimisation of (i) RIS phase configurations, (ii) multi-tone SWIPT waveforms, and (iii) nonlinear power-splitting ratios. A weighted MMSE inner loop maximizes the data rate, while an outer alternating optimisation applies semidefinite relaxation to enforce passive-element constraints and SAR compliance. Full-stack simulations at 0.3 THz with 20 GHz bandwidth and up to 256 RIS elements show that APF (i) improves the rate–energy Pareto frontier by 30–75% over recent adaptive baselines; (ii) achieves a 150% gain in harvested energy and a 440 Mbps peak per-user rate; (iii) reduces energy-efficiency variance by half while maintaining a Jain fairness index of 0.999;; and (iv) caps SAR at 1.6 W/kg, which is 20% below the IEEE C95.1 safety threshold. The algorithm converges in seven iterations and executes within <3 ms on a Cortex-A78 processor, ensuring compliance with real-time 6G control budgets. The proposed architecture supports sustainable THz-powered networks for smart factories, digital-twin logistics, wire-free extended reality (XR), and low-maintenance structural health monitors, combining high-capacity communication, safe wireless power transfer, and carbon-aware operation for future 6G cyber–physical systems. Full article

The continuous, effective operation of grid-connected photovoltaic (GCPV) systems depends on dependable inverter failure detection. Early, precise fault diagnosis improves general system dependability, lowers maintenance costs, and saves downtime. Although computing efficiency remains a difficulty, particularly in resource-limited contexts, machine learning-based fault detection presents interesting prospects in accuracy and responsiveness. By streamlining data complexity and allowing faster and more effective fault diagnosis, dimensionality reduction methods play vital role. Using dimensionality reduction and ML techniques, this work explores inverter fault detection in GCPV systems. Photovoltaic inverter operational data was normalized and preprocessed. In the next step, dimensionality reduction using Principal Component Analysis (PCA) and autoencoder-based feature extraction were explored. For ML training four classifiers which include Random Forest (RF), logistic regression (LR), decision tree (DT), and K-Nearest Neighbors (KNN) were used. Trained on the whole standardized dataset, the RF model routinely produced the greatest accuracy of 99.87%, so efficiently capturing complicated feature interactions but requiring large processing resources and time of 36.47sec. LR model showed reduction in accuracy, but very fast training time compared to other models. Further, PCA greatly lowered computing demands, especially improving inference speed for LR and KNN. High accuracy of 99.23% across all models was maintained by autoencoder-derived features. Full article

Against the backdrop of China’s “dual-carbon” initiative, this study innovatively applies a process-based life cycle assessment (PLCA) methodology, meticulously tracking energy and carbon flows across material production, transportation, and maintenance processes. By comparing six asphalt pavement maintenance technologies in Xinjiang, the research reveals that milling and resurfacing (MR) exhibits the highest energy consumption 250,809 MJ/10³ m²) and carbon emissions (15,095.67 kg CO₂/10³ m²), while preventive techniques like hot asphalt grouting reduce emissions by up to 87%. The PLCA approach uncovers a critical insight: 40–60% of total emissions originate from the raw material production phase, with cement and asphalt identified as primary contributors. This granular analysis, unique in regional road maintenance research, challenges traditional assumptions and emphasizes the necessity of upstream intervention. By contrasting reactive and preventive strategies, the study validates that early-stage maintenance aligns seamlessly with circular economy principles. Tailored to a local arid climate and vast transportation network, the study concludes that prioritizing preventive maintenance, adopting low-carbon materials, and optimizing logistics can significantly decarbonize road infrastructure. These region-specific strategies, underpinned by the novel application of PLCA, not only provide actionable guidance for local policymakers but also offer a replicable framework for sustainable road development worldwide, bridging the gap between scientific research and practical decarbonization efforts. Full article

The global shift toward sustainable transportation has gained increasing interest, promoting the use of electric vehicles (EVs) as an environmentally friendly alternative to conventional vehicles as a result of a complex interaction between economic incentives, social dynamics, and environmental imperatives. This study is based on the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) to understand the key factors influencing consumers’ intentions in the Sultanate of Oman toward adopting electric vehicles. It is based on a mixed methodology combining quantitative data from a questionnaire of 448 participants, analyzed using ordinal logistic regression, with qualitative thematic analysis of in-depth interviews with 18 EV owners. Its results reveal that performance expectations, trust in EV technology, and social influence are the strongest predictors of EV adoption intentions in Oman. These findings suggest that some issues related to charging infrastructure, access to maintenance services, and cost-benefit ratio are key considerations that influence consumers’ intention to accept and use EVs. Conversely, recreational motivation is not a statistically significant factor, which suggests that consumers focus on practical and economic motivations when deciding to adopt EVs rather than on their enjoyment of driving the vehicle. The findings of this study provide valuable insights for decision-makers and practitioners to understand public perceptions of electric vehicles, enabling them to design effective strategies to promote the adoption of these vehicles in the emerging sustainable transportation market of the future. Full article

