Search Results (180)

Fast charging improves the usability of consumer electronics and electric vehicles (EVs) by reducing range anxiety and downtime but accelerates battery degradation and raises safety concerns. Optimizing operational conditions during fast-charging is critical to mitigating aging and ensuring safety. This study evaluated multilayer Gr/NMC811 cells under various conditions, including depths of discharge (DODs of 68%, 84%, and 100%), upper charge cutoff voltages (4.1–4.2 V), and post-charge rest periods (2–30 min), using a 20 min fast charging protocol for up to 500 cycles (up to 150,000 miles of EV use assuming 3.3 mi/kWh vehicle level energy efficiency). Surprisingly, higher DODs under fast charging improved battery life and performance compared to lower DODs. Reducing the upper charge cut-off voltage helped mitigate degradation. A brief 2 min rest period after charging further reduced aging effects. The primary aging modes were loss of lithium inventory and cathode active material. Although minor lithium plating was observed within 500 cycles, it did not affect performance significantly. These findings suggest that, with optimized conditions, cells can sustain hundreds of fast charge cycles—equivalent to over 100,000 miles of EV use—without significant adverse effects on performance or longevity. Full article

Reliable observation of water resources is a major challenge for sustainable development, particularly in the river-centric deltaic countries like Bangladesh, where the data is generally scarce. Leveraging operational satellites, this study presents a real-time capable water level (WL), discharge (Q), and floodplain monitoring framework implemented for the Brahmaputra River in Bangladesh. The multi-satellite approach presented here combined satellite altimetry, synthetic aperture radar (SAR), and optical imagery. A set of WL time series is obtained first from Jason-2/3 and Sentinel-3 altimetry, while a combination of Sentinel-1 SAR and Sentinel-2 optical images is used to extract the floodplain extent. Seasonal Rating Curve (RC) models are then developed to estimate Q from the river WL (altimetry) and width (imagery). The altimetry WL measurement is further complemented by the width-based Q utilizing an inverse RC. Furthermore, the water level is combined with a floodplain map to extract floodplain topography and its evolution. The proposed framework provides consistent and reliable observations in the Brahmaputra River, with a bias, root mean-squared errors (RMSEs), and correlation coefficient of 0.03 m, 0.68 m, and 0.96 for WL, and −168.22 m³/s, 4161.46 m³/s, and 0.97 for Q, respectively, relative to a mean discharge of approximately 30,000 m³/s. The locations of high erosion–accretion across the river reach are also well-captured in the evolving floodplain maps. By integrating multiple satellite altimetry missions with SAR and optical imagery, the multi-satellite approach reduces the effective monitoring interval for both water level and discharge from approximately 10 days (single-mission altimetry) to about 4 days, enabling improved capture of extreme events such as floods. As the operational satellites used in this study are expected to provide long-term observations, the proposed framework supports sustainable monitoring of floodplain dynamics in Bangladesh and other similar data-poor environments, towards informed water management under ongoing climatic and anthropogenic changes. Full article

This paper examines how hour-ahead forecasting uncertainty propagates to microgrid operation under intermittent renewable generation. Using hourly public data for Ontario and focusing on the FSA K0K in 2018, we evaluate four representative months (January, April, July, and December) to capture seasonal dynamics. We benchmark three univariate forecasting approaches for load demand, photovoltaic (PV) generation, and wind generation under a consistent 24-to-1 input setup, including GRU, LSTM, and a persistence baseline. We report point-forecast metrics (RMSE, MAE, and $R^2$) and also provide 90% prediction intervals (PI90) using conformal calibration to quantify uncertainty. To assess downstream impact, forecasts are coupled with a dual-branch MATLAB/Simulink microgrid model. One branch uses True profiles and the other uses forecast-driven Pred inputs, while both branches share the same rule-based EMS and BESS constraints. System performance is evaluated using time-series comparisons and monthly key performance indicators (KPIs) covering grid import and export, grid peak power, battery throughput, and state-of-charge (SoC) statistics. We further report an illustrative cost sensitivity under a flat tariff and a throughput-based degradation proxy. Results show that forecasting performance is target dependent. GRU achieves the best overall point accuracy for load and PV, whereas wind is strongly driven by short term persistence at the one hour horizon, and in this measurement only setup without meteorological covariates the persistence baseline can match or outperform the deep learning models. In the microgrid simulations, Pred and True trajectories remain qualitatively consistent, and SoC-related indicators and peak power remain comparatively consistent across months. In contrast, energy-flow indicators, especially grid export and battery throughput, show larger deviations and dominate the observed cost sensitivity. Overall, the findings suggest that compact hour-ahead forecasts can be adequate to preserve operational reliability under a constraint-driven EMS, while forecast improvements mainly translate into economic efficiency gains rather than reliability-critical benefits. Full article

