Search Results (113)

Offshore wind turbines are rapidly scaling in size, which amplifies the need for credible integrated load analyses that consistently resolve the coupled dynamics among rotor–nacelle–tower systems and their support substructures. This study presents a comprehensive ultimate limit state (ULS) load assessment for a fixed jack-up-type substructure (hereafter referred to as K-wind) coupled with the IEA 15 MW reference wind turbine. Unlike conventional monopile or jacket configurations, the K-wind concept adopts a self-installable triangular jack-up foundation with spudcan anchorage, enabling efficient transport, rapid deployment, and structural reusability. Yet such a configuration has never been systematically analyzed through full aero-hydro-servo-elastic coupling before. Hence, this work represents the first integrated load analysis ever reported for a jack-up-type offshore wind substructure, addressing both its unique load-transfer behavior and its viability for multi-MW-class turbines. To ensure numerical robustness and cross-code reproducibility, steady-state verifications were performed under constant-wind benchmarks, followed by time-domain simulations of standard prescribed Design Load Case (DLC), encompassing power-producing extreme turbulence, coherent gusts with directional change, and parked/idling directional sweeps. The analyses were independently executed using two industry-validated solvers (Deeplines Wind v5.8.5 and OrcaFlex v11.5e), allowing direct solver-to-solver comparison and establishing confidence in the obtained dynamic responses. Loads were extracted at the transition-piece reference point in a global coordinate frame, and six key components (Fx, Fy, Fz, Mx, My, and Mz) were processed into seed-averaged signed envelopes for systematic ULS evaluation. Beyond its methodological completeness, the present study introduces a validated framework for analyzing next-generation jack-up-type foundations for offshore wind turbines, establishing a new reference point for integrated load assessments that can accelerate the industrial adoption of modular and re-deployable support structures such as K-wind. Full article

Repurposing lithium-ion (Li-ion) batteries for second-life applications, such as stationary energy storage, offers significant economic and environmental benefits as these cells reach the end of their initial service life. Accurate and scalable classification of used Li-ion cell chemistries is essential for efficient sorting and safe repurposing, especially when manufacturer metadata is unavailable. This study presents a robust, automated machine learning (AutoML) framework, implemented in MATLAB R2024b and its toolboxes, for classifying three commercial 18,650 cell chemistries (LFP, NMC, and NCA) using long-term cycle life data. The workflow integrates structured data ingestion, segmentation, and multi-tiered feature engineering, extracting over 75 diagnostic features per cycle, including statistical, cumulative, segment-specific, and differential curve metrics. Feature selection is performed using principal component analysis and sequential forward selection, while Bayesian optimisation within AutoML identifies the optimal classification model. The resulting K-Nearest Neighbours classifier achieves over 99% test accuracy, demonstrating the effectiveness of the approach. This framework enables research-grade, metadata-independent classification and provides a scalable foundation for future industrial battery sorting and second-life applications. Full article

We model the short-period cataclysmic variable EZ Lyn with MESA binary evolution and infer its present-day parameters through a staged statistical search. First, we compute a coarse grid of tracks in $(M_{1,0},P_0)$ at fixed $M_{2,0}$ and rank snapshots by a profile likelihood. We then resample the neighbourhood of the minimum to build a refined $\Delta {\chi }^2$ surface. Finally, we sample this surface with an affine-invariant MCMC to obtain posteriors, using a likelihood that treats the one-sided constraint on the donor temperature and the ambiguity of component roles in the binary output. The best-fit snapshot reproduces the observables and identifies EZ Lyn as a period bouncer with a substellar donor. We infer $M_{\mathrm{WD}}=0.850\pm 0.019{\mathrm{M}}_{\odot }$, $M_2=0.0483\pm 0.0137{\mathrm{M}}_{\odot }$, $R_{\mathrm{WD}}=0.0092\pm 0.0001{\mathrm{R}}_{\odot }$, $R_2=0.099\pm 0.005{\mathrm{R}}_{\odot }$, $T_{\mathrm{WD}}=11,500\pm 20\mathrm{K}$, and $T_2=1600\pm 50\mathrm{K}$. The instantaneous mass-transfer rate at the best-fit snapshot is $\dot{M}=3.66\times {10}^{-11}{\mathrm{M}}_{\odot }{\mathrm{yr}}^{-1}$, consistent with the secular range implied by the white-dwarf temperature. Independent checks from the Roche mean-density relation, surface gravities, and the semi-empirical donor sequence support the solution. In population context, EZ Lyn lies in the period-minimum spike and on the low-mass tail of the donor mass–period plane. The classification is robust to modest displacements along the shallow $\Delta {\chi }^2$ valley. We release inlists, tracks, and analysis scripts for reproducibility. Full article

