Search Results (22,341)

The increasing prevalence of biofilm-associated infections caused by Cutibacterium acnes has stimulated interest in food-derived natural products as alternative sources of anti-biofilm agents. This study investigated the effects of processing and extraction conditions on the phytochemical composition, antibacterial activity, and anti-biofilm properties of Dioscorea hispida Dennst. Reflux extraction of dried yam with 80% ethanol produced the crude extracts with the highest yields (1.39–1.80%), whereas fresh yam yielded 0.51–0.97% extract. Using Gas–liquid chromatography–mass spectrometry (GLC-MS) analysis, linoleic acid ethyl ester, n-hexadecanoic acid, 9,12-octadecadienoic acid (Z,Z)-, and stigmasterol were identified as the major constituents. Among the tested extracts, DH-W-F-H (D. hispida-water washing-fresh-hexane) and DH-W-F-E (D. hispida-water washing-fresh-ethanol) were extracted from fresh yam using hexane and ethanol, respectively, while DH-W-D-E (D. hispida-water washing-dry-ethanol) was isolated from dried yam using ethanol and exhibited the strongest antibacterial activity, with minimum inhibitory concentrations (MIC) ranging from 64 to 2048 µg/mL. These extracts demonstrated pronounced concentration-dependent inhibition of biofilm formation by Staphylococcus epidermidis, Staphylococcus aureus, and Cutibacterium acnes. The strongest anti-biofilm activity was observed against C. acnes, with biofilm formation nearly eliminated at MIC concentrations. Moreover, all three extracts significantly reduced established C. acnes biofilms, with DH-W-F-H exhibiting greater eradication efficacy than vancomycin under the tested conditions. To elucidate the underlying mechanism, major fatty acid derivatives were evaluated against C. acnes lipase (_CALipase), a virulence factor associated with biofilm development, using molecular docking, molecular dynamics simulations, and the Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) binding free-energy calculations. The compounds exhibited favorable interactions with _CALipase, with linoleic acid ethyl ester (FA2) showing the strongest binding affinity, stable protein–ligand interactions throughout a 200 ns simulation, and the most favorable binding free energy. Collectively, the biological and computational findings suggest that fatty acid-rich extracts from processed D. hispida suppress biofilm formation through an antivirulence mechanism involving _CALipase inhibition. These results highlight the potential of D. hispida as a source of metabolites for the development of functional food ingredients and value-added cosmetic and dermatological applications. Full article

This study aimed to evaluate bone regeneration in critical-size rat calvarial defects treated with a xenogeneic bone graft alone or combined with autologous platelet concentrates prepared using two distinct centrifugation protocols, one based on horizontal centrifugation to produce horizontal platelet-rich fibrin (H-PRF) and the other based on vertical fixed-angle centrifugation to produce leukocyte–platelet-rich fibrin (L-PRF). Calvarial defects were created in 24 rats and allocated to four groups: blood clot (C), xenograft (XEN), xenograft plus L-PRF (XEN+L-PRF), and xenograft plus H-PRF (XEN+H-PRF) (n = 6/group). Calcein and alizarin were administered at 14 and 30 days, respectively. After 35 days, specimens were analyzed by micro-computed tomography, confocal laser scanning microscopy, histomorphometry, and histopathology. Data were analyzed using ANOVA and Tukey’s post hoc test (p < 0.05). All treated groups showed higher bone volume and lower trabecular separation than group C. The XEN+L-PRF group showed significantly higher bone volume than the XEN group. The XEN+H-PRF group showed higher bone volume, connectivity density, alizarin labeling, mineral apposition rate, and newly formed bone area, as well as lower trabecular separation, than the other groups (p < 0.05). Under the experimental conditions evaluated, both PRF protocols improved bone regeneration when combined with a xenogeneic bone graft, whereas the H-PRF protocol was associated with superior structural and histomorphometric bone regeneration outcomes and with more favorable spatiotemporal mineralization kinetics. Full article

