Search Results (36,711)

The precise determination of highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy levels is critical for understanding the photophysical and photochemical properties of carbon dots (C-dots), which directly govern their performance in optoelectronic, catalytic, and sensing applications. However, the lack of distinct redox peaks in cyclic voltammetry (CV) curves of C-dots poses a major challenge to conventional energy level calculation methods. Herein, we propose a novel strategy to calculate the HOMO–LUMO energy levels of C-dots by combining electron transfer (ET) kinetics with Marcus theory. A series of quinones (electron acceptors, EAs) and ferrocene derivatives (electron donors, EDs) were employed to quench the fluorescence of C-dots, and the ET rate constants (K) were derived from fluorescence lifetime measurements. The CV curves of EAs and EDs provided their respective oxidation and reduction potentials, which were used as reference energy levels. The UV–Vis absorption spectra confirmed that the fluorescence quenching mechanism was dominated by ET rather than energy transfer. Based on Marcus theory, the free energy change (ΔG) of ET reactions was correlated with K, and the HOMO and LUMO energy levels of C-dots were calculated to be −1.84 V (vs. SCE) and +1.60 V (vs. SCE), respectively. This study not only provides a reliable method for determining the energy levels of C-dots without distinct redox peaks but also deepens the understanding of ET mechanisms between C-dots and small molecules. The proposed strategy is expected to be extended to other fluorescent nanomaterials with similar CV limitations. Full article

Background: Inflammation is central to myocardial injury and repair after ST-segment elevation myocardial infarction (STEMI). C-reactive protein (CRP) is an established biomarker of systemic inflammation, but its prognostic thresholds across patient subgroups are not well defined. Methods: In this retrospective cohort study, admission CRP was analyzed in 958 consecutive STEMI patients admitted to University Hospital Salzburg 2018–2020 and categorized into four groups (Serum CRP < 5.0, 5.0–9.9, 10.0–15, and >15.0 mg/dL). Mortality was assessed during short- (30, 90, and 180 days) and long-term (1, 3, and 5 years) follow-up. Kaplan–Meier analyses compared survival, Cox regression tested associations, and receiver operating characteristic (ROC) curves determined discriminatory value and optimal cut-offs. Results: Elevated admission CRP was associated with larger infarct size, impaired left ventricular function, and increased mortality. Kaplan–Meier curves showed progressively poorer survival with higher CRP, with worst outcomes at >15 mg/dL. At 30, 90, and 180 days, CRP demonstrated moderate discrimination (AUC 0.628, 0.653, and 0.654; all p < 0.001), with predictive cut-offs 11–15 mg/dL in the overall cohort. Subgroup analyses revealed markedly lower thresholds in vulnerable populations. Diabetic patients showed cut-offs 5–6 mg/dL with the highest AUC values (up to 0.714). Younger patients and smokers exhibited thresholds near 9–10 mg/dL, while subacute STEMI presentations demonstrated lower cut-offs compared with acute infarction. These findings indicate that the prognostic value of CRP is context-dependent rather than uniform. Conclusions: Admission CRP predicts short-term mortality after STEMI, with subgroup-specific cut-offs emerging below conventional thresholds, highlighting profiles where modest inflammatory activation carries disproportionate risk. Full article

Meticulous segmentation of medical images requires obtaining both local and global spatial detailed information. The conventional U-Net model excels at local spatial feature extraction through residual convolutional blocks but struggles to capture global features. To resolve this issue, we propose the vision transformer U-NeT (ViTUNet) model framework, which combines the self-attention mechanism of the vision transformer (ViT) to capture global information while maintaining the extraction of local features via U-NeT. This proposed architecture introduces vision transformers to the existing residual convolution blocks in the U-Net encoder path, thereby capturing both local and global features. The decoder path then rebuilds this information into high-quality segmentation maps with accurately highlighted boundaries/edges. This model is utilized to segment carious lesions in bitewing dental radiographs. These images are pre-processed using augmentation, morphological operations, and segmentation to identify the boundaries/edges of the regions of interest (caries/cavity). The proposed method is evaluated on an augmented dataset containing 3000 image–watershed mask pairs. It was trained on 2400 training images and tested on 600 testing images. The experimental results exemplified significant improvements in segmentation performance, achieving $98.45\%$ validation accuracy, $97.88\%$ validation Dice coefficient, and $95.87\%$ validation intersection over union (IoU) metric scores. These results are superior compared to other conventional and state-of-the-art U-NeT models, thus highlighting the impact of transformer-based hybrid architectures in improving medical image segmentation tasks. Full article

