Search Results (1,264)

The present study explores the application of RNNs for the prediction and propagation of flood waves along a section of the Bârsa River, Romania, as a fast alternative to classical hydraulic models, aiming to identify new ways to alert the population. Five neural architectures were analyzed as follows: S-RNN, LSTM, GRU, Bi-LSTM, and Bi-GRU. The input data for the neural networks were derived from 2D hydraulic simulations conducted using HEC-RAS software, which provided the necessary training data for the models. It should be mentioned that the input data for the hydraulic model are synthetic hydrographs, derived from the statistical processing of recorded floods. Performance evaluation was based on standard metrics such as NSE, $R^2$ MSE, and RMSE. The results indicate that all studied networks performed well, with NSE and $R^2$ values close to 1, thus validating their capacity to reproduce complex hydrological dynamics. Overall, all models yielded satisfactory results, making them useful tools particularly the GRU and Bi-GRU architectures, which showed the most balanced behavior, delivering low errors and high stability in predicting peak discharge, water level, and flood wave volume. The GRU and Bi-GRU networks yielded the best performance, with RMSE values below 1.45, MAE under 0.3, and volume errors typically under 3%. On the other hand, LSTM architecture exhibited the most significant instability and errors, especially in estimating the flood wave volume, often having errors exceeding 9% in some sections. The study concludes by identifying several limitations, including the heavy reliance on synthetic data and its local applicability, while also proposing solutions for future analyses, such as the integration of real-world data and the expansion of the methodology to diverse river basins thus providing greater significance to RNN models. The final conclusions highlight that RNNs are powerful tools in flood risk management, contributing to the development of fast and efficient early warning systems for extreme hydrological and meteorological events. Full article

During high-current vacuum arcing, asymmetric arcing with off-center plasma columns may occur due to stochastic discharge initiation and mechanical motion, receiving less research attention than symmetric arcing. The objective of this paper is to numerically analyze the influence law of asymmetric arc ignition on arc parameters. For 60 mm diameter contacts, three arc conditions of symmetric arcing, 33% arc offset, and 67% arc offset were modeled. The results show that the arc offset causes asymmetry in the arc’s distribution. For 33% offset, the pressure and number density on the side away from the root of the arc is about 50% of root values, while these parameters fall below 20% for the 67% offset. Simultaneously, arc offset elevates peak parameter values: under 33% offset, maxima for ion pressure, ion density, ion temperature, electron temperature, and current density rise 12%, 11%, 6%, 6%, and 14% versus symmetric arcing; during 67% offset, these escalate significantly to 67%, 61%, 12%, 18%, and 47%. This study contributes to providing reference for the analysis of vacuum interruption processes under asymmetric arcing conditions. Full article

Asynchrony between the movement of water and sediment in a reservoir will affect long-term maintenance of the reservoir’s capacity to a certain extent. Based on water and sediment data on the Three Gorges Reservoir (TGR) measured over the years and a river network model, optimization of the dispatching mode of the reservoir’s sand peak process was studied, and the corresponding water and sediment dispatching indicators were provided. The results show that (1) sand peak discharge dispatching of the TGR can be divided roughly into three stages, namely the flood detention period, the sediment transport period, and the sediment discharge period. (2) According to the process of the flood peak and the sand peak, a division method for each period is proposed. (3) A corresponding scheduling index is proposed according to the characteristics of the sand peak process and the needs of flood control scheduling. This research can provide operational indicators for the operation and management of the sediment load in the TGR and also provide technical support for sustainable reservoirs similar to TGR. Full article

