Search Results (1,646)

Digital twins (DTs) offer significant promise for condition-based maintenance of floating offshore wind turbines (FOWTs); however, existing solutions typically compromise either on physical rigor or real-time computational performance. This paper presents a real-time DT framework that resolves this trade-off by embedding a hydro-elastic reduced-order model (ROM) that accurately captures structural dynamics and fluid–structure interaction. Integrated in a cloud-ready Internet of Things architecture, the ROM reconstructs full-field displacements, von Mises stresses, and fatigue metrics with near real-time responsiveness. Validation on the 5 MW OC4-DeepCWind semi-submersible platform shows that the ROM reproduces finite-element (FEM) displacements and stresses with relative errors below 1%. A three-hour load case is solved in 0.69 min for displacements and 3.81 min for stresses on a consumer-grade NVIDIA RTX 4070 Ti GPU—over two orders of magnitude faster than the full FEM model—while one million fatigue stress histories (1000 hotspots × 1000 operating scenarios) are processed in 37 min. This efficiency enables continuous structural monitoring, rapid *what-if* assessments and timely decision-making for targeted inspections and adaptive control. By effectively combining physics-based reduced-order modeling with high-throughput computation, the proposed framework overcomes key barriers to DT deployment: computational overhead, physical fidelity and scalability. Although demonstrated on a steel platform, the approach is readily extensible to composite structures and multi-turbine arrays, providing a robust foundation for cost-effective and reliable deep-water wind-energy operations. Full article

Highway culverts are essential components of transportation infrastructure, designed to convey water beneath highways and protect embankments from flooding. However, extreme flood events often impose hydraulic loads, overtopping, and debris accumulation that can trigger erosion, scour, blockage, and in severe cases, catastrophic washout. This paper presents a comprehensive review of highway culvert behavior under flooding conditions, integrating insights from hydraulics, geotechnical engineering, and structural performance. The review is organized around four themes: (1) types of flooding and their interactions with culverts; (2) hydraulic performance during flood events; (3) common failure modes, including scour, debris blockage, and structural instability; and (4) mitigation strategies to enhance resilience. Advances in hydraulic modeling, including 1D, 2D, 3D, and CFD approaches, are summarized, with attention to their accuracy, applicability limits, and validation needs. Representative experimental, numerical, and empirical studies are grouped by common properties to highlight key findings and constraints. Finally, emerging research opportunities are discussed, including the need for quantitative relationships between culvert geometry and flood intensity, methods to assess structural capacity loss during flooding, and the integration of artificial intelligence and computer vision for rapid post-flood inspection. This synthesis establishes a foundation for more robust evaluation, design, and maintenance strategies, supporting the long-term resilience of highway culverts in an era of increasingly frequent and severe floods. Full article

Accelerated oxidation experiments on Chinese 0# diesel fuel were performed with a self-designed aging reactor system. Five experimental conditions covering pressures ranging from atmospheric pressure to 0.8 MPa, temperatures ranging from room temperature (25 °C) to 80 °C, and their synergistic effects were adopted to simulate the long-term oxidation of diesel fuel. The extent of deterioration was evaluated based on the measurement of three key indicators, i.e., oxidation stability, wear scar diameter, and viscosity. Thermogravimetric analysis (TGA) tests were performed, and the measured thermogravimetric (TG) curves and derivative thermogravimetric (DTG) curves were used to evaluate the effects of reactor material, heating rate, bath gas, and reactive gas on the deterioration and vaporization processes of diesel fuel. Based on a comparison of the deterioration indicators of diesel fuel collected from the accelerated oxidation experiments and oil depots serving actual operating emergency diesel generators (EDGs), a rapid assessment method of real-time diesel deterioration was explored. Based on the experimental observations, the affecting mechanisms of the increases in temperature and oxygen partial pressure were discussed. Two test methods of accelerated oxidation, with the respective conditions of 0.8 MPa/80 °C and atmospheric pressure/80 °C, were proposed, which could effectively compress the time needed for long-term storage simulations (e.g., 200 h lab aging equals three years of actual operation). The optional temperature and pressure windows for acceleration oxidation were confirmed (40–80 °C/0.3–0.8 MPa). These results are valuable for the further understanding of the processes of deterioration and vaporization of diesel fuel. Full article

