Search Results (1,249)

Background: Severe hypoglycemia is a major reason for emergency medical service (EMS) activation among patients with diabetes. However, real-world epidemiology, including onset location, timing, caller identity, and prehospital management, remains insufficiently described. This study aimed to characterize these cases and assess prehospital interventions and patient outcomes. Methods: We conducted a retrospective, descriptive study using EMS transport records and emergency department (ED) data from two core hospitals and their regional EMS systems in Japan between January 2018 and December 2023. Included patients were those transported by EMS for hypoglycemia with a corresponding ED diagnosis. Extracted data included patient characteristics, episode location and time, EMS caller identity, prehospital interventions, and clinical outcomes. Results: Among 237 episodes, the median age was 74 years and 59.9% were male. Most events occurred at home (78.1%) and during evening or nighttime hours (51.9%). Family members were the most frequent EMS callers (67.5%), yet 12.5% of patients received bystander medical intervention. EMS teams performed most prehospital interventions (68.8%), primarily intravenous glucose administration (65.2%). At EMS arrival, 16.0% were fully conscious and 21.1% were comatose. Hospitalization occurred in 44.3%. The hospitalization rate was 34.2% among patients who received prehospital intervention and 53.2% among those who did not. Conclusions: Most hypoglycemia episodes were discovered by family members, but bystander intervention was uncommon. Differences in hospitalization rates were observed according to the presence and timing of prehospital intervention. Full article

Background: Monitoring health-related variables across a competitive season is essential to understand factors associated with performance in Paralympic athletes. However, evidence on the interplay between sleep, mood states, nutritional factors, injuries, and performance remains limited. Objective: To examine the associations between injuries, sleep parameters, nutritional factors, mood states, and performance in Para powerlifting athletes during a competitive cycle. Methods: Twenty-four athletes from the Brazilian National Para powerlifting team were assessed at three time points: baseline (~3 months pre-competition), pre-competition (upon arrival), and post-competition (day after the event). Data were collected using standardized instruments and analyzed in R. Descriptive statistics, Mann–Whitney U tests, Spearman’s correlations, Friedman tests, and individual delta values (Δ) were applied. Results: No significant between-group differences were observed in pre-competition cross-sectional analyses. Longitudinally, sleep duration was the only variable consistently differing between performance groups. Athletes who matched or improved performance showed greater sleep stability, whereas those who did not improve exhibited larger post-competition increases in sleep duration. Negative mood states decreased over time, and baseline vigor was higher in the higher-performing group. Sleep duration changes were negatively correlated with performance variation (ρ = −0.575, p = 0.003). Conclusions: Sleep duration was the variable most consistently associated with performance variation. Mood changes reflected reduced negative affect over time. Findings support longitudinal monitoring in Para powerlifting, although caution is warranted due to the observational design and small sample. Full article

To reduce charging time and improve operational efficiency at integrated energy stations (IESs) for electric vehicles (EVs), this paper develops a sustainability-oriented collaborative optimization model by coupling vehicle routing behavior with charging decision-making. Firstly, a dynamic road network model is established to simulate vehicle arrivals at IESs from different network nodes. Then, considering grid peak–valley electricity prices, station electricity procurement costs and EV charging demand, a dynamic pricing strategy for IESs is proposed to guide EVs to charge at off-peak hours so as to realize peak shaving and valley filling for the power grid. Meanwhile, the NSGA-III algorithm is improved through the introduction of Good Point Set initialization and an adaptive crossover mechanism, and the Good Point Set initialization and Adaptive Crossover NSGA-III (GPS-AC-NSGA-III) algorithm is proposed to solve the scheduling optimization problem. Finally, the CRITIC-based TOPSIS method is employed to identify the optimal compromise solution from the Pareto-optimal set. Case studies further prove the effectiveness of the proposed multi-objective collaborative optimization model for EVs and IESs. Compared with scenarios without dynamic Dijkstra-based navigation and dynamic pricing, the IES daily revenue increased by 39.83%, pollutant emissions decreased by 0.4%, and the peak-to-valley load difference ratio was reduced by 4.94%. The results indicate that dynamic Dijkstra-based vehicle routing improves travel efficiency, while the proposed dynamic pricing strategy enhances station profitability and smooths grid load fluctuations. Overall, the proposed framework contributes to sustainable transportation and energy systems by reducing pollutant emissions, improving energy efficiency, and enhancing the operational stability of integrated energy infrastructure, thereby supporting the transition toward low-carbon and sustainable urban energy systems. Full article