Tidal prediction is essential for navigation safety, coastal risk management, and climate adaptation. This study develops and validates a hybrid harmonic analysis–artificial intelligence (HA–AI) framework to improve decadal tidal forecasting at five tide gauge stations along the Korean coast. Using ten years of hourly sea-level observations (2015–2025), harmonic decomposition captures deterministic astronomical components, while station-specific long short-term memory (LSTM) models learn residual nonlinear dynamics. Validation against the independent 2025 dataset demonstrates substantial accuracy gains compared with harmonic analysis alone. Across all stations, the hybrid approach reduced root mean square error (RMSE) by 16–40% (average 32.3%), with RMSE values of 8.1–10.8 cm, mean absolute errors (MAEs) of 6.3–8.9 cm, and correlation coefficients (R) ranging from 0.76 to 0.96. At Busan, RMSE was reduced from 15.1 cm (HA) to 9.9 cm (hybrid), while at Sokcho, improvement reached 40.1%. Uncertainty analysis further confirmed reliability, with 46.2% of residuals contained within ±2σ bounds. These results highlight the hybrid framework’s ability to integrate physical interpretability with adaptive skill, ensuring robust and transferable forecasts across heterogeneous coastal settings. The findings provide practical value for navigation, flood preparedness, and climate-resilient coastal planning, and demonstrate the potential of hybrid models as an operational forecasting tool. Full article

Determining a high-precision national geoid is a fundamental step in modernizing Kazakhstan’s vertical reference system. However, the country’s vast territory, complex topography, and limited coverage of modern terrestrial and airborne gravimetric surveys present significant challenges. In this context, Soviet-era gravimetric maps at a 1:200,000 scale remain the only consistent nationwide data source, yet their reliability has not previously been rigorously assessed within modern gravity standards. This study presents the first comprehensive validation of Soviet-era gravimetric surveys using two independent approaches. The first approach is about the comparison of gravity anomalies with the global geopotential models EGM2008, EIGEN-6C4 and XGM2019e_2159. The second approach is about the direct evaluation against absolute gravity measurements from the newly established Qazaqstan Gravity Reference Frame (QazGRF). The analysis demonstrates that, after applying systematic corrections, the Soviet-era gravimetric survey retains high information content. The mean discrepancy with QazGRF measurements is 0.7 mGal with a standard deviation of 2.5 mGal, and more than 90% of the evaluated points deviate by less than ±5 mGal. Larger inconsistencies, up to 20 mGal, are confined to mountainous and geophysically complex regions. In addition, several artifacts inherent to the global models were identified, suggesting that the integration of validated regional gravimetric data can also support future improvements of global gravity models. A key finding was the detection of an artifact in the global models on sheet M43. Its presence was confirmed by comparison with terrestrial gravimetric data and inter-model differences. It was established that the anomaly is caused by inaccuracies in the terrestrial “fill-in” component of the EGM2008 model, which subsequently inherited by later global solutions. The results confirm that Soviet gravimetric maps, once critically re-evaluated and tied to absolute observations, can be effectively integrated with global models. This integration delivers reliable, high-resolution inputs for regional gravity-field modeling. It establishes a robust scientific and practical foundation for constructing the first national geoid of Kazakhstan and for implementing a unified state coordinate and height system. It also helps enhance the accuracy of global geopotential models. Full article

Ensuring the authenticity of meat products is a critical issue for consumer protection, regulatory compliance, and the integrity of local food systems. In this study, attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy combined with chemometric and machine learning models was applied to differentiate Maltese from non-Maltese pork. Spectral datasets were subjected to a range of preprocessing techniques, including Savitzky–Golay first and second derivatives, detrending, orthogonal signal correction (OSC), and standard normal variate (SNV). Linear methods such as principal component analysis–linear discriminant analysis (PCA-LDA), the soft independent modeling of class analogy (SIMCA), and partial least squares regression (PLSR) were compared against nonlinear approaches, namely support vector machine regression (SVMR) and artificial neural networks (ANNs). The results revealed that derivative preprocessing consistently enhanced spectral resolution and model robustness, with the fingerprint region (1800–600 cm⁻¹) yielding the highest discriminative power. While PCA-LDA, SIMCA, and PLSR achieved high accuracy, SVMR and ANN models provided a superior predictive performance, with accuracies exceeding 0.99 and lower misclassification rates under external validation. These findings highlight the potential of FTIR spectroscopy combined with nonlinear chemometrics as a rapid, non-destructive, and cost-effective strategy for meat authentication, supporting both consumer safety and sustainable food supply chains. Full article

