Search Results (8,942)

The increasing penetration of renewable energy and the frequent occurrence of extreme weather events have significantly heightened the uncertainty in power system operations. Simultaneously, the scarcity of renewable energy output samples under extreme meteorological conditions constrains the accurate assessment of extreme risks in system planning and dispatch. To bridge this gap, this work aims to propose a method for generating extreme wind power output scenarios that possess both diversity and statistical accuracy under limited sample conditions. To address this, this paper proposes a method for generating scenarios of extreme wind power output guided and constrained by statistical features. First, multidimensional statistical features are extracted from historical wind power output scenarios and combined, and a quantile threshold method is applied to screen out extreme wind power output scenarios. Subsequently, based on differentiated application requirements of the power system, extreme scenarios undergo preliminary classification followed by category-specific clustering analysis, achieving refined classification of the scenario set. Building on this, an improved generative adversarial network model is constructed, and the Wasserstein distance and gradient penalty mechanism are introduced to enhance training stability. Additionally, a statistical feature self-attention mechanism and feature loss function are designed to effectively constrain the consistency between generated scenarios and real scenarios in key statistical features. Results demonstrate that the proposed method can generate a set of extreme wind power output scenarios with both diversity and statistical accuracy under limited sample conditions, providing effective data support for system safety operation and risk prevention and control. Full article

Recent developments in computer vision have significantly enhanced the automation and objectivity of sports analytics. This paper proposes a novel deep learning-based framework for estimating football possession directly from broadcast video, eliminating the reliance on manual annotations or event-based data that are often labor-intensive, subjective, and temporally coarse. The framework incorporates two structurally improved object detection models: YOLOv8-P2S3A for football detection and YOLOv8-HWD3A for player detection. These models demonstrate superior accuracy compared to baseline detectors, achieving 79.4% and 71.1% validation average precision, respectively, while maintaining low computational latency. Team identification is accomplished through unsupervised DBSCAN clustering on jersey color features, enabling robust and label-free team assignment across diverse match scenarios. Object trajectories are maintained via the Norfair multi-object tracking algorithm, and a temporally aware refinement module ensures accurate estimation of ball possession durations. Extensive experiments were conducted on a dataset comprising 20 full-match Video clips. The proposed system achieved a root mean square error (RMSE) of 4.87 in possession estimation, outperforming all evaluated baselines, including YOLOv10n (RMSE: 5.12) and YOLOv11 (RMSE: 5.17), with a substantial improvement over YOLOv6n (RMSE: 12.73). These results substantiate the effectiveness of the proposed framework in enhancing the precision, efficiency, and automation of football analytics, offering practical value for coaches, analysts, and sports scientists in professional settings. Full article

This study investigates the impact of various data-augmentation techniques on the performance of neural networks in EEG-based motor imagery three-class event classification. EEG data were obtained from a publicly available open-source database, and a subset of 25 patients was selected for analysis. The classification task focused on detecting two types of motor events: imagined movements of the left hand and imagined movements of the right hand. EEGNet, a convolutional neural network architecture optimized for EEG signal processing, was employed for classification. A comprehensive set of augmentation techniques was evaluated, including five time-domain transformations, three frequency-domain transformations, two spatial-domain transformations and two generative approaches. Each method was tested individually, as well as in selected two- and three-method cascade combinations. The augmentation strategies were tested using three data-splitting methodologies and applying four ratios of original-to-generated data: 1:0.25, 1:0.5, 1:0.75 and 1:1. Our results demonstrate that the augmentation strategies we used significantly influence classification accuracy, particularly when used in combination. These findings underscore the importance of selecting appropriate augmentation techniques to enhance generalization in EEG-based brain–computer interface applications. Full article

Introduction: Cardiac amyloidosis, primarily comprising transthyretin amyloid cardiomyopathy (ATTR-CM) and light-chain amyloidosis with cardiac involvement (AL-cardiac), is an increasingly recognized contributor to the global heart failure burden. Management has shifted from supportive care to disease-modifying agents targeting specific stages of the amyloid cascade. This registry-based review qualitatively characterizes the current pharmacological clinical trial landscape through a registry-based analysis. Methods: A structured qualitative analysis of ClinicalTrials.gov was conducted for interventional trials registered between January 2015 and November 2025. Following PRISMA principles, studies were screened to include pharmacological interventions with explicit cardiac targeting while excluding neuropathy-dominant amyloidosis. Trial-level data regarding therapeutic classes, study phases, enrollment, and primary outcome domains were extracted and synthesized. Results: A total of 18 trials met the inclusion criteria (14 ATTR-CM; 4 AL-cardiac), representing a total enrollment of 4924 participants across 11 unique agents. Five therapeutic classes were identified: amyloid-clearing monoclonal antibodies (44.4% of trials), TTR silencers, TTR stabilizers, fibril-modifying agents, and cardiac phenotype-directed therapies. Monoclonal antibodies represented the largest class by both trial count and enrollment (3075 participants). Clinical events (n = 7) and safety/tolerability (n = 5) were the most frequent primary outcome domains. ATTR-CM trials dominated the landscape, accounting for 77.7% of the total study population, while parallel-group placebo-controlled designs were the most common study architecture (n = 10). Conclusions: The therapeutic landscape for cardiac amyloidosis is transitioning toward stage-specific, mechanism-based interventions. While ATTR-CM currently dominates research efforts, the expansion of silencers and monoclonal antibodies reflects an increasing capacity to intercept the amyloid cascade at distinct molecular checkpoints. However, significant heterogeneity in outcome measures and the shift toward diagnosing milder disease pose challenges for demonstrating clinical efficacy. Future priorities include standardized progression markers and addressing barriers to global access for these high-cost therapies. Full article

