Search Results (620)

According to the UN World Water Development Report 2024, global food production has more than doubled over the past three decades, placing increasing pressure on freshwater systems due to climate change, urban expansion, and intensified pollution events. This study presents a Monitoring and Mitigation Framework (MMF) for real-time river contamination detection, contamination source identification, and estimation of Chemical Oxygen Demand (COD) concentrations at the source. The framework is based on Inverse Estimation (IE) algorithms using feed-forward neural networks trained on approximately 85,000 simulated pollution events for the Ebro River (Spain). Each event represents a 52 h contamination episode monitored at two locations with a 10 min sampling interval, covering a wide range of COD concentrations. For low-concentration scenarios (<1000 mg/L), the TensorFlow-based regression model achieved a Mean Absolute Relative Error (MARE) of 0.26% and a Mean Square Relative Error (MSRE) of 1.82%, while for higher concentrations (>1000 mg/L), the scikit-learn implementation provided superior performance with MARE below 1.85%. Source location identification achieved an accuracy of 81%, increasing to 97% when allowing adjacent river sections. Overall, the MMF is a scalable, low-cost, real-time decision-support tool for water authorities such as the Confederación Hidrográfica del Ebro (CHE) to detect, track, and mitigate pollution events. Full article

Soil amendments are widely applied to improve soil fertility and structure, yet their performance in cold regions is constrained by low accumulated temperatures, frequent freeze–thaw (FT) cycles, and permafrost sensitivity. In this review, ‘cold regions’ refers to high-latitude and high-altitude areas characterized by long winters and seasonally frozen soils and/or permafrost. We screened the peer-reviewed literature using keyword-based searches supplemented by backward/forward citation tracking; studies were included when they assessed amendment treatments in cold region soils and reported measurable changes in physical, chemical, biological, or environmental indicators. Across organic, inorganic, biological, synthetic, and composite amendments, the most consistent benefits are improved aggregation and nutrient retention, stronger pH buffering, and the reduced mobility of potentially toxic elements. However, effectiveness is often site-specific and may be short-lived, and unintended risks—including greenhouse gas emissions, contaminant accumulation, and thermal disturbances—can offset gains. Cold-specific constraints are dominated by limited thermal regimes, FT disturbance, and the trade-off between surface warming for production and permafrost protection. We therefore propose integrated countermeasures: prescription-based amendment portfolios tailored to soils and seasons; the prioritization and screening of local resources; coupling with engineering and land surface strategies; a minimal cold region MRV loop; and the explicit balancing of agronomic benefits with environmental safeguards. These insights provide actionable pathways for sustainable agriculture and ecological restoration in cold regions under climate change. Full article

Computational musicology and music information retrieval research on Korean Pansori requires reliable analysis of vocal energy and tempo variation across rhythmic patterns known as jangdan. In this work, a jangdan is treated as a downbeat period: analogous to downbeats in Western music, it denotes both a rhythmic pattern type and the temporal span between two consecutive downbeats. Under this formulation, jangdan tracking is equivalent to downbeat tracking, allowing conventional downbeat-tracking methods to be directly applied to Pansori. Downbeat tracking in Pansori is challenging due to expressive rhythmic cycles, flexible tempi, and sparse accompaniment, which limit the generalization of systems trained on Western music. This paper proposes a rhythm-pattern-aware downbeat (i.e., jangdan) tracking framework based on offline and online Temporal Convolutional Networks (TCNs) and RoFormer-based models. A jangdan-aware Dynamic Bayesian Network (DBN) constrains minimum and maximum downbeat intervals using prior rhythmic knowledge. Using 22.4 h of annotated Pansori recordings, the proposed approach consistently outperforms general-purpose downbeat trackers across all jangdan patterns, with the offline RoFormer and tuned DBN achieving the strongest results. The improved jangdan inference enables detailed analysis of vocal energy and tempo variation. An A-weighted, beat-level vocal energy labeling method reveals characteristic energy contours aligned with specific jangdan cycles, while tempo analysis shows how performers modulate pacing in relation to rhythmic structure. These results demonstrate that identifying jangdan as a downbeat analog and incorporating rhythm-pattern-aware decoding substantially improves downbeat reliability and enables fine-grained analysis of temporal expressivity in Korean Pansori. Full article