Integrated sensing and communications (ISAC) requires tight coordination between spatial signal design and multiple-access strategies to balance communication throughput and sensing accuracy under shared spectral and hardware constraints. However, existing ISAC frameworks with rate-splitting multiple access (RSMA) typically rely on fixed antenna arrays and decoupled optimization, which fundamentally limit their ability to adapt to fast channel variations and dynamic sensing requirements. This paper introduces a fluid antenna-enabled RSMA-assisted ISAC architecture, in which movable antenna ports are exploited as a new spatial degree of freedom to enhance adaptability in both communication and sensing operations. Fluid antenna systems (FAS) are deployed at both the base station and user terminals, allowing dynamic port selection that reshapes the effective channel and sensing beampattern in real time. We formulate a joint sum-rate maximization problem subject to explicit sensing-quality constraints, capturing the coupled impact of antenna port selection, RSMA rate allocation, and multi-beam transmit design. The proposed framework maximizes the communication sum-rate while ensuring that the sensing functionality satisfies a predefined sensing quality constraint. This constraint-based ISAC formulation guarantees that sufficient sensing power is directed toward the target while optimizing communication performance. The resulting optimization involves strongly coupled discrete and continuous decision variables, rendering conventional optimization methods ineffective. To address this challenge, a hierarchical deep reinforcement learning (HDRL) framework is developed, where an upper-layer deep Q-network (DQN) determines discrete antenna port selection and a lower-layer twin delayed deep deterministic policy gradient (TD3) algorithm optimizes continuous beamforming and rate-splitting parameters. Numerical results demonstrate that the proposed approach significantly improves system performance, achieving higher communication sum-rate while satisfying sensing requirements under dynamic propagation conditions. Full article

Water exhibits unique interfacial properties that arise from the collective organization of its hydrogen-bond network. Establishing clear links between molecular-scale interactions and macroscopic observables remains a central challenge in understanding the behavior of liquid water. In this work, we combine experimental measurements of the contact angle of sessile water drops with quantum-chemical modeling of small water clusters (H₂O)_n (n = 2–6) to explore multiscale effects of hydrogen-bond cooperativity. The cluster calculations reveal a nonlinear, saturating evolution of hydrogen-bond geometries with increasing cluster size, reflecting the onset of cooperative many-body effects. Experimentally, the evolution of the apparent contact angle during evaporation is quantified using both conventional geometry and a non-invasive geometrical-optical method based on analysis of the dark refractive ring, which provides independent validation against conventional goniometric measurements. The evaporation dynamics are further interpreted within the diffusion-limited framework of the Popov model, indicating that the temporal evolution of the apparent contact angle is primarily consistent with geometry-controlled mass loss under diffusion-limited conditions, rather than requiring variations in intrinsic surface energy. By combining macroscopic contact-angle measurements with molecular-level cluster analysis, this study offers a qualitative multiscale perspective in which minimal cooperative hydrogen-bond motifs provide molecular context for interpreting interfacial behavior, without implying direct quantitative prediction of macroscopic interfacial observables. Full article

Redundant robot manipulators are widely used in constrained operations and tasks in complex environments. However, when multiple task constraints and inequality constraints coexist, motion planning becomes significantly more difficult. In high-dimensional configuration spaces, conventional planners are prone to local minima and may generate trajectories that are difficult to execute in real time. To address these issues, this paper proposes a hierarchical, redundancy-driven real-time replanning framework. First, we perform Cartesian sampling on the task-constraint manifold to reduce the search dimension and generate multiple candidate joint configurations for each Cartesian sample via a redundancy mapping. During connection, manipulability and executability margin are used as evaluation metrics, so that redundant degrees of freedom are explicitly exploited in tree expansion and configuration selection. Second, at the local execution layer, we employ a null-space manipulability optimization strategy to continuously improve dexterity while keeping the primary task unchanged and combine it with a priority-based hard inequality constraint filtering mechanism to project the nominal motion onto the feasible set under joint limits, velocity bounds, and safety-distance constraints in real time. Unlike existing approaches that treat global planning and local control as loosely coupled modules, the proposed framework unifies redundancy reconfiguration, feasibility maintenance, and topological replanning within a single closed-loop structure, thereby reinterpreting local minima as event-triggered topology-switching conditions. To handle the mismatch between dynamic environments and real-time perception, we further introduce a feasibility-margin monitoring mechanism that triggers event-based replanning based on changes in manipulability, constraint scaling, and safety distance, enabling fast topology-level switching and escape from local minima. Simulation and experimental results show that the proposed method effectively restores manipulability through redundancy-driven configuration adjustment and achieves a higher success rate of local recovery under dynamic obstacle intrusion. In forced replanning scenarios, the framework further demonstrates faster environmental response and lower replanning overhead while maintaining better task-constraint stability compared with existing approaches. Full article