Fiscal cyclicality occupies a central position in macroeconomic stabilization, but existing empirical studies have focused predominantly on determining whether fiscal behaviour is procyclical or countercyclical, with relatively limited attention being paid to the dynamic evolution and intensity of fiscal response over time. The purpose of this study is to develop a dynamic framework for assessing the direction, intensity and temporal evolution of fiscal cyclicality in the European Union. This research analyses 27 Member States during the period 2001–2025. The fiscal cyclicality coefficient is estimated using five-year rolling ordinary least squares (OLS) regressions of the budget balance on the output gap, allowing fiscal cyclicality to vary across countries and over time. In order to enhance the reliability of the estimates, the computed coefficients are winsorised and used to construct a continuous measure of fiscal cyclicality intensity and a normalized Score index. The findings reveal substantial heterogeneity in fiscal cyclicality across EU Member States and over time, indicating a predominant countercyclical behaviour during significant macroeconomic shocks, while also demonstrating significant differences in the strength and stability of fiscal responses. Countercyclical observations account for 83.07% of the rolling-window estimates, compared with 11.82% procyclical and 5.11% acyclical observations. The additional robustness tests indicate that the main structure of the estimated coefficient series is preserved under alternative treatments of extreme observations, the exclusion of individual countries and the use of an earlier information set for the output gap. Analysing fiscal cyclicality solely through its direction therefore provides an incomplete representation of fiscal behaviour. The proposed framework extends the existing literature by simultaneously considering the direction, intensity and dynamics of fiscal cyclicality over time, providing a more comprehensive basis for comparative analysis of fiscal policy and future assessments of fiscal sustainability. For policymakers, the resulting Score provides an additional quantitative indicator for monitoring changes in the intensity of fiscal behaviour, and when considered jointly with the estimated coefficient, changes in its direction may complement existing assessments of fiscal policy within the European Semester and the European economic governance framework. Full article

The utilisation of computer vision in urban studies has become common practice due to its capacity to diminish the financial burden associated with field surveys. Monocular Depth Estimation (MDE) is a recent branch of computer vision that has been shown to be capable of predicting three-dimensional information from a single image. Street View Imagery (SVI) refers to the collection of a substantial dataset comprising urban images. The purpose of this paper is to analyse the potential of applying MDE to Volunteered SVI (VSVI) in urban studies. Following the processes of acquisition and screening, a total of 102 MDE and 42 studies employing SVI are utilised to delineate this potential association. The number of MDE models, training strategies and the volume of training, validation and evaluation datasets have all increased over the years. Notably, MDE models have significantly improved in accuracy through the KITTI benchmark test. Despite the gap between MDE developers and urban study practitioners, as well as the misuse of VSVI, the application of MDE in SVI-based urban studies is substantial. Based on observable potentials, this study further proposes a future framework for using MDE in VSVI-based urban studies, which can contribute to the field of computer vision in a built environment. Full article

Land subsidence amplifies the impacts of sea-level rise (SLR) in low-lying deltas, yet some Coastal Vulnerability Index (CVI) assessments omit this or rely on global estimates, drastically understating deltaic vulnerability. This study presents the first CVI assessment along Ghana’s coast to integrate validated, spatially resolved land subsidence derived from Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR). Nine geological, geomorphological, hydrodynamic, and anthropogenic variables were quantified across 72 contiguous grid cells spanning ~150 km of the Volta Delta’s coastline and ranked on a 1–5 vulnerability scale. Three composite indices were computed using a common quantile distribution: an index excluding subsidence (CVI_-sub), an index with a spatially uniform regional vertical land motion (CVI_+(u-sub)), and an index incorporating spatially resolved local InSAR subsidence (CVI_+(v-sub)). High to very high vulnerability ranks rose from 28% of cells without subsidence, through 44% with the regional estimate, to 75% with the local InSAR; every grid cell recorded a higher CVI once subsidence was incorporated (Wilcoxon signed-rank test, p < 0.001). Vulnerability peaked along the Keta and Songor lagoonal margins. Omitting measured subsidence understates deltaic vulnerability, and this approach offers a transferable method for data-sparse deltas. Full article