The rapid development of autonomous driving systems has increased the demand for scalable frameworks capable of modeling vehicle motion patterns in complex traffic environments. This paper proposes a big data spatio-temporal modeling architecture that integrates Apache Spark version 4.0.1 (SparkML) with Long Short-Term Memory (LSTM) networks to analyze and classify vehicle trajectory patterns. The proposed SparkML–LSTM framework exploits Spark’s distributed processing capabilities and LSTM’s strength in sequential learning to handle large-scale traffic trajectory data efficiently. Experiments were conducted using the DETRAC dataset, which is a large-scale benchmark for vehicle detection and multi-object tracking consisting of more than 10 h of video captured at 24 different locations. The videos were recorded at 25 frames per second with a resolution of 960 × 540 pixels and annotated across more than 140,000 frames, covering 8.250 vehicles and approximately 1.21 million bounding box annotations. The dataset provides detailed annotations, including vehicle categories (Car, Bus, Van, Others), weather conditions (Sunny, Cloudy, Rainy, Night), occlusion ratio, truncation ratio, and vehicle scale. Based on the extracted trajectory features, vehicle motion patterns were categorized into predefined movement classes derived from trajectory dynamics. The experimental results demonstrate strong classification performance. These findings suggest that the proposed SparkML–LSTM architecture is effective for large-scale spatio-temporal trajectory modeling and traffic behavior analysis, and can serve as a foundation for higher-level decision-making modules in intelligent transportation system. Full article

Although the impact of COVID-19, caused by SARS-CoV-2, may appear to have diminished in recent years, the emergence of new variants still continues to cause significant global health and economic challenges. While numerous metabolomic studies have explored serum-based alterations linked to the infection, investigations utilizing urine as a biological matrix remain notably limited. This gap is especially significant given the potential advantages of urine, a non-invasive and easily obtainable biofluid, in clinical settings. In the context of patients in intensive care units (ICUs), temporal monitoring through such non-invasive samples may offer a practical and effective approach for tracking disease progression and tailoring therapeutic interventions. This study retrospectively explored the longitudinal metabolomic alterations in COVID-19 patients admitted to the ICU, stratified into three prognostic outcome groups: healthy discharged (HD), polyneuropathic syndrome (PS), and Exitus. A total of 32 urine samples, collected at four distinct time points per patient during April 2020 and preserved at −80 °C, were analyzed by proton nuclear magnetic resonance (¹H-NMR) spectroscopy for comprehensive metabolic profiling. Statistical evaluation using two-way ANOVA and ANOVA–Simultaneous Component Analysis (ASCA) identified significant prognostic variations (p < 0.05) in the levels of taurine, 3-hydroxyvaleric acid and formic acid. Complementary supervised classification via random forest modeling yielded moderate predictive performance with out-of-bag error rate of 40.6% based on prognostic categories. Particularly, taurine, 3-hydroxyvaleric acid and formic acid levels were highest in the PS group. However, no significant temporal changes were observed for any metabolite in analyses. Additionally, metabolic pathway analysis conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database highlighted the “taurine and hypotaurine metabolism” pathway as the most significantly affected (p < 0.05) across prognostic classifications. Harnessing urinary metabolomics, as indicated in our preliminary study, could offer valuable insights into the dynamic metabolic responses of ICU patients, thereby facilitating more personalized and responsive critical care strategies in COVID-19 patients. Full article