This study evaluates the feasibility of employing rainwater as an alternative feedstock for hydrogen production via electrolysis. While conventional systems typically rely on high-purity water—such as deionized or distilled variants—these can be cost-prohibitive and environmentally intensive. Rainwater, being naturally available and minimally treated, presents a potential sustainable alternative. In this work, a series of comparative experiments was conducted using a proton exchange membrane electrolyzer system operating with both deionized water and rainwater collected from different Austrian locations. The chemical composition of rainwater samples was assessed through inductively coupled plasma, ion chromatography and visual rapid tests to identify impurities and ionic profiles. The electrolyzer’s performance was evaluated under equivalent operating conditions. Results indicate that rainwater, in some cases, yielded comparable or marginally superior efficiency compared to deionized water, attributed to its inherent ionic content. The study also examines the operational risks linked to trace contaminants and explores possible strategies for their mitigation. Full article

This paper investigated the tunnel slags generated from a specific tunnel project to systematically assess their environmental risk through phase composition, chemical composition, acidification potential, and heavy metal speciation. Leaching experiments were conducted under various influencing factors, including particle size, time, liquid-to-solid ratio, pH, temperature. The release concentration of heavy metals from the tunnel slag particles follows the following order: Zn > Cu > Cr. This is primarily attributed to the preferential release of Zn under acidic conditions due to its high acid-soluble state, while Cr, which is predominantly present in the residual state, exhibits very low mobility. Furthermore, decreased particle sizes, increased liquid-to-solid ratios, elevated leaching temperatures, extended leaching times, and lower pH values can effectively promote the dissolution of heavy metals from the tunnel slag. The cumulative leaching curves of Cr, Cu, and Zn from the three types of tunnel slags conform to the Elovich equation (R² > 0.88), indicating that the release process of heavy metals is primarily controlled by diffusion mechanisms. The S- and Fe/Mg-rich characteristics of D3 confers a high acidification risk, accompanied by a rapid and persistent heavy metal release rate. In contrast, D2, which is influenced by the neutralizing effect of carbonate dissolution, releases heavy metals at a steady rate, while D1, which is dominated by inert minerals like quartz and muscovite, exhibits the slowest release rate. It is recommended that waste management engineering prioritize controlling S- and Fe/Mg-rich tunnel slags (D3) and mitigating risks of elements like Zn and Cu under acidic conditions. This study provides a scientific basis and technical support for the environmentally safe disposal and resource utilization of tunnel slag. Full article

The term heart disease refers to a wide range of conditions that impact the heart and blood vessels. It continues to be a major global cause of morbidity and mortality. The narrowing or blockage of blood vessels, which can result in major medical events like heart attacks, angina (chest pain) or strokes, is a common issue linked to heart disease. In order to lower the risk of serious complications and facilitate prompt medical intervention, early diagnosis and prediction are essential. This study developed predictive models that can precisely identify people at risk by applying a variety of machine learning algorithms to a structured dataset on heart disease. Blood pressure, cholesterol, age, gender, and other health-related indicators are among the 13 essential characteristics that make up the dataset. Numerous machine learning models such as Naïve Bayes, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Random Forest, and others were trained using these features. Using the RapidMiner platform, which offered a visual environment for data preprocessing, model training, and performance analysis, all models were created and assessed. The best-performing model was the Naïve Bayes classifier which achieved an impressive accuracy rate of 90% after extensive testing and comparison of performance metrics like accuracy precision and recall. This outcome shows how well the model can predict heart disease in actual clinical settings. By supporting individualized health recommendations, enabling early diagnosis, and facilitating timely treatment, the effective application of such models can significantly benefit patients and healthcare professionals. Furthermore, heart disease incidence can be considerably decreased by identifying and addressing modifiable risk factors such as high blood pressure, elevated cholesterol, smoking, diabetes, and physical inactivity. In summary, machine learning has the potential to improve the identification and treatment of heart-related disorders. This study highlights the value of data-driven methods in healthcare and indicates that incorporating predictive models into standard medical procedures may enhance patient outcomes, lower healthcare expenses, and improve public health administration. Full article

