Search Results (2,441)

The Strait of Messina poses unique challenges for small vessels due to strong currents and complex wave conditions, which critically affect structural integrity and operational safety. This study proposes an integrated methodology that combines seakeeping analysis, a comparison with classification society rules, and fatigue life assessment within a unified and computationally efficient framework. A panel-based approach was used to compute vessel motions and vertical bending moments at different speeds and wave directions. Hydrodynamic loads derived from Response Amplitude Operators (RAOs) were compared with regulatory limits and applied to fatigue analysis. A further innovative aspect is the use of high-resolution bathymetric data from the Strait of Messina, enabling a realistic representation of local currents and sea states and providing a more accurate assessment than studies based on idealized conditions. The results show that forward speed amplifies bending moments, reducing safe wave heights from 2 m at rest to about 0.5 m at 16 knots. Fatigue analysis indicates that aluminum hulls are highly vulnerable to 2–3 m waves, while steel and titanium show no significant damage. The proposed workflow is transferable to other vessel types and supports safer design and operation. The case study of the Strait of Messina, the busiest and most challenging maritime corridor in Italy, confirms the validity and practical importance of the approach. By combining hydrodynamic and structural analyses into a single workflow, this study establishes the foundation for predictive maintenance and real-time structural health monitoring, with significant implications for navigation safety in complex sea environments. Full article

Obstructive coronary artery disease (CAD) is common in patients undergoing transcatheter aortic valve implantation (TAVI). While invasive coronary angiography (ICA) is the gold standard for coronary evaluation, coronary computed tomography angiography (cCTA) is gaining interest for its potential to exclude obstructive CAD during pre-procedural imaging. This study aimed to assess the diagnostic accuracy of cCTA in ruling out significant CAD in TAVI candidates. We retrospectively analyzed 95 TAVI candidates (mean age 77.7 ± 8.5 years) who underwent both cCTA and ICA. Diagnostic performance of cCTA—sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy—was assessed using ICA as the reference, in both patient- and vessel-based models. Obstructive CAD was defined as ≥50% luminal stenosis or occlusion of a stent/bypass graft. ICA detected obstructive CAD in 27 patients (28.4%). Excluding non-evaluable cases, cCTA showed a negative predictive value (NPV) of 97% (patient-level) and 95% (vessel-level), with a diagnostic accuracy of 85% and 87%, respectively. Including all patients, regardless of scan quality, the NPV remained high (97%), although overall accuracy dropped to 67% (patient-level) and 66% (vessel-level). cCTA demonstrated high accuracy in excluding significant CAD, with a stable NPV of 95–97%. The relatively high rate of non-diagnostic scans and the single-center, retrospective design suggest that its role should be considered complementary to ICA, potentially reducing—but not replacing—the need for ICA in selected TAVI candidates. Full article

The present work reports the bioguided isolation of constituents from the ethanol extract of Holarrhena floribunda stem bark, their identification by UHPLC-ESI-QTOF-MS/MS identification, and the in silico prediction of the pharmacokinetic and toxicity parameters. The crude extract, along with its n-hexane and alkaloid-rich fractions, displayed moderate to good antiplasmodial activity in vitro against chloroquine-sensitive (3D7) and multidrug-resistant (Dd2) strains of Plasmodium falciparum, with IC₅₀ values ranging from 6.54 to 43.54 µg/mL. Seventeen steroidal alkaloids (1–17) were identified in the most active fraction using UHPLC-ESI-QTOF-MS/MS, based on their fragmentation patterns and analysis with the Structural Similarity Network Annotation Platform for Mass Spectrometry (SNAP-MS). Furthermore, bioguided isolation of the ethanol extract yielded twenty-one compounds (3, 5, 10, 14–16, 18–31), whose structures were elucidated by spectroscopic methods. Among them, compounds 5, 14, and 27 showed the highest potency against the two strains of P. falciparum, with IC₅₀ values between 25.97 and 55.78 µM. In addition, the in silico prediction of pharmacokinetic parameters and drug-likeness using the SwissADME web tool indicated that most of the evaluated compounds (1, 3–5, and 14–16) complied with Lipinski’s rule of five. Full article

