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Abstract Due to climate and human activity, water supply in Jharkhand, India, fluctuates. This study measured AWS and AWA in Jharkhand, India. MODIS NDVI and Terra Climate data processed by Google Earth Engine (GEE) are used in the analysis. Based on mean annual values for NDWI, run-off, precipitation, and ET, AWS and AWA are categorised. Results show significant regional differences in ET, precipitation, runoff, AWS, and AWA in Jharkhand. Purbi Singhbhum has far higher ET rates than Garhwa, Palamu, and Chatra. High AWS levels in Sahibganj, Godda, Pakur, Garhwa, and Kodarma indicate serious water scarcity. Therefore, these locations need focused water management approaches. Different districts including Giridih, Chatra, Jamtara, Latehar, Simdega, and Hazaribagh have different levels of AWS, highlighting the need for individualised treatments. The AWA index helps resource managers make educated decisions by detecting water-scarce places. Climate variability affects temperatures and precipitation, worsening AWS in high-AWS areas. Water scarcity mitigation requires local infrastructure development and water management measures. To verify remote sensing data accuracy, future study should improve data resolution and include in-situ measurements. These findings can help policymakers and stakeholders in Jharkhand create efficient water resource management strategies to reduce water stress and assure water supply.

Abstract Rajasthan faces significant environmental challenges, including the pressing need for effective climate change mitigation strategies. Recognizing afforestation as a vital tool in this endeavor, this study leverages the latest in remote sensing and geospatial analysis to map out the state's afforestation potential. The goal is to assess land suitability across Rajasthan for afforestation projects and evaluate the potential carbon sequestration capabilities of different tree species. This aims to inform sustainable land management strategies that can contribute to climate change mitigation. By integrating satellite imagery, land cover data, terrain analysis, and vegetation indices, the study evaluates environmental factors such as slope, soil moisture, and vegetation health to identify areas optimal for tree planting. A carbon sequestration model was also developed to estimate sequestration rates based on species-specific growth patterns. Findings indicate that approximately 40% of Rajasthan is suitable for afforestation, with potential carbon sequestration rates ranging from 2 to 8 tons per hectare per year. Species such as Azadirachta indica (Neem) and Prosopis cineraria (Khejri) are identified as particularly effective for short-term sequestration, while Phyllanthus emblica (Amla) and Ziziphus mauritiana (Ber) are better suited for long-term carbon capture. This research highlights the importance of targeted afforestation using suitable tree species as a nature-based solution (NBS) for climate change mitigation in Rajasthan. The study offers a data-driven approach to enhancing ecosystem resilience and supports decision-making for climate adaptation in arid regions, highlighting the significant potential of Rajasthan's land for carbon sequestration through afforestation.

This research investigates the potential of utilizing recycled concrete aggregate (RCA) and recycled sand (RS), derived from crushed concrete cubes, as sustainable alternatives in construction materials. The study comprehensively evaluates the properties of RCA and RS, focusing on workability, impact resistance, abrasion resistance, and compressive strength to determine their viability as substitute construction materials. A notable finding is RS’s enhanced fire and heat resistance when used as a fine aggregate in mortar blends, mixed with cement and Sinicon PP in a 3:1 ratio. The experimental analysis included thorough assessments of uniformity, durability, and curing time, alongside Scanning Electron Microscopy (SEM) for structural examination. Results show that RCA has an Aggregate Impact Value (AIV) of 5.76% and a Los Angeles Abrasion Value (LAA) of 21.78%, demonstrating excellent strength of the recycled aggregates. The mortar mix was also prepared using recycled sand, cement, and Sinicon PP, and its stability was confirmed through soundness tests, which resulted in a 0.53 mm expansion and a satisfactory consistency level of 44%. Ultrasonic pulse velocity (UPV) tests also indicated high-quality concrete formation using RCA and RS. SEM imaging corroborated this by revealing a bond between the cement paste and the aggregates. Incorporating RS and RCA in concrete mixtures impressively yielded a compressive strength of 26.22 N/mm2 in M20-grade concrete. The study concludes that using RCA and RS waste materials in the construction sector underlines that sustainable practices can be integrated without compromising material quality. This approach aligns with sustainable development goals and fosters a more environmentally friendly construction industry.

This research focuses on the urban expansion occurring in the Kamrup Metropolitan District—an area experiencing significant urbanization—with the aim of understanding its patterns and projecting future growth. The research covers the period from 2000 to 2022 and projects growth up to 2052, providing insights for sustainable urban planning. The study utilizes the maximum likelihood method for land use/land cover (LULC) delineation and the Shannon entropy technique for assessing urban sprawl. Additionally, it integrates the cellular automata (CA)-Markov model and the analytical hierarchy process (AHP) for future projections. The results indicate a considerable shift from non-built-up to built-up areas, with the proportion of built-up areas expected to reach 36.2% by 2032 and 40.54% by 2052. These findings emphasize the importance of strategic urban management and sustainable planning. The study recommends adaptive urban planning strategies and highlights the value of integrating the CA Markov model and AHP for policymakers and urban planners. This can contribute to the discourse on sustainable urban development and informed decision-making.

Groundwater is crucial for agriculture, domestic use, and industry. This study represents groundwater potential zones in the Amravati district, Maharashtra, India, using the analytical hierarchy process (AHP) and multi-influencing factor (MIF) techniques. These techniques are employed for a detailed spatial analysis, which is essential for sustainable groundwater management. The study integrates data layers including lithology, geomorphology, land use/land cover, drainage density, lineament density, rainfall, soil, elevation, and slope to evaluate the groundwater potential. The resulting groundwater potential map classifies the area into five categories: poor, fair, moderate, good, and excellent based on groundwater availability. The study reveals that 9% of the area has poor groundwater potential, 37% fair, 28% moderate, 13% good, and 13% excellent. This map is instrumental for stakeholders and policymakers, as it aids in resource allocation and the formulation of sustainable groundwater management strategies. Through the application of AHP and MIF techniques, this study effectively maps the groundwater potential, providing an essential tool for evidence-based decision-making for water resource management in the Amravati district. HIGHLIGHTS The analytical hierarchy process and multi-influencing factor techniques are used for detailed groundwater mapping.; Factors like lithology and rainfall are integrated for comprehensive evaluation.; Key spatial data are provided for sustainable water management in the Amravati district.; High-potential areas are identified for efficient resource allocation.; The need for targeted conservation is highlighted in low-potential areas.;

The mapping and monitoring of different types of Glacial lakes through the Geospatial techniques is vital to show the impact of climate changes on the Glacier and alleviate hazards that result from the bursting of Glacial Lakes and cause catastrophic consequences to human lives. The main goal of the present work was to map and analyze different types of glacial lakes in Bhutan during the years 1990, 2000, and 2017. Several sets of satellite images, Landsat-TM for 1990, Landsat ETM + for 2000, and Landsat 8-OLI satellite image for 2017, were used to estimate the changes in the glacial lakes and the inventory study. Several glacial lakes, i.e., moraine-dammed lake, supra glacial lake, lateral moraine lake, erosional lake, medial moraine lake, and end moraine lake, were mapped within these periods. It was found that there was a rapid increase in glacial lakes from 1990 to 2017. The number of glacial lakes in 1990 was increased from 213 to 436 in 2017. It was also observed that the spatial dimensions of some of the glacial lakes increased. The study revealed five end moraine lakes, 40 lateral moraine lakes, 50 supra glacial lakes, 239 erosional lakes, and 15 other moraines dammed lakes in 2017.

The construction of large-scale infrastructure projects like dams can substantially alter local hydrological systems, prompting concerns about their impacts on river water levels and discharge. This study was undertaken in response to the compelling need to investigate and analyze the potential implications of water scarcity, particularly within the framework of the Bisalpur Dam located on the Banas River. This study uses non-parametric trend analysis tests - Mann-Kendall (MK) and Modified Mann-Kendall (MMK) for river discharge and water level trend analysis, and Sen's Slope and Pettit's test for detecting changes or trends. Significant shifts in these parameters were identified post-construction. From 1960 to 2011, the Dam's construction led to a marked decrease in peak discharge (MK/MMK statistics Zc = −3.24; Pc = 0.001), with a Sen's slope of −21.51 MCM/year. Before the Dam's construction, the peak discharge reached a maximum of 7680 MCM in 1981–82. The gauge station's water level significantly decreased (MK/MMK statistics Zc = −3.46; Pc = 0.001) at an annual rate of −0.035 m/year. This level, peaking at 201.59 m in 1981–82, declined from an average of 196.04 m–194.5 m post-construction. The findings underscore the extensive impact of dam construction on local hydrological parameters. These results hold significant implications for future infrastructure projects, emphasizing the critical need for comprehensive environmental impact assessments to be conducted before project implementation. This understanding can aid in designing interventions with reduced environmental impacts, leading to more sustainable infrastructure development.

This study conducts a comprehensive analysis of hydrological patterns in the Kalisindh and Parbati Rivers, highlighting the Eastern Rajasthan Canal Project (ERCP) as pivotal for bolstering regional water security. Employing an array of data sources, this research utilizes Polynomial Regression and neural network forecasting to dissect flow patterns, identifying significant virgin flow peaks in the mid-1980s and early 2000s for the Kalisindh River, and a notable peak in 2006–2007 for the Parbati River. Analysis reveals that the specific discharge rate of the Parbati River is diminishing at twice the rate of the Kalisindh River, with annual decreases of approximately 0.0038 cumecs/km2 for Parbati, compared to 0.0019 cumecs/km2 for Kalisindh. Furthermore, runoff volumes indicate that the Parbati River, specifically at the Khatoli Gauge & Discharge (G&D) site, experiences significantly higher runoff—28,137.912 million cubic meters (MCM)—in contrast to 15,795.094 MCM for the Kalisindh River at the Barod G&D site. The findings accentuate the necessity for science-based water management strategies to effectively combat water scarcity and climate change impacts. The ERCP emerges as a crucial initiative for sustainable management of the Kalisindh and Parbati Rivers in Rajasthan, underscoring its potential to serve as a model for sustainable water and river basin management.

India is confronted with the substantial issue of plastic debris due to the absence of an efficient waste management infrastructure. Recycled plastic has the potential to enhance various construction materials, such as roofing tiles, paving blocks, and insulation. The aforementioned materials possess notable attributes such as high strength, low weight, and exceptional resistance to extreme temperatures and humidity. The objective of this study is to ascertain feasible alternatives for manufacturing road paver blocks utilizing plastic waste (Polyethene terephthalate (PET)), and M-sand (stone dust). Three variations of a discarded plastic cube measuring 150 mm × 150 mm × 150 mm were prepared for the experiment. The experimental findings indicated that a ratio of 1:4 was determined to be the most effective in achieving the desired level of compressive strength. I-section road and brick paver blocks were produced as an alternative to the traditional concrete ones. Compressive strength tests were performed on I-sections and brick paver blocks, revealing that the 1:4 mix ratio exhibited the highest average compressive strength for both materials. The findings indicated that including plastic waste positively impacted the compressive strength of the I-sections and brick paver blocks. Additionally, the quality grading of these materials was evaluated using an ultrasonic pulse velocity test. The ultrasonic pulse velocity test results demonstrated a high-quality grading for the I-sections and brick paver blocks. Scanning electron microscopy (SEM) tests assessed the microstructural behavior and performance. The results of this study demonstrate that incorporating plastic waste in combination with M-sand can effectively improve the mechanical characteristics of composite materials, rendering them viable for use in construction-related purposes.

The NH 58 area in India has been experiencing an increase in landslide occurrences, posing significant threats to local communities, infrastructure, and the environment. The growing need to identify areas prone to landslides for effective disaster risk management, land use planning, and infrastructure development has led to the increased adoption of advanced geospatial technologies and statistical methods. In this context, this research article presents an in-depth analysis aimed at developing a landslide susceptibility zonation (LSZ) map for the NH 58 area using remote sensing, GIS, and logistic regression analysis. The study incorporates multiple geo-environmental factors for analysis, such as slope aspect, curvature, drainage density, elevation, fault distance, flow accumulation, geology, geomorphology, land use land cover (LULC), road distance, and slope angle. Utilizing 50% of the landslide inventory data, the logistic regression model was trained to determine correlations between causal factors and landslide occurrences. The logistic regression model was then employed to calculate landslide probabilities for each mapping unit within the NH 58 area, which were subsequently classified into relative susceptibility zones using a statistical class break technique. The model’s accuracy was verified through ROC curve analysis, resulting in a 92% accuracy rate. The LSZ map highlights areas near road cut slopes as highly susceptible to landslides, providing crucial information for land use planning and management to reduce landslide risk in the NH 58 area. The study’s findings are beneficial for policymakers, planners, and other stakeholders involved in regional disaster risk management. This research offers a comprehensive analysis of landslide-influencing factors in the NH 58 area and introduces an LSZ map as a valuable tool for managing and mitigating landslide risks. The map also serves as a critical reference for future research and contributes to the broader understanding of landslide susceptibility in the region.

The main aim of this study is to comprehensively analyze the dynamics of land use and land cover (LULC) changes in the Bathinda region of Punjab, India, encompassing historical, current, and future trends. To forecast future LULC, the Cellular Automaton–Markov Chain (CA) based on artificial neural network (ANN) concepts was used using cartographic variables such as environmental, economic, and cultural. For segmenting LULC, the study used a combination of ML models, such as support vector machine (SVM) and Maximum Likelihood Classifier (MLC). The study is empirical in nature, and it employs quantitative analyses to shed light on LULC variations through time. The result indicates that the barren land is expected to shrink from 55.2 km2 in 1990 to 5.6 km2 in 2050, signifying better land management or increasing human activity. Vegetative expanses, on the other hand, are expected to rise from 81.3 km2 in 1990 to 205.6 km2 in 2050, reflecting a balance between urbanization and ecological conservation. Agricultural fields are expected to increase from 2597.4 km2 in 1990 to 2859.6 km2 in 2020 before stabilizing at 2898.4 km2 in 2050. Water landscapes are expected to shrink from 13.4 km2 in 1990 to 5.6 km2 in 2050, providing possible issues for water resources. Wetland regions are expected to decrease, thus complicating irrigation and groundwater reservoir sustainability. These findings are confirmed by strong statistical indices, with this study’s high kappa coefficients of Kno (0.97), Kstandard (0.95), and Klocation (0.97) indicating a reasonable level of accuracy in CA prediction. From the result of the F1 score, a significant issue was found in MLC for segmenting vegetation, and the issue was resolved in SVM classification. The findings of this study can be used to inform land use policy and plans for sustainable development in the region and beyond.

Because textile industries are intensely water-consuming and generate a huge quantity of wastewater, the present study examines the scope of using solar thermal technology to treat wastewater from textile industries. A hybrid technology, comprising a compound parabolic concentrator-based solar thermal system in conjunction with a Membrane Distillation (MD) system, is experimented with for wastewater treatment in textile industries. The MD system requires a water temperature of around 90 °C for efficient functioning. The advanced MD technology using waste heat combined with solar heat to meet the system’s thermal load is technologically evaluated for an experimental textile industry in India. Moreover, the present study critically analyses the techno economics of the proposed hybrid technology. A detailed financial analysis has revealed that, besides technological superiority, the recommended technology is also financially rewarding for wastewater treatment in the textile industry. To cope with the delayed payback period, financial incentives are recommended so that the system becomes a lucrative technological option.

This study delves into the patterns of urban expansion in Kabul, using Landsat and Sentinel satellite imagery as primary tools for analysis. We classified land use and land cover (LULC) into five distinct categories: water bodies, vegetation, barren land, barren rocky terrain, and buildings. The necessary data processing and analysis was conducted using ERDAS Imagine v.2015 and ArcGIS 10.8 software. Our main objective was to scrutinize changes in LULC across five discrete decades. Additionally, we traced the long-term evolution of built-up areas in Kabul from 1973 to 2020. The classified satellite images revealed significant changes across all categories. For instance, the area of built-up land reduced from 29.91% in 2013 to 23.84% in 2020, while barren land saw a decrease from 33.3% to 28.4% over the same period. Conversely, the proportion of barren rocky terrain exhibited an increase from 22.89% in 2013 to 29.97% in 2020. Minor yet notable shifts were observed in the categories of water bodies and vegetated land use. The percentage of water bodies shrank from 2.51% in 2003 to 1.30% in 2013, and the extent of vegetated land use showed a decline from 13.61% in 2003 to 12.6% in 2013. Our study unveiled evolving land use patterns over time, with specific periods recording an increase in barren land and a slight rise in vegetated areas. These findings underscored the dynamic transformation of Kabul’s urban landscape over the years, with significant implications for urban planning and sustainability.

This study investigated the historical climate data and future projections under the SSP5-8.5 scenario for Jammu, Kashmir (J&K), and its adjoining regions in India. Agriculture is a critical economic pillar of this region, making it highly vulnerable to climate change. This study focused on temperature and precipitation trends. Statistical analysis and modeling methods, including cloud computing, were employed to predict changes and assess their impact on agricultural productivity and water resources. The results indicated that by 2100, the mean maximum and minimum temperatures are projected to increase by approximately 2.90 °C and 2.86 °C, respectively. Precipitation variability is expected to rise, with a mean increase of 2.64 × 10−6 mm per day. These changes have significant consequences for crop yield, water stress, and ecosystem dynamics. An analysis of Gross Primary Productivity (GPP) as a proxy for agricultural productivity using linear regression revealed a concerning trend. Although the total GPP of the study area remained stable over time, it declined by −570 g yr−1 in 2010, coinciding with a 1 °C temperature rise. Projections based on the expected 3 °C temperature increase by 2100 suggest a total GPP loss of −2500 g yr−1. These findings highlight the urgent need for proactive adaptation measures, including sustainable agricultural practices, improved water management, and enhanced socioeconomic infrastructure, to mitigate the impact of climate change and ensure long-term resilience and food security in the region.

This study employs advanced geospatial analytical techniques to evaluate the vulnerability of Nigeria’s coastal states and their constituent local government areas to flood hazards, which represent a critical and escalating risk within the coastal hazard paradigm intensified by climate change phenomena. The study’s objective is to utilize geospatial data to delineate and quantify the intensity and distribution of flood susceptibility, thus establishing a foundational framework for developing comprehensive disaster management strategies in response to the challenges posed by climate variability. The research uses satellite imagery and geographic information system (GIS)-based hydrological modeling to delineate regions susceptible to flooding, synthesizing topographical and hydrological data to stratify areas into discrete flood susceptibility categories. The findings indicate that the Delta coastal State of Nigeria contains extensive medium to high-risk flood zones spanning 8304.57 km2. While the Bayelsa coastal State of Nigeria presents critical areas at high to very high flood risk, encompassing 5506.61 km2 at high risk and 1826.88 km2 at very high risk, this highlights the urgent necessity for immediate and strategic mitigation measures. This research highlights the critical importance of geospatial technology in shaping disaster management and enhancing community resilience against increasing flood frequencies. As Nigeria’s coastal regions face escalating flood susceptibility, advanced geospatial methods are vital for assessing and mitigating these climate-induced threats, contributing to climate-resilient planning and aligning with Sustainable Development Goal 13: Climate Action. The study’s geospatial approach delivers precise flood risk evaluations and guides targeted mitigation efforts, marking significant progress in managing coastal hazards in a changing climate.

Amid global concerns regarding climate change and urbanization, understanding the interplay between land use/land cover (LULC) changes, the urban heat island (UHI) effect, and land surface temperatures (LST) is paramount. This study provides an in-depth exploration of these relationships in the context of the Kamrup Metropolitan District, Northeast India, over a period of 22 years (2000–2022) and forecasts the potential implications up to 2032. Employing a high-accuracy supervised machine learning algorithm for LULC analysis, significant transformations are revealed, including the considerable growth in urban built-up areas and the corresponding decline in cultivated land. Concurrently, a progressive rise in LST is observed, underlining the escalating UHI effect. This association is further substantiated through correlation studies involving the normalized difference built-up index (NDBI) and the normalized difference vegetation index (NDVI). The study further leverages the cellular automata–artificial neural network (CA-ANN) model to project the potential scenario in 2032, indicating a predicted intensification in LST, especially in regions undergoing rapid urban expansion. The findings underscore the environmental implications of unchecked urban growth, such as rising temperatures and the intensification of UHI effects. Consequently, this research stresses the critical need for sustainable land management and urban planning strategies, as well as proactive measures to mitigate adverse environmental changes. The results serve as a vital resource for policymakers, urban planners, and environmental scientists working towards harmonizing urban growth with environmental sustainability in the face of escalating global climate change.

Protecting groundwater from contamination is today’s most current environmental protection topic. What can man do in his environment to reduce the harmful impact of contamination on the environment, and thus the immediate effect on groundwater? Agricultural production is an ongoing source of groundwater contamination due to the increasingly frequent use of nitrates in fertilizers, which are washed out from the soil into groundwater due to precipitation. This paper investigates three wellfields in the north of the Republic of Croatia near the town of Varaždin. With the application of the RAPS method, the dependence of nitrate concentration in groundwater on the amount of precipitation was established. The analysis results show the connection of the observed parameters, especially in the upper aquifer layer. In this layer, the coefficients of correlation are greater than 0.80 at all locations, which shows a strong positive connection between the parameters. In the lower aquifer, the values of the coefficients of correlation are lower, and the results mostly indicate a weak correlation. The obtained results will serve as a starting point for future studies, which will aim to precisely determine the factors that influence groundwater quality in the observed area.

Forests play a vital role in maintaining ecological balance and provide numerous benefits. The monitoring and managing of large-scale forest plantations can be challenging and expensive. In recent years, advancements in remote sensing technologies, such as lightweight drones and object-oriented image analysis, have opened up new possibilities for efficient and accurate forest plantation monitoring. This study aimed to explore the utility of lightweight drones as a cost-effective and accurate method for mapping plantation characteristics in two 50 ha forest plots in the Nayla Range, Jaipur. By combining aerial photographs collected by the drone with photogrammetry and limited ground survey data, as well as topography and edaphic variables, this study examined the relative contribution of drone-derived plantation canopy information. The results demonstrate the immense potential of lightweight drones and object-oriented image analysis in providing valuable insights for optimizing silvicultural operations and planting trees in complex forest environments.

This study employed an advanced geospatial methodology using the Google Earth Engine (GEE) platform to assess soil erosion in the Satluj Watershed thoroughly. To achieve this, the Revised Universal Soil Loss Equation (RUSLE) model was integrated into the study, which was revealed through several analytical tiers, each with a unique function. The study commenced with estimating the R factor, which was carried out using annual precipitation data from the Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS). The erodibility of the soil, which the K factor describes, was then calculated using the USDA soil texture classifications taken from the Open Land Map. The third layer emphasizes the LS factor, which analyzes slope data and how they affect soil erosion rates, using digital elevation models. To understand the impact of vegetation on soil conservation, the fourth layer presents the C factor, which evaluates changes in land cover, and the Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 data. The P factor incorporates MODIS data to assess the types of land cover and slope conditions. Combining these layers with the RUSLE model produces a thorough soil loss map, revealing different levels of soil erosion throughout the Satluj Watershed. The preliminary findings indicate that 3.3% of the watershed had slight soil loss, 0.2% had moderate loss, and 1.2% had high soil erosion rates. And 92% had severe rates of soil erosion. After a thorough investigation, the detected regions were divided into risk classifications, providing vital information for the watershed’s land management and conservation plans. The mean soil loss throughout the watershed was determined to be 10,740 tons/ha/year. This novel method creates a strong foundation for evaluating soil erosion, while also highlighting the value of the cloud-based geospatial analysis and the RUSLE model in comprehending intricate environmental processes.

Land Surface Temperature refers back to the calculation of ground temperature that is made with extraordinary factors. Due to changes in the land surface temperature of the area, the climate and vegetation are also affected. The Normalized Difference Vegetation Index (NDVI) serves as an indicator of vegetation abundance to estimate the land surface temperature (LST) vegetation relationship. Landsat 7 ETM+ and Landsat 8 OLI TIRS images for four years (2005, 2010, 2015, and 2020) are used to study the LST changes in the research area (Jammu & Kashmir). Correlations between NDVI and LST are calculated. Through the study, it is clear that the Normalized Difference Vegetation Index plays a major role in the calculation of land surface temperature. In this study, the change in the higher temperature noted in the year 2005 was 200C, and in the year 2020, the highest temperature was 250C. If we talk about the lower temperature, the change is 10C. Both Jammu and Kashmir have semi-deciduous vegetation that includes subtropical pine forests.

In this paper, an extraction and characterization of nano fibrils has been performed to understand the behavior of cotton fibers. Basically, fibers are used to form some light weight composite materials and they provide strength to matrix, but a natural fiber contains number of bio-properties which are not necessary to build up a composite material. So, here a quality method is required to extract those properties from it. Many researches had been performed over the extraction method of unnecessary properties from natural fibers. Cellulose is the only property of natural fibers which helps to increase the tensile strength of natural fibers. To extract the cellulose from the cotton, natural fibers undergo some chemical treatments such as alkali treatment, bleaching, and acidic treatment. After the extraction of cellulose, fiber undergoes various testing for the morphological and thermal behavior of extracted fibers. These tested fibers were used to make composite. Validation and designing of composite had been done by using ANSYS. After development of composite using epoxy as a matrix, these composites went under some non-destructive and destructive testing.

Type 1 diabetes (T1D) is a chronic autoimmune disease characterized by the destruction of pancreatic β-cells, leading to insulin deficiency. Insulin replacement therapy is the current standard of care for T1D, but it has significant limitations. However, stem cell-based replacement therapy has the potential to restore β-cell function and achieve glycaemic control eradicating the necessity for drugs or injecting insulin externally. While significant progress has been made in preclinical studies, the clinical translation of stem cell therapy for T1D is still in its early stages. In continuation, further research is essentially required to determine the safety and efficacy of stem cell therapies and to develop strategies to prevent immune rejection of stem cell-derived β-cells. The current review highlights the current state of cellular therapies for T1D including, different types of stem cell therapies, gene therapy, immunotherapy, artificial pancreas, and cell encapsulation being investigated, and their potential for clinical translation.

Flood susceptibility mapping plays a crucial role in flood risk assessment and management. Accurate identification of areas prone to flooding is essential for implementing effective mitigation measures and informing decision-making processes. In this regard, the present study used high-resolution remote sensing products, i.e., synthetic aperture radar (SAR) images for flood inventory preparation and integrated four machine learning models (Random Forest: RF, Classification and Regression Trees: CART, Support Vector Machine: SVM, and Extreme Gradient Boosting: XGBoost) to predict flood susceptibility in Metlili watershed, Morocco. Initially, 12 independent variables (elevation, slope angle, aspect, plan curvature, topographic wetness index, stream power index, distance from streams, distance from roads, lithology, rainfall, land use/land cover, and normalized vegetation index) were used as conditioning factors. The flood inventory dataset was divided into 70% and 30% for training and validation purposes using a popular library, scikit-learn (i.e., train_test_split) in Python programming language. Additionally, the area under the curve (AUC) was used to evaluate the performance of the models. The accuracy assessment results showed that RF, CART, SVM, and XGBoost models predicted flood susceptibility with AUC values of 0.807, 0.780, 0.756, and 0.727, respectively. However, the RF model performed better at flood susceptibility prediction compared to the other models applied. As per this model, 22.49%, 16.02%, 12.67%, 18.10%, and 31.70% areas of the watershed are estimated as being very low, low, moderate, high, and very highly susceptible to flooding, respectively. Therefore, this study showed that the integration of machine learning models with radar data could have promising results in predicting flood susceptibility in the study area and other similar environments.

Evaluating inherent vulnerability, an intrinsic characteristic becomes imperative for the formulation of adaptation strategies, particularly in highly complex and vulnerable regions of Himalayas. Jammu City, situated in the north-western Himalayas within a transitional zone between the Himalayan range and the plains, is not only susceptible to intense seismic activities but also faces multiple hazards, including floods, earthquakes, avalanches, and landslides. In recent years, the region has experienced growth in population with rapid progress in infrastructure development, encompassing the construction of highways, dams, and tunnels as integral components of urban development initiatives. Therefore, this study has been conducted to assess the inherent vulnerability index (VI) in Jammu City at ward level as a function of sensitivity, adaptive capacity, and exposure, using ecological and social indicators in GIS environment. The primary objective was to identify the most vulnerable area and ascertain the corresponding municipal ward, aiming to formulate a comprehensive ranking. The 22 indicators analysed were from four major components, namely social, infrastructure, technological, and ecological. The ecological indicators like Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), and Land use/Land cover were derived from Landsat 8 OLI satellite data. The results show that the majority of the area of the city falls into the moderate (20%), high (25.49%), and very high (25.17%) vulnerability categories, respectively, clustered in north-western and south-western transects with densely populated residential areas. The results can assist policymakers in identification of components of inherent vulnerability for focused resource management and formulating adaptation strategies to address the current stressors in the region.

Groundwater pollution in Rajasthan, India, poses significant challenges due to the region’s heavy reliance on this resource for drinking and irrigation. Given the increasing water scarcity and overexploitation, this study assesses the susceptibility of groundwater pollution in this semi-arid area. We applied and compared vulnerability mapping methods, DRASTIC and SINTACS, and their modified versions. These methodologies considered various geological and environmental factors such as depth-to-water table, recharge, aquifer conductivity, soil, and topography. The modified versions also integrated land use and temperature data for enhanced sensitivity. Validation was achieved by comparing contaminant data from the Central Ground Water Board (CGWB), India, focusing on primary contaminants such as fluoride, nitrate, chloride, and total dissolved solids (TDS). The results strongly align with the modified methodologies and observed groundwater ion values. Specifically, more than half of the 300 sample points analyzed indicated TDS values exceeding the permissible 300 ppm limit, with over 80 points surpassing 500 ppm. The vulnerability was classified into the following five categories: very low; low; medium; high; and very high. Notably, 30.53% of the area displayed “very high” vulnerability under the modified DRASTIC model. Districts like Jalore, Pali, Sirohi, and Jodhpur emerged as highly vulnerable zones, while areas within Udaipur, Kota, and Jaipur, among others, showed very high vulnerability. This research highlights the importance of conducting groundwater vulnerability assessments, especially for regions grappling with water scarcity like Rajasthan. The findings from this research are pivotal in guiding sustainable ground water resource management, as well as advocating continual monitoring and effective groundwater conservation strategies in the region.

The selection of an appropriate scaffold is imperative for the successful development of alternative animal protein in the form of cultured meat or lab-grown meat. Decellularized tissues have been suggested as a potential scaffold for cultured meat production owing to their capacity to support an optimal environment and niche conducive to cell proliferation and growth. This approach facilitates the systematic development of 3D tissues in the laboratory. Decellularized scaffold biomaterials have characteristics of high biocompatibility, biodegradation, and various bioactivities, which could potentially address the limitations associated with synthetic bio-scaffold materials. The present study involved the derivation and characterization of a decellularized scaffold from mushroom tissue following subsequent assessment of the scaffold’s capacity to support myogenic differentiation. Mushroom sections were soaked in nuclease and detergent solution for 4 days. Furthermore, decellularization was confirmed by histology and DAPI staining, which showed the removal of cellular components and nuclei. Myoblast cells were seeded onto decellularized tissue, which exhibited excellent cytocompatibility and promoted myogenic growth and differentiation. The study’s findings can serve as a foreground for the generation of an edible and natural scaffold for producing a safe and disease-free source of alternative animal protein, potentially reducing the burden on the health sector caused by conventional animal protein production and consumption.

The aim of this study is to analyze land cover changes and their effects on land surface temperature (LST) and normalized difference vegetation index (NDVI) in Muzaffarpur district, Bihar, India. The research utilized Landsat 5 and 8 satellite images taken every five years from 1990 to 2020 to classify seven land cover types, namely built-up areas, wetlands, fallow lands, croplands, vegetation, and water bodies, using the Artificial Neural Network technique in ENVI 5.1. The resulting land cover maps reveal a significant decrease in cropland area during the studied period, while fallow land area decreased from 48.06% to 35.79%. Analysis of LST and NDVI data showed a strong negative correlation (R2 < -0.0057) for all years, except for a weak positive correlation (R2 > 0.006). NDVI values were highest in agricultural lands with the lowest LST values, while fallow land areas showed the opposite trend. The study suggests that vegetation and fallow land are crucial determinants of the spatial and temporal variations in NDVI and LST, relative to urban and water cover categories.

Property tax is the primary source of revenue for municipal bodies. In India, municipal corporations are facing issues in property tax collection, and the primary reason for it is a lack of count of assessed properties under its jurisdiction. Also, the storage of information on the properties is mainly based on manual efforts, which leads to data redundancy and failure to appropriate tax collection. Geographical Information Systems (GIS) consists of technology, personnel, and resources to create, maintain, visualize, search, and share geospatial data and services. The study has been carried out in the Hauz Khas Ward, South Delhi Municipal Corporation, Delhi. This paper aims to develop a spatial database for property tax management. It includes capturing the building footprint, road, land use such as parks, paved area, drains, and demarcation of boundaries such as locality slums, based on a regular grid net with a cell size of 250 m by 250 m. The generated geospatial database has been finally used to evaluate parameters for property tax calculation. Moreover, this spatial database can be organized as different models for any web-based application for municipal services. This study provides a working example of a GIS-based property tax collection solution for whole of India and other South-Asian countries.

In this research, the SWAT+ model was employed to elucidate hydrological dynamics within the Beas Basin. The primary objectives encompassed the calibration of the SWAT model for accurate water balance quantification, annual simulation of salient hydrological components, and a decadal analysis of trends in fluvial discharge and sediment transport. The methodology encompasses simulating hydrological data with the SWAT+ model, followed by calibration and validation using flow data from Larji and Mahadev hydroelectric plants. The model’s efficacy in depicting streamflow and other hydrological components is corroborated by statistical measures such as the Nash–Sutcliffe efficiency and PBIAS. The water balance analysis delivers insights into the basin’s hydrological characteristics, including surface flow, water yield, and evapotranspiration. The temporal analysis exposes intricate seasonal and interannual variability in flow and sediment discharge, while spatial distribution highlights heterogeneity across the basin. These findings have practical implications for water resource management, including optimizing water allocation, hydroelectric power generation, irrigation, and environmental concerns. Limitations, such as data quality and model simplifications, are acknowledged, and future data collection and observations are recommended for improved model performance. In essence, these researches enhance understanding of the Beas Basin’s hydrology, setting a course for future investigations to integrate more data sources, refine model parameters, and consider climate and land-use changes for a richer comprehension of the basin’s hydrological dynamics.

The Severe Acute Respiratory Syndrome Coronavirus Disease 2019 (COVID-19) pandemic has presented unprecedented challenges to global health and economic stability. Intriguingly, the necessary lockdown measures, while disruptive to human society, inadvertently led to environmental rejuvenation, particularly noticeable in decreased air pollution and improved vegetation health. This study investigates the lockdown’s impact on vegetation health in Jharkhand, India, employing the Google Earth Engine for cloud-based data analysis. MODIS-NDVI data were analyzed using spatio-temporal NDVI analyses and time-series models. These analyses revealed a notable increase in maximum vegetation greenery of 19% from April 2019 to 2020, with subsequent increases of 13% and 3% observed in March and May of the same year, respectively. A longer-term analysis from 2000 to 2020 displayed an overall 16.7% rise in vegetation greenness. While the maximum value remained relatively constant, it demonstrated a slight increment during the dry season. The Landsat data Mann–Kendall trend test reinforced these findings, displaying a significant shift from a negative NDVI trend (1984–2019) to a positive 17.7% trend (1984–2021) in Jharkhand’s north-west region. The precipitation (using NASA power and Merra2 data) and NDVI correlation were also studied during the pre- and lockdown periods. Maximum precipitation (350–400 mm) was observed in June, while July typically experienced around 300 mm precipitation, covering nearly 85% of Jharkhand. Interestingly, August 2020 saw up to 550 mm precipitation, primarily in Jharkhand’s southern region, compared to 400 mm in the same month in 2019. Peak changes in NDVI value during this period ranged between 0.6–0.76 and 0.76–1, observed throughout the state. Although the decrease in air pollution led to improved vegetation health, these benefits began to diminish post-lockdown. This observation underscores the need for immediate attention and intervention from scientists and researchers. Understanding lockdown-induced environmental changes and their impact on vegetation health can facilitate the development of proactive environmental management strategies, paving the way towards a sustainable and resilient future.

Introduction: Despite the advancements in perinatal care in past decades, perinatal asphyxia remains a serious problem leading to significant perinatal morbidity and mortality. Therefore, foetal monitoring during the intrapartum period is of paramount importance. Among various methods of fetal monitoring, cardiotocography is a form of electronic foetal monitoring in which there is simultaneous recording of foetal heart rate and uterine contractions. Materials and Methods: This cross-sectional observational study was done in the labour room and neonatal intensive care unit (NICU) of a teaching Municipal Hospital in North India including 500 pregnant women of age group 18-45 years with singeleton fetus of gestation ≥36 weeks without any known congenital anomaly. Intrapartum cardiotocography (CTG) for 20 minutes was done within 12 hours prior to delivery and babies born to them were observed for birth asphyxia as Apgar score

To describe the behaviour of watercourses, numerous parameters should be considered, such as water flow, depth/water level, velocity, and the amount of deposit in a waterbed. In addition, many other parameters should be analysed to understand the mechanism of specific watercourses. The most typical parameters are those associated with water quality, such as water temperature, and pH value, and temperature, as well as various climate parameters, such as precipitation, evaporation, and insolation, which are visualised in the form of a time series. The measurement frequency can differ for each parameter. Depending on the number of available measurements, hourly, daily, monthly, and yearly data can be obtained, which can then be used to form a time series. Many well-established and applicable procedures and models for time-series analyses exist, including the rescaled adjusted partial sums method (RAPS). This method is devised to detect possible irregularities and/or fluctuations within the original time series, which cannot be achieved using typical time-series analysis methods. This paper presents a comprehensive review of the application of the RAPS method in hydrology, where the advantages and disadvantages of the method are discussed.

Flooding is a natural disaster that can ruin natural resources, cause casualties, and harm the environment. Floods are known for their deadly and destructive impacts. To predict important information about flooding, such as the extent of inundation and water surface elevations at specific locations, hydraulic modeling and Flood Inundation Mapping (FIM) must be used. A crucial tool for flood monitoring is the HEC-RAS software and remote sensing (RS) technology, in combination with geographic information systems (GIS). The primary emphasis in this discipline revolves around the definition of flood zones and the preparation of flood hazard maps for at-risk regions. This map could allocate land development priorities, and prepare services for future emergency aid and relief interventions in at-risk locations.

Healthcare site selection assumes an imperative part in healthcare development and management. From part of the public authority, proper medical site selection will help the distribution of clinical assets, coordinating with the arrangement of medical care with the social and economic demands, organizing the metropolitan and rural healthcare administration advancement, and facilitating social logical inconsistencies. Site suitability analysis is a variety of analysis utilized in GIS to work out the simplest place or site for one thing. The main objective of the current study was to select a site for new healthcare services with geospatial technologies to intermix spatial and non-spatial data to create a weighted result. The current study had been done into three phases, where many processes are intermixed into a single phase. In the first phase of analysis, distance, density, and proximity were mapped to seek out poor and lower accessible areas of healthcare from existing healthcare. To selecting new healthcare sites, four-factor criteria (Buffer around road and rail, land use land cover and buffer around settlement,) and some constrain criteria considered in the second phase of analysis. Finally, the shortest network path analysis has been done in the third phase to determine the shortest and best route from selected healthcare sites towards district medical college. The current study presents some suitable sites in the poor and inaccessible areas of the district. This study will be very helpful for the decision support system of healthcare management in the future.

Natural capital is the wealth of nations that determine their economic status. Worldwide, vulnerable people depend on natural capital for employment, salaries, wealth, and livelihoods and, in turn, this determines the developmental index of the nation to which they belong. In this short review, we have tried to sum up the ideas and discussions over natural capital’s role in ascribing economic status to countries as well as the need for natural resource management and sustainability. This paper aimed to discuss how humanity’s prosperity is intertwined with the services that ecosystems provide, and how poor natural resource management (NRM) has adversely affected human well-being. Our preselected criteria for the review paper led us to evaluate 96 peer-reviewed publications from the SCOPUS database, which is likely the most comprehensive archive of peer-reviewed scientific literature as well as WoS, PUBMED, and Google Scholar databases. Our review revealed that the availability of ecological services is crucial for clean water and air, food and fodder, and agricultural development. We further discussed important concepts regarding sustainability, natural capital and economics, and determinants of human well-being vis-à-vis the intergenerational security of natural wealth. To ensure current and future human well-being, we conclude that an in-depth understanding of the services that ecosystems provide is necessary for the holistic management of the Earth system.

Continuous groundwater quality monitoring is crucial for ensuring safe drinking and irrