Search Results (6,893)

Global commodity shocks are associated with volatility in frontier financial markets, affecting exchange rates and equity indices. This study examined volatility transmission from global commodity shocks to Kenya’s USD/KES exchange rate and the NSE 20 Share Index using daily data from November 1997 to December 2024. While GARCH specifications capture clustering, they are sensitive to structural breaks and regime changes, which distort persistence and weaken risk measures. Machine learning approaches provide alternatives capable of capturing nonlinear dependencies, abrupt volatility bursts, and regime-independent dynamics. Empirical evidence demonstrates that the 2008 Global Financial Crisis and COVID-19 induced permanent volatility regime changes. This study examined volatility transmission from global commodity shocks to a frontier financial market, focusing on the USD/KES and the NSE 20 Share Index. Structural break-detection was integrated through the Iterative Cumulative Sum of Squares algorithm, alongside APARCH, FIGARCH models and ML architectures (XGBoost, LSTM). In Kenya volatility is characterized by strong persistence and long-memory dynamics, with limited evidence of leverage effects. Break-adjusted models improve inference by correcting spurious persistence, while machine learning approaches demonstrate superior tracking of volatility during stress regimes. Volatility transmission is nonlinear, break-sensitive, and state-dependent; hybrid ML–econometric methods enhance crisis forecasting and regime-sensitive financial stability analysis. Full article

Mediterranean low-pressure systems or cyclones are responsible for many extreme events affecting the region. This study presents a comprehensive analysis of Mediterranean cyclones from 1940 to 2024 using high-resolution ERA5 reanalysis data. This study implements a detection algorithm based on geopotential height minima on three different pressure levels (1000 hPa, 850 hPa and 700 hPa). Cyclone tracks in this study are constructed by linking identified low-pressure centers at successive time steps using a nearest neighbor tracking algorithm. The number of cyclones at 1000 hPa is filtered by matching them with upper levels and restricting them within 150 km from the coast, covering the entire Mediterranean region, which we divided into three subregions: the western Mediterranean, the eastern Mediterranean, and the Black Sea. Seasonal analysis was performed for winter (December–February), spring (March–May), summer (June–August), and autumn (September–November). Our results have recorded 39,933 individual cyclone tracks, where the majority (25,265 cyclones; 63.3%) are short-lived (24–72 h). Regionally, the western Mediterranean has the highest cyclone density, followed by the Black Sea and the eastern Mediterranean. While there is only a small increase in total numbers, a notable increase in cyclone activity is observed during the summer months, particularly in August, with a statistically significant rise of 18.4% since 1980 across the whole Mediterranean region. In the western Mediterranean, this August intensification was even 23.8%. As a result of this, the annual peak of cyclone activity has shifted from May/June to August. Full article

Meaningful partitioning of 2D binary shapes remains a challenging problem in shape analysis because many existing methods rely mainly on local geometric rules or skeleton simplification, which often struggle to separate the main body of a shape from its protruding parts in a perceptually meaningful way. This limitation becomes more evident in shapes with thin limbs, branching structures, or irregular extensions, where preserving topology while achieving human-consistent decomposition is difficult. We present a fully automatic framework for the hierarchical partitioning of 2D binary shapes into semantically meaningful core bodies and protruding limbs (tails). The pipeline begins by generating candidate structural lines through multi-directional centroid tracking along horizontal, vertical, and diagonal (±45°) bands. Three direction-specific Sugeno fuzzy controllers first evaluate these lines based on normalized length, angular alignment, and minimum distance to the boundary. A second pair of fuzzy systems then classifies segments as either tails or core parts using thickness statistics derived from the distance transform. For ambiguous merged tail groups, iterative midpoint splitting is applied until stable labeling is achieved. High-curvature boundary corners are then detected via signed turning-angle analysis, and candidate cutting rays are assessed through exact region splitting, tail area measurement, and label purity analysis. An adaptive third-stage fuzzy controller ranks these candidates according to cut length, purity, and area. The highest-scoring non-overlapping cuts are executed iteratively, progressively peeling peripheral parts while preserving the overall topology and symmetry of the shape. The proposed framework is evaluated on a targeted subset of 32 categories from the 2D Shape Structure Dataset Results on this evaluated subset indicate that the method produces coherent and topologically consistent partitions, with competitive agreement with the available human-annotated references. This training-free framework provides an interpretable tool for 2D shape analysis, with potential applications in object recognition, computer animation, and symmetry studies. Full article

Background: Race walking, an Olympic discipline, produces an increase in energy cost and a change in the recruitment pattern of muscle fibres compared with running, yet the cardiorespiratory responses of elite race walkers to severe-intensity exercise remain poorly characterised. Objectives: (i) To determine whether exhaustive exercise performed at vΔ50 elicits VO₂max in young elite race walkers, and (ii) to compare the temporal and metabolic profiles of this effort with those of similarly trained runners. Methods: Fourteen elite junior athletes (seven race walkers and seven runners) completed an incremental test to determine velocity at the lactate threshold (vLT), vVO2max, and VO2max, followed by a constant-velocity trial at individual vΔ50 performed to voluntary exhaustion on a 400 m track. Breath-by-breath VO2, heart rate, capillary blood lactate concentration, and time to exhaustion, time limit (Tlim) were measured. Results: At vΔ50 (≈94% vVO2max), the race walkers reached VO2max, with no detectable VO2 slow component (SC) in six of seven participants. In contrast, runners exhibited a significant SC (8 ± 3% of total VO2). The energy cost (EC) was 16% higher in race walking than in running (p < 0.01). Conclusions: In elite junior race walkers, it seems that vΔ50 reliably elicits VO2max primarily due to a high baseline oxygen cost rather than a progressive VO2 SC, contrasting with the kinetic response observed in running. These discipline-specific responses suggest that interval training in race walking should be prescribed using walking-specific thresholds. This study is preliminary, given the small sample size; further studies with larger cohorts are warranted. Full article

Ginseng Alternaria leaf and stem blight, caused by Alternaria panax, imposes substantial yield and economic losses to the ginseng cultivation industry. Current diagnostic methods for ginseng diseases primarily rely on pathogen isolation from infected tissues, a procedure that is laborious, time-consuming, and inherently low in sensitivity. This study has therefore developed a rapid, specific and sensitive SYBR Green-based quantitative real-time PCR (qPCR) assay for detecting A. panax in plants, seeds, and soil. The developed qPCR assay exhibited high sensitivity and repeatability, with a detection limit of 0.074 fg/μL of target amplicon DNA (0.619 ng/μL of genomic DNA) and a coefficient of variation below 2%. In artificially inoculated tissues (leaves, stems and seeds), Ct values decreased progressively with increasing incubation time, reflecting pathogen proliferation. Analysis of field-collected leaves and stems showed a strong overall correlation between Ct values and visual disease grades. Surveying of ginseng-growing areas revealed that A. panax was detected in asymptomatic leaves and stems at rates of 12.12% and 14.29%, respectively, and in 14.46% of soil samples and 23.73% of seed samples. This qPCR assay presented here provides a robust tool for forecasting early disease, tracking the primary inoculum of the pathogen and its transmission chains, and screening of both ginseng seed lots and candidate soils for ginseng Alternaria leaf and stem blight prior to planting. Full article

Chronic kidney disease (CKD) is a prevalent condition in cats and recognized as a leading cause of mortality among cats generally, yet it is difficult to detect by cat caregivers, especially in its early stages. This retrospective study aimed to enhance detection of CKD by cat caregivers through a smart litter box monitor technology that tracks feline elimination behaviors. We established cohorts of cats with CKD and cats with no known conditions and analyzed behavioral features captured by the monitor. A mixed-effects model identified significant differences, which were then integrated into a machine learning framework for predictive modeling. The model achieved a weighted F1-score of 92.7% in training using cross-validation and 89.9% in validation, demonstrating high precision for CKD predictions. Key findings included a behavioral profile unique to cats with CKD characterized by increased urination frequency, longer elimination durations, and reduced post-elimination covering behavior. These results suggest that the smart litter box monitor can provide valuable, continuous, and non-invasive data for CKD detection and management. Full article