Bangladesh has significantly reduced child undernutrition, yet micronutrient deficiencies and diet-related non-communicable diseases remain pressing challenges. While the afordability of healthy diets is recognized as a key determinant of nutrition outcomes, limited attention has been paid to the uncertainties that affect diet costs and access over time. This paper addresses this gap by exploring the major drivers of uncertainty in the cost of healthy diets in Bangladesh and their implications for nutrition policy. This study emloyed foresight tools to explore future uncertainties affecting the cost and accessibility of healthy diets in Bangladesh. Key drivers of change, such as climate variability, market dynamics, income inequality, and dietary behavior, were identified through a structured expert workshop. Two critical uncertainties were selected using the 2 × 2 scenario planning method: food price volatility and changing dietary patterns. These formed the basis for four plausible future scenarios, each illustrating different trajectories for nutrition and food system outcomes. This foresight approach supports proactive, multisectoral policymaking by highlighting potential risks and opportunities for ensuring affordable, nutritious diets in a changing context. The resulting scenarios underscore the need for integrated, multisectoral strategies to build resilient food systems, improve the affordability of nutrient-rich foods, and promote dietary behavior change. Full article

Poly- and perfluoroalkyl substances (PFASs) are persistent environmental pollutants widely used in industrial applications due to their thermal stability and chemical resistance. However, their persistence in the environment and potential health risks, including developmental and immunological issues, have raised significant concerns. This review highlights the industrial uses, environmental fate, and bioaccumulation of PFASs, emphasizing their widespread presence in air, water, soil, and biota. Major sources of PFAS contamination include industrial discharges, wastewater treatment, and military sites. The atmospheric transport of PFASs contributes to their deposition in remote ecosystems, while aquatic and soil contamination stems from both point and nonpoint sources. Bioaccumulation studies reveal that PFASs accumulate in organisms, leading to potential human exposure through food, water, and consumer products. This review calls for further research to address knowledge gaps in PFAS detection, behavior, and health impacts, while advocating for improved regulations to limit their release and exposure. Full article

Background/Objectives: The widespread use of sulfamethoxazole (SMX) has led to increasing antibiotic resistance, and there is a need for improved formulations to enhance its therapeutic effectiveness. In this study, we investigated the biocidal potential of SMX composite crystals incorporated with functionalized poly(lactic-co-glycolic acid) (nfPLGA) and nano-graphene oxide (nGO). Methods: The composites, namely SMX-nfPLGA and SMX-nGO, were synthesized via antisolvent precipitation and evaluated using Kirby–Bauer disk diffusion assays. Results: Incorporation of nfPLGA and nGO significantly improved SMX solubility, increasing it from 0.029 mg/mL to 0.058 mg/mL and 0.063 mg/mL, respectively. Additionally, the log partition coefficient (log P or K_w) also improved from 1.4 to 0.86 for nGO and 0.92 for nfPLGA composites. Both formulations exhibited improved antibacterial activity with distinct time-dependent bactericidal effects. Compared to pure SMX, the SMX-nfPLGA showed 60% and 53% greater bacterial inhibition at concentrations of 50 mg/mL and 100 mg/mL, respectively. Although SMX-nGO was slightly less potent, it still surpassed pure SMX, with 50% and 33% higher inhibition at the same concentrations. Conclusions: Importantly, neither nfPLGA nor nGO showed any biocidal effects, confirming that the observed enhancement was due to improved SMX solubility caused by their incorporation. These findings suggest that embedding solubility-enhancing nanoparticles into the existing crystal structure of the antibiotic is a promising strategy for enhancing the effectiveness. Full article