Background/Objectives: The 2020 Infectious Diseases Society of America (IDSA) guidelines recommend a single 200 mg dose of doxycycline within 72 h of tick removal after a high-risk bite for Lyme disease prophylaxis. However, limited data are available on prescribing practices related to this recommendation in highly endemic Lyme disease areas. Methods: We conducted a retrospective chart review on adult patients (aged ≥ 18 years) who received a single dose of oral doxycycline for Lyme disease prevention for the period 2022–2024 within a rural Wisconsin health system. Patient and provider prescribing characteristics were evaluated. Manual data abstraction was performed on a random sample of 155 prescribing events to assess adherence to IDSA guidelines. Results: A total of 2404 prophylaxis prescriptions were identified; 44% were prescribed to older adults between 65 and 79 years of age, 54% were prescribed to males, and 66% were prescribed to patients living in rural areas. Prescriptions peaked in spring and summer months, consistent with the known seasonal trends in tick activity. Prescribing was distributed relatively evenly across provider types, with the majority (77%) of cases occurring in outpatient and urgent care settings. Upon manual abstraction, doxycycline was indicated in 12% with the remainder either classified as possibly indicated or not indicated due to suboptimal documentation and nonadherence. Conclusions: Our study identified high rates of incomplete documentation and uncertainty in guideline concordance in a Lyme-endemic health system, highlighting the opportunities to support evidence-based prescribing and to improve documentation practices. Full article

The convergence of Lean manufacturing and Industry 4.0 requires digital infrastructures capable of transforming high-frequency telemetry into actionable insights. However, architectures that integrate near real-time data with closed-loop process control remain scarce, particularly in the food-processing industry. This study proposes a “Lean 4.0” framework based on a six-layer Digital Twin (DT) architecture to digitise waste detection and optimise a medium-scale bakery. The methodology integrates a heterogeneous Industrial Internet of Things (IIoT) network comprising 17 ESP32 (Espressif Systems, Shanghai, China)-based monitoring nodes. Data collection is managed via an edge-centric MQTT–InfluxDB (version 2.7, InfluxData, San Francisco, CA, USA) data pipeline. Furthermore, the analytics layer employs discrete-event simulation in Siemens Plant Simulation (version 2302, Siemens Digital Industries Software, Plano, TX, USA), constraint programming with Google OR-Tools (version 9.8, Google LLC, Mountain View, CA, USA), and machine learning models (Isolation Forest and SARIMA). Multi-month validation in a brownfield bakery, including a 60-day continuous monitoring test, demonstrated that the proposed architecture reduced production cycle time by 24.4% and inter-operational waiting time by 51.2%. Moreover, manual planning time decreased by 87.4% through the use of low-code scheduling interfaces. In addition, state-based control of critical ovens reduced energy consumption by 23.06%. These findings indicate that combining deterministic simulation and combinatorial optimisation with data-driven analytics provides a scalable blueprint for implementing cyber-physical systems in food-processing SMEs. This approach effectively bridges the gap between traditional Lean principles and data-driven smart manufacturing. Full article

Decoding reaching movements from non-invasive brain signals is a key challenge for the development of naturalistic brain–computer interfaces (BCIs). While this decoding problem has been addressed via traditional machine learning, the exploitation of deep learning is still limited. Here, we evaluate a convolutional neural network (CNN) for decoding movement direction during a delayed center-out reaching task from the EEG. Signals were collected from twenty healthy participants and analyzed using EEGNet to discriminate reaching endpoints in three scenarios: fine-direction (five endpoints), coarse-direction (three endpoints), and proximity (two endpoints) classifications. To interpret the decoding process, the CNN was coupled with explanation techniques, including DeepLIFT and occlusion tests, enabling a data-driven analysis of spatio-temporal EEG features. The proposed approach achieved accuracies well above chance, with accuracies of 0.45 (five endpoints), 0.64 (three endpoints) and 0.70 (two endpoints) on average across subjects. Explainability analyses revealed that directional information is predominantly encoded during movement preparation, particularly in parietal and parietal–occipital regions, consistent with known visuomotor planning mechanisms and with EEG analysis based on event-related spectral perturbations. These results demonstrate the feasibility and interpretability of CNN-based EEG decoding for reaching movements, providing insights relevant for both neuroscience and the prospective development of non-invasive BCIs. Full article