Background: Ward patients who experience clinical deterioration are at high risk of mortality. Conventional rapid response systems (RRS) using track-and-trigger protocols have not consistently demonstrated improved outcomes. This study evaluated the impact of an artificial intelligence (AI)-based cardiac arrest prediction model. Methods: This 1-year, prospective, non-randomized interventional trial assigned hospitalized patients with AI-based software as a medical device (AI-SaMD) high-risk alerts to groups based on their subsequent clinical response; those reassessed or treated within 24 h comprised the AI-SaMD-guided cohort, while the remainder formed the usual care cohort. Alerts prompted an optional but not mandatory treatment review. The primary outcome was ward-based cardiac arrest; the secondary outcome was in-hospital mortality. Multivariable regression analysis was used to adjust for potential confounders. Results: Of 35,627 general ward admissions, 2906 triggered an AI-SaMD alert. Among these, 1409 (48.4%) were allocated to the AI-SaMD-guided cohort. The incidence of cardiac arrest significantly decreased from 2.07% to 1.06% (adjusted risk ratio (RR), 0.54; 95% confidence interval (CI), 0.20–0.88; p < 0.01). In-hospital mortality also significantly declined (adjusted RR, 0.65; 95% CI, 0.32–0.98; p < 0.05). Conclusions: AI-SaMD-guided alerts were associated with reductions in cardiac arrest and in-hospital mortality without requiring additional resources, supporting their integration into current clinical workflows to improve patient safety and optimize RRS performance. Full article

A fast modelling framework is presented for loss estimation in current-source microinverters. The power stage is modelled with ideal switches and simplified magnetics to keep simulations lightweight, while dedicated estimators reconstruct core, conduction, and switching losses from simulated waveforms using Steinmetz-based and analytical models. The method is demonstrated on an interleaved active-clamp flyback with H-bridge unfolder but remains topology-agnostic and applicable to other current source (CS) DC/DC variants. Control includes maximum power point tracking (MPPT) with voltage-reference tracking, a PID loop, simplified grid synchronization, and peak-current regulation. Dynamic tests under irradiance and grid-voltage variations confirm stable operation and correct MPPT behaviour. A steady-state loss breakdown at 0.75 p.u. irradiance predicts ~97% overall efficiency, consistent with reported microinverter performance. The framework enables rapid design exploration and efficiency prediction without full device-level modelling, balancing accuracy and computational speed. Full article

Lithium-ion batteries (LIBs) face the safety hazard of thermal runaway (TR). Gas-sensing-based monitoring is one of the viable warning approaches for batteries during operation, and TR warning using semiconductor gas sensors has garnered widespread attention. This review presents a comprehensive analysis of the latest advances in this field. It details the gas release characteristics during the TR failure process and identifies H₂, electrolyte vapor, CO, CO₂, and CH₄ as effective TR warning markers. The core of this review lies in an in-depth critical analysis of gas-sensing materials designed for these target gases, systematically summarizing the design, performance, and application research of semiconductor gas-sensing materials for each aforementioned gas in battery monitoring. We further summarize the current challenges of this technology and provide an outlook on future development directions of gas-sensing materials, including improved selectivity, integration, and intelligent advancement. This review aims to provide a roadmap that directs the rational design of next-generation sensing materials and fast-tracks the implementation of gas-sensing technology for enhanced battery safety. Full article

This study provides a practice-oriented sensitivity analysis of DeePC for pressure management in water distribution systems. Two public benchmark systems were used, Fossolo (simpler) and Modena (more complex). Each run fixed a monitored node and pressure reference, applied the same randomized identification phase followed by closed-loop control, and quantified performance by the mean absolute error (MAE) of the node pressure relative to the reference value. To better characterize closed-loop behavior beyond MAE, we additionally report (i) the maximum deviation from the reference over the control window and (ii) a valve actuation effort metric, normalized to enable fair comparison across different numbers of valves and, where relevant, different control update rates. Motivated by the need for practical guidance on how hydraulic boundary conditions and algorithmic choices shape DeePC performance in complex water networks, we examined four factors: (1) placement of an additional internal PRV, supplementing the reservoir-outlet PRVs; (2) the control time step $(\Delta t)$; (3) a uniform reservoir-head offset $(\Delta h)$; and (4) DeePC regularization weights ${(\lambda }_g,{\lambda }_u,{\lambda }_y)$. Results show strong location sensitivity, in Fossolo, topologically closer placements tended to lower MAE, with exceptions; the baseline MAE with only the inlet PRV was 3.35 [m], defined as a DeePC run with no additions, no extra valve, and no changes to reservoir head, time step, or regularization weights. Several added-valve locations improved the MAE (i.e., reduced it) below this level, whereas poor choices increased the error up to ~8.5 [m]. In Modena, 54 candidate pipes were tested, the baseline MAE was 2.19 [m], and the best candidate (Pipe 312) achieved 2.02 [m], while pipes adjacent to the monitored node did not outperform the baseline. Decreasing $\Delta t$ across nine tested values consistently reduced MAE, with an approximately linear trend over the tested range, maximum deviation was unchanged (7.8 [m]) across all $\Delta t$ cases, and actuation effort decreased with shorter steps after normalization. Changing reservoir head had a pronounced effect: positive offsets improved tracking toward a floor of ≈0.49 [m] around $\Delta h$ ≈ +30 [m], whereas negative offsets (below the reference) degraded performance. Tuning of regularization weights produced a modest spread (≈0.1 [m]) relative to other factors, and the best tested combination (λy, λg, λu) = (10², 10⁻³, 10⁻²) yielded MAE ≈ 2.11 [m], while actuation effort was more sensitive to the regularization choice than MAE/max deviation. We conclude that baseline system calibration, especially reservoir heads, is essential before running DeePC to avoid biased or artificially bounded outcomes, and that for large systems an external optimization (e.g., a genetic-algorithm search) is advisable to identify beneficial PRV locations. Full article