There has been significant interest in deploying unmanned aerial vehicles (UAVs) for their ability to perform precise and rapid remote mapping and inspection of critical environmental assets for structural health monitoring. This case study investigates the use of UAV-based LiDAR and photogrammetry at Melbourne Water’s Western Treatment Plant (WTP) to routinely monitor high-density polyethylene floating covers on anaerobic lagoons. The proposed approach integrates LiDAR and photogrammetry data to enhance the accuracy and efficiency of generating digital elevation models (DEMs) and orthomosaics by leveraging the strengths of both methods. Specifically, the photogrammetric images were orthorectified onto LiDAR-derived DEMs as the projection plane to construct the corresponding orthomosaic. This method captures precise elevation points directly from LiDAR, forming a robust foundation dataset for DEM construction. This streamlines the workflow without compromising detail, as it eliminates the need for time-intensive photogrammetry processes, such as dense cloud and depth map generation. This integration accelerates dataset production by up to four times compared to photogrammetry alone, while achieving centimetre-level accuracy. The LiDAR-derived DEM achieved higher elevation accuracy with a root mean square error (RMSE) of 56.1 mm, while the photogrammetry-derived DEM achieved higher in-plane accuracy with an RMSE of up to 35.4 mm. An analysis of cover deformation revealed that the floating cover had elevated rapidly within the first two years post-installation before showing lateral displacement around the sixth year, which was also evident from a significant increase in wrinkling. This approach delivers valuable insights into cover condition that, in turn, clarifies scum accumulation and movement, thereby enhancing structural integrity management and supporting environmental sustainability at WTP by safeguarding methane-rich biogas for renewable-energy generation and controlling odours. The findings support the ongoing collaborative industry research between Monash University and Melbourne Water, aimed at achieving comprehensive structural and prognostic health assessments of these high-value assets. Full article

The dynamic modulus of asphalt mixtures (|E*|) is a key mechanical parameter in the design of road pavements, yet direct laboratory testing is time- and resource-intensive. This study evaluates two predictive models for estimating |E*| using data from 62 asphalt mixtures containing reclaimed asphalt: a grey relational analysis–multiple linear regression (GRA-MLR) hybrid model and a mechanistic sigmoidal model. The results showed that the GRA-MLR model effectively identifies influential variables but achieved moderate predictive accuracy (R² values varying from 0.4743 to 0.6547). In contrast, the sigmoidal model outperformed across all temperature conditions (R² > 0.96) and produced predictions deviating by less than ±20% from measured values. Temperature-dependent shifts in factor influence were observed, with stiffness and gradation dominating at low temperatures and reclaimed asphalt (RA) content becoming more significant at higher temperatures. While the GRA-MLR model is advantageous, offering rapid assessments and early-stage evaluations, the sigmoidal model offers the precision suited for detailed design. Integrating both models can balance computational efficiency and provide a balanced strategy, with strong predictive reliability to advance mechanistic–empirical pavement design. Full article

Cell towers play a key role in providing telecommunications infrastructure, especially in remote mountainous regions. This paper presents an approach to the efficient design of 42-metre-high cell towers intended to install high-power equipment in remote mountainous regions of the Carpathians (750 m above sea level). The region requires rapid deployment of many standardized towers adapted to geographical features. The main design challenges were the limited space available for the base, the impact of extreme weather conditions, and the need for a fast project implementation due to the critical importance of ensuring stable communication. Special methodological attention is given to how the transition between pyramidal and prismatic segments in cell tower shafts influences overall structural performance. The effect of this geometric boundary on structural efficiency and material usage has not been addressed in previous studies. A dedicated investigation shows that positioning the transition at a height of 33 m yields the best compromise between stiffness and weight, minimizing a generalized penalty function that accounts for both the horizontal displacement of the tower top and its total mass. Modal analysis confirms that the chosen configuration maintains a natural frequency of 1.68 Hz, ensuring a safe margin from resonance. For the final analysis of the behavior of towers with elements of different cross-sectional shapes, finite element modeling was used for a detailed numerical study of their structural and performance characteristics. This allowed us to assess the impact of geometric constraints of structures and take into account the most unfavorable combinations of static and dynamic loads. The study yields a concise rule of thumb for towers with compact foundations, namely that the pyramidal-to-prismatic transition should be placed at roughly 78–80% of the total tower height. Full article