In this work, we propose a localization method based on an exogenous radar with multi-base station and the synchronization signal block (SSB) in 5G downlink signals. We combine physical cell identities (PCIs)-based identification with the extensive cancellation algorithm (ECA) to reconstruct and cancel the present strongest SSB signal, thereby obtaining reference signal receiving power (RSRP) values of them in descending order of strength. Then, we designed a two-stage localization method. Firstly, we determined the target’s coarse location based on the directional characteristics of different SSB beams. Subsequently, we compared the RSRP values extracted from the actually received signals against those pre-obtained when the target is at various reference points. The reference point corresponding to the closest match was selected as the estimated target position. We conducted simulations under various signal-to-noise ratio (SNR) levels, reference point densities, and signal jitter conditions. The simulation results demonstrate that the method outperforms techniques such as Fang’s method for time difference of arrival (Fang-TDOA) and observed time difference of arrival (OTDOA). Full article

Lorentz invariance violation (LIV) can alter the group velocity of photons by modifying their dispersion relation, manifesting as differences in the arrival times of photons with different energies. This effect can accumulate over long propagation distances, making gamma-ray bursts (GRBs) a key tool for probing Lorentz invariance violation. By analyzing spectral lag data from 360 measurements across 90 GRBs using Markov Chain Monte Carlo (MCMC) sampling, and under the assumption that all GRBs share a common intrinsic time delay function, we report a maximum a posteriori value of the energy scale of quantum gravity at linear order $E_{QG}=8.96\times {10}^{14}$ GeV, though the data are also compatible with Lorentz invariance ($E_{\mathrm{QG}}=\infty$) to within $2.8\sigma$. Furthermore, we are $95\%$ confident that $E_{QG}\ge 6.67\times {10}^{14}$ GeV. Full article

The current development of ultra-wide band localization systems focuses on reducing the number of infrastructure nodes (anchors). In certain areas and applications the full three-dimensional position is not necessary; therefore, constraining the solution brings an opportunity to use fewer anchors. In this work, soft constraining of lateral and vertical position components for Time Difference of Arrival positioning in a corridor-like scenario is presented. Implementation in extended and unscented Kalman filter solvers is described. Tests in a real environment suggests that the constraints enable reliable along-track position estimation even with two or three anchors in sight, and the accuracy is better than 30 $\mathrm{c}$$\mathrm{m}$ (RMS). Moreover, the soft nature of constraints allows for uncertainty in the constraint definition. Full article

Background: Refugees arriving from conflict zones often continue to experience trauma and are at increased risk of anxiety and depression. Those seeking asylum form a group at higher risk of suffering adverse mental health outcomes, with higher needs for psychosocial and therapeutic care. This study aimed to determine metabolic changes potentially associated with psychological distress in refugees from Syria, using a saliva-based metabolomics approach via proton nuclear magnetic resonance (1H NMR) spectroscopy. Methods: Participants were recruited from Lethbridge Family Services and categorized into high and low stress burden groups using questionnaires assessing depression (PHQ-9) and generalized anxiety (GAD-7). Salivary metabolomic profiles from 26 female and 32 male participants were analyzed using supervised and unsupervised multivariate statistical methods to identify metabolic differences linked to composite stress, depression, and anxiety. Results: Salivary metabolic profiles showed the most prominent differences associated with anxiety in female participants and depression in male participants. Multivariate statistical analyses identified 31 metabolites and 13 biological pathways that were significantly altered according to mental health status, with the greatest changes observed in glycolysis/gluconeogenesis, sphingolipid metabolism, and taurine/hypotaurine metabolism. Conclusions: These findings indicate that salivary 1H NMR metabolomic profiling can identify a quantifiable “metabolic fingerprint” related to impaired mental health and psychological distress in a cost-effective, objective, and non-invasive way. This analytical strategy shows potential as a screening tool to support effective decision-making, enabling early identification of individuals at highest risk who require timely emotional and medical support. Full article

Waveguide-like propagation in elongated underground environments—utility corridors, logistics tunnels—generates dense multipath that can cause the earliest or strongest resolvable channel impulse response (CIR) component to originate from a specular reflection rather than the direct line-of-sight (LOS) path. In the single-anchor CIR-tap-based implementations common to practical ultra-wideband (UWB) systems, baseline estimators such as phase-difference-of-arrival (PDOA) and MUSIC rely on selecting a single dominant CIR component, producing large angle-of-arrival (AoA) errors whenever the selected path is a reflection. We propose a multipath credibility selection (MCS) AoA estimator, MCS-AoA, that does not require explicit LOS/NLOS classification. The algorithm scores each resolvable CIR component with four credibility factors—amplitude significance, time-of-flight (TOF) consistency, inter-baseline phase–geometry agreement, and cross-baseline coherence—and fuses retained candidates into a credibility-weighted spatial covariance matrix for 2D MUSIC search. Field experiments on a custom five-channel coherent UWB platform compare MCS-AoA against six baselines—PDOA, MUSIC, MVDR/Capon, TLS-ESPRIT, PwMUSIC, and DNN-AoA. In an underground corridor (5–40 m), MCS-AoA achieves an azimuth/elevation MAE of 1.00°/1.46°, outperforming all baselines (PDOA: 2.26°/2.49°; MUSIC: 1.76°/2.40°; next-best PwMUSIC: 1.44°/2.17°); in a logistics tunnel (5–80 m), it achieves a 1.19° overall azimuth MAE. Simulations corroborate these gains, with a 0.71° azimuth RMSE at 80 m (69.3% reduction over PDOA) and 86.6% of estimates falling within 1°. Full article