Reliability testing for Single Event Effects (SEEs) requires accurate localization of heavy-ion tracks from projection images. Conventional localization often relies on handcrafted features and geometric fitting, which is sensitive to noise and difficult to accelerate in hardware. This paper presents a lightweight detector based on YOLOv5n that treats charge tracks in Topmetal pixel sensor projections as distinct objects and directly regresses the track angle and intercept, along with bounding boxes, in a single forward pass. On a synthetic dataset, the model achieves a precision of 0.9626 and a recall of 0.9493, with line-parameter errors of 0.3930° in angle and 0.4842 pixels in intercept. On experimental krypton beam data, the detector reaches a precision of 0.92 and a recall of 0.96, with a position resolution of 52.05 μm. We further deploy the model on an Xilinx Alveo U200, achieving an average per-frame accelerator latency of 3.1 ms while preserving measurement quality. This approach enables accurate, online track localization for SEE monitoring on Field-Programmable Gate Array (FPGA) platforms. Full article

To address coverage gaps in high-latitude space weather monitoring caused by constraints in energy, bandwidth, and labeled samples, this study presents a systematic solution deployed in Hailar, China. We constructed a Cloud–Edge–Terminal system featuring wind–solar hybrid energy and RK3588-based edge computing, achieving six months of stable ionospheric–geomagnetic observation under −40 °C. Furthermore, we propose a Dual-Adversarial Recurrent Autoencoder (DA-RAE) for anomaly detection. Utilizing a single-source domain strategy, the model learns physical manifolds from quiet-day data, enabling zero-shot anomaly perception in the unsupervised target domain. Field tests in March 2025 demonstrated superior generalized anomaly detection capabilities, successfully identifying both transient space weather events and environmental equipment faults (baseline drifts). This work validates the value of edge intelligence for autonomous operations in extreme environments, providing a reproducible paradigm for global ground-based networks. Full article

Socio-cultural tourism factors include folk music, cuisine and gastronomic brands, domestic handicrafts, crafts, folk customs, events, local tourist culture and cultural–historical heritage, language, social life of residents, and other factors. Important natural factors are the geographical and tourist location, features of relief, hydrographic potential, types of climates, plant and animal species, and others. Socio-cultural factors, together with natural factors, can create the basic characteristics of a destination. This research used the two landscapes of outstanding features (LOFs) that are part of the wider area of Serbia’s capital city, Belgrade. The selected areas are the main excursion and tourist centers, which possess significant natural and cultural characteristics for the development of sustainable tourism (STO). The main characteristics of these LOFs are forest ecosystems, which have an impact on tourism and recreation. The article used a quantitative methodology, based on the survey technique, which was used to collect data. A total of 1120 respondents were surveyed. Respondents expressed their views on claims related to space factors, which can influence the development of tourism and recreation. By analyzing the results, it can be concluded that there is an impact of factors on satisfaction with STO. Full article

The early diagnosis of invasive candidiasis remains challenging in immunocompromised and other high-risk patients, prompting interest in artificial intelligence models for assisting clinical decision-making. We conducted a PROSPERO-registered systematic review and meta-analysis of artificial intelligence-based predictive models for the early identification of invasive Candida infections. We searched multiple databases for studies reporting model performance in hospitalized immuno-compromised patients. Data on study characteristics, model details, validation strategy, and diagnostic accuracy were extracted. A bivariate random-effects meta-analysis was performed for candidemia prediction models with compatible data. Eight studies met inclusion criteria. Models were typically developed using retrospective hospital data with heterogeneous populations and predictors. Five candidemia studies provided threshold-based performance data for meta-analysis. Pooled sensitivity and specificity for candidemia prediction were 81.3% (95% confidence interval (CI) 72.9–87.6%) and 81.6% (95% CI 68.4–90.1%), respectively. Most models achieved high negative predictive values, whereas positive predictive values were modest, reflecting low event prevalence. The risk of bias was generally moderate to high (PROBAST), and the certainty of evidence was low (GRADE) due to study limitations and indirectness. AI models show promise for early candidemia identification with moderate diagnostic accuracy. They may be useful as decision-support tools, but further multicenter prospective validation is needed before routine clinical adoption. Full article