Accurate prediction of reinforced concrete shear strength is essential for structural safety, yet datasets often contain a mix of raw geometric and material properties alongside physics-informed engineered features, making optimal feature selection challenging. This study introduces a statistically principled framework that advances feature reduction for neural networks in three novel ways. First, it extends the artificial neural network-based signal-to-noise ratio (ANN-SNR) method, previously limited to classification, into regression tasks for the first time. Second, it couples ANN-SNR with a confidence-interval (CI)-based stopping rule, using the lower bound of the baseline ANN’s R² confidence interval as a rigorous statistical threshold for determining when feature elimination should cease. Third, it systematically evaluates both raw experimental variables and physics-informed engineered features, showing how their combination enhances both robustness and interpretability. Applied to experimental concrete shear strength data, the framework revealed that many low-SNR features in conventional formulations contribute little to predictive performance and can be safely removed. In contrast, hybrid models that combined key raw and engineered features consistently yielded the strongest performance. Overall, the proposed method reduced the input feature set by approximately 45% while maintaining results statistically indistinguishable from baseline and fully optimized models (R² ≈ 0.85). These findings demonstrate that ANN-SNR with CI-based stopping provides a defensible and interpretable pathway for reducing model complexity in reinforced concrete shear strength prediction, offering practical benefits for design efficiency without compromising reliability. Full article

The development of the Connected and Autonomous Vehicle (CAV) and Hybrid Electric Vehicle (HEV) provides a new effective means for the optimization of eco-driving strategies. However, the existing research has not effectively considered the cooperative speed optimization and power allocation problem of the Connected and Autonomous Plug-in Hybrid Electric Vehicle (CAPHEV) platoon. To this end, a hierarchical eco-driving strategy is proposed, which aims to enhance driving efficiency and fuel economy while ensuring the safety and comfort of the platoon. Firstly, an improved car-following model is proposed, which considers the motion states of multiple preceding vehicles. On this basis, a platoon cooperative car-following decision-making method based on model predictive control is designed. Secondly, a distributed energy management strategy is constructed, and a bionic optimization algorithm based on the behavior of nutcrackers is introduced to solve nonlinear problems, so as to solve the energy distribution and management problems of powertrain systems. Finally, the tests are conducted under the driving cycle of the Urban Dynamometer Driving Schedule (UDDS) and the Highway Fuel Economy Test (HWFET). The results show that the proposed strategy can ensure the driving safety of the CAPHEV platoon in different scenes, and has excellent tracking accuracy and driving comfort. Compared with the rule-based strategy, the equivalent energy consumption of UDDS and HWFET is reduced by 20.7% and 5.5% in the battery’s healthy charging range, respectively. Full article

Road traffic crashes (RTCs) have emerged as a major global cause of fatalities, with the number of accident-related deaths rising rapidly each day. To mitigate this issue, it is essential to develop early prediction methods that help drivers and riders understand accident statistics relevant to their region. These methods should consider key factors such as speed limits, compliance with traffic signs and signals, pedestrian crossings, right-of-way rules, weather conditions, driver negligence, fatigue, and the impact of excessive speed on RTC occurrences. Raising awareness of these factors enables individuals to exercise greater caution, thereby contributing to accident prevention. A promising approach to improving road traffic accident severity classification is the stacking ensemble method, which leverages multiple machine learning models. This technique addresses challenges such as imbalanced datasets and high-dimensional features by combining predictions from various base models into a meta-model, ultimately enhancing classification accuracy. The ensemble approach exploits the diverse strengths of different models, capturing multiple aspects of the data to improve predictive performance. The effectiveness of stacking depends on the careful selection of base models with complementary strengths, ensuring robust and reliable predictions. Additionally, advanced feature engineering and selection techniques can further optimize the model’s performance. Within the field of artificial intelligence, various machine learning (ML) techniques have been explored to support decision making in tackling RTC-related issues. These methods aim to generate precise reports and insights. However, the stacking method has demonstrated significantly superior performance compared to existing approaches, making it a valuable tool for improving road safety. Full article