Background: Left ventricular global longitudinal strain (GLS) measured by speckle-tracking echocardiography (STE) has become a key marker of myocardial systolic function, yet normal reference values remain heterogeneous, and the magnitude of physiological sex differences is not fully defined. We performed a systematic review and meta-analysis to establish pooled GLS reference estimates in healthy individuals, quantify sex-related differences, and contextualize deformation findings relative to conventional systolic function. Methods: A systematic search of PubMed, Scopus, and EMBASE identified observational studies reporting GLS in healthy adults assessed by two-dimensional or three-dimensional STE. Random-effects meta-analysis using standardized mean differences (SMD) compared GLS between women and men. Descriptive pooled reference values were derived using weighted median and interquartile range (IQR) reconstruction from study-level distributions. Meta-regression analyses explored demographic, clinical, and methodological sources of heterogeneity. A complementary analysis evaluated sex-related differences in left ventricular ejection fraction (LVEF) within the same populations. Results: Thirty-two studies, including 19,157 healthy individuals, were analyzed. The pooled population had a weighted median age of 47.5 years and 53% female participants. Overall, GLS demonstrated a weighted median of 20.3% (IQR 17.8–22.5). Women showed higher GLS values than men (20.8% [18.4–23.1] vs. 19.4% [17.0–21.6]). Meta-analysis of 28 studies confirmed significantly greater GLS in females (SMD 0.487, 95% CI 0.409–0.565; p < 0.001), with consistent findings across imaging modalities and no subgroup interaction. Between-study heterogeneity was substantial (I² = 82.7%), although effect direction was uniform. Meta-regression analyses identified no significant moderators, and sensitivity analyses confirmed stable estimates without publication bias. Segmental analysis demonstrated a physiological base-to-apex strain gradient. In contrast, LVEF was largely comparable between sexes, with no clinically meaningful difference (SMD 0.257, 95% CI 0.186–0.327; p < 0.001), indicating preserved global systolic performance despite differences in myocardial deformation. Conclusions: GLS demonstrates a consistent physiological range in healthy populations, with women exhibiting higher longitudinal deformation than men, independent of the imaging modality. These findings support the adoption of sex-specific GLS reference values and highlight the complementary roles of deformation and volumetric indices in improving the interpretation of myocardial function and reducing misclassification in clinical practice. Full article

Background/Objectives: Compared to conventional test methods, point-of-care tests (POCTs) offer advantages for optimising care in patient groups at risk of vitamin D deficiency. However, their diagnostic accuracy in clinical settings has not previously been systematically assessed. We conducted a systematic review to assess the diagnostic accuracy of current point-of-care technology (POCT) for diagnosing vitamin D deficiency in adults and children. Methods: We searched Embase, MEDLINE and Web of Science on 3 December 2024 and also conducted forward and backward citation searching. We included studies from all patient groups and clinical settings where the index test had been conducted and processed at point of care, with a comparator of any laboratory reference standard test. We assessed risk of bias and applicability concerns for the included studies using published tools. The review was registered in advance (PROSPERO reference CRD42024618338). Results: After screening, five articles relating to four studies were included. These assessed five index POCTs against reference standard laboratory tests (liquid chromatography tandem mass spectrometry in three of the four included studies). The number of samples per comparison ranged from 6 to 20. There was variation in the level of agreement between POCT and laboratory reference standard tests. We also identified incomplete reporting of key study features, which prevented definitive assessment of several domains of the risk of bias and applicability tools. Conclusions: There is currently insufficient peer-reviewed evidence from clinical evaluations to recommend any particular POCT for vitamin D. Future studies should recruit adequate sample size and complete reporting of study design features and diagnostic accuracy measures. Full article

