Search Results (2,327)

In the context of small, battery-powered systems, a lightweight, reusable architecture is needed for integrated measurement, visualization, and cloud telemetry that minimizes hardware complexity and energy footprint. Existing solutions require high resources. This limits their applicability in Internet of Things (IoT) devices with low power consumption. The present work demonstrates the process of design, implementation and experimental evaluation of a single-cell lithium-ion battery monitoring prototype, intended for standalone operation or integration into other systems. The architecture is compact and energy efficient, with a reduction in complexity and memory usage: modular architecture with clearly distinguished responsibilities, avoidance of unnecessary dynamic memory allocations, centralized error handling, and a low-power policy through the usage of deep sleep mode. The data is stored in a cloud platform, while minimal storage is used locally. The developed system combines the functional requirements for an embedded external battery monitoring system: local voltage and current measurement, approximate estimation of the State of Charge (SoC) using a look-up table (LUT) based on the discharge characteristic, and visualization on a monochrome OLED display. The conducted experiments demonstrate the typical U(t) curve and the triggering of the indicator at low charge levels (LOW − SoC ≤ 20% and CRITICAL − SoC ≤ 5%) in real-world conditions and the absence of unwanted switching of the state near the voltage thresholds. Full article

Background/Objectives: Donor kidney function measured by glomerular filtration rate (GFR) is widely used as a selection criterion in kidney transplantation (KT). This study addresses the knowledge gap regarding the relationship between donor GFR at organ procurement and graft function in deceased donor KT. Methods: We retrospectively analyzed 918 deceased donor KTs and compared donor GFRs at procurement and recipient GFRs after KT at hospital discharge and in the one-year follow-up. The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula was used to estimate and compare GFRs. Donor baseline GRF was defined as the last available estimated GRF prior to organ procurement. The Kaplan–Meier analysis was used to estimate recipient and graft survival. Results: The median donor GFR was 92.8 mL/min/1.73 m², while the median recipient GFR at hospital discharge was 37.5 mL/min/1.73 m² (−60% to donor baseline, p < 0.001), increasing to 51.4 mL/min/1.73 m² (+37%, p < 0.001) at one-year follow-up. One-year graft and patient survival rates were 95.3% and 98.1%, respectively. Except for grafts from donors with a GFR < 15 mL/min/1.73 m² due to acute renal failure that resulted in a significantly higher delayed graft function (DGF) rate and inferior graft survival (71.4%), no correlation was observed between baseline GFRs and DGF occurrence nor graft survival. Conclusions: Excellent results can be achieved in KT with subnormal donor GFR. The decision to refuse a kidney offer for KT should not solely be based on donor GFR. Kidneys from donors with very low GFR (<15 mL/min/1.73 m²) may be transplanted, but our observation is based on a very small sample (n = 7) and should therefore be interpreted with caution, particularly given the associated higher risk of DGF and lower graft survival. Full article

The Mixed-Conduction Model for oxide films is used to quantitatively interpret in situ electrochemical and ex situ surface analytical results on the corrosion of AISI 316L (an internal reactor material) and Alloy 690 (a steam generator tube material) in small modular reactor primary coolant with the addition of soluble Zn. The model parameters of alloy oxidation and corrosion release are estimated with the time of exposure up to 168 h and anodic polarization potential (up to −0.25 V vs. standard hydrogen electrode) using fitting of the transfer function to experimental impedance spectra. Model parameters of individual alloy constituents are estimated by fitting of the model equations to the atomic fraction profiles of respective elements in the formed oxide obtained by Glow-Discharge Optical Emission Spectroscopy (GDOES). Conclusions on the effect of Zn addition on film growth and cation release processes in boron-free SMR coolant are drawn and future research directions are outlined. Full article