Arc fault detection in photovoltaic systems is crucial, since it may cause incidents like fires and explosions. So far, most existing methods rely on an arc’s local features and do not characterize arc faults globally, which may lead to detection failure in noisy environments. In this paper, a fundamentally different method is proposed that relies on an arc’s global features instead of local ones. The core idea of the method is that the physical mechanisms of the arc fault signals and the normal signals are so different that they are thought to be generated by two independent sources. Based on this insight, independent component analysis (ICA) is introduced to decompose the photovoltaic system’s DC currents. By using ICA, the DC current signals with an arc fault can be decomposed into two independent signals, while the normal signals without an arc fault cannot be decomposed into two such independent signals. This indicates that arc faults can be detected by using the concept of “independence”. Then, the dynamic time warping algorithm was used to determine the independence level of the ICA outputs so as to realize end-to-end arc fault detection. Experimental results showed that our method has better performance than traditional methods in terms of detection accuracy and robustness against environmental disturbances. Full article

As climate extremes increasingly threaten global food security, precision tools for early detection of crop stress have become vital, particularly for root crops such as potato (Solanum tuberosum L.) and sweet potato (Ipomoea batatas L. Lam.), which are especially susceptible to environmental stressors throughout their life cycles. In this study, plants were monitored from the initial onset of seasonal stressors, including spring drought, heat, and episodes of excessive rainfall, through to harvest, capturing the full range of physiological and biochemical responses under seasonal, simulated conditions in greenhouses. The spectral data were obtained from regions of interest (ROIs) of each cultivar’s leaves, with over 3000 data points extracted per cultivar; these data were subsequently used for model development. A comprehensive classification framework was established by employing machine learning models, Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Partial Least Squares-Discriminant Analysis (PLS-DA), to detect stress across various growth stages. Furthermore, severity levels were objectively defined using photoreflectance indices and principal component analysis (PCA) data visualizations, which enabled consistent and reliable classification of stress responses in both individual cultivars and combined datasets. All models achieved high classification accuracy (90–98%) on independent test sets. The application of the Successive Projections Algorithm (SPA) for variable selection significantly reduced the number of wavelengths required for robust stress classification, with SPA-PLS-DA models maintaining high accuracy (90–96%) using only a subset of informative bands. Furthermore, SPA-PLS-DA-based chemical imaging enabled spatial mapping of stress severity within plant tissues, providing early, non-invasive insights into physiological and biochemical status. These findings highlight the potential of integrating hyperspectral imaging and machine learning for precise, real-time crop monitoring, thereby contributing to sustainable agricultural management and reduced yield losses. Full article

Background/Objectives: Patients with chronic kidney disease (CKD) face higher risks of infections, poor vaccine responses, and cardiovascular diseases, leading to increased morbidity and mortality due to immune dysfunction and frailty. This study aims to evaluate immune status and frailty in CKD patients across different treatments, examine the influence of frailty on immune status, and link these factors to mortality. Methods: A total of 174 participants were included (end-stage renal disease, ESRD n = 40; hemodialysis, HD n = 40; peritoneal dialysis, n = 36; kidney transplant patients, n = 40; healthy subjects n = 18). Immunophenotyping of lymphocyte and monocyte subpopulations was performed, and frailty was assessed using the Edmonton Frail Scale. Principal component analysis (PCA) integrated immune and frailty variables to define an “immuno-fragile profile,” and survival was monitored for up to six years. Results: CKD patients, especially those on HD, showed decreased lymphocyte counts and proinflammatory monocyte subpopulations with increased expression of costimulatory molecules (B7.2/CD86 and ICAM-1/CD54). Frailty was most prevalent in HD patients (53%), with notable sex differences. PCA identified three components—lymphocyte counts, monocyte co-stimulatory expression, and frailty—that together explained 70% of the variance. Survival analysis revealed that patients with lower lymphocyte counts and higher frailty scores had increased mortality risk, especially in the HD and ESRD groups. Cox regression confirmed that the immuno-fragile profile independently predicted mortality. Conclusions: The integration of immune alterations and frailty defines an immuno-fragile profile strongly associated with mortality in CKD patients, which may serve as a robust prognostic tool to improve risk stratification and guide personalized interventions in clinical practice. Full article