In recent years, social networks have shown a great influx of new users and traffic. As their popularity grows, so does the interest in researching ways to process the information available, in order to produce useful knowledge. One direction is making personalized recommendations based on users’ preferences and on their social behavior and related characteristics in general. Static recommendations, however, are proven to be highly inaccurate, since as time progresses, people tend to change their preferences, making different decisions than the ones predicted previously. This calls for an adaptive algorithm that shifts according to the changes in preferences and habits of the users. Handling the stream of information is challenging, as the new data can severely change the recommendations to many users. In this work, we propose a novel streaming Point-of-Interest recommendation algorithm that explicitly incorporates location-aware features into its dynamic update mechanism, enabling continuous adaptation to newly arriving data. The proposed approach is experimentally evaluated based on real-life data sets containing the network structure as well as check-in information. The results demonstrate high accuracy, achieving at the same time significant performance gains with respect to runtime costs compared to conventional approaches. Full article

Early and reliable detection of P-wave arrivals is critical for seismic monitoring and earthquake early warning, particularly under low signal-to-noise ratio (SNR) and non-stationary noise conditions. This study presents an automatic detection method based on singular value decomposition (SVD) entropy computed in sliding time windows with local signal filtering. Within this framework, the P-wave onset is interpreted as a local structural change in the signal rather than a simple energy increase. SVD entropy captures the redistribution of energy among dominant signal components, providing high sensitivity to the initial P-wave arrival even at moderate and low noise levels ($SNR\ge 2$). The method was validated using real seismic data from four regional stations operating under different noise conditions. Analysis of detection parameters revealed strong station dependence. For stations affected by low-frequency drift, polynomial detrending was identified as a necessary preprocessing step to ensure a stable entropy response and reliable detection. The proposed approach achieves detection accuracies of up to 93–98% at $SNR\ge 2$, significantly outperforming the classical STA/LTA algorithm and demonstrating performance comparable to modern deep learning methods. Since the method does not require model training or labeled datasets, it provides an interpretable and computationally efficient solution for automatic seismic monitoring. These properties make the proposed approach particularly suitable for real-time seismic monitoring systems and distributed sensor networks operating under limited computational resources. All computational stages were performed at the Farabi Supercomputer Centre of Al-Farabi Kazakh National University. The method requires no model training or labeled data, making it an interpretable, robust, and computationally efficient solution for automatic seismic monitoring and early warning systems. Full article

We present PIRATE (Precision Imaging Real-time Autonomous Tracker and Explorer), a fully autonomous unmanned surface vehicle designed to enable self-operating data collection and persistent tracking of mobile underwater targets through the tight integration of acoustic localization, onboard visual perception, and closed-loop navigation. PIRATE employs a single mobile acoustic receiver to estimate target position using time-difference-of-arrival (TDoA) measurements acquired at different times and locations through planned autonomous motion and uses these estimates to drive adaptive vehicle behavior and activate fine-grained visual sensing in real time. This architecture enables sustained target-driven operation, in which navigation, acoustic monitoring, and visual processing are dynamically coordinated based on mission context and localization uncertainty. The system integrates real-time AI-based visual detection and tracking with automatic mission control, allowing visual perception to operate opportunistically within an acoustically guided tracking loop rather than as a standalone sensing modality. Field experiments in a shallow-water environment demonstrate reliable autonomous navigation, single-receiver acoustic localization with meter-scale accuracy, and stable onboard visual inference under sustained operation. By enabling coupled acoustic tracking and onboard visual perception in a fully autonomous surface platform free of external infrastructure, PIRATE provides a practical foundation for fine-scale behavioral observation, adaptive marine monitoring, and long-duration studies of mobile underwater organisms. We demonstrate this advantage with two possible applications. Full article