Background/Objectives: Patellar instability is a common orthopedic condition affecting pediatric and adolescent populations, particularly during periods of rapid growth and increased sports participation. Recurrent patellar dislocation in skeletally immature patients is frequently associated with underlying anatomical risk factors such as patella alta, trochlear dysplasia, or increased tibial tubercle–trochlear groove distance. Methods: This narrative review summarizes the current evidence on the epidemiology, diagnostic approach, and arthroscopic management of patellar instability in skeletally immature patients. Results: Arthroscopy has become an essential tool in both the diagnosis and treatment of patellar instability, allowing for minimally invasive assessment of patellofemoral alignment, chondral pathology, and ligament integrity. It also enables precise surgical interventions such as physeal-sparing medial patellofemoral ligament reconstruction, which remains the preferred stabilization technique for patients with open physes due to its safety and efficacy. Emerging innovations, including robotic-assisted tunnel placement, bioengineered scaffolds for cartilage repair, and three-dimensional modeling for surgical planning, have the potential to improve outcomes and arthroscopic surgical precision in this population. Despite these advances, major challenges such as a lack of pediatric-specific outcome measures, variability in surgical indications and rehabilitation protocols, and limited long-term follow-up data remain. Conclusions: Optimizing outcomes in pediatric and adolescent patients with patellar instability requires individualized growth-aware strategies and multidisciplinary collaborations. By integrating technological innovation with patient-centered care, clinicians can continue to refine the arthroscopic management of patellofemoral instability in young patients. Full article

The rapid proliferation of generative artificial intelligence (GenAI) in educational settings has created unprecedented opportunities for language instruction, yet empirical evidence regarding its efficacy in primary-level English as a Foreign Language contexts remains scarce, particularly concerning pre-service teachers’ implementation experiences during formative practicum periods. This investigation, conducted in a public school in a non-Anglophone country during the Spring of 2025, examined the impact of GenAI-driven gamified activities on elementary pupils’ English language competencies while exploring novice educators’ professional development trajectories through a mixed-methods quasi-experimental approach with comparison groups. Four third-grade classes (n = 119 individuals aged 8–9) in a public school were assigned to either ChatGPT-mediated voice-interaction games (n = 58) or conventional non-digital activities (n = 61) across six 45 min lessons spanning three weeks, with four female student-teachers serving as instructors during their culminating practicum. Quantitative assessments of grammar, listening comprehension, and pronunciation occurred at baseline, post-intervention, and one-month follow-up intervals, while reflective journals captured instructors’ evolving perceptions. Linear mixed-effects modeling revealed differential outcomes across linguistic domains: pronunciation demonstrated substantial advantages for GenAI-assisted learners at both immediate and delayed assessments, listening comprehension showed moderate benefits with superior overall performance in the experimental condition, while grammar improvements remained statistically equivalent between groups. Thematic analysis uncovered pre-service teachers’ progression from technical preoccupations toward sophisticated pedagogical reconceptualization, identifying connectivity challenges and assessment complexities as primary barriers alongside reduced performance anxiety and individualized pacing as key facilitators. These findings suggest selective efficacy of GenAI across language skills while highlighting the transformative potential and implementation challenges inherent in technology-enhanced elementary language education. Full article

Stony corals are long-lived, calcifying cnidarians that can be preserved within archaeological strata, offering insights into past seawater conditions, anthropogenic influences, and harbor dynamics. This study analyzes sub-fossil Cladocora sp. colonies from ancient Akko, Israel, dated to the Hellenistic period (~335–94 BCE), alongside modern Cladocora caespitosa from Haifa Bay, Israel. We employed micromorphology, stable isotope analysis, and DNA sequencing to assess species identity, colony growth form, and environmental conditions experienced by the corals. Comparisons suggest that Hellenistic Akko corals grew in high-light, cooler-water, high-energy environments, potentially with exposure to terrestrial waste. The exceptional preservation of these colonies indicates rapid burial, possibly linked to ancient harbor activities or extreme sedimentation. Our results demonstrate the utility of scleractinian corals as valuable paleoenvironmental archives, capable of integrating both biological and geochemical proxies to reconstruct past marine conditions. By linking archaeological and ecological records, this multidisciplinary approach provides a comprehensive understanding of historical coastal dynamics, including ancient harbor use, climate variability, and anthropogenic impacts. Full article