The fatigue constitutive model under cyclic loading is of vital importance for studying the fatigue deformation characteristics of soft rocks. In this paper, based on the classical Bingham model, a modified Bingham fatigue model for describing the fatigue deformation characteristics of soft rocks under cyclic loading was developed. Firstly, the traditional constant-viscosity component was replaced by an improved nonlinear viscoelastic component related to the number of cycles. The elastic component was replaced by an improved nonlinear elastic component that decays as the number of cycle loads increases. Meanwhile, by decomposing the cyclic dynamic loads into static loads and alternating loads, a one-dimensional nonlinear viscoelastic-plastic Bingham fatigue model was developed. Furthermore, a rock fatigue yield criterion was proposed, and by using an associated flow rule compatible with this criterion, the one-dimensional fatigue model was extended to a three-dimensional constitutive formulation under complex stress conditions. Finally, the applicability of the developed Bingham fatigue model was verified through fitting with experimental data, and the parameters of the model were identified. The model fitting results show high consistency with experimental data, with correlation coefficients exceeding 0.978 and 0.989 under low and high dynamic stress conditions, respectively, and root mean square errors (RMSEs) below 0.028. Comparative analysis between theoretical predictions and existing soft rock fatigue test data demonstrates that the developed Bingham fatigue model more effectively captures the complete fatigue deformation process under cyclic loading, including the deceleration, constant velocity, and acceleration phases. With its simplified component configuration and straightforward combination rules, this model provides a valuable reference for studying fatigue deformation characteristics of rock materials under dynamic loading conditions. Full article

Accurate prediction of system degradation and remaining useful life (RUL) is essential for reliable health monitoring of Lithium-ion (Li-ion) batteries, as well as other dynamic systems. While evolving systems can offer adequate adaptability to the nonstationary and nonlinear behavior of battery degradation, existing methods often face challenges such as uncontrolled rule growth, limited adaptability, and reduced accuracy under noisy conditions. To address these limitations, this paper presents a smart evolving fuzzy predictor with customized firefly optimization (SEFP-FO) to provide a better solution for battery RUL prediction. The proposed SEFP-FO technique introduces two main contributions: (1) An activation- and distance-aware penalization strategy is proposed to govern rule evolution by evaluating the structural relevance of incoming data. This mechanism can control rule growth while maintaining model convergence. (2) A customized firefly algorithm is suggested to optimize the antecedent parameters of newly generated fuzzy rules, thereby enhancing prediction accuracy and improving the predictor’s adaptive capability to time-varying system conditions. The effectiveness of the proposed SEFP-FO technique is first validated by simulation using nonlinear benchmark datasets, which is then applied Full article

Background: Emergency laparotomy (EL) carries high morbidity and mortality relative to elective abdominal surgery. While Enhanced Recovery After Surgery (ERAS) principles improve outcomes in elective care, their translation to emergencies is inconsistent. The emergency department (ED) provides a window for rapid risk stratification and pre-optimization, provided that interventions do not delay definitive surgery. Methods: We conducted a PRISMA-ScR–conformant scoping review to map ED-initiated, ERAS-aligned strategies for EL. PubMed, Scopus, and Cochrane were searched in February 2025. Eligible sources comprised ERAS guidelines, systematic reviews, cohort studies, consensus statements, and programmatic reports. Evidence was charted across five a priori domains: (i) ERAS standards, (ii) comparative effectiveness, (iii) ED-feasible pre-optimization, (iv) risk stratification (Emergency Surgery Score [ESS], frailty, sarcopenia), and (v) oncological emergencies. Results: Thirty-four sources met inclusion. ERAS guidelines codify rapid assessment, multimodal intraoperative care, and early postoperative rehabilitation under a strict no-delay rule. Meta-analysis and cohort data suggest ERAS-aligned pathways reduce complications and length of stay, though heterogeneity persists. ED-feasible measures include multimodal analgesia, goal-directed fluids, early safe nutrition, respiratory preparation, and anemia/micronutrient optimization (IV iron, vitamin B12, folate, vitamin D). Sarcopenia, frailty, and ESS consistently predicted adverse outcomes, supporting targeted bundle activation. Evidence from oncological emergencies indicates feasibility under no-delay governance. Conclusions: A minimal, ED-initiated, ERAS-aligned bundle is feasible, guideline-concordant, and may shorten hospitalization and reduce complications in EL. We propose a practical framework that links rapid risk stratification, opportunistic pre-optimization, and explicit continuity into intra- and postoperative care; future studies should test fidelity, costs, and outcome impact in pragmatic emergency pathways. Full article