Climate wave data, derived from significant wave height (SWH) altimetry, provide accurate information towards nearshore and coastal areas. Their use is crucial to enhance our capabilities of observing, understanding, and forecasting storm waves, even in complex coastal basins. In this study, SWOT nadir data were combined with nine existing altimeters for assessing waves and monitoring their evolution during storms in the English Channel, near UK–French coasts. Validation against wave buoys and numerical models shows high accuracy, with correlations around 95%, decreasing to 85% when buoy track offsets > 50 km, producing the largest errors. The multi-source approach enables depth-resolved monitoring, with SWH mapping revealing ~20–25% modulation in the Channel and ~36% dissipation near the Seine Bay during storms. Spectral analysis of multi-source altimeter-derived merged observations improve time-sampling, resolving high-frequency variability from monthly to daily scales and capturing ~75% of storms. Most storm wave features along altimetry tracks are resolved, with CFOSAT mapping nearshore areas and SWOT capturing coastal zones, both achieving ~80% variance. This temporal and spatial monitoring would be further enhanced with SWOT’s 2D wide swath. This finding provides a complementary, comprehensive understanding of coastal waves and offers valuable input for data assimilation, to improve storm wave estimates in coastal basins. Full article

Hybrid AC/DC microgrids (HADCMGs), which integrate renewable energy sources and battery storage systems, often face significant stability challenges due to their inherently low inertia and highly variable power inputs. To address these issues, this paper proposes a novel, robust composite controller based on backstepping fast terminal sliding mode control (BFTSMC). This controller is further enhanced with a virtual capacitor to emulate synthetic inertia and with a fractional power-based reaching law, which ensures smooth and finite-time convergence. Moreover, the proposed control strategy ensures the effective coordination of power sharing between AC and DC sub-grids through bidirectional converters, thereby maintaining system stability during rapid fluctuations in load or generation. To achieve optimal control performance under diverse and dynamic operating conditions, the controller gains are adaptively tuned using the marine predators algorithm (MPA), a nature-inspired metaheuristic optimization technique. Furthermore, the stability of the closed-loop system is rigorously established through control Lyapunov function analysis. Extensive simulation results conducted in the MATLAB/Simulink environment demonstrate that the proposed controller significantly outperforms conventional methods by eliminating steady-state error, reducing the settling time by up to 93.9%, and minimizing overshoot and undershoot. In addition, real-time performance is validated via processor-in-the-loop (PIL) testing, thereby confirming the controller’s practical feasibility and effectiveness in enhancing the resilience and efficiency of HADCMG operations. Full article

(1) Background: Wetlands are ecologically significant ecosystems that support biodiversity and contribute to essential environmental functions such as water purification, carbon storage and flood regulation. However, these ecosystems face increasing pressures from land-use change and degradation, prompting the need for scalable and accurate classification methods to support conservation and policy efforts. In this research, our motivation was to test whether high-spatial-resolution PlanetScope imagery can be used with pixel-based machine learning to support the mapping and monitoring of wetlands at a national scale. (2) Methods: This study compared four machine learning classification models—Random Forest (RF), XGBoost (XGB), Histogram-Based Gradient Boosting (HGB) and a Multi-Layer Perceptron Classifier (MLPC)—to detect and map wetland areas across New Zealand. All models were trained using eight-band SuperDove satellite imagery from PlanetScope, with a spatial resolution of ~3 m, and ancillary geospatial datasets representing topography and soil drainage characteristics, each of which is available globally. (3) Results: All four machine learning models performed well in detecting wetlands from SuperDove imagery and environmental covariates, with varying strengths. The highest accuracy was achieved using all eight image bands alongside features created from supporting geospatial data. For binary wetland classification, the highest F1 scores were recorded by XGB (0.73) and RF/HGB (both 0.72) when including all covariates. MLPC also showed competitive performance (wetland F1 score of 0.71), despite its relatively lower spatial consistency. However, each model over-predicts total wetland area at a national level, an issue which was able to be reduced by increasing the classification probability threshold and spatial filtering. (4) Conclusions: The comparative analysis highlights the strengths and trade-offs of RF, XGB, HGB and MLPC models for wetland classification. While all four methods are viable, RF offers some key advantages, including ease of deployment and transferability, positioning it as a promising candidate for scalable, high-resolution wetland monitoring across diverse ecological settings. Further work is required for verification of small-scale wetlands (<~0.5 ha) and the addition of fine-spatial-scale covariates. 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