Robust pose estimation and high-fidelity scene reconstruction in dynamic environments represent core challenges in the field of Visual Simultaneous Localization and Mapping (SLAM). Although 3D Gaussian Splatting (3DGS)-based techniques have demonstrated significant potential, existing methods typically assume static scenes and struggle to address the inconsistency between photometric and geometric observations in dynamic settings, leading to a notable degradation in pose estimation and map accuracy. To address these issues, this paper presents a novel dynamic SLAM method: Loop Closure with 3D Gaussian Splatting for Dynamic SLAM (LCD-Splat). Taking RGB-D images as input, LCD-Splat integrates Mask R-CNN with an improved multi-view geometry approach to detect dynamic objects, generating static scene maps and filling in occluded backgrounds. By leveraging 3DGS submaps and a frame to model tracking strategy, LCD-Splat achieves dense map construction. The method initiates online loop closure detection and employs a novel coarse to fine 3DGS registration algorithm to compute loop closure constraints between submaps. Global consistency is ultimately ensured through robust pose graph optimization. Experimental results on real-world datasets such as TUM RGB-D and Bonn demonstrate that LCD-Splat outperforms existing state-of-the-art SLAM methods in terms of tracking, scene reconstruction, and rendering performance. This approach provides novel insights for high-precision SLAM in dynamic environments and holds significant implications for scene understanding in complex settings. Full article

Human lifespan is increasing in parallel with the development levels of societies. Consequently, the number of elderly individuals worldwide is also rising day by day. One of the most significant risks these individuals face is falling. In this study, fall and daily activity data were collected from different home environments using a continuous-wave (CW) radar. Micro-Doppler signatures were generated from 700 data samples obtained from 10 individuals. Furthermore, the dataset was expanded by doubling the number of spectrogram images through data augmentation. The YOLO architecture, generally used in vision-based studies for object detection and tracking, was preferred for radar-based fall and activity detection. Classifications were performed with different YOLO structures, and comparative results are presented. At this stage, binary (fall/non-fall) and multi-class (seven different classes) classifications were carried out, achieving 100% accuracy for binary classification and 88.02% for multi-class classification. Additionally, the generalizability of the proposed architecture is demonstrated using the Leave-One-Subject-Out (LOSO) approach on the collected data and through the analysis of a public dataset. These results demonstrate the applicability of YOLO architectures in radar-based fall detection studies. Full article

Wastewater surveillance is an effective approach for monitoring populations of antibiotic-resistant bacteria and tracking the spread of antimicrobial resistance (AMR) across different settings. In this study, hospital and municipal wastewater were collected monthly for 12 months from multiple locations in the greater Pittsburgh area to quantify the presence of antibiotic-resistant bacteria and investigate their genomic diversity. After quantitative culturing on six different selective media types, a total of 150 isolates were speciated by 16S rRNA sequencing, which revealed diverse pathogenic and non-pathogenic taxa, including Klebsiella spp. (n = 28), Pseudomonas spp. (n = 20) and Aeromonas spp. (n = 37). A subset of isolates (n = 46) underwent whole genome sequencing, which identified several antibiotic resistance genes of clinical concern, such as bla_KPC (n = 17), bla_NDM (n = 6) and bla_IMP (n = 6), and revealed genetic similarities between wastewater isolates and clinical isolates collected from infected patients at a Pittsburgh-area medical center. In addition, analysis of plasmids carried by wastewater isolates revealed closely related plasmids present in isolates from different species and sampling locations. Overall, these findings suggest that both hospital and municipal wastewater act as interconnected reservoirs of antimicrobial resistance. Integrating wastewater surveillance with clinical and genomic data could enable the early detection of emerging resistance threats and support proactive infection-control strategies. Full article