Mid-sized Philippine cities commonly rely on fixed-time traffic signal plans that cannot respond to short-term, demand-driven surges, resulting in measurable idle time at stop lines, increased delay, and unnecessary emissions, while adaptive signal control has demonstrated performance benefits, many existing solutions depend on centralized infrastructure and high-bandwidth connectivity, limiting their applicability for resource-constrained local government units (LGUs). This study reports a field deployment of TrafficEZ, a lightweight edge AI signal controller that reallocates green splits locally using traffic-density approximations derived from cabinet-mounted cameras. The controller follows a macroscopic, cycle-level control abstraction consistent with Transportation System Models (TSMs) and does not rely on stationary flow–density–speed (fundamental diagram) assumptions. The system estimates queued demand and discharge efficiency on-device and updates green time each cycle without altering cycle length, intergreen intervals, or pedestrian safety timings. A quasi-experimental pre–post evaluation was conducted at three signalized intersections in El Salvador City using an existing 125 s, three-phase fixed-time plan as the baseline. Observed field results show average per-vehicle delay reductions of 18–32%, with reclaimed effective green translating into approximately 50–200 additional vehicles per hour served at the busiest approaches. Box-occupancy durations shortened, indicating reduced spillback risk, while conservative idle-time estimates imply corresponding CO₂ savings during peak periods. Because all decisions run locally within the signal cabinet, operation remained robust during backhaul interruptions and supported incremental, intersection-by-intersection deployment; per-cycle actions were logged to support auditability and governance reporting. These findings demonstrate that density-driven edge AI can deliver practical mobility, reliability, and sustainability gains for LGUs while supporting evidence-based governance and performance reporting. Full article

This study applies spectral analysis and singular spectrum analysis (SSA) to mean annual runoff of the Dniester River for 1950–2024 to identify dominant periodic components governing the hydrological regime of this transboundary basin shared by Ukraine and Moldova. The novelty lies in a basin-specific integration in the first systematic application of a combined spectral–SSA framework to the Dniester River, enabling consistent characterization of runoff variability and assessment of large-scale natural drivers. Time series from three gauging stations are analysed to develop data-driven runoff models and medium-term forecasts. Four stable groups of periodic variability are identified, with characteristic timescales of approximately 30, 11, 3–5.8, and 2 years, corresponding to major atmospheric–oceanic oscillations (AMO, NAO, PDO, ENSO, QBO) and the 11-year solar cycle. Cross-spectral and coherence analyses reveal a statistically significant relationship between solar activity and river discharge, with an estimated lag of about 2 years. SSA reconstructions explain more than 80% of discharge variance, indicating high model reliability. Forecast comparisons show that spectral methods tend to amplify long-term trends, CNN–LSTM models produce conservative trajectories, while a hybrid ensemble approach provides the most balanced and physically interpretable projections. Ensemble forecasts indicate reduced runoff during 2025–2028, followed by recovery in 2029–2034, supporting long-term water-resources planning and climate adaptation. Full article

Electric vehicles (EVs) are central to low-carbon urban mobility, but range anxiety persists. In real fleet operations, vehicles are rarely discharged to low State-of-Charge (SOC), so the remaining driving range (RDR) labels are incomplete, hindering accurate RDR prediction and analysis of operating conditions. This paper proposes a label completion framework that reconstructs low SOC mileage and a hybrid mileage-factor-oriented residual regressor (MF-CMR) to learn mileage factors under SOC imbalance. Applied to one year of data from eight EVs in Guangzhou, China, the method achieves a mean absolute error of 0.88 and a coefficient of determination of 0.64, yielding completed trip-level RDR labels whose distribution centers around 241.73 km. Using the completed labels, a two-way analysis of variance (ANOVA) with ambient temperature and driving style as factors shows that temperature is the dominant determinant of RDR, while driving style exerts a secondary but substantial effect, with a significant interaction. Together, the label completion framework and the quantified impacts of temperature and driving style enable more reliable RDR estimation from fleet logs, offering a quantitative basis for dispatching policies, charging margins, and eco-driving guidance in EV fleet services involving long distance trips or low SOC deep discharge scenarios. Full article