This study addresses the theoretical development and computational implementation of probabilistic-statistical methods for solving the stationary problems of two-phase filtration of two immiscible, incompressible fluids in a porous medium. The object of investigation is a second-kind Fredholm integral equation, denoted Equation, which arises from applying the Levi function to the regular filtration problem. It is proved that, provided $a\left(x\right)\equiv a=const>0$, the norm of the integral operator in the space $C\left(\bar{\Omega }\right)$ is strictly less than unity, which guarantees the existence, uniqueness, and convergence of successive approximations. Building on this result, a Neumann–Ulam scheme is constructed: the generating Markov chain is described, a probabilistic representation of the solution is derived, and the asymptotic unbiasedness of the idealized estimators and the bounded finite $\epsilon \left(\delta \right)$-bias and finite variance of the practically implementable estimators are established. A generalization of the Neumann–Ulam scheme is proposed and theoretically justified—a dominant-part extraction method for the operator—which affords a substantial reduction in variance whenever the norm of the residual integral operator is small. A computational experiment includes a variable-coefficient test case to examine the practical behavior of the proposed methods beyond the constant-coefficient setting considered in the theoretical results. The proposed algorithms are shown to outperform the standard finite-difference scheme in the accuracy of the solution at an individual point of the domain, an advantage that becomes particularly pronounced for multidimensional problems. Full article

Autonomous mobile robots are increasingly considered for campus delivery and service logistics, where route efficiency can reduce unnecessary travel under spatial constraints. This study develops an obstacle-aware routing framework that combines a 1 m occupancy grid, A* shortest-path computation, and an Improved Mayfly Optimization Algorithm (IMOA). The A* stage constructs a pairwise distance matrix using orthogonal costs of 1, diagonal costs of $\sqrt{2}$, an octile heuristic, and a no-corner-cutting rule; IMOA then optimizes the closed visiting order through random-key decoding, elite 2-opt, and stagnation handling. Validation comprises ten independent benchmark instances, the fixed G40 scenario, and a campus-derived G-real application. Under a common budget of 50,000 full-tour evaluations and 30 independent runs, a Friedman test detected overall differences across the ten instances (χ²(7) = 66.2488, p = 8.434 × 10⁻¹²). After Holm correction, IMOA significantly outperformed GA, PSO, GWO, ACO, and MOA, showed no significant difference from MS2OPT, and had a worse average rank than the deterministic LKH reference, which achieved the best overall rank. On G-real, IMOA obtained a median distance of 8178.37 m, compared with 8223.99 m for MS2OPT; this difference was not significant, while LKH achieved the lowest deterministic cost of 8076.46 m. A three-instance exploratory ablation ranked IMOA first and consistently identified elite 2-opt as the principal observed improvement source; component-level inference remains exploratory because only three instances were available. These findings establish routing-efficiency gains under the evaluated protocol. Such gains may support more resource-efficient campus logistics, but energy consumption and carbon emissions were not evaluated. Full article

Vision-based fire and smoke detection must be both accurate and lightweight for edge cameras and unmanned aerial vehicles (UAVs). Most lightweight fire detectors, however, are validated on a single dataset from a single run, which leaves the accuracy–efficiency trade-off and its external validity only partially examined. Rather than a new state of the art, we take an evaluation-centered stance and study two lightweight operating points of YOLO11n: an accuracy-first variant (LFS-YOLO11-A) that adopts the Wise-IoU (WIoU) loss at no extra parameters, and a lightweight variant (LFS-YOLO11-B) that further adds GhostConv. Both are evaluated on the public D-Fire dataset, retrained on a second dataset (DFS) over eight seeds, and profiled across GPU/CPU under PyTorch and ONNX Runtime. LFS-YOLO11-A matches the baseline on D-Fire (mAP@0.5 0.760 versus 0.758), while LFS-YOLO11-B reduces parameters by 12.4% and computation by 11%, both exceeding 160 FPS end-to-end (batch = 1) on a desktop-class GPU. On DFS, WIoU yields a small, exploratory +0.6-point improvement (nominal paired p = 0.037; seed-sensitive, with a confidence interval lower bound near zero), whereas an apparent +6.1-point gain from an early, uncontrolled run proved to be train/test contamination introduced before the data pipeline was frozen, not a genuine effect. Frozen-pipeline, multi-seed, two-benchmark evaluation is therefore necessary to separate genuine effects from the artifacts of uncontrolled single runs in lightweight fire and smoke detection. Full article

