Search Results (983)

The escalating severity of microplastic pollution and oily wastewater discharge has intensified the demand for recyclable, multifunctional, and environmentally benign materials. In this study, we present a composite polyurethane (PU) sponge constructed through the synergistic integration of cuttlefish-ink nanoparticles (CINPs), Ti₃C₂T_X MXene, and polydimethylsiloxane (PDMS). The synergistic CINP@MXene framework imparts high photothermal conversion efficiency and structural stability, while the PDMS coating confers superhydrophobicity. The resulting sponge demonstrates efficient oil absorption and oil–water separation capabilities, alongside a stable photothermal response, achieving a temperature of 84.1 °C within 10 s under 1.5 Sun irradiation. Notably, the sponge absorbed approximately 0.05 g of crude oil within 10 s, the saturated absorption capacity of crude oil under 1.5 solar days was 24.52 g/g, and the adsorption rate of 5 g crude oil within 4 min was 91.4%. Furthermore, it exhibits remarkable adsorption performance toward common microplastics and nanoplastics. Overall, the CINPs@MXene/PU/PDMS sponge represents a versatile and scalable platform with significant potential for addressing challenges in oily wastewater treatment, solar-assisted oil recovery, and microplastic remediation, thereby contributing to sustainable environmental protection efforts. Full article

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

Eutrophication remains a persistent challenge in the Romanian Black Sea coastal zone, driven by excess nutrient inputs from riverine and coastal sources and further intensified by climate change. This study assesses eutrophication dynamics and explores mitigation options using an integrated framework that combines in situ observations, satellite-derived chlorophyll a data, machine learning, and system dynamics modelling. Water samples collected during two field campaigns (2023–2024) were analyzed for nutrient concentrations and linked with chlorophyll a products from the Copernicus Marine Service. Random Forest analysis identified dissolved inorganic nitrogen, phosphate, salinity, and temperature as the most influential predictors of chlorophyll a distribution. A system dynamics model was subsequently used to explore relative ecosystem responses under multiple management scenarios, including nutrient reduction, enhanced zooplankton grazing, and combined interventions. Scenario-based simulations indicate that nutrient reduction alone produces a moderate decrease in chlorophyll a (45% relative to baseline conditions), while restoration of grazing pressure yields a comparable response. The strongest reduction is achieved under the combined scenario, which integrates nutrient reduction with biological control and lowers normalized chlorophyll a levels by approximately two thirds (71%) relative to baseline. In contrast, a bloom-favourable scenario results in a several-fold increase in chlorophyll a of 160%. Spatial analysis highlights persistent eutrophication hotspots near the Danube mouths and urban discharge areas. These results demonstrate that integrated strategies combining nutrient source control with ecological restoration are substantially more effective than single-measure interventions. The proposed framework provides a scenario-based decision-support tool for ecosystem-based management and supports progress toward achieving Good Environmental Status under the Marine Strategy Framework Directive. Full article

Porous NiCo₂O₄ nanomaterials were synthesized using in situ-generated polyacrylamide as a template, with cobalt nitrate, nickel nitrate, and urea serving as raw materials. XRD and FESEM analyses confirm the successful formation of spinel-structured NiCo₂O₄ electrode materials featuring a 3D macroporous/mesoporous architecture and an average crystalline size of approximately 8.1 nm, obtained through calcination of the amorphous precursor. Electrochemical evaluation of the as-prepared NiCo₂O₄ reveals that the specific capacitance retained at 10 A g⁻¹ reaches 88.9% of the value measured at 1 A g⁻¹, demonstrating excellent rate capability. Furthermore, the material exhibits a gradual increase in specific capacity over 3000 charge–discharge cycles, achieving a capacitance retention of up to 246.5%, which indicates good cycling stability and superior capacity retention. 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

Pumped-storage power stations (PSPSs) are vital for grid stability, yet pump-turbines (PTs) operating in the S-shaped region often induce severe hydraulic instability. To reveal the mechanism of these self-excited oscillations, this study establishes a nonlinear mathematical model based on rigid water column theory and a cubic polynomial approximation of the PT’s nonlinear characteristics. Both analytical derivations and numerical simulations were conducted. Analytical results indicate that, in the absence of surge tanks, self-excited oscillations occur when the PT’s negative hydraulic impedance modulus exceeds the pipeline impedance. With a single surge tank, the system behaves analogously to the Van der Pol oscillator, exhibiting oscillations that converge to a stable limit cycle governed by system parameters. Numerical simulations for a dual-surge-tank system further reveal that, due to initial negative damping, the PT transitions to alternative stable equilibria. Crucially, the transition direction is governed by the polarity of the initial disturbance: negative perturbations lead to the regular turbine region, while positive ones lead to the reverse pump region. Additionally, pipe friction causes the steady-state discharge to deviate slightly from the theoretical static value, with deviations remaining below 2.96%. This work provides a theoretical basis for stability prediction in PSPSs. Full article