Background: Metabolic syndrome is associated with early impairments in cellular bioenergetics that are not fully captured by conventional body composition measures. Molecular hydrogen, produced endogenously through gut microbial fermentation and measurable in breath, has been implicated in redox and mitochondrial regulation. Whether breath hydrogen relates to preservation of intracellular, metabolically active tissue in metabolic syndrome remains unclear. Objectives: To examine the association between breath hydrogen concentration and an integrated cellular bioenergetic phenotype derived from intracellular body composition indices in sedentary adults with metabolic syndrome. Methods: Twenty-eight sedentary, middle-aged adults (51.2 ± 7.9 years, 19 females) with metabolic syndrome underwent fasting breath hydrogen assessment and multifrequency bioelectrical impedance analysis. A composite cellular bioenergetic phenotype was derived using principal component analysis of body cell mass, intracellular water, total body potassium, and glycogen. Associations between breath hydrogen and the composite phenotype were evaluated using Spearman correlation with bootstrapped confidence intervals, Theil-Sen regression, and Bayesian linear regression adjusted for age, sex, and waist circumference. Sensitivity analyses included fat-free mass. Results: A single principal component explained 98.6% of the variance across intracellular variables, indicating a highly coherent cellular bioenergetic phenotype. Breath hydrogen concentration was positively associated with this phenotype (ρ = 0.43, p = 0.021; BCa 95% CI 0.07–0.70). Theil-Sen regression confirmed a robust positive association (β = 0.017 per ppm hydrogen; 95% CI 0.002–0.046). Bayesian models showed posterior distributions centered on positive effect sizes, independent of central adiposity. In contrast, the association with fat-free mass alone was borderline. Conclusions: Breath hydrogen concentration reflects an integrated intracellular bioenergetic phenotype in sedentary adults with metabolic syndrome, tracking cellular quality rather than lean mass quantity. Breath hydrogen may serve as a non-invasive biomarker of cellular bioenergetic integrity and a potential tool for phenotype-guided metabolic interventions. Full article

Green supply chain management (GSCM) has become essential for organizations seeking to balance environmental sustainability, regulatory compliance, and economic resilience. However, selecting appropriate green supply chain strategies constitutes a complex multicriteria decision-making (MCDM) problem due to diverse sustainability practices, conflicting objectives, dynamic market conditions, and significant uncertainty in expert evaluations. To address these challenges, this study proposes an intelligent multicriteria group decision-making (MCGDM) framework to assess 15 GSCM strategies across 15 environmental, operational, economic, and regulatory criteria. The framework employs complex fractional orthopair fuzzy sets (C_FOFS) to model uncertainty, expert hesitation, and complex-valued judgments. Expert weights are determined using the analytic hierarchy process (AHP), while criteria weights are derived objectively through the entropy method. A modified technique for order preference by similarity to the ideal solution (TOPSIS) is applied to obtain a robust ranking of alternatives. Evaluations from five multidisciplinary experts ensure practical relevance and validity. The results indicate enhanced uncertainty modeling, improved ranking stability, and greater interpretability compared with conventional fuzzy and deterministic approaches. The proposed framework provides a transparent and effective decision support tool for strategic GSCM planning. Full article

This article proposes a novel time-space two-grid high-order compact difference scheme for the one-dimensional nonlinear Schrödinger equation subject to Dirichlet boundary conditions. In comparison with the fully nonlinear compact difference scheme, the proposed methodology combines a small-scale nonlinear fourth-order compact difference algorithm on a time-space coarse grid and a large-scale linearized correction compact difference algorithm on a fine grid. In contrast to the time two-grid compact difference method, the proposed scheme applies the two-grid technique in both the spatial and temporal domains, thereby further improving computational efficiency. Solutions from the coarse grid are projected onto the fine grid via a temporally linear and spatially cubic Lagrange interpolation operator. Unconditional stability and optimal convergence rates, which are fourth-order in space and second-order in time, are proven in both the discrete $L^2$ and $L^{\infty }$ norms, without any constraints on the grid ratio. In addition to the standard techniques of the energy method, a discrete Sobolev inequality and an a priori error estimate are employed to demonstrate stability and high-order convergence. Finally, the theoretical results are validated through numerical experiments, which confirm the robustness and reliability of the proposed approach. A single-soliton experiment demonstrates that, compared with the fully nonlinear compact difference scheme, the proposed method achieves a significant reduction in CPU time while maintaining a comparable level of accuracy. Additional experiments further illustrate the algorithm’s effectiveness in simulating two-soliton interactions and soliton birth. These findings establish the proposed scheme as a highly efficient alternative to conventional nonlinear approaches. Full article

