Search Results (172)

(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

Background/Objectives: The co-formulation of active pharmaceutical ingredients (APIs) is a growing strategy in biopharmaceutical development, particularly when it comes to improving solubility and bioavailability. This study explores a co-precipitation method to prepare co-formulated crystals of griseofulvin (GF) and dexamethasone (DXM), utilizing nanostructured, functionalized polylactic glycolic acid (nfPLGA) as a solubility enhancer. Methods: An antisolvent precipitation technique was employed to incorporate nfPLGA at a 3% concentration into the co-formulated GF and DXM, referred to as DXM-GF-nfPLGA. The dissolution performance of this formulation was compared to that of the pure drugs and the co-precipitated DXM-GF without nfPLGA. Results: Several characterization techniques, including electron microscopy (SEM), RAMAN, FTIR, TGA, and XRD, were used to analyze the nfPLGA incorporation and the co-precipitated co-formulations. The inclusion of nfPLGA significantly enhanced the dissolution and initial dissolution rate of both GF and DXM in the DXM-GF-nfPLGA formulation, achieving a maximum dissolution of 100%, which was not attained by the pure drugs or the DXM-GF formulation. The incorporation of nfPLGA also reduced the amount of time taken to reach 50% (T₅₀) and 80% (T₈₀) dissolution. T₅₀ values decreased from 52 and 82 min (for pure DXM and GF) to 23 min for DXM-GF-nfPLGA, and the T₈₀ improved to 50 min for DXM-GF-nfPLGA, significantly outpacing the pure compounds. Furthermore, incorporating nfPLGA into the crystal structures greatly accelerated the dissolution rates, with initial rates reaching 650.92 µg/min for DXM-GF-nfPLGA compared to 540.60 µg/min for DXM-GF, while pure GF and DXM showed lower rates. Conclusions: This work demonstrates that nfPLGA incorporation enhances dissolution performance by forming water channels within the API crystal via hydrogen-bonding interactions. This innovative nfPLGA incorporation method holds promise for developing hydrophobic co-formulations with faster solubility and dissolution rates. Full article

Brain tumors (BT) are also known as intracranial diseases, which occur due to uncontrolled cell growth in the brain. Detecting and classifying the brain tumors at the initial stage is crucial to saving the patient’s life. A radiologist uses MRI scans to identify and classify the various types of BT using a manual approach. However, it is inaccurate and time-consuming because of the many images. In machine learning, convolutional neural networks (CNN) are one significant algorithm that can extract features automatically with high accuracy. The drawback of this algorithm is that it can extract features without knowing micro and macro features. The proposed architecture of parallel CNN (PCNN) can extract the features by knowing the micro and macro features from two separate window sizes and, at first, augmenting the normalized data using geometric transformation to enhance the number of images. Then, micro and macro features are extracted using the proposed architecture, PCNN, alongside batch normalization to reduce the overfitting problem. Finally, three kinds of tumors—glioma, meningioma, pituitary—and a no tumor condition are classified using various classifiers like Softmax, KNN, and SVM. The proposed PCNN-SVM obtained the best accuracy of 96.1% with the special features compared with the other pertained model. Full article

The growing world population necessitates more efficient food production, particularly in agriculture. Traditional irrigation techniques usually result in overwatering or underwatering, which wastes energy and water and reduces agricultural productivity. Smart agriculture optimizes food production, resource management, and labor. This study introduces an intelligent irrigation and monitoring system that uses the Internet of Things (IoT) to automate water pump management and monitor sunlight, temperature, and humidity levels without human interaction. The system’s hardware components include a soil moisture sensor, a sunlight sensor, temperature and humidity (DHT11) sensors, an ESP32 microcontroller, and a pump motor. The sensors are in charge of gathering the information that the ESP32 microcontroller needs in order to properly operate the pump motor. To operate and monitor data from the sensors remotely, the ESP32 is also integrated with the well-known Ubidots server via a wireless sensor network. Initially, sensors such as the DHT11, soil moisture sensors, and sunlight level sensors collect data from the field and send it to the ESP32 microcontroller. The microcontroller then compares the received data to the previously stored data. If the values are greater than the threshold, the associated devices turn on and update the sensor value and pump motor condition to the Ubidots server. Full article