Unmanned aerial vehicles (UAVs) have become essential platforms for remote sensing in challenging environments such as high-dynamic-range (HDR) scenes and low-texture areas. However, conventional frame-based visual–inertial simultaneous localization and mapping (SLAM) systems often suffer from motion blur and overexposure during high-speed UAV flight, leading to state estimation failure. To address numerical instability in marginalization, weak scene adaptability, and insufficient outlier suppression in event-based stereo visual–inertial SLAM systems for aerial applications, we propose HESVI, a hybrid marginalization and adaptive heterogeneous kernel state estimation method for UAV remote sensing. Our method first establishes a focal-length-driven cross-modal inverse depth consistency constraint to couple image and event inverse depths, providing high-quality priors for optimization. Such lightweight prior generation is designed with the limited onboard computing resources of UAV platforms in mind. A hybrid marginalization strategy is then introduced, employing block-parallel tall–skinny QR (TSQR) acceleration based on Householder reflections alongside dynamic Tikhonov regularization and first-estimates Jacobian (FEJ) linearization to balance computational efficiency and numerical stability. Furthermore, an adaptive heterogeneous Cauchy kernel maps differentiated thresholds to image and event features according to their average effective tracking lengths, enabling dynamic outlier suppression. Experiments on the VECtor, MVSEC, and HKU datasets demonstrate that HESVI achieves the best absolute trajectory error (ATE) on the vast majority of the evaluated sequences, with average ATE reductions of 47.2%, 41.2%, and 26.8% over PL-EVIO, ESIO, and ESVIO, where each average is computed only over the sequences on which the corresponding baseline runs successfully. The method also exhibits excellent performance in complex remote-sensing scenarios and generalization tests. HESVI effectively enhances the numerical stability, scene adaptability, and localization accuracy of event-based stereo visual–inertial SLAM systems in challenging UAV remote-sensing environments. Full article

This study evaluates a hierarchical workflow for fault diagnosis in transmission systems by integrating established machine-learning techniques for fault-type classification and zonal fault location with a complementary double-ended traveling-wave procedure for point-location estimation. The contribution lies at the level of process integration and comparative evaluation rather than in the proposal of a new machine-learning or traveling-wave algorithm. The methodology was evaluated using the IEEE 9-bus test system. Symmetrical and asymmetrical short-circuit scenarios were automatically simulated at multiple positions along six transmission lines using DIgSILENT PowerFactory, and the resulting oscillographic records were exported in COMTRADE format, producing a database of 2952 fault events. Phase voltages and currents, together with positive-, negative-, and zero-sequence components, were used to evaluate Decision Trees, Self-Organizing Maps (SOM), Artificial Neural Networks (ANN), and k-Nearest Neighbors (KNN) for fault-type classification and zonal fault location. Under the simulated noise-free conditions and the adopted fixed hold-out partition, all four algorithms correctly classified the 591 fault-type testing observations, yielding 100% test-set accuracy. This result characterizes the specific evaluation subset considered in the study; repeated, cross-validated, or grouped partitions were not performed, and neighboring simulated fault positions may therefore be represented across the training and testing subsets. For zonal fault location, the ANN exhibited the strongest and most consistent observed performance in the retained 590-event evaluation set, with class-specific recall values between approximately 0.96 and 0.99 across the six fault zones, whereas the Decision Tree provided a favorable compromise between zonal discrimination and computational efficiency. Some model-specific hyperparameter values from the original executions are unavailable in the retained experimental record, which limits exact replication of those original configurations; the reported results correspond to the evaluated executions documented in this study. As a complementary third component of the workflow, the double-ended traveling-wave procedure based on discrete wavelet analysis was illustrated for one AG event simulated at 25% of the transmission-line length, producing a normalized point-location estimate of approximately 25.07% from the local terminal. This single-event analysis demonstrates the operation of the traveling-wave processing sequence, while broader multi-event validation is outside the present experimental scope. Overall, the results demonstrate the coordinated application of fault-type classification, zonal fault location, and traveling-wave-based point-location refinement within a common diagnostic workflow. The findings should be interpreted within the deterministic simulation conditions, fixed evaluation subsets, and experimental records considered in this study. Full article