Quantifying future changes in extreme events and associated flooding is challenging yet fundamental for stormwater managers. Along the U.S. Atlantic Coast, Eastern North Carolina (ENC) is frequently exposed to catastrophic floods from extreme rainfall that is typically associated with tropical cyclones. This study presents a novel approach that uses rainfall data from five dynamically and statistically downscaled (DD and SD) global climate models under two scenarios to visualize a potential future extent of flooding in ENC. Here, we use DD data (at 36-km grid spacing) to compute future changes in precipitation intensity–duration–frequency (PIDF) curves at the end of the 21st century. These PIDF curves are further applied to observed rainfall from Hurricane Matthew—a landfalling storm that created widespread flooding across ENC in 2016—to project versions of “Matthew 2100” that reflect changes in extreme precipitation under those scenarios. Each Matthew-2100 rainfall distribution was then used in hydrologic models (HEC-HMS and HEC-RAS) to simulate “2100” discharges and flooding extents in the Neuse River Basin (4686 km²) in ENC. The results show that DD datasets better represented historical changes in extreme rainfall than SD datasets. The projected changes in ENC rainfall (up to 112%) exceed values published for the U.S. but do not exceed historical values. The peak discharges for Matthew-2100 could increase by 23–69%, with 0.4–3 m increases in water surface elevation and 8–57% increases in flooded area. The projected increases in flooding would threaten people, ecosystems, agriculture, infrastructure, and the economy throughout ENC. Full article

Modern utilities face unprecedented pressures as trends in digital transformation and democratized energy choice empower consumers to engage in peak shaving, flexible load management, and adopt grid automation and intelligence solutions. A powerful confluence of architectural, technological, and socio-economic forces is transforming the U.S. electricity market, triggering significant changes in electricity production, transmission, and consumption. Utilities are embracing digital twins and repurposed Utility 2.0 concepts—distributed energy resources, microgrids, innovative electricity market designs, real-time automated monitoring, smart meters, machine learning, artificial intelligence, and advanced data and predictive analytics—to foster operational flexibility and market efficiency. This analysis qualitatively evaluates how digitalization, Battery Energy Storage Systems (BESSs), and adaptive strategies to mitigate rebound effects collectively advance smart duck curve management. By leveraging digital platforms for real-time monitoring and predictive analytics, utilities can optimize energy flows and make data-driven decisions. BESS technologies capture surplus renewable energy during off-peak periods and discharge it when demand spikes, thereby smoothing grid fluctuations. This review explores the benefits of targeted digital transformation, BESSs, and managed rebound effects in mitigating the duck curve problem, ensuring that energy efficiency gains translate into actual savings. Furthermore, this integrated approach not only reduces energy wastage and lowers operational costs but also enhances grid resilience, establishing a robust framework for sustainable energy management in an evolving market landscape. Full article

Flood control infrastructure is essential for the development of cities and the population’s well-being. The goal is to protect human and economic resources by reducing the inundation area and controlling the flood level and peak discharges. Detention basins can do this by storing a large volume of water to be released after the peak discharge. By doing this, a large amount of energy is stored, which can be recovered via micro-hydropower. In addition, as the release flow is controlled and almost constant, Pumps as Turbines (PAT) could be a feasible and economic option in these cases. Thus, this study investigates the feasibility of micro-hydropower (MHP) in urban detention basins, using the Santa Lúcia detention basin in Belo Horizonte as a case study. The methodology involved hydrological modeling, hydraulic analysis, and economic and environmental assessment. The results demonstrated that PAT selection has a crucial role in the feasibility of the MHP, and exploiting rainfall with lower intensities but higher frequencies is more attractive. Using multiple PATs with different operating points also showed promising results in improving energy production. In addition to the economic benefits, the MHP in the detention basin produces minimal environmental impact and, as it exploits a wasted energy source, it also reduces the carbon footprint in the urban water cycle. Full article

An extreme flood event occurred in Slovenia in August 2023. This study evaluated the influence of this extreme flood on the design discharges in Slovenia. This evaluation was based on flood frequency analysis for the data from 33 gauging stations. Analyses were conducted with and without the 2023 peak discharge, i.e., for the periods 1961–2022 and 1961–2023, using eight different theoretical distribution functions. In addition, specific discharge values for the 2023 flood event were analyzed and compared with regional envelope curves for Europe. The findings of the study indicate that the impact of a single flood event on the design discharge values can be substantial. Moreover, an analysis of the specific discharges resulting from the 2023 flood event in Slovenia reveals that the values for all gauging stations considered are below the regional envelopes. Concurrently, the analysis indicates that a flood event larger than the 2023 event may occur in the future. Full article