Precision agriculture (PA), leveraging wireless sensor networks (WSNs) for efficient data collection, is set to revolutionize intelligent farming. However, challenges such as energy efficiency, data collection time, data quality, redundant data transmission, latency, and limited WSN lifespan persist. We propose a novel edge computing-driven WSN framework (ECDWF) for PA, designed to enhance network longevity by optimizing data transmission to the base station (BS) and enhancing energy dissipation by abolishing data redundancy through aggregation. This framework involves a two-step data aggregation process: within clusters, where the cluster head (CH) aggregates data, and at a central network point, where an edge computing-enabled gateway node (GN) performs further aggregation. Our MATLAB simulation evaluates the proposed ECDWF against the Low-energy adaptive clustering hierarchy (LEACH) protocol and two classic sensing strategies, Effective Node Sensing (ENS) and Periodically Sensing with All Nodes (PSAN). Results reveal significant energy efficiency, quality of data (QoD) transmission, and network lifespan improvements. Due to reduced long-range transmissions, nodes in our scheme dissipate energy over 2500 rounds, compared to 1000 rounds in LEACH. Our method sends data packets to the CH and base station (BS) for 2500 rounds at 3.6 × 10¹⁰ bits, while LEACH stops at 1000 rounds at 2 × 10¹⁰ bits data transmission rate. Our approach improves network stability and lifetime, with the first node dying at 2070 rounds, versus 999 rounds in LEACH, and the last node remaining functional until 2476 rounds compared to 1000 rounds in LEACH. Our proposed system, ECDWF, outperforms PSAN and ENS in latency, data collection time (DCT), and energy usage. At 50 Mbps, the communication latency of ECDWF is just 8 s, compared to 24 s for ENS and 45 s for PSAN. ECDWF maintains a QoD of 100% across various valid sensor and node counts, surpassing ENS and PSAN. Our contribution integrates edge computing with WSN for PA, enhancing energy utilization and data aggregation. This approach effectively tackles data redundancy, transmission efficiency, and network longevity, providing a robust solution for precision agriculture. Full article

Trichophyton (T.) mentagrophytes ITS genotype VIII, also known as Trichophyton indotineae, is a new species of the T. mentagrophytes/T. interdigitale complex and its first records, albeit under a different species name, are from the Indian subcontinent, Middle Eastern Asia, and West Asia. T. mentagrophytes genotype VIII (T. indotineae) has spread globally and has now been documented in over 30 countries. The aim of this study was to investigate the occurrence and proportion of terbinafine- and itraconazole-resistant isolates of T. mentagrophytes ITS genotype VIII (T. indotineae) in Bangladesh. This was part of an official collaborative project between IADVL (Indian Association of Dermatologists, Venereologists, and Leprologists) and Bangabandhu Sheikh Mujib Medical University (BSMMU), Bangladesh. Over a period of 6 months, ninety-nine patients of chronic recalcitrant tinea corporis were recruited from BSMMU hospital. Species identification was performed by fungal culture and morphological observation of the upper and lower surfaces of fungal colonies, as well as by using fluorescent microscopy. In addition, a PCR (polymerase chain reaction)-ELISA was performed to group the patients into those with the T. mentagrophytes/T. interdigitale complex. The internal transcribed spacer (ITS) gene was sequenced. Samples were tested for resistance to terbinafine and itraconazole by mutational analyses of the squalene epoxidase (SQLE) and the ergosterol 11B (ERG11B) genes. A total of 79/99 samples showed a positive culture. In 76 of these isolates, T. mentagrophytes ITS genotype VIII (T. indotineae) could be reliably identified both by culture and molecular testing. Resistance testing revealed terbinafine resistance in 49 and itraconazole resistance in 21 patients. Among these, 11 patients were resistant to both the antifungal agents. Mutations L393S, L393F, F397L, and F397I of the SQLE gene were associated with terbinafine resistance. Resistance to itraconazole could not be explained by mutations in the ERG11B gene. Infections with T. mentagrophytes ITS genotype VIII (T. indotineae) have become a public health issue with potentially global ramifications. About 62% of samples from Bangladesh showed resistance to terbinafine, making oral itraconazole the most effective drug currently available, although resistance to itraconazole and both terbinafine and itraconazole also exists. Full article