This exploratory study examines the implementation of a GenAI-supported curriculum redesign in a project-based multimedia computing course by comparing students’ experiences in Python-based and GenAI-supported production pathways in relation to creativity, applied digital competence, and perceived professional readiness. A mixed-methods, quasi-experimental design was employed to compare two naturally occurring undergraduate cohorts at a Gulf-region university. Data was collected from 29 students through post-course surveys, open-ended reflections, project reports, student-generated artefacts, and instructional observations. Quantitative data were analyzed using nonparametric statistical tests and effect size reporting, while qualitative evidence was examined through inductive thematic analysis supported by human-in-the-loop AI-assisted coding. The findings indicate that the GenAI-supported cohort reported significantly higher project satisfaction, perceived ease of use, and job relevance, with additional positive trends in creativity and professional output. Qualitative evidence and illustrative project examples further suggest that students experienced fewer technical barriers and greater opportunities for multimodal production, particularly in community-facing projects. Based on these findings, the study presents the INSPIRE framework, a seven-stage, practice-informed conceptual model for integrating GenAI into project-based computing education. Although limited by a small sample and single-institution context, the study provides exploratory comparative evidence and a practice-informed framework that offers actionable guidance for responsible and structured GenAI integration in higher education. Full article

Accurate measurement of vibro-acoustic transfer functions is essential for ship noise control. Because direct testing requires high-power excitation sources that are difficult and costly to deploy, the reciprocity method has attracted increasing attention. Its application to full-scale ships, however, has long been hindered by an unresolved theoretical question: whether hull-mounted acoustic coatings compromise force–sound reciprocity. To answer this question, this study combines theoretical analysis, numerical simulation, and anechoic water-tank experiments to investigate a typical horn-type cavity acoustic coating. Theoretical analysis shows that, because its complex stiffness tensor remains symmetric, a linear viscoelastic coating with geometrically asymmetric cavities still preserves reciprocity. Numerical simulations of a stiffened double-layer cylindrical shell covered with the coating show that the forward and reciprocal transfer functions coincide to well within 1 dB over the entire computed band. Anechoic water-tank experiments on a scaled model show that applying the coating raises the band-averaged reciprocity error by only 0.2 dB, from 1.4–1.5 dB to 1.6–1.7 dB in the 2–5 kHz band. These results provide evidence that, for the tested coating and structural configuration under anechoic conditions, the horn-cavity coating introduces no significant principle-based error into reciprocity testing—a first quantitative step towards removing the long-standing theoretical obstacle to applying reciprocity methods to coated, full-scale ships. Full article