Intelligence metrics based on benchmark performance or population norms are useful for measuring comparative ability within defined test environments, but they do not directly evaluate the structural coherence of an agent’s trajectory across time, domains, and perturbations. This article introduces Reflexive Signature Intelligence (RSI) as a bounded theoretical framework for addressing that different problem. RSI is developed within a causal-symmetric informational perspective in which intelligence is understood as the capacity of a system to maintain and restore alignment with a structurally constrained invariant without collapsing the open gradient of development. On this basis, the paper formulates the Principle of Bounded Subjectivity and the Prohibition of Finality as framework-level principles, arguing that intelligence should be assessed not as arrival at a completed end state but as the quality of an asymptotic trajectory. The framework is then operationalized on two coupled levels: a micro-level proposed as a future measurement program linked heuristically to resilience and prediction-error dynamics, and a macro-level expressed through five dimensions of structural integrity, including reflexive regulation, cross-domain integration, internal consistency, stabilization, and signature-setting. The article concludes by outlining implications for AI evaluation and alignment, with particular relevance for distinguishing full agents, partial systems, and human–AI composite configurations. Full article

We analyze three distinct approaches to time of arrival (TOA) distributions for discrete quantum systems using a spin-$\frac{1}{2}$ particle in a constant magnetic field as a paradigmatic example. We argue that these distributions should not be regarded as competing predictions for the same notion of arrival time, but rather relate to fundamentally different notions whose relevance depends on the physical context. These results are used to analyze information propagation arrival time distributions in XX spin chain systems, and discuss potential applications in quantum information science. Full article

Out-of-hospital cardiac arrest (OHCA) remains a leading cause of sudden death worldwide, with wide variation in reported incidence and outcomes driven by heterogeneity in registries, emergency medical services (EMS) organization, and case definitions. Despite substantial advances in resuscitation systems, survival after EMS-treated OHCA generally remains below 10%, and outcomes are critically time dependent. Delays in emergency call activation, bystander cardiopulmonary resuscitation (CPR), and—most importantly—early defibrillation are associated with a rapid decline in return of spontaneous circulation and favorable neurological recovery. This narrative review synthesizes current evidence and implementation strategies aimed at reducing “time-to-CPR” and “time-to-shock,” with a specific focus on public-access defibrillation (PAD) as a tool to mitigate avoidable delay. Randomized trials and large registry studies consistently demonstrate that automated external defibrillator (AED) use before EMS arrival is a key determinant of survival in patients with shockable rhythms. However, the real-world effectiveness of PAD remains limited by suboptimal AED placement, restricted 24/7 accessibility, low public awareness, and underutilization driven by fear and lack of confidence. We compare different PAD delivery models—including EMS-based, police and first-responder-based, and fully integrated community systems—and summarize evidence supporting targeted, high-yield AED deployment and cost-effectiveness. In addition, we review emerging strategies to reduce avoidable delay and strengthen the early links of the chain of survival, such as school-based training programs, smartphone- and SMS-based citizen-responder networks, improved dispatch recognition of cardiac arrest (including artificial intelligence–supported tools), and drone-enabled AED delivery. Across these approaches, patient benefit critically depends on system integration, alert performance, and true AED accessibility. Finally, we describe the Italian “Progetto Vita” experience as a community-integrated model explicitly designed to minimize avoidable delay through widespread AED deployment, lay responder training, and real-time integration with EMS. We conclude by outlining future priorities, including the development of robust national OHCA registries and scalable solutions for the high burden of cardiac arrests occurring at home, such as population-level deployment of low-cost, ultra-portable AEDs. Full article

Background/Objectives: Hospitals prioritize effective resource allocation and patient satisfaction as key performance indicators. Improving the performance of the ultrasonography department remains a major challenge for hospital management due to the inherently unplanned and stochastic nature of its operations. Arrival patterns vary throughout the day, and examination durations differ depending on patients’ clinical pathways and examination types. This study focuses on the ultrasonography department of a private healthcare facility located in one of the most densely populated regions of Istanbul. The primary objective of this study was to improve departmental performance in terms of average waiting time, total time spent in the system, and resource utilization. Methods: To address the variability in patient arrivals and service times across different ultrasonography procedures, a simulation-based optimization approach was employed. Current system performance was evaluated, and multiple alternative operational scenarios were developed and simulated. In addition, the potential impact of Internet of Things applications on the performance of the ultrasonography department was investigated by incorporating alternative system configurations into the simulation model. Results: The simulation results enabled a comparative evaluation of alternative scenarios based on key performance indicators. The findings demonstrate that optimized system configurations can significantly reduce patient waiting times and total system time while improving resource utilization. The inclusion of Internet of Things applications further contributed to performance improvements in the selected scenarios. Conclusions: The proposed simulation-based approach provides a systematic decision-support framework for evaluating alternative operational scenarios in ultrasonography departments. By optimizing resource allocation and leveraging Internet of Things applications, hospital managers can improve operational efficiency and patient satisfaction. The results highlight the value of data-driven decision-making in managing complex and stochastic healthcare systems. Full article