The Sylhet Division of Bangladesh, highly susceptible to monsoon flooding, requires effective flood risk management to reduce socio-economic losses. Flood frequency analysis is an essential aspect of flood risk management and plays a crucial role in designing hydraulic structures. This study applies regional flood frequency analysis (RFFA) using L-moments to identify homogeneous hydrological regions and estimate extreme flood quantiles. Records from 26 streamflow gauging stations were used, including streamflow data along with corresponding physiographic and climatic characteristic data, obtained from GIS analysis and ERA5 respectively. Most stations showed no significant monotonic trends, temporal correlations, or spatial dependence, supporting the assumptions of stationarity and independence necessary for reliable frequency analysis, which allowed the use of cluster analysis, discordancy measures, heterogeneity tests for regionalization, and goodness-of-fit tests to evaluate candidate distributions. The Generalized Logistic (GLO) distribution performed best, offering robust quantile estimates with narrow confidence intervals. Multiple Non-Linear Regression models, based on catchment area, elevation, and other parameters, reasonably predicted ungauged basin peak discharges (R² = 0.61–0.87; RMSE = 438–2726 m³/s; MAPE = 41–74%) at different return periods, although uncertainty was higher for extreme events. Four homogeneous regions were identified, showing significant differences in hydrological behavior, with two regions yielding stable estimates and two exhibiting greater extreme variability. Full article

This paper presents an end-to-end solution towards authenticated positioning using only Galileo E1B signal by utilizing the Open Service Navigation Message Authentication (OSNMA). One of the primary objectives of this work is to offer a complete OSNMA-based authenticated position solution by releasing FGI-GSRx-v2.1.0 (an open-source software-defined multi-constellation GNSS receiver) update. The idea is to bridge the gap between two open-source implementations by the Finnish Geospatial Research Institute (FGI): FGI-GSRx and FGI-OSNMA (an open-source Python software package). FGI-GSRx-v2.1.0 utilizes FGI-OSNMA as an OSNMA computation engine to generate the authentication events with the information of whether a tag is valid or not. FGI-GSRx computes the position authentication at the navigation layer with the Galileo E1B satellites that are OSNMA verified and have $C/N_0$ greater than 30 dB-Hz. OSNMA-based position authentication is shown through the findings from two real-world open sky use cases: (i) a clean nominal scenario and (ii) a spoofing scenario recorded during the Jammertest 2023 in Andøya, Norway. In the case of the spoofing scenario, the software receiver stops offering an authenticated position solution. A detailed comparison between the authenticated and non-authenticated position solutions also highlights the damage spoofing could cause to the end user in deviating the user’s position on a target spoofed location. Full article