The maritime defence settlements of the Ming Dynasty are a key component of China’s military cultural heritage. This study examines the three coastal defence sectors of Fujian by establishing a three-tier evaluation framework utilising GIS spatial analysis and the Analytic Hierarchy Process (AHP) for quantitative assessment. The findings reveal that the synergy between military outposts significantly enhances overall defence effectiveness, while the independent defence capability of each stronghold is crucial for withstanding external threats. A comprehensive evaluation further indicates that the Fujian central coastal defence sector, characterized by its robust economy and densely distributed fortifications, demonstrates the highest level of defensive performance. By systematically quantifying the defensive performance of Fujian’s maritime defence settlements, this study develops an evaluation model that provides a scientific basis and decision support for value assessment, sustainable conservation, and adaptive reuse of this category of military cultural heritage. Full article

Background/Objectives: Age-related macular degeneration (AMD) is the leading cause of visual impairment among the elderly. Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality that enables detailed visualisation of retinal vascular layers. However, clinical assessment of OCTA images is often challenging due to high data volume, pattern variability, and subtle abnormalities. This study aimed to develop automated algorithms to detect and quantify AMD in OCTA images, thereby reducing ophthalmologists’ workload and enhancing diagnostic accuracy. Methods: Two texture-based algorithms were developed to classify OCTA images without relying on segmentation. The first algorithm used whole local texture features, while the second applied principal component analysis ($PCA$) to decorrelate and reduce texture features. Local texture descriptors, including rotation-invariant uniform local binary patterns ($LB{P}^{2riu}$), local binary patterns ($LBP$), and binary robust independent elementary features ($BRIEF$), were combined with machine learning classifiers such as support vector machine (SVM) and K-nearest neighbour (KNN). OCTA datasets from Manchester Royal Eye Hospital and Moorfields Eye Hospital, covering healthy, dry AMD, and wet AMD eyes, were used for evaluation. Results: The first algorithm achieved a mean area under the receiver operating characteristic curve (AUC) of $1.00\pm 0.00$ for distinguishing healthy eyes from wet AMD. The second algorithm showed superior performance in differentiating dry AMD from wet AMD (AUC $0.85\pm 0.02$). Conclusions: The proposed algorithms demonstrate strong potential for rapid and accurate AMD diagnosis in OCTA workflows. By reducing manual image evaluation and associated variability, they may support improved clinical decision-making and patient care. Full article

Obesity is a major public health challenge linked to increased risks of chronic diseases. Effective prevention and intervention strategies require accurate classification and identification of key determinants. This study aims to develop a robust deep learning framework to enhance the accuracy and interpretability of obesity classification using comprehensive datasets, and to compare its performance with both traditional and state-of-the-art deep learning models. We propose a hybrid deep learning framework that combines an improved Mamba model with an attention-enhanced bidirectional LSTM (ABi-LSTM). The framework utilizes the Obesity and CDC datasets. A feature tokenizer is integrated into the Mamba model to improve scalability and representation learning. Channel-independent processing is employed to prevent overfitting through independent feature analysis. The ABi-LSTM component is used to capture complex temporal dependencies in the data, thereby enhancing classification performance. The proposed framework achieved an accuracy of 93.42%, surpassing existing methods such as ID3 (91.87%), J48 (89.98%), Naïve Bayes (90.31%), Bayesian Network (89.23%), as well as deep learning-based approaches such as VAE (92.12%) and LightCNN (92.50%). Additionally, the model improved sensitivity to 91.11% and specificity to 92.34%. The hybrid model demonstrates superior performance in obesity classification and determinant identification compared to both traditional and advanced deep learning methods. These results underscore the potential of deep learning in enabling data-driven personalized healthcare and targeted obesity interventions. Full article