Remote sensing enables large-scale, image-based assessments of river dynamics, offering new opportunities for hydrological monitoring. We present a publicly available dataset consisting of 281,024 satellite and aerial images of U.S. rivers, constructed using an Application Programming Interface (API) and the U.S. Geological Survey’s National Hydrography Dataset. The dataset includes images, primary keys, and ancillary geospatial information. We use a manually labeled subset of the images to train models for detecting rapids, defined as areas where high velocity and turbulence lead to a wavy, rough, or even broken water surface visible in the imagery. To demonstrate the utility of this dataset, we develop an image segmentation model to identify rivers within images. This model achieved a mean test intersection-over-union ($IoU$) of 0.57, with performance rising to an actual $IoU$ of 0.89 on the subset of predictions with high confidence (predicted $IoU$ > 0.9). Following this initial segmentation of river channels within the images, we trained several convolutional neural network (CNN) architectures to classify the presence or absence of rapids. Our selected model reached an accuracy and F1 score of 0.93, indicating strong performance for the classification of rapids that could support consistent, efficient inventory and monitoring of rapids. These data provide new resources for recreation planning, habitat assessment, and discharge estimation. Overall, the dataset and tools offer a foundation for scalable, automated identification of geomorphic features to support riverine science and resource management. Full article

Existing baseflow estimation methods—such as exponential recession models, linear reservoir approaches, and digital filtering techniques—seldom account for anthropogenic disturbances or evapotranspiration-induced streamflow alterations. To address this limitation, a physically based baseflow equation that explicitly integrates human water withdrawals and evapotranspiration losses has been introduced. The governing equation was reformulated from a nonlinear storage–discharge relationship and validated against multi-decadal streamflow records in the Middle Yellow River Basin (MYRB). Results demonstrate that the proposed model accurately reproduces observed recession behavior across diverse sub-basins (NSE ≥ 0.94; RMSE ≤ 152 m³ s⁻¹). By providing reliable baseflow estimates, the equation enables quantitative assessment of eco-hydrological benefits and informs cost-effective water-resource investments. Furthermore, long-term baseflow simulations driven by climate projections offer a scientific basis for evaluating climate-change impacts on regional water security. Full article

Wastewater treatment systems retain a significant proportion of microplastics (MPs) derived from domestic and industrial discharges; however, these emerging pollutants are not completely removed and tend to accumulate in the biological sludge generated during the treatment process. In this study, three biological-type wastewater treatment plants (WWTPs) located in Acapulco, Mexico, were analyzed. The concentrations of MPs in the biological sludge ranged from 830 to 9300 particles/L. Using differential scanning calorimetry (DSC), the predominant polymers identified were high-density polyethylene (HDPE), polyethylene terephthalate (PET), and polypropylene (PP). It was estimated that the monthly concentrations of MPs in the sludge could reach up to 5.36 × 10⁹ particles/L, while the annual concentrations could rise to 3.55 × 10¹⁰ particles/L. These findings highlight the urgent need to review and update the regulatory framework related to the use of residual sludge for agricultural purposes, since high loads of MPs and their transfer pose a potential risk to soil quality, ecosystem health, and long-term environmental sustainability. Full article

Accurate modeling of Battery Energy Storage Systems (BESS) is essential for optimizing system performance, ensuring operational safety, and extending service life in applications ranging from electric vehicles (EV) to large-scale grid storage. However, the simplifications inherent in conventional battery models often hinder optimal system design and operation, leading to conservative performance limits, inaccurate State-of-Energy (SOE) estimation, and reduced overall efficiency. This paper presents a framework for advanced battery modeling, developed to achieve higher fidelity in SOE estimation and improved power-capability prediction. The proposed model introduces a dynamic energy-based representation of the charging and discharging processes, incorporating a functional dependence of instantaneous power on stored energy. Experimental validation confirms the superiority of this modeling framework over existing state-of-the-art models. The proposed approach reduces SOE estimation error to 0.1% and cycle-time duration error to 0.82% compared to the measurements. Consequently, the model provides more accurate predictions of the maximum charge and discharge power limits than state-of-the-art solutions. The enhanced predictive accuracy improves energy utilization, mitigates premature degradation, and strengthens safety assurance in advanced battery management systems. Full article