The Ganges–Brahmaputra–Meghna (GBM) delta, characterized by complex topography and hydrological conditions, is highly susceptible to recurrent flooding, particularly in its coastal regions where tidal dynamics hinder floodwater discharge. This study integrates Synthetic Aperture Radar (SAR) imagery with machine learning (ML) techniques to assess near real-time flood inundation patterns associated with extreme weather events, including recent cyclones between 2017 to 2024 (namely, Mora, Titli, Fani, Amphan, Yaas, Sitrang, Midhili, and Remal) as well as intense monsoonal rainfall during the same period, across a large spatial scale, to support disaster risk management efforts. Three machine learning algorithms, namely, random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN), were applied to flood extent data derived from SAR imagery to enhance flood detection accuracy. Among these, the SVM algorithm demonstrated the highest classification accuracy (75%) and exhibited superior robustness in delineating flood-affected areas. The analysis reveals that both cyclone intensity and rainfall magnitude significantly influence flood extent, with the western coastal zone (e.g., Morrelganj and Kaliganj) being most consistently affected. The peak inundation extent was observed during the 2023 monsoon (10,333 sq. km), while interannual variability in rainfall intensity directly influenced the spatial extent of flood-affected zones. In parallel, eight major cyclones, including Amphan (2020) and Remal (2024), triggered substantial flooding, with the most severe inundation recorded during Cyclone Remal with an area of 9243 sq. km. Morrelganj and Chakaria were consistently identified as flood hotspots during both monsoonal and cyclonic events. Comparative analysis indicates that cyclones result in larger areas with low-level inundation (19,085 sq. km) compared to monsoons (13,829 sq. km). However, monsoon events result in a larger area impacted by frequent inundation, underscoring the critical role of rainfall intensity. These findings underscore the utility of SAR-ML integration in operational flood monitoring and highlight the urgent need for localized, event-specific flood risk management strategies to enhance flood resilience in the GBM delta. Full article

Electric vehicles (EVs) offer a sustainable solution for reducing carbon emissions in the transportation sector. However, their increasing widespread adoption poses significant challenges for local distribution grids, many of which were not designed to accommodate the heightened and irregular power demands of EV charging. Components such as transformers and distribution networks may experience overload, voltage imbalances, and congestion—particularly during peak periods. While upgrading grid infrastructure is a potential solution, it is often costly and complex to implement. The unpredictable nature of EV charging behavior further complicates grid operations, as charging demand fluctuates throughout the day. Therefore, efficient integration into the grid—both for charging and potential discharging—is essential. This paper reviews recent studies on the impacts of high EV penetration on distribution grids and explores various strategies to enhance grid performance during peak demand. It also examines promising optimization methods aimed at mitigating negative effects, such as load shifting and smart charging, and compares their effectiveness across different grid parameters. Additionally, the paper discusses key challenges related to impact analysis and proposes approaches to improve them in order to achieve better overall grid performance. Full article

Supercapacitors, with their remarkable attributes such as including a high power density, an extended cycle life, and inherent safety, have emerged as a major research area for electrochemical energy storage. In this paper, phenylenediamine and glyoxal were used as raw material to prepare p-phenylenediamine glyoxal (PGo) with one single step. p-phenylenediamine glyoxal-ferrocene (PGo-Fc_x, x = 1, 0.3, 0.2, 0.1) composites were synthesized based on a poly-Schiff base. FTIR and XRD results indicated that ferrocene doping preserves the intrinsic PGo framework while inducing grain refinement, as evidenced by the narrowing of the XRD diffraction peaks. SEM observations further revealed distinct morphological evolution. CV (cyclic voltammetry), EIS (electrochemical impedance spectroscopy), and GCD (galvanostatic charge–discharge) were conducted on PGo-Fc_x in order to examine its electrochemical performance. The PGo-Fc_0.3 in PGo-Fc_x electrode material had a specific capacitance of 59.6 F/g at a current density of 0.5 A/g and 36 F/g at a current density of 10 A/g. Notably, even after undergoing 5000 charging–discharging cycles at 10 A/g, the material retained 76.2% of its specific capacitance compared to the initial cycle. Therefore, taking conductive polymers and metal oxide materials for modification can improve the stability and electrochemical performance of supercapacitors. Full article