Stroke is one of the leading causes of mortality and long-term neurological disability worldwide, and early diagnosis through accurate disease classification directly affects treatment success. Rapid differentiation of hemorrhagic and ischemic stroke on computed tomography (CT) images, together with accurate determination of the acute and chronic phase in ischemic cases, is of critical importance in the clinical decision-making process. In this study, StrokeCT-2C5K a two-center dataset comprising 5000 cranial CT images, was assembled specifically for this work. The images were reviewed by radiology specialists and assigned to one of four diagnostic categories: normal, hemorrhagic stroke, acute ischemic stroke, or chronic ischemic stroke. A Squeeze-and-Excitation (SE-Attention) mechanism was then integrated into DenseNet-121, ResNet-50, and EfficientNet-B3. From a biomimetic perspective, this channel-recalibration process provides a functional analogy to biological selective attention by giving greater weight to informative responses while reducing the influence of less relevant ones. All models were trained under the same training, validation, and test protocol; the standard CNN architectures were compared with their SE-Attention-enhanced counterparts. The results showed that the SE-Attention mechanism enables more effective learning of lesion-specific discriminative features by adaptively recalibrating channel-wise information, yielding an average classification accuracy improvement of 0.93 percentage points across all three architectures. The most pronounced improvements were observed in distinguishing ischemic from hemorrhagic stroke, as well as in distinguishing acute from chronic ischemic stroke. These findings show that a selective-information-processing strategy functionally analogous to biological attention can improve multi-class stroke classification across different CNN backbones. Full article

This work introduces efficient signal denoising methods based on wavelet thresholding (WT) and Hankel matrix-based Singular Value Decomposition (HSVD) with a randomized algorithm. The sequential hybrid frameworks of these techniques are investigated—both WT followed by HSVD (WT-HSVD) and HSVD followed by WT (HSVD-WT)—across varying noise levels. Numerical tests are performed through different benchmark signals, including dual harmonic, damped sine, and dual frequency signals, as well as a natural phonocardiogram recording. Different types of corrupted noises, including white Gaussian, colored (brown), and impulsive noises, are considered in the numerical tests. For moderate and high noise levels, hybrid frameworks can significantly improve the final accuracy of the denoised signals, as measured by reconstruction error metrics and Signal-to-Noise Ratio. These hybrid frameworks are also shown to be advantageous in compensating for suboptimal parameter selections that may occur when using either method individually. Furthermore, to mitigate the computational burden inherent to exact SVD, this work employs a randomized Singular Value Decomposition (rSVD) algorithm. Depending on the signal size, the proposed hybrid frameworks with rSVD achieve a 30% to 80% reduction in CPU execution time while maintaining denoised signal reconstruction accuracy across test cases. Full article

The accelerated deployment of electric vehicles requires planning tools able to quantify how much charging infrastructure can be integrated into distribution systems without violating operational constraints. This paper proposes a multi-scenario optimization framework for the siting and sizing of electric vehicle charging stations in radial distribution networks. The problem is formulated as a mixed-integer nonlinear programming model in which candidate-station slots, binary siting decisions, integer EV assignments, hourly power-flow constraints, voltage limits, thermal limits, charger power factor, and charging strategy are coordinated. The objective function combines hosting capacity maximization with active energy losses and voltage deviation terms through a scalarized formulation. Unmanaged and managed charging strategies are evaluated under weekday and weekend operating scenarios. Four adaptive population-based optimizers are analyzed under identical computational conditions: particle swarm optimization, a population-based genetic algorithm, JAYA, and the multi-verse optimizer. Monte Carlo random sampling is included separately as a non-adaptive baseline without memory or learning. The methodology is tested on a modified 33-bus distribution system using Colombian demand profiles and line-current limits. The campaign includes 720 cases and 7200 independent runs. In the 720-case stochastic campaign, the largest feasible solution serves 765 EVs, equivalent to 5.508 MW, with a minimum voltage of 0.9084 p.u. and a maximum loading of 99.83%. Statistical validation shows no significant Holm-adjusted pairwise differences among the adaptive algorithms in hosting capacity, while PSO provides the most robust feasibility behavior. Supplementary robustness analyses quantify the influence of candidate-site definition, objective scaling, voltage limits, base charging-power scale, and native-load growth. A complementary deterministic 69-bus assessment under a normalized branch-current envelope preserves the qualitative managed-versus-unmanaged trend, with feasible sequential allocations of 779 and 225 equivalent EV charging units, respectively. The proposed framework provides a reproducible basis for identifying robust EVCS locations, estimating hosting capacity, and quantifying tradeoffs among charging capacity, network losses, voltage performance, and computational effort. Full article