This study investigates the process of using multi-walled carbon nanotube (MWCNT) coatings to enhance lamp heating temperatures for solar thermal absorption applications. The primary focus is studying the effects of the self-organized honeycomb structures of CNTs formed on silicon substrates on different cell area ratios (CARs). The drop-casting process was used to develop honeycomb-structured MWCNT-coated absorbers with varying CAR values ranging from ~60% to 17%. The optical properties were investigated within the visible (400–800 nm) and near-infrared (934–1651 nm) wavelength ranges. Although fully coated MWCNT absorbers showed the lowest reflectance, honeycomb structures with a ~17% CAR achieved high-temperature absorption. These structures maintained 8.4% reflectance at 550 nm, but their infrared reflection dramatically increased to 80.5% at 1321 nm. The solar thermal performance was assessed throughout a range of irradiance intensities, from 0.04 W/cm² to 0.39 W/cm². The honeycomb structure with a ~17% CAR value consistently performed better than the other structures by reaching the highest absorption temperatures (ranging from 52.5 °C to 285.5 °C) across all measured intensities. A direct correlation was observed between the reflection ratio (visible: 550 nm/infrared: 1321 nm) and the temperature absorption efficiency, where lower reflection ratios were associated with higher temperature absorption. This study highlights the significant potential for the large-scale production of cost-effective solar thermal absorbers through the application of optimized honeycomb-structured absorbers coated with MWCNTs. These contributions enhance solar energy efficiency for applications in water heating and purification, thereby promoting sustainable development. Full article

Sandbars are an integral part of the alluvial river’s geophysical system due to these rivers’ wide sediment availability and varied transport capacity. The sandbars’ evolution and translation considerably influence the stability of the riverbank. However, while designing the riverbank protection structures (RBPS), the impact of such sandbars is often overlooked, as the evolution of such bars is quite uncertain in terms of location, amplitude, and translation. This study evaluates the localized impact of sandbars on bank protection structures in two types of alluvial rivers: meandering (Ganges) and braided (Brahmaputra-Jamuna), utilizing time series satellite images, hydraulic characteristics, and numerical modeling. We found that sandbar development initiates width adjustment in both meandering and braided rivers when the ratio of width to depth surpasses 90. In the case of meandering rivers, riverbank erosion mostly occurs as a result of the presence of alternate bars or point bars. Sandbars in a meandering river (Ganges) can lead to an approximate 18% increase in flow depth. The depth-averaged velocity is anticipated to rise by approximately 29%, and the tractive force may increase by a factor of 1.6. On the other hand, the braided river (the Brahmaputra-Jamuna) underwent significant bank erosion due to the presence of both free unit and hybrid types of bars. In such rivers, the depth of the flow may experience a notable increase of 18%, while the depth-averaged velocity undergoes an approximate increase of 50%, and the tractive force has the potential to grow by a factor of 5.3. Consequently, we recommend allowing the natural evolution of sandbars while preserving the riverbank (where needed only) through RBPS, considering these additional loads. Full article

Climatic change and environmental degradation have become a worldwide discourse. Green innovation is commonly viewed as a means of lowering environmental pollution in the era of climate change. Considering this, the primary purpose of this study is to investigate the effects of environmental technology (ET) on CO₂ emissions by controlling Saudi Arabia’s ICT use, energy use, energy intensity, and financial development. It uses a quantile-based multiple-threshold “nonlinear autoregressive distributed lag (NARDL)” estimation utilizing data from 1990 to 2020. It also conducts the ARDL and NARDL estimation techniques simultaneously for comparative outcomes. The Toda–Yamamoto (T-Y) causality assessment also crosschecks the primary multiple-threshold NARDL estimates. The outcomes reveal that ET promotes environmental pollution due to its low scale compared to the Kingdom’s technological base. ICT improves environmental quality, and energy consumption deteriorates it. All three estimation techniques confirm these findings. The multiple-threshold NARDL estimation appears robust and reveals damaging impacts of energy intensity and financial development on emissions. The T-Y causality assessment also authenticates the primary estimation outcomes. The outcomes have important implications for policymakers to focus on enhancing patents for ET, raising ICT diffusion, reducing energy intensity through generating more renewable energies, expanding financial support for ICT and green investments, and ensuring a sustainable environment. Full article