In response to the inability to quickly assess wind noise performance during the early stages of automotive styling design, this paper proposes a method for predicting interior wind noise by integrating automotive point cloud models with the Gray Wolf Optimization Residual Network model (GWO-ResNet). Based on wind tunnel test data under typical operating conditions, the point cloud model of the test vehicle is compressed using an auto-encoder and used as input features to construct a nonlinear mapping model between the whole vehicle point cloud and the wind noise level at the driver’s left ear. Through adaptive optimization of key hyperparameters of the ResNet model using the gray wolf optimization algorithm, the accuracy and generalization of the prediction model are improved. The prediction results on the test set indicate that the proposed GWO-ResNet model achieves prediction results that are consistent with the actual measured values for the test samples, thereby validating the effectiveness of the proposed method. A comparative analysis with traditional ResNet models, GWO-LSTM models, and LSTM models revealed that the GWO-ResNet model achieved Mean Absolute Percentage Error (MAPE) and mean squared error (MSE) of 9.72% and 20.96, and 9.88% and 19.69, respectively, on the sedan and SUV test sets, significantly outperforming the other comparison models. The prediction results on the independent validation set also demonstrate good generalization ability and stability (MAPE of 10.14% and 10.15%, MSE of 23.97 and 29.15), further proving the reliability of this model in practical applications. The research results provide an efficient and feasible technical approach for the rapid evaluation of wind noise performance in vehicles and provide a reference for wind noise control in the early design stage of vehicles. At the same time, due to the limitations of the current test data, it is impossible to predict the wind noise during the actual driving of the vehicle. Subsequently, the wind noise during actual driving can be predicted by the test data of multiple working conditions. Full article

Plant-derived toxins such as ricin and abrin represent some of the most potent biological agents known, posing significant threats to public health and security due to their high toxicity, relative ease of extraction, and widespread availability. These ribosome-inactivating proteins (RIPs) have been implicated in politically and criminally motivated events, underscoring their critical importance in the context of biodefense. Public safety agencies, including law enforcement, customs, and emergency response units, require rapid, sensitive, and portable detection methods to effectively counteract these threats. However, many existing screening technologies lack the capability to detect biotoxins unless specifically designed for this purpose, revealing a critical gap in current biodefense preparedness. Consequently, there is an urgent need for robust, field-deployable detection platforms that operate reliably under real-world conditions. End-users in the security and public health sectors demand analytical tools that combine high specificity and sensitivity with operational ease and adaptability. This review provides a comprehensive overview of the biochemical characteristics of ricin and abrin, their documented misuse, and the challenges associated with their detection. Furthermore, it critically assesses key detection platforms—including immunoassays, mass spectrometry, biosensors, and lateral flow assays—focusing on their applicability in operational environments. Advancing detection capabilities within frontline services is imperative for effective prevention, timely intervention, and the strengthening of biosecurity measures. Full article

The increasing threat of terrorism involving Radiological Dispersal Devices (RDDs) necessitates comprehensive evaluation and preparedness strategies, especially in densely populated public areas. This study aims to assess the potential consequences of RDD detonation, focusing on the effective doses received by individuals and the ground deposition of radioactive materials in a hypothetical urban environment. Utilizing the HotSpot code, simulations were performed to model the dispersion patterns of ¹³⁷Cs and ²⁴¹Am under varying meteorological conditions, mirroring the complexities of real-world scenarios as outlined in recent literature. The results demonstrate that ¹³⁷Cs dispersal produces a wider contamination footprint, with effective doses exceeding the public exposure limit of 1 mSv at distances up to 1 km, necessitating broad protective actions. In contrast, ²⁴¹Am generates higher localized contamination, with deposition levels surpassing cleanup thresholds near the release point, creating long-term remediation challenges. Dose estimates for first responders highlight the importance of adhering to operational dose limits, with scenarios approaching 100 mSv under urgent rescue conditions. Overall, the findings underscore the need for rapid dose assessment, early shelter-in-place orders, and targeted decontamination to reduce population exposure. These insights provide actionable guidance for emergency planners and first responders, enhancing preparedness protocols for RDD incidents in major urban centers. Full article