Previous in vivo studies have clearly demonstrated that the intravenous administration of the carotenoid astaxanthin (AST) suppresses the excitability of rat trigeminal spinal nucleus caudalis (SpVc) neurons. This action is hypothesized to be mediated through the inhibition of both voltage-gated Ca²⁺ (Cav) channels and excitatory glutamate receptor transmission. The objective of this study was to determine whether acute intravenous administration of AST alleviates the hyperexcitability of SpVc wide dynamic range (WDR) neurons in a rat model of inflammation. Neuronal responses to both nociceptive and non-nociceptive mechanical stimulation were evaluated using an in vivo electrophysiological model. One day following inflammation induced by Complete Freund’s Adjuvant (CFA), the mechanical escape threshold was significantly reduced compared to pre-injection baseline values. Subsequently, extracellular single-unit recordings were performed on SpVc WDR neurons in anesthetized, inflamed rats. The neuronal responses to both non-noxious and noxious orofacial mechanical stimuli were then analyzed. Acute intravenous administration of AST at 1 and 5 mM elicited a dose-dependent reduction in the mean firing frequency of SpVc WDR neurons in response to noxious mechanical stimuli. This inhibition peaked within 10 min and was fully reversed after approximately 25 min. Importantly, AST preferentially inhibited the discharge frequency of SpVc WDR neurons in response to noxious stimulation, exhibiting a significantly greater effect than on the response evoked by non-noxious stimulation (41.5 ± 3.0% vs. 20.7 ± 4.2%, p < 0.05). Collectively, these findings demonstrate that acute intravenous administration of AST effectively suppresses noxious synaptic transmission within the SpVc during inflammation. We propose that this suppressive effect is mediated by the inhibition of upregulated Cav channels and glutamate receptors. Consequently, AST is implicated as a promising therapeutic candidate for the management of trigeminal inflammatory pain, given its potential for a favorable safety profile compared to conventional treatments. Full article

This study investigates the creepage discharge method applied to a cold cathode to enhance discharge performance. Creepage discharge refers to the movement of electrical discharges along an insulating surface, increasing electron emission from dielectric materials. The research examines discharge behavior by varying the position of a ceramic pipe on the cathode and proposes a new average current calculation method. Findings indicate that when the ceramic pipe tip extends beyond the cathode, discharge intensity increases. The ceramic-covered cathode (CCC) configuration achieved an average discharge current exceeding 600 mA, compared to about 500 mA for the conventional rod-wire cathode (CRC). The CCC design also enabled arc plasma generation at voltages below 5 kV, whereas the CRC required 4–7 kV to achieve similar effects. For industrial applications, the CCC setup produced approximately 700 mA, whereas the CRC reached approximately 600 mA. These results demonstrate that the CCC configuration offers superior discharge current generation and plasma-column discharge. Full article

With accelerating urbanization, rivers have been severely polluted, resulting in widespread black and odorous waterways. The coagulation–sedimentation and contact oxidation bypass treatment process is characterized by low operational cost and simple operation and management. In this study, a coagulation–sedimentation–contact oxidation biofilter process was developed to treat heavily polluted malodorous blackwater. Among the tested biofilm carriers, rigid aramid fiber exhibited the fastest biofilm formation and the best pollutant removal performance. Based on a comprehensive evaluation of effluent quality and treatment capacity, the optimal operating conditions of the proposed process were identified as a PAC dosage of 50 mg/L, an air-to-water ratio of 7:1, and a hydraulic retention time (HRT) of 2 h. Under these conditions, the effluent concentrations of chemical oxygen demand (COD), ammonia nitrogen (NH₄⁺-N), and suspended solids (SSs) were consistently maintained below 30, 5, and 5 mg/L, respectively. Moreover, the optimized system demonstrated strong resistance to shock loading, maintaining stable operation at influent COD and SS concentrations of approximately 150 mg/L and 40 mg/L, respectively, while complying with the Class A Discharge Standard of Pollutants for Municipal Wastewater Treatment Plants. This study provides an efficient treatment strategy for malodorous blackwater remediation. Full article