An accurate assessment of the state of health (SOH) of lithium-ion batteries (LIBs) is crucial for ensuring the safety and reliability of energy storage systems and electric vehicles. However, existing methods face challenges: physics-based models are computationally complex, traditional data-driven methods rely heavily on manual feature engineering, and single models lack the ability to capture both local and global degradation patterns. To address these issues, this paper proposes a novel hybrid LSTM–Transformer model for LIB SOH estimation using actual measurement data. The model integrates Long Short-Term Memory (LSTM) networks to capture local temporal dependencies with the Trans-former architecture to model global degradation trends through self-attention mechanisms. Experimental validation was conducted using eight 18650 Nickel Cobalt Manganese (NCM) LIBs subjected to 750 charge–discharge cycles under room temperature conditions. Sixteen statistical features were extracted from voltage and current data during constant current–constant voltage (CC-CV) phases, with feature selection based on the Pearson correlation coefficient and maximum information coefficient analysis. The proposed LSTM–Transformer model demonstrated superior performance compared to the standalone LSTM and Transformer models, achieving a mean absolute error (MAE) as low as 0.001775, root mean square error (RMSE) of 0.002147, and mean absolute percentage error (MAPE) of 0.196% for individual batteries. Core features including cumulative charge (CC Q), charging time, and voltage slope during the constant current phase showed a strong correlation with the SOH (absolute PCC > 0.8). The hybrid model exhibited excellent generalization across different battery cells with consistent error distributions and nearly overlapping prediction curves with actual SOH trajectories. The symmetrical LSTM–Transformer hybrid architecture provides an accurate, robust, and generalizable solution for LIB SOH assessment, effectively overcoming the limitations of traditional methods while offering potential for real-time battery management system applications. This approach enables health feature learning without manual feature engineering, representing an advancement in data-driven battery health monitoring. Full article

Background: Community-acquired pneumonia (CAP) is a leading cause of morbidity and mortality in the elderly. While pneumococcal vaccination is a core preventive measure, it remains unclear whether its association with severe CAP is uniform across all elderly subgroups. Our study aimed to evaluate the overall association of pneumococcal vaccination with the risk of severe CAP in hospitalized patients aged ≥ 65 years and to explore potential heterogeneity in this association using a causal forest model. Methods: We conducted a retrospective cohort study of patients discharged between January 2023 and June 2025, aged ≥ 65 years, with a primary diagnosis of CAP. We used multivariable logistic regression to estimate the average association and a causal forest model to explore heterogeneous patterns in the conditional average treatment effect (CATE). Results: Among 1906 included patients (severe CAP: 924; non-severe CAP: 982), PPSV23 vaccination was independently associated with reduced odds of all-cause severe CAP (adjusted OR = 0.610, 95% CI: 0.401–0.930). The causal forest model yielded an average treatment effect (ATE) estimate of −0.112 (95% CI: −0.200 to −0.023), corresponding to an 11.2 percentage-point reduction in absolute risk. Exploratory analysis suggested potential heterogeneity: the association appeared most pronounced in patients aged 65–74 years (CATE = −0.122) and showed an attenuating trend in older groups. Age was the primary variable associated with heterogeneity, followed by hypertension, SARS-CoV-2 infection, and sex. Conclusions: In this observational cohort study, PPSV23 vaccination was associated with a reduced risk of severe CAP in elderly inpatients under strong assumptions of no unmeasured confounding. Exploratory analyses suggested potential heterogeneity in this association, which appeared to attenuate with advancing age and may be influenced by comorbidities. These hypothesis-generating findings indicate that further investigation is needed to determine whether prevention strategies should be tailored for the very old and those with specific chronic conditions. Full article

Background/Objectives: Post-COVID-19 condition (PCC) remains poorly characterized beyond two years, particularly among intensive care unit (ICU) survivors. We aimed to describe the prevalence, persistence, and late consequences of PCC up to 32 months after discharge in an ICU cohort. Methods: This single-center longitudinal cohort included 170 adults with confirmed SARS-CoV-2 infection admitted to an ICU in Cáceres (Spain) between March 2020 and March 2021. 94 survivors entered follow-up at discharge and 3, 6, 12, 18, 24, and 32 months. PCC manifestations were grouped into five organ system domains (respiratory, cardiovascular, renal, infectious, and musculoskeletal/neuromuscular) and recorded only when supported by clinician-confirmed diagnoses or diagnostic tests. Prevalence at each visit, persistence, and new onset of manifestations between 3 and 6 months, and the cumulative incidence of new chronic diseases between 18 and 32 months were estimated with 95% confidence intervals. Results: Any PCC manifestation was almost universal at discharge (96.8% [95% CI, 91.1–98.9]) and remained high at 12 months (85.2% [95% CI, 76.3–91.2]), declining to 48.6% at 24 months and 25.7% at 32 months. Respiratory manifestations predominated early and were largely resolved by 32 months, whereas musculoskeletal/neuromuscular involvement remained relatively stable. From 18 to 32 months, 36.5% (95% CI, 26.4–47.9) of survivors developed at least one chronic condition, most frequently cardiovascular disease (14.9% [95% CI, 8.5–24.7]). Conclusions: Long-term PCC manifestations and incident chronic diseases are common among ICU COVID-19 survivors, underscoring the need for prolonged follow-up and post-ICU care. Full article