This paper presents an experimental study validating the feasibility of Radio Frequency Identification (RFID) marker systems as a complementary solution for autonomous vehicle (AV) localization in Global Navigation Satellite System (GNSS)-degraded urban environments. A novel synchronized dynamic testbed featuring hardware-level integration with wheel revolution tracking enables precise correlation of RFID marker reads with vehicle angular position. Experimental results demonstrate that multi-antenna configurations achieve consistently high read success rates (up to 99.6% at 0.5 m distance), sub-meter localization accuracy (~55 cm marker spacing), and reliable performance at average urban speeds (36 km/h simulated velocity). Spatial diversity from four strategically positioned antennas overcomes multipath interference and orientation challenges inherent to high-speed RFID reading. Processing latency remains well within the 58 ms time budget critical for autonomous navigation. These findings validate RFID’s potential for smart road infrastructure integration and demonstrate a scalable, cost-effective solution for enhancing AV safety and decision-making capabilities through contextual information transmission. Full article

(1) Background: In hospitals, mattresses are often relocated for cleaning or patient transfer, leading to mismatches between actual and recorded bed locations. Manual updates are time-consuming and error-prone, requiring an automatic localization system that is cost-effective and easy to deploy to ensure traceability and reduce nursing workload. (2) Purpose: This study presents a pragmatic, large-scale implementation and validation of a BLE-based localization system using RSSI measurements. The goal was to achieve reliable room-level identification of smart mattresses by leveraging existing hospital infrastructure. (3) Results: The system showed stable signals in the complex hospital environment, with a 12.04 dBm mean gap between primary and secondary rooms, accurately detecting mattress movements and restoring location confidence. Nurses reported easier operation, reduced manual checks, and improved accuracy, though occasional mismatches occurred when receivers were offline. (4) Conclusions: The RSSI-based system demonstrates a feasible and scalable model for real-world asset tracking. Future upgrades include receiver health monitoring, watchdog restarts, and enhanced user training to improve reliability and usability. (5) Method: RSSI–distance relationships were characterized under different partition conditions to determine parameters for room differentiation. To evaluate real-world scalability, a field validation involving 266 mattresses in 101 rooms over 42 h tested performance, along with relocation tests and nurse feedback. Full article

This study examined the longitudinal patterns of concurrent aerobic and resistance training in young elite rowers to address the limited understanding of how training volume, modality, and periodisation interact across a season, and to introduce a novel rowing-specific resistance training classification. A retrospective design was used to analyse group training data over 36 weeks (n = 9; 20.6 ± 0.5 years), and individual case studies over 55 weeks (n = 4; 21.6 ± 0.4 years). Aerobic loads, resistance training tonnage, and ergometer performance (power output) were tracked, with resistance exercises categorised as rowing-specific, upper accessory, lower accessory, or core. Weekly aerobic volume averaged 14.0 ± 5.0 h, and rowing-specific resistance accounted for 48–57% of total tonnage (14.13 × 10³ ± 7.41 × 10³ kg). Exploratory analyses suggested an inverse relationship between aerobic, and resistance loads across training phases and trends toward improved ergometer power in three of four case athletes. High concurrent loads also appeared to coincide with occasional missed or modified sessions in several cases. These findings highlight the importance of managing concurrent loads to support consistent training while offering a practical resistance training classification that may enhance monitoring and decision-making for developing rowers. Full article