Safe and efficient operation of port cranes is vital for maintaining the efficiency of global maritime logistics. However, traditional non-destructive testing methods face significant limitations in harsh port environments, such as periodic inspection intervals, restricted access to structural components, and a lack of real-time monitoring. This review explores the emerging paradigm of remote non-destructive testing through the integration of vibration and acoustic emission sensors with Internet of Things platforms. By enabling continuous, real-time monitoring, these sensor systems can detect early indicators of mechanical degradation, structural fatigue, and corrosion. This study synthesizes findings from over 100 peer-reviewed sources and identifies a significant gap in the application of these technologies to port cranes. Although vibration and acoustic emission sensors have been widely studied in various fields, their application to port cranes remains underexplored, presenting a novel and promising avenue for future research and practical applications. The unique operational demands and structural complexities of port cranes, coupled with their critical role in global trade logistics, make them ideal for leveraging these sensors in tandem with Internet of Things solutions. This integration not only overcomes the limitations of traditional non-destructive testing methods, but also offers substantial benefits, including enhanced safety, reduced inspection costs, and improved operational efficiency. This review concludes by proposing future research directions to enhance sensor performance, data analytics, and Internet of Things integration, paving the way for predictive maintenance strategies that increase operational uptime and improve safety in port crane operations. Full article

This perspective explores the use of biochar, a carbon-rich material derived from biomass, as a sustainable solution for mitigating volatile organic compounds (VOCs) emitted during asphalt production and use. VOCs from asphalt contribute to ozone formation and harmful secondary organic aerosols (SOAs), which negatively impact air quality and public health. Biochar, with its high surface area and capacity to adsorb VOCs, provides an effective means of addressing these challenges. By tailoring biochar’s surface chemistry, it can efficiently capture VOCs, while also offering long-term carbon sequestration benefits. Additionally, biochar enhances the durability of asphalt, extending road lifespan and reducing maintenance needs, making it a promising material for sustainable infrastructure. Despite these promising benefits, several challenges remain. Variations in biochar properties, driven by differences in feedstock and production methods, can affect its performance in asphalt. Moreover, the integration of biochar into existing plant operations requires the further development of methods to streamline the process and ensure consistency in biochar’s quality and cost-effectiveness. Standardizing production methods and addressing logistical hurdles will be crucial for biochar’s widespread adoption. Research into improving its long-term stability in asphalt is also needed to ensure sustained efficacy over time. Overcoming these challenges will be essential for fully realizing biochar’s potential in sustainable infrastructure development Full article

Effective trail management is essential for preventing environmental degradation and promoting sustainable recreational use. This study proposes a GIS-based, explainable machine learning framework for predicting forest trail degradation using exclusively environmental variables, eliminating the need for costly visitor monitoring data that remains unavailable in most operational forest settings. Field surveys conducted in Geumjeongsan, South Korea, classified trail segments as degraded or non-degraded based on physical indicators such as erosion depth, trail width, and soil hardness. Environmental predictors—including elevation, slope, trail slope alignment (TSA), topographic wetness index (TWI), vegetation type, and soil texture—were derived from spatial analysis. Three machine learning algorithms (Binary Logistic Regression, Random Forest, and Gradient Boosting) were systematically compared using confusion matrix metrics and AUC-ROC (Area Under the Receiver Operating Characteristic Curve). Random Forest (RF) was selected for its strong performance (AUC-ROC = 0.812) and seamless integration with SHAP (SHapley Additive exPlanations) for transparent interpretation. Spatial block cross-validation achieved an AUC-ROC of 0.729, confirming robust spatial generalization. SHAP analysis revealed vegetation type as the most significant predictor, with hardwood forests showing higher degradation susceptibility than mixed forests. A susceptibility map generated from the RF model indicated that 40.7% of the study area faces high to very high degradation risk. This environmental-only approach enables proactive trail management across data-limited forest systems globally, providing actionable insights for sustainable trail maintenance without requiring visitor use data. Full article