3D multi-object tracking (MOT) for autonomous driving remains challenging due to frequent identity switches in crowded scenes, trajectory fragmentation during occlusions, and the difficulty of adapting association strategies to varying scene complexities. While existing methods rely on fixed geometric or appearance-based associations, they struggle to handle ambiguous cases and detection failures. We present an adaptive multi-level 3D MOT framework that achieves robust tracking through three key innovations: (1) multi-granularity temporal modeling that captures both fine-grained short-term motion and coarse long-term trends via dual-scale spatio-temporal attention, enabling accurate motion prediction across different object dynamics; (2) Transformer-based Appearance Association that employs cross-attention to model global inter-object relationships, resolving ambiguous associations in crowded scenarios where geometric cues alone fail; and (3) scene-adaptive learned thresholds that automatically adjust association strictness based on object density, motion complexity, and occlusion levels, avoiding the one-size-fits-all limitations of fixed thresholds. Our hierarchical four-level tracking strategy progressively handles cases from easy geometric matching (Level 1) to complex interval-frame recovery (Level 4), with SOT-based virtual detection generation bridging detector failures. Extensive experiments on the nuScenes benchmark demonstrate state-of-the-art performance. Full article

Building Information Modeling (BIM) has fundamentally changed the way interdisciplinary coordination works in construction projects; however, the theoretical mechanisms underlying open collaboration standards in this field remain insufficiently explored. This article fills this gap by presenting a systematic analysis of the BIM Collaboration Format (BCF) through the lens of reification and serialization, two fundamental concepts in information systems theory. Although the BCF format is widely used in the industry and implemented in major BIM tools for clash detection and issue tracking, the existing literature treats it primarily as an operational tool, overlooking the deeper information systems principles that govern its architecture. The analysis demonstrates that BCF achieves reification by transforming informal coordination knowledge—such as verbally communicated clashes, scattered email threads, and undocumented design decisions—into first-class objects (Topic, Comment, Viewpoint) equipped with unique identifiers, typed attributes, ownership, temporal metadata, and formalized inter-object relationships. Further analysis was conducted on BCF’s serialization mechanisms, including XML encoding for file exchange, JSON for RESTful API communication, and ZIP archiving as a distribution container, each of which was selected to balance human readability, schema validation, compression, and cross-platform portability. The complementarity of these two mechanisms was examined: reification determines what to preserve and in what structure, while serialization determines how to encode and in what format, which together enable interoperable, auditable, and automatable coordination workflows in heterogeneous software environments. The analysis was illustrated with a real-world BCF example from a major infrastructure project in Poland, demonstrating practical alignment between theoretical constructs and their implementation. The research results provide both a conceptual foundation for researchers working on openBIM standards and practical guidance for practitioners seeking to optimize issue management, the implementation of a Common Data Environment (CDE), and the specification of Exchange Information Requirements (EIR). The study contributes new knowledge in three areas: (1) To the best of the authors’ knowledge, it provides the first systematic theoretical analysis of BCF through the lens of reification and serialization, filling a gap between the format’s widespread practical use and its limited theoretical understanding. (2) It demonstrates how the formal criteria of reification (unique identity, typed attributes, ownership, temporal metadata, and inter-object relationships) map onto specific BCF entities, offering a transferable analytical framework for evaluating other openBIM standards. (3) It identifies the complementarity of reification and serialization as a design principle that can guide the development of future standards for digital twins and IoT-based facility management. Full article

Background/Objectives: Bloodstream infections are among the most severe infectious diseases, with mortality rates up to 25%. Delays as short as one hour in the diagnosis or initiation of the appropriate antimicrobial therapy can significantly worsen patient outcomes. Methods: This retrospective study, in an Italian 900-bed hospital from January 2019 to December 2024, evaluates the impact of a 24/7 reorganization of the clinical microbiology laboratory, adding a night shift to ensure around-the-clock processing and introducing a fast-track diagnostic pathway to prioritize the blood cultures from critically ill patients (called urgent blood cultures) in terms of turnaround times for Gram staining, microorganism identification, and resistance marker detection. Results: A total of 194,171 blood cultures were processed. Following the implementation of the 24/7 model, the median Gram stain turnaround time decreased from 4.46 to 1.40 h, microorganism identification turnaround time decreased from 5.75 to 2.35 h, and resistance marker turnaround time from 6.97 to 2.68 h. Significant reductions were observed especially during night shifts. Urgent blood cultures yielded a higher positivity rate (16.22% vs. 13.04%) and included the isolation of time-critical bacteria that can cause meningitis, such as Streptococcus pneumoniae. Conclusions: The continuous around-the-clock processing of blood culture and prioritized blood cultures for critically ill patients significantly reduced reporting times, particularly overnight. This model enhances early sepsis management and exemplifies how tailored and precision microbiology, supported by strong interdisciplinary collaboration and effective communication, can enhance earlier targeted antimicrobial treatment. Full article