The background error covariance matrix (BE) is a fundamental component of data assimilation (DA) systems. Its impact on both the DA process and subsequent forecast performance depends on model configuration and the types of observations assimilated. However, few studies have specifically examined BE behavior in the context of satellite DA for regional tropical cyclone (TC) prediction. In this study, we develop the BE and evaluate its structure for a TC forecasting system over the western North Pacific. A total of six BEs are modeled using three control variable (CV) schemes (aligned with the CV5, CV6, and CV7 options available in the Weather Research and Forecasting DA system (WRFDA)) with training data from two distinct periods: the TC season and the winter season. Results demonstrate that the BE structure is sensitive to the training data used. The performance of TC-season BEs derived from different CV schemes is assessed for TC track forecasting through the assimilation of microwave sounder satellite brightness temperature data. The evaluation is based on a set of 14 cases from 2018 that exhibited large official track forecast errors. The CV7 BE, which uses the x- and y-direction wind components as CVs, captures finer small-scale momentum error features and yields greater forecast improvement at shorter lead-times (24 h). In contrast, the CV6 BE, which employs stream function (ψ) and unbalanced velocity potential (χ_u) as CVs, incorporates more large-scale momentum error information. The inherent multivariate couplings among analysis variables in this scheme also allow for closer fits to satellite microwave brightness temperature data, which is particularly crucial for forecasting TCs that primarily develop over oceans where conventional observations are scarce. Consequently, it enhances the large-scale environmental field more effectively and delivers superior forecast skill at longer lead times (48 h and 72 h). Full article

This study introduced Softball Pitching Plane (SPP), a best-fit geometric plane designed to characterize the throwing arm spatial trajectory during the windmill softball pitch. The purpose was to evaluate the reliability of this planar representation and determine whether deviations from the SPP were associated with ball velocity. Forty-nine adolescent softball pitchers each performed 15 drop-ball pitches (735 total pitches). Kinematics were recorded using a 15-sensor electromagnetic tracking system. A weighted orthogonal least-squares algorithm was applied to compute the best-fit plane across three intervals (WU–BR, TOP–BR, and DS–BR). Reliability was assessed using within-subject variability, leave-one-trial-out error, and ICCs. Linear mixed-effects models were used to examine associations between SPP parameters and ball velocity. The downswing–ball release interval of the wrist trajectory showed the most stable planar pattern (RMS = 0.053 m). SPP parameters demonstrated high reliability (CV ≤ 4.2%; ICC = 0.81–0.90). RMS deviation negatively predicted ball velocity at both within-pitcher (−0.11 km·h⁻¹ per cm, p = 0.003) and between-pitcher levels (−0.40 km·h⁻¹ per cm, p = 0.03). These findings indicate that, in adolescent softball pitchers, the SPP provides a reliable geometric description of throwing-arm motion during the downswing–ball release phase, with reduced deviation associated with higher pitch velocity. Full article

Image-guided surgical procedures demand tracking systems that combine high accuracy, low latency, and minimal footprint to ensure safe and precise navigation in the operating room. To address these requirements, we developed a monocular optical tracking system based on a single near-infrared camera with directional illumination and compact retroreflective markers designed for short-range measurement. Small dodecahedral markers carrying fiducial patterns on each face were fabricated to enable robust detection in confined and variably illuminated surgical environments. Their non-metallic construction ensures compatibility with CT and MRI, and they can be sterilized using standard autoclave procedures. Multiple fiducial families, detection strategies, and optical hardware configurations were systematically assessed to optimize accuracy, depth of field, and latency. Among the evaluated options, the ArUco MIP_36h12 family provided the best overall performance, yielding a translational error of 0.44 ± 0.20 mm and a rotational error of 0.35 ± 0.16° across a working distance of 30–70 cm, based on static position estimates, with a total system latency of 32 ± 8 ms. These results indicate that the proposed system offers a compact, versatile, and precise solution suitable for high-accuracy navigated and image-guided surgery. Full article