Background: Post-traumatic epilepsy (PTE) is a recognized complication of traumatic brain injury (TBI), yet its risk following mild and moderate TBI remains underappreciated. Although mild TBI represents the majority of cases in clinical practice, a subset of patients develop unprovoked seizures months or even years post-injury. This review aims to synthesize current evidence on the incidence and predictors of PTE in mild and moderate TBI and to propose a clinically actionable decision-support tool for early risk stratification. Methods: We performed a narrative review of peer-reviewed studies published between 1985 and 2024 that reported on the incidence, risk factors and predictive models of PTE in patients with mild (Glasgow Coma Scale [GCS] 13–15) and moderate (GCS 9–12 or imaging-positive) TBI. Data from 24 studies were extracted, focusing on neuroimaging findings, early post-traumatic seizures, EEG abnormalities and clinical risk factors. These variables were integrated into a rule-based algorithm, which was implemented using Streamlit to enable real-time clinical decision-making. The decision-support tool incorporated five domains: injury severity, early post-traumatic seizures, neuroimaging findings (including contusion location and hematoma type), clinical and demographic variables (age, sex, psychiatric comorbidities, prior TBI, neurosurgical intervention) and EEG abnormalities. Results: PTE incidence following mild TBI ranged from <1% to 10%, with increased risk observed in patients presenting with intracranial hemorrhage or early seizures. From moderate TBI, incidence rates were consistently higher (6–12%). Key predictors included early seizures, frontal or temporal contusions, subdural hematoma, multiple contusions and midline shift. Additional risk-enhancing factors included prolonged loss of consciousness, male sex, psychiatric comorbidities and abnormal EEG patterns. Based on these features, we developed a decision-support tool that stratifies patients into low-, moderate- and high-risk categories for developing PTE. Conclusions: Even in non-severe cases, patients with mild and moderate TBI who exhibit high-risk features remain vulnerable to long-term epileptogenesis. Our proposed tool provides a pragmatic, evidence-based framework for early identification and follow-up planning. Prospective validation studies are needed to confirm its predictive accuracy and optimize its clinical utility. Full article

This paper proposes an AI-based approach to adapting the data write latency in multicloud systems (MCSs) that supports data consistency across geo-distributed replicas of cloud service providers (CSPs). The proposed approach allows for dynamically forming adaptation scenarios based on the proposed model of multi-criteria optimization of data write latency. The generated adaptation scenarios are aimed at maintaining the required data write latency under changes in the intensity of the incoming request flow and network transmission time between replicas in CSPs. To generate adaptation scenarios, the features of the algorithmic Latord method of data consistency, are used. To determine the threshold values and predict the external parameters affecting the data write latency, we propose using learning AI models. An artificial neural network is used to form rules for changing the parameters of the Latord method when the external operating conditions of MCSs change. The features of the Latord method that influence data write latency are demonstrated by the results of simulation experiments on three MCSs with different configurations. To confirm the effectiveness of the developed approach, an adaptation scenario was considered that allows reducing the data write latency by 13% when changing the standard deviation of network transmission time between DCs of MCS. Full article

The fault diagnosis of complex systems is essential for ensuring operational safety. The belief rule base (BRB), a rule-driven framework based on expert knowledge, is widely applied in fault diagnosis because of its ability to manage uncertainty. However, existing BRB models rely heavily on large amounts of high-quality labeled data, and their performance decreases when labels are scarce or noisy. To address this limitation, a perturbed self-training-based BRB method (PS-BRB) is proposed. In this approach, pseudo-labels for unlabeled samples are first inferred by an initial BRB, and Gaussian noise is introduced into the inputs to simulate perturbations. Samples that produce consistent predictions before and after perturbation are retained through class consistency checking. The Jensen–Shannon (JS) divergence then measures the difference between belief distributions, and high-quality pseudo-labels are selected according to the 90th percentile criterion. These pseudo-labels are incorporated into the training set to optimize BRB rules and parameters. The method is validated on two bearing datasets, and the results show improved diagnostic accuracy and applicability, which indicates potential for use in practical engineering scenarios. Full article