Reliable forecasting of karst spring discharge is critical for sustainable groundwater resource management under the dual pressures of climate change and intensified anthropogenic activities. This study proposes a Heterogeneous Spatiotemporal Graph Attention Network (H-STGAT) to predict spring discharge dynamics at Shentou Spring, Shanxi Province, China. Unlike conventional spatiotemporal networks that treat all relationships uniformly, our model derives its heterogeneity from a graph structure that explicitly categorizes spatial, temporal, and periodic dependencies as unique edge classes. Specifically, a dual-layer attention mechanism is designed to independently extract hydrological features within each relational channel while dynamically assigning importance weights to fuse these multi-source dependencies. This architecture enables the adaptive capture of spatial heterogeneity, temporal dependencies, and multi-year periodic patterns in karst hydrological processes. Results demonstrate that H-STGAT outperforms both traditional statistical and deep learning models in predictive accuracy, achieving an RMSE of 0.22 m³/s and an NSE of 0.77. The model reveals a long-distance recharge pattern dominated by high-altitude regions, a finding validated by independent isotopic evidence, and accurately identifies an approximately 4–6 month lag between precipitation and spring discharge, which is consistent with the characteristic hydrological lag identified through statistical cross-covariance analysis. This research enhances the understanding of complex mechanisms in karst hydrological systems and provides a robust predictive tool for sustainable groundwater management and ecological conservation, while offering a generalizable methodological framework for similar complex karst hydrological systems. Full article

Plasma-activated water (PAW) has gained attention across agricultural, medical, cosmetic, and sterilization fields due to its production of reactive oxygen and nitrogen species (ROS and RNS). Although PAW has been primarily explored for seed germination and sterilization in agriculture, its role as a nutrient source and physiological regulator remains less understood. In this study, PAW generated by a surface dielectric barrier discharge (SDBD) system contained approximately 1000 ppm nitrate (NO₃⁻) and was designated as PAW1000. Diluted PAW solutions were applied to sprout crops—wheat (Triticum aestivum), barley (Hordeum vulgare), radish (Raphanus sativus), and broccoli (Brassica oleracea var. italica)—grown under hydroponic and soil-based conditions. PAW100 and PAW200 treatments enhanced growth, increasing fresh biomass by up to 26%, shoot length by 22%, and root length by 18%, depending on the species. In silico analysis identified nitrogen-responsive transcripts among several autophagy-related genes. Consistent with this, fluorescence microscopy of Arabidopsis thaliana GFP-StATG8 lines revealed increased autophagosome formation following PAW treatment. The growth-promoting effect of PAW was diminished in atg4 mutants, indicating that autophagy contributes to plant responses to PAW-derived ROS and RNS. Together, these findings demonstrate that diluted PAW generated by SDBD enhances biomass accumulation in sprout crops, and that autophagy plays a regulatory role in mediating PAW-induced physiological responses. 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

Electric vehicles (EVs) rely on a battery pack as their primary energy source, making it a critical component for their operation. To guarantee safe and correct functioning, a Battery Management System (BMS) is employed, which uses variables such as State of Charge (SOC) to set charge/discharge limits and to monitor pack health. In this article, we propose a Multilayer Perceptron (MLP) network to estimate the SOC of a 14.8 V battery pack installed in a robotic vacuum cleaner. Both offline and online (real-time) tests were conducted under continuous load and with rest intervals. The MLP’s output is compared against two commonly used approaches: NARX (Nonlinear Autoregressive Exogenous) and CNN (Convolutional Neural Network). Performance is evaluated via statistical metrics, Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), and we also assess computational cost using Operational Intensity. Finally, we map these results onto a Roofline Model to predict how the MLP would perform on an automotive-grade microcontroller unit (MCU). A generalization analysis is performed using Transfer Learning and optimization using MLP–Kalman. The best performers are the MLP–Kalman network, which achieved an RMSE of approximately 13% relative to the true SOC, and NARX, which achieved approximately 12%. The computational cost of both is very close, making it particularly suitable for use in BMS. Full article

Japanese modern irrigation management is considered a successful model of water governance worldwide. However, debates continue over whether this success is due to natural water abundance or to water management practices. This study evaluates pre-modern water scarcity risk for six irrigation schemes, developed during that period in the Kinu River Basin (1603–1868); a period without large reservoirs, canal systems, or modern regulatory technologies. As the methodology, pre-modern river flows were reconstructed by removing the effects of four modern dams from the present-day river discharge, adjusting the conveyance efficiency, changes in paddy field area, rainfall input, and return flows. Water demand was assessed using Japanese irrigation standards of 5 mm/d (minimum water demand corresponding to evapotranspiration) and 20 mm/d (easy management), and risk was evaluated under both the prior appropriation and Equal Water Distribution rules. Results show that modern flow in the dry season is approximately 25 m³/s, whereas reconstructed natural flow during drought years declines to 10–18 m³/s, and about 15 m³/s after rainfall adjustment. Under the 20 mm/d demand scenario, scarcity occurred in four schemes (2 of 17 years in the third scheme and 7 of 17 years for the sixth scheme), while no scarcity occurred under the minimum-demand scenario (5 mm/d), even during low-flow conditions. This indicates that the available water in these schemes was at a level where drought damage could occur under extensive irrigation management, but could be avoided by intensive irrigation management to supply the minimum necessary water to all paddy fields. Full article