This study investigates the dynamic response mechanisms of discharge capacity in the Han River Estuary to hydrological process changes at the Yangtze–Han River confluence. By constructing a one-dimensional hydrodynamic model for the 265 km Xinglong–Hankou reach, we quantitatively decouple the synergistic effects of riverbed scouring (mean annual incision rate: 0.12 m) and Three Gorges Dam (TGD) operation through four orthogonal scenarios. Key findings reveal: (1) Riverbed incision dominates discharge variation (annual mean contribution >84%), enhancing flood conveyance efficiency with a peak flow increase of 21.3 m³/s during July–September; (2) TGD regulation exhibits spatiotemporal intermittency, contributing 25–36% during impoundment periods (September–October) by reducing Yangtze backwater effects; (3) Nonlinear interactions between drivers reconfigure flow paths—antagonism occurs at low confluence ratios (R < 0.15, e.g., Cd increases to 45 under TGD but decreases to 8 under incision), while synergy at high ratios (R > 0.25) reduces Hanchuan Station flow by 13.84 m³/s; (4) The 180–265 km confluence-proximal zone is identified as a sensitive area, where coupled drivers amplify water surface gradients to −1.41 × 10⁻³ m/km (2.3× upstream) and velocity increments to 0.0027 m/s. The proposed “Natural/Anthropogenic Dual-Stressor Framework” elucidates estuary discharge mechanisms under intensive human interference, providing critical insights for flood control and trans-basin water resource management in tide-free estuaries globally. Full article

The mechanisms of ictal discharge initiation remain incompletely understood, particularly the paradoxical role of inhibitory fast-spiking interneurons in seizure generation. Using simultaneous whole-cell recordings of interneurons and pyramidal neurons combined with extracellular [K⁺]_o monitoring in mouse entorhinal cortex-hippocampal slices (4-aminopyridine model of epileptiform activity), we identified a critical transition sequence: interneurons displayed high-frequency firing during the preictal phase before entering depolarization block (DB). DB onset coincided with the peak of rate of extracellular [K⁺] accumulation. Pyramidal cells remained largely silent during interneuronal hyperactivity but started firing within 1.1 ± 0.3 s after DB onset, marking the transition to ictal discharges. This consistent sequence (interneuron DB → [K⁺]_o rate peak → pyramidal cell firing) was observed in 100% of entorhinal cortex recordings. Importantly, while neurons across all entorhinal cortical layers synchronously fired during the first ictal discharge, hippocampal CA1 neurons showed fundamentally different activity: they generated high-frequency interictal bursts but did not participate in ictal events, indicating region-specific seizure initiation mechanisms. Our results demonstrate that interneuron depolarization block acts as a precise temporal switch for ictogenesis and suggest that the combined effect of disinhibition and K⁺-mediated depolarization triggers synchronous pyramidal neuron recruitment. These findings provide a mechanistic framework for seizure initiation in focal epilepsy, highlighting fast-spiking interneurons dysfunction as a potential therapeutic target. Full article