As the threat of unstable braided river geomorphology to the resilience of local communities grows, a better understanding of the morphological changes in a river subject to climate is essential. However, little research has focused on the long-term planform change of the braided reaches and its response to hydrological changes. The reach around Majuli Island (Majuli Reach), the first and typical braided reach of the Brahmaputra River emerging from the gorge, experiences intense geomorphological change of the channels and loss of riparian area every year due to the seasonal hydrological variability. Therefore, focusing on the Majuli Reach, we quantitatively investigate changes in its planform morphology from 1990 to 2020 using remote sensing images from the Landsat dataset and analyze the influence of discharge in previous years on channel braiding. The study shows that the Majuli Reach is characterized by a high braiding degree with an average Modified Plan Form Index (MPFI) of 4.39, an average reach width of 5.58 km, and the development of densely migrating bars and active braided channels. Analysis shows a control point near Borboka Pathar with little morphological change, and the braided channel shows contrasting morphological changes in the braiding degree, bars, and main channel between the reach upstream and downstream of it. The area of the riparian zone of the Majuli Reach decreased by more than 50 km² during the study period due to migration of the main channel toward the island. The braiding degree of Majuli Reach is positively correlated with the discharge in previous years, with the delayed response time of the MPFI to discharge being just 3–4 years, indicating the unstable feature of the Majuli Reach with varied hydrology conditions. Full article

Heavy metal cations such as Ag⁺, Pb²⁺, and Hg²⁺ can accumulate in living organisms, posing severe risks to biological systems, including humans. Therefore, removing heavy metal cations from wastewater is crucial before discharging them to the environment. However, trace levels and high-capacity removal of the heavy metals remain a critical challenge. This work demonstrates the synthesis and characterization of [Mo₂S₁₂]²⁻ intercalated cobalt aluminum-layered double hydroxide, CoAl―Mo₂S₁₂―LDH (CoAl―Mo₂S₁₂), and its remarkable sorption properties for heavy metals. This material shows high efficiency for removing over 99.9% of Ag⁺, Cu²⁺, Hg²⁺, and Pb²⁺ from 10 ppm aqueous solutions with a distribution constant, K_d, as high as 10⁷ mL/g. The selectivity order for removing these ions, determined from the mixed ion state experiment, was Pb²⁺ < Cu²⁺ ≪ Hg²⁺ < Ag⁺. This study also suggests that CoAl―Mo₂S₁₂ is not selective for Ni²⁺, Cd²⁺, and Zn²⁺ cations. CoAl―Mo₂S₁₂ is an efficient sorbent for Ag⁺, Cu²⁺, Hg²⁺, and Pb²⁺ ions at pH~12, with the removal performance of both Ag⁺ and Hg²⁺ cations retaining > 99.7% across the pH range of ~2 to 12. Our study also shows that the CoAl―Mo₂S₁₂ is a highly competent silver cation adsorbent exhibiting removal capacity (q_m) as high as ~918 mg/g compared with the reported data. A detailed mechanistic analysis of the post-treated solid samples with Ag⁺, Hg²⁺, and Pb²⁺ reveals the formation of Ag₂S, HgS, and PbMoO₄, respectively, suggesting the precipitation reaction mechanism. Full article

Nanofluids have the proven capacity to significantly improve the thermal efficiency of a heat exchanging system due to the presence of conductive nanoparticles. The aim of this study is to simulate the forced convection on a non-Newtonian hybrid with a nanofluid (Al₂O₃-TiO₂-H₂O) in a hexagonal enclosure by the Galerkin finite element method (GFEM). The physical model is a hexagonal enclosure in two dimensions, containing a heated cylinder embedded at the center. The bottom, middle left, and right walls of the enclosure are all considered cold ($T_c$), while the top wall is considered to be moving, and the remaining middle, upper left, and right walls have the adiabatic condition. The Prandtl number ($Pr$ = $6.2$), Reynolds number ($Re$ = 50, 100, 300 and 500), power law index (n = $0.6$, $0.8$, $1.0$, $1.2$ and $1.4$), volume fractions of nanoparticles ($\varphi$ = $0.00$, $0.01$, $0.02$, $0.03$ and $0.04$), and Hartmann numbers ($Ha$ = 0, 10, 20 and 30) are considered in the model. The findings are explained in terms of sensitivity tests and statistical analysis for various $Re$ numbers, n, and $Ha$ numbers employing streamlines, isotherms, velocity profiles, and average Nusselt numbers. It is observed that the inclusion of $\varphi$ improves the convective heat transfer at the surging values of $Re$. However, if the augmenting heat transfer requires any control mechanism, integrating a non-zero $Ha$ number is found to stabilize the system for the purpose of thermal efficacy. Full article