This research utilizes machine learning (ML)-based malicious node detection techniques to effectively incorporate security and trustworthiness into fifth-generation (5G) and Vehicular Ad hoc Network (VANET) systems, in contrast to traditional methods that do not employ modern techniques. VANET may be vulnerable due to vehicle mobility, network openness, and the conventional network architecture. Therefore, security and trust management using modern methodologies, such as ML approaches, is essential for 5G-enabled VANET integration, which has become a paramount concern. And due to limitations imposed by traditional security methods, which are unable to identify malicious nodes in VANET completely, processing delays are longer. Therefore, this research utilizes the VANET malicious-node dataset designed for real-time malicious node/attack detection in VANET. The proposed ML methodology uses a Random Forest (RF) and an optimized ensemble ML classifier, such as XGBoost and LightGBM, which require a security and trustworthiness solution provided by the RF Trust Extended Authentication (TEA). We simulate vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) mobility, communication behaviors, and trust metrics to assess the accuracy of malicious-vehicular-node features for the identification and detection of attacks, including False Injection, Sybil, blackhole, and Denial-of-Service (DoS). The proposed ML methodology also identifies these attack patterns, providing a realistic dataset for Intelligent Transportation System (ITS) research. In contrast, traditional VANET methods do not. We compared the performance of the proposed ML method with other literature-standard ML and RF methods using metrics such as accuracy, confusion matrices, and precision, Recall, and F1-score to measure effectiveness. In our proposed machine learning (ML) method, we achieve 99% accuracy in classifying MVN and predicting both attack, including False Injection, Sybil, blackhole, and Denial-of-Service (DoS), and benign classes, with precision, recall, and F1-score of 100% each, and establish a trustworthiness score of 100%, Whilst the standard models, such as other VANET methods achieved an accuracy of only 95%, with precision, recall, and F1-score of 98%, without a confusion matrix to confirm the model’s performance. Full article

This paper presents an intelligent cascaded fractional-order proportional–integral (CFO-PI) control strategy optimized using a genetic algorithm (GA) for a 1.5 MW DFIG-based multi-rotor wind turbine (MRWT) system. The primary objective is to enhance operational performance and power quality. The proposed method is evaluated against the conventional direct power control scheme using a traditional PI regulator (DPC-PI) to demonstrate its effectiveness. Comparative analysis shows substantial performance improvements achieved by the CFO-PI approach. Specifically, active power ripple is reduced by 61.71% compared to DPC-PI, resulting in smoother power delivery and improved grid compatibility. In addition, the steady-state error of active power decreases by 72.60%, indicating improved tracking accuracy. For reactive power, a 52.03% reduction in ripple is observed, while current ripple is reduced by approximately 56%, reflecting enhanced waveform quality. These results highlight the CFO-PI controller’s capability to maintain better power quality and steady-state performance relative to conventional DPC-PI. Overall, the GA-optimized CFO-PI controller provides a promising alternative for improving dynamic performance and power quality in DFIG-based MRWT systems. Full article

To address the issues of slow dynamic response and poor disturbance rejection in three-phase interleaved parallel buck converters under disturbance conditions such as voltage or load transients, an improved sliding mode auto-disturbance rejection control (SM-ADRC) strategy is proposed. Firstly, the traditional ADRC algorithm suffers from reduced disturbance observation accuracy in the extended state observer (ESO) due to discontinuous switching of the nonlinear function at segment boundaries. To address this, a novel nonlinear function is designed using an interpolation fitting method. Concurrently, an improved ESO is constructed based on deviation-control principles, utilising the deviation between each state variable and its observed value. Secondly, an enhanced state error feedback law combines an improved exponential approach law with an integral sliding mode surface, thereby enhancing the control system’s robustness. Finally, simulation comparisons of output voltage fluctuations and power response speeds under various operating conditions validate the superiority and feasibility of the proposed SM-ADRC strategy over both the conventional ADRC strategy and PI control strategy. Full article

Line spectrum purification is a fundamental task in underwater detection and identification tasks. A dual-input architecture based on Dense U-net is introduced to extract clean line spectra from strong interference. The U-net model features a symmetric encoder–decoder structure that accepts two-dimensional data as both input and output. The DenseBlock, a core component of DenseNets, offers greater parameter efficiency compared to conventional convolutional layers. In this paper, standard convolutional layers inside the original U-net are replaced by DenseBlocks. This model possesses two input channels, thus allowing the time–frequency feature of the interference and that of the interference–target mixture to be fed simultaneously. With supervised learning, the model is capable of eliminating the strong interference components and background noise from the superimposed spectrum, thereby producing a purified target line spectrum. Compared to traditional interference suppression methods, this approach offers higher feature accuracy and greater signal-to-interference-and-noise ratio (SINR) gain. Moreover, the model is trainable using simulation datasets and then deployed to real-world measurements, demonstrating strong generalization capabilities—a valuable property given the limited availability of labeled samples in underwater detection tasks. Being data-driven, this method operates without requiring prior assumptions about the array configuration, and consequently exhibits greater resilience to array imperfections relative to conventional model-based interference suppression techniques. Simulation and experimental results demonstrate that the proposed method achieves an output SINR improvement of more than 8 dB under low SINR conditions and exhibits significantly better robustness to array position errors than conventional methods, verifying its excellent line spectrum purification capability. Full article