Phase change materials (PCMs) have significant potential for utilization due to their high energy storage density and excellent safety in energy storage. In this research, a flexible heat storage device using the stable supercooling of sodium acetate trihydrate composite is developed, enabling on-demand heat release through controlled solidification initiation. The solidification and heat release characteristics are investigated in experiments. The results indicate that the heat release characteristics of this heat storage device are closely linked to the crystallization process of the PCM. During the experiment, based on whether external intervention was needed for the solidification process, the PCM manifested two separate solidification modes—specifically, spontaneous self-solidification and triggered-solidification. Meanwhile, the heat release rates, temperature changes, and crystal morphologies were observed in the two solidification modes. Compared with spontaneous self-solidification, triggered-solidification achieved a higher peak surface temperature (53.6 °C vs. 46.2 °C) and reached 45 °C significantly faster (5 min vs. 15 min). Spontaneous self-solidification exhibited slower, uncontrollable heat release with dendritic crystals, while triggered-solidification provided rapid, controllable heat release with dense filamentous crystals. This controllable switching between modes offers key practical advantages, allowing the device to provide either rapid, high-power heat discharge or slower, sustained release as required by the application. According to the crystal solidification theory, the different supercooling degrees are the main reasons for the two solidification modes exhibiting different solidification characteristics. During solidification, the growth rate of SAT crystals exhibits substantial disparities across diverse experiments. In this research, the maximum axial growth rate is 2564 μm/s, and the maximum radial growth rate is 167 μm/s. Full article

With ongoing global warming, glacier lake outburst floods (GLOFs) and associated debris flows pose increasing threats to downstream communities and infrastructure. Glacial lakes differ in their triggering factors and breach mechanisms, necessitating event-specific analysis. This study investigates the GLOF risk of Jiongpuco Lake, located in the southeastern part of the Tibetan Plateau, using an integrated approach combining remote sensing, field surveys, and numerical modeling. Results show that the lake has expanded significantly—from 2.08 km² in 1990 to 5.43 km² in 2021—with the most rapid increase observed between 2015 and 2016. InSAR data and optical imagery indicate that surrounding moraine deposits remain generally stable. However, ice avalanches from the glacier terminus are identified as the primary trigger for lake outburst via wave-induced overtopping. Mechanical and geomorphological analyses suggest that the moraine dam is resistant to downcutting erosion, reinforcing overtopping as the dominant failure mode. To assess potential impacts, three numerical simulation scenarios were conducted based on different avalanche volumes. Under the extreme scenario involving a 5-million m³ ice avalanche, the modeled peak discharge at the dam site reaches approximately 19,000 m³/s. Despite the high flood magnitude, the broad and gently sloped downstream terrain facilitates rapid attenuation of flood peaks, resulting in limited impact on downstream settlements. These findings offer critical insights for GLOF hazard assessment, disaster preparedness, and risk mitigation under a changing climate. Full article

Background: Nutritional disorders may affect short-term recovery after major orthopaedic surgery, but evidence is lacking. This study assessed whether and how different nutritional disorders diagnosed at admission could influence early recovery after hip replacement. Methods: A prospective analytical study was designed to include 60 patients scheduled for elective primary hip replacement and assess their nutritional status to diagnose 5 malnutrition phenotypes: undernutrition, sarcopenia, obesity, sarcopenic obesity, and sarcopenic undernutrition. Outcome measures were 24 h change in neutrophils, 72 h change in haemoglobin, and 10-day gait speed regain. Results: Haemoglobin reached the nadir at day 2–3 and partially recovered by day 10 in all patients, with sarcopenia and undernutrition being the strongest predictors of the postoperative drop (−2.37 g∙dL⁻¹ and −0.80 g∙dL⁻¹, p < 0.05). Neutrophils peaked immediately after surgery and returned to baseline levels at discharge, with sarcopenic undernutrition displaying a blunted response after surgery (−16.20%, p < 0.01). Undernutrition was found to be the most influential preoperative variable on gait speed recovery, but with a marginal effect. None of the patients covered the reference energy and protein needs through diet in the 10 postoperative days. Conclusions: In this cohort, nutritional disorders with reduced body function and reserves (sarcopenia and undernutrition) grounded a greater vulnerability to surgery in terms of early stress response and short-term recovery. This calls for both advanced planning of nutritional prehabilitation strategies for these conditions and adequate postoperative nutritional support. Full article