To address the significant degradation of steering performance in dual-motor steer-by-wire (DMSBW) systems caused by the coupling of communication delays and motor faults, a robust fault-tolerant control strategy is proposed under the dual-motor collaborative driving mode. First, a matrix polytopic model is employed to describe the nonlinearities introduced by delays, establishing a delay-dependent DMSBW system dynamics model. Second, for electrical faults such as internal motor short circuits that cause a sudden drop in rotational speed, an adaptive motor-switching fault-tolerant mechanism is designed based on a smooth monitoring function to achieve rapid fault detection and steering function reconstruction. Furthermore, considering the coupled impact of delays and faults, a robust linear quadratic regulator (LQR) controller with feedforward compensation is designed to enhance system fault tolerance and robustness. Simulation results demonstrate that under steering wheel angle step input with delays, the proposed strategy achieves a rapid response without significant overshoot, and the steady-state tracking error is significantly reduced. In variable-speed single lane change maneuvers with coupled delays and severe motor faults, the peak and root mean square (RMS) errors of the front wheel angle are reduced to 0.0112 rad and 0.0031 rad, respectively. Compared to the delay-compensated nonlinear model predictive control (NMPC) and sliding mode control (SMC) strategies that do not account for delays, the peak error is reduced by 15.79% and 45.37%, while the RMS error decreases by 27.91% and 35.42%, respectively. Additionally, the peak and RMS errors of the sideslip angle and yaw rate are substantially reduced, validating the strategy’s excellent steering fault tolerance, robustness, and vehicle handling stability. Full article

The Camino de Santiago, a UNESCO-listed pilgrimage route, has experienced sustained growth in visitor numbers, challenging municipalities to preserve cultural integrity while ensuring service quality. This study reviews people-counting technologies and proposes a smart pilgrim management framework grounded in flux measurement systems to support data-driven and sustainable decision-making. Drawing on the smart tourism literature, the conceptual framework integrates infrared counters, mobile tracking solutions, and GPS/Wi-Fi data to generate real-time insights into pilgrim flows. A pilot simulation illustrates how these data can inform operational and strategic planning. The framework enables local authorities to monitor pedestrian movements, anticipate service demands (sanitation, accommodation, and safety), and detect overcrowding in sensitive heritage areas. By incorporating technological solutions into traditionally low-tech pilgrimage settings, municipalities can transition from reactive to proactive management approaches. The paper contributes a scalable and ethically grounded framework tailored to heritage pilgrimage routes, advancing smart tourism applications in culturally significant contexts. Full article

We apply rolling-window econometric methods, including GARCH(1,1) estimation, Bai–Perron structural break detection, CUSUM stability testing, and Granger causality analysis in bivariate VAR frameworks, to analyze the temporal dynamics of market integration in cryptocurrency perpetual futures, tracking funding rate correlations, arbitrage prevalence, and volatility persistence across 26 exchanges and 812 symbols over two months (November 2025 through January 2026). Using 53 overlapping seven-day rolling windows on 9.1 million hourly observations, we find that the two-tiered market structure previously documented in a static snapshot (centralized exchanges tightly integrated, decentralized exchanges fragmented) persists qualitatively but varies substantially in magnitude, with the integration gap ranging from $-0.041$ to $0.222$. Structural break tests detect no discrete regime shifts; the market evolves through gradual drift. GARCH(1,1) analysis reveals that near-integrated (IGARCH) volatility behavior, previously reported as a general property, appears in only 24.5% of windows, concentrated in specific time periods. Granger causality tests show that mid-tier exchanges lead the largest venue (Binance) more frequently than the reverse, challenging a simple size-based price discovery hierarchy. Intraday spread patterns are statistically significant and linked to funding rate settlement mechanics, with spreads peaking approximately two hours after standard settlement times. These findings have implications for systemic risk assessment: market surveillance frameworks that focus on the largest venue may miss price discovery signals originating from mid-tier exchanges. Full article