Background/Objectives: Pain, anxiety, and distress are common yet frequently insufficiently managed issues for patients undergoing radiology and radiotherapy procedures. Immersive technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), are emerging as innovative non-pharmacological approaches to alleviate such burdens through engaging interventions. This review, combining scoping and narrative methodologies, seeks to examine the current application, efficacy, and integration of these technologies to enhance patient care and wellbeing within diagnostic and oncological environments. Methods: Employing a mixed scoping and narrative review approach, this study conducted a systematic search of PubMed, EMBASE, Scopus, and Web of Science databases (no date restrictions—search included studies up to May 2025) to identify relevant studies utilizing VR, AR, MR, or XR for mitigating pain, anxiety, or distress in patients undergoing radiology or radiotherapy. Two independent reviewers selected eligible papers, with data extracted systematically. The narrative analysis supplemented the scoping review by providing contextual insights into clinical relevance and technological challenges. Results: The screening process identified 76 articles, of which 27 were assessed for eligibility and 14 met the inclusion criteria. Most studies focused on oncology and primarily employed VR as the immersive technology. VR has shown promising effects in reducing anxiety and pain—particularly during radiotherapy sessions and invasive procedures—and in supporting patient education through engaging, immersive experiences, making it a valuable approach meriting further investigation. Patient acceptance was notably high, especially among those with elevated distress levels. However, findings in radiology were less consistent, likely due to shorter procedure durations limiting the effectiveness of VR. The variability in outcomes highlights the importance of tailoring immersive interventions to specific procedures and patient needs. The narrative component identified key barriers, such as regulatory hurdles, standardization issues, and implementation challenges, that need addressing for broader clinical adoption. Conclusions: Immersive digital therapeutics are evolving from preliminary research tools toward more structured incorporation into clinical practice. Their future success relies on harmonizing technological advancements with patient-focused design and robust clinical evidence. Achieving this will require collaborative efforts among researchers, industry stakeholders, and healthcare providers. The integration of scoping and narrative review methods in this study offers a comprehensive perspective on the current landscape and informs strategic directions for advancing immersive technologies in radiology and radiotherapy. Full article

We present a computational approach for performing the Gaussian decomposition (GD) of experimental spectral data, called evolutionary Gaussian decomposition (EGD). The key feature of EGD is its ability to estimate the optimal number of Gaussian components required to fit a target function, which can be any experimental functional dependence. The efficiency and robustness of EGD are achieved through the use of the differential evolution (DE) algorithm, which allows us to tune the performance of the method. Based on statistics from the independent trials of DE, EGD can determine the number of Gaussians above which further improvement in fit quality does not occur. EGD works by collecting statistics on local minima in the vicinity of the estimated optimal number of Gaussians, and, if necessary, repeats this process several times during optimization until the desired results are obtained. The method was tested using both synthetic spectral-like functions and measured spectra of photosynthetic pigments. In addition to the local minima statistics, the most significant factors that affect the results of the analysis were the median and minimum values of the cost function. These values were obtained for each different number of Gaussian functions used in the evaluation process. Full article

Within the hazardous construction industry, roofers represent one of the most at-risk workforces, with high fatalities and injury rates largely driven by Work-Related Musculoskeletal Disorders (WMSDs). The primary precursor to these disorders is muscle fatigue, yet its objective assessment remains a significant challenge for implementing proactive safety management. To address this gap, this study details the implementation and validation of an AI-driven predictive analytics framework for automated fatigue detection using surface electromyography (sEMG) signals. Data was collected as participants (novice roofers) performed strenuous, simulated roofing tasks involving sustained standing, stooping, and kneeling postures. A key innovation is a data-driven labeling methodology using Weak Monotonicity (WM) trend analysis to automate the generation of objective labels. After a feature selection process yielded seven significant features, an evaluation of standard models confirmed that their classification performance was highly posture-dependent, motivating a more robust, hybrid solution. The framework culminates in a high-performance hybrid machine learning model. This architecture synergistically combines a Transformer–LSTM network for deep feature extraction with an XGBoost classifier. The model outperformed all standalone approaches, achieving over 82% accuracy across all postures with consistently strong fatigue F1-scores (0.77–0.78). The entire framework was validated using a stringent Leave-One-Subject-Out (LOSO) cross-validation protocol to ensure subject-independent generalizability. This research provides a validated component for AI-enhanced safety management systems. Future work should prioritize field validation with professional workers to translate this framework into practical, real-world ergonomic monitoring systems. Full article