This study presents a comprehensive numerical simulation of reservoir dam failure based on the two-dimensional hydrodynamic model MIKE 21. To reproduce the real accident process, a detailed digital elevation model derived from LiDAR survey data was constructed, incorporating valley microtopography, river channel geometry, and hydraulic structure elements. The modeling was performed in a stepwise manner and included the simulation of breach formation using a time-varying digital elevation model, the drawdown of the reservoir, and the propagation of the dam-break flood wave in the downstream reach, as well as an assessment of the hydrodynamic impact of the flow on small dams located further downstream. The simulations produced spatiotemporal distributions of flow depths and velocities, quantified the temporal evolution of reservoir water volume, and determined overflow parameters at the small dams. Based on the analysis of bed shear stress distribution, zones of increased hydrodynamic loading were identified and compared with observed damage areas. The results confirm the applicability of the adopted modeling framework for detailed reconstruction of dam-break events. The proposed approach can be applied both to the analysis of past dam failures and for predictive purposes when assessing the potential consequences of possible accidents at other reservoirs. The methodology enables preliminary evaluation of inundation zones, erosion intensity, and impacts on downstream hydraulic structures, making it a valuable tool for safety assessment and the planning of protective measures in areas with complex terrain conditions. Full article

Interpreting event completion is a core difficulty in second language acquisition, as it underpins temporal reference and communication. This study investigates how L1 Urdu Pakistani learners of English acquire telicity, a semantic property that distinguishes completed and ongoing events. The analysis centers on bounded and unbounded object noun phrases (NPs) in marking telic/atelic events within accomplishment predicates. In English, telicity is compositionally encoded through verb types, object NPs, and temporal adverbials, whereas Urdu relies on aspectual morphology, creating challenges for learners in mapping event completion. The study is framed within the Full Transfer Full Access (FTFA) model and the Interpretability Hypothesis (IH). Data were collected through an Acceptability Judgment Task (AJT) administered to Pakistani ESL learners at elementary, intermediate, and advanced levels, alongside a native English control group. Results support the FTFA model, revealing a significant developmental trajectory where accuracy in distinguishing telic/atelic contrasts increases with proficiency. At the elementary level, an L1-based accuracy gradient emerged across NP types, reflecting the transfer of Urdu nominal underspecification. While advanced learners demonstrated successful restructuring in bounded contexts, partial support for the IH was found in atelic contexts. Continued divergence from native judgements in unbounded NP conditions highlights a persistent mapping deficit at the syntax–semantics interface. The study advances second language event semantics, emphasizing the role of object structure and cross-linguistic influence in the acquisition of L2 event boundaries. Full article

Severe linear convective windstorms (SLCWs) account for 66% of the total windthrow area in Northern Eurasian forests. However, in many cases, these events do not result in forest damage. The aim of this study is to reveal the features of storms that determine whether or not they cause forest damage. The study examines the relationship between windthrow occurrence and the characteristics of SLCW (seasonality, wind gusts and accompanying rainfall), as well as their formation environments. The sample includes 351 SLCW events that occurred in Northern Eurasian forests between 1986 and 2024. These are subdivided into two subsamples: 181 SLCW events with wind gusts of ≥25 m s⁻¹, but without substantial damage to forests (SR_ND), and 170 SLCW events associated with windthrow (SR_WD). Since the subsamples are similar in terms of forest stand characteristics, their differences are likely linked to differences in the characteristics of the SLCWs themselves. In general, SR_WD events are accompanied by stronger wind gusts and rainfall than SR_ND events. The difference in rainfall amounts is more pronounced in the Integrated Multi-satellitE Retrievals for Global Precipitation Monitoring (GPM IMERG) satellite-derived data than in the data from weather stations. Springtime events contribute substantially more to SR_ND (26%) than to SR_WD (12%). According to the ERA5 reanalysis, SR_WD events form in conditions of greater thermodynamic instability and stronger wind shear than SR_ND events, i.e., under conditions that are generally more favorable for more severe windstorms. Obtained results can be further used to assess probable changes in forest damage caused by SLCW events based on projections of rainfall intensity and convective variables in a changing climate. Full article

Background/Objectives: Patient-Controlled Analgesia (PCA) is a well-established strategy for managing postoperative pain, but its use in the Intensive Care Unit (ICU) remains poorly defined, poorly standardized, and fragmented. The aim of this scoping review is to map the extent, nature, and characteristics of the available evidence on the use of PCA in ICU patients, identifying key areas of uncertainty and knowledge gaps that require further study. Methods: Scoping review reported according to the PRISMA-ScR guidelines. Results: 12 relevant studies were identified. Available evidence suggests that PCA can provide pain control comparable to traditional techniques in post-cardiac surgery patients in the ICU, while data on its use in non-surgical patients are limited. The studies reported good feasibility and a generally favorable safety profile, with a low incidence of significant respiratory events thanks to intensive monitoring. Methodological variability prevents direct comparisons between studies. Conclusions: PCA supports personalized pain management based on patient-specific clinical conditions and response. However, more standardized studies are needed to define its role. Full article