Legal judgment prediction is an emerging application of artificial intelligence in the legal domain, offering significant potential to enhance legal decision support systems. Such systems can improve judicial efficiency, reduce burdens on legal professionals, and assist in early-stage case assessment. This study focused on predicting whether a legal case would be Accepted or Rejected using only the Fact section of court rulings. A key challenge lay in processing long legal documents, which often exceeded the input length limitations of transformer-based models. To address this, we proposed a two-step methodology: first, each document was segmented into sentence-level inputs compatible with AraBERT—a pretrained Arabic transformer model—to generate sentence-level predictions; second, these predictions were aggregated to produce a document-level decision using several methods, including Mean, Max, Confidence-Weighted, and Positional aggregation. We evaluated the approach on a dataset of 19,822 real-world cases collected from the Saudi Arabian Commercial Court. Among all aggregation methods, the Confidence-Weighted method applied to the AraBERT-based classifier achieved the highest performance, with an overall accuracy of 85.62%. The results demonstrated that combining sentence-level modeling with effective aggregation methods provides a scalable and accurate solution for Arabic legal judgment prediction, enabling full-length document processing without truncation. Full article

Quantum interference can strongly enhance thermoelectric response, with higher-order “supernodes” predicted to yield scalable gains in thermopower and efficiency. A central question, however, is whether such features are intrinsically more fragile to dephasing. Using Büttiker voltage–temperature probes, we establish an order-selection rule: the effective near-node order is set by the lowest among coherent and probe-assisted channels. Supernodes are therefore fragile in an absolute sense because their transmission is parametrically suppressed with order. However, once an incoherent floor dominates, the fractional suppression of thermopower, efficiency, and figure of merit becomes universal and order-independent. Illustrating these principles with benzene- and biphenyl-based junction calculations, we show that the geometry of environmental coupling—through a single orbital or across many—dictates whether coherence is lost by order reduction or by floor building. These results yield general scaling rules for the thermoelectric response of interference nodes under dephasing. Full article

Background and Objectives: The growing academic interest within the biomedical sciences regarding the diagnostic capabilities of multimodal language models, such as ChatGPT-4o, is clear. However, their ability to interpret oral clinical images remains insufficiently explored. This exploratory pilot study aimed to provide preliminary observations about the diagnostic validity of ChatGPT-4o in identifying oral squamous cell carcinoma (OSCC), oral leukoplakia (OL), and oral lichen planus (OLP) using only clinical photographs, without the inclusion of additional clinical data. Materials and Methods: Two general dentists selected 23 images of oral lesions suspected to be OSCC, OL, or OLP. ChatGPT-4o was asked to provide a probable diagnosis for each image on 30 occasions, generating a total of 690 responses. The responses were then evaluated against the reference diagnosis set up by an expert to calculate sensitivity, specificity, predictive values, and the area under the ROC curve. Results: ChatGPT-4o demonstrated high specificity across the three conditions (97.1% for OSCC, 100% for OL, and 96.1% for OLP), correctly classifying 90% of OSCC cases (AUC = 0.81). However, this overall accuracy was largely driven by correct negative classifications, while the clinically relevant sensitivity for OSCC was only 65%. In spite of that, sensitivity was highly variable: 60% for OL and just 25% for OLP, which limits its usefulness in a clinical setting for ruling out these conditions. The model achieved positive predictive values of 86.7% for OSCC and 100% for OL. Given the small dataset, these findings should be interpreted only as preliminary evidence. Conclusions: ChatGPT-4o demonstrates potential as a complementary tool for the screening of OSCC in clinical oral images. Nevertheless, the pilot nature of this study and the reduced sample size highlight that larger, adequately powered studies (with several hundred cases per pathology) are needed to obtain robust and generalizable results. Nevertheless, its sensitivity remains insufficient, as a significant proportion of true cases were missed, underscoring that the model cannot be relied upon as a standalone diagnostic tool. Full article