Background: The prognostic relevance of surgical timing at glioblastoma recurrence remains uncertain, and definitions of early versus delayed reoperation vary widely. Whether earlier surgery provides meaningful survival or functional benefit has not been clearly established. Methods: Databases including PubMed, Embase, Scopus, and Web of Science were searched from inception to May 2025. Eighteen observational studies met the inclusion criteria, fourteen of which provided extractable hazard ratios for survival. The primary outcome was overall survival after reoperation; secondary outcomes included functional status (ΔKPS or discharge home) and major postoperative complications. Random-effects models with Hartung–Knapp adjustment were used, with subgroup analyses stratified by KPS, extent of resection, and eloquence. Results: Across 2267 reoperated patients from 14 survival studies, earlier reoperation was associated with significantly longer survival (pooled HR 0.86; 95% CI 0.78–0.95). Subgroup analyses showed stronger effects in patients with KPS ≥ 70 (HR 0.81; 95% CI 0.72–0.92), non-eloquent tumors (HR 0.84; 95% CI 0.75–0.94), and near-total/gross-total resection (HR 0.79; 95% CI 0.68–0.93). Functional outcomes were pooled from 9 studies (n = 1182), demonstrating higher odds of postoperative stability or improvement with early surgery (OR 1.28; 95% CI 1.12–1.46). Major complications were reported in 9 studies (n = 1344) and did not differ between groups (OR 0.98; 95% CI 0.81–1.19). Sensitivity analyses and influence diagnostics showed consistent effect estimates and no undue single-study influence. Conclusions: Earlier reoperation for recurrent glioblastoma is associated with improved survival and better functional outcomes without increased morbidity in appropriately selected patients. Surgical timing should be incorporated into multidisciplinary planning. Prospective studies with standardized timing definitions and time-dependent modeling are needed to validate these findings. Full article

Coconut coir, a key substrate in soilless cultivation, presents challenges for accurate moisture detection because of its complex internal structure, which limits the understanding of water infiltration and redistribution. This study employed RGB image recognition techniques combined with machine learning algorithms to systematically investigate the effects of initial moisture content (10%, 20%, and 30%), coarse-to-fine coir volume ratio (1:0, 1:1, and 0:1), and emitter discharge rate (1.0, 1.5, and 2.0 L h⁻¹) on wetting front morphology, water transport dynamics, and moisture variation within coir substrates. Morphological features of the wetting front were extracted from images and incorporated into three machine learning models—Support Vector Regression (SVR), Random Forest (RF), and Polynomial Regression—to construct a predictive framework for coir moisture estimation. The results showed that the SVR model achieved the best predictive performance in coarse coir substrates (R² = 0.89, RMSE = 3.37%), whereas Polynomial Regression performed best in mixed substrates (R² = 0.861, RMSE = 4.34%). All models exhibited lower accuracy in fine coir, particularly at high moisture levels. Under the same irrigation volume, increasing the initial moisture content enhanced both the water transport rate and the wetting front extent, with the aspect ratio (AR) decreasing from approximately 2.0 to 1.3, indicating a morphological transition of the wetting front from a “thumb-shaped” to a “hemispherical” pattern. Coarse particles facilitated vertical infiltration, while fine particles exhibited stronger water retention. By integrating RGB image recognition with machine learning approaches, this study achieved reliable prediction of coir moisture content and proposed an optimal management strategy using mixed substrates with an initial moisture content of 20–30% to balance infiltration efficiency and water-holding capacity while minimizing percolation risk. These findings provide a robust technical pathway for precise water management in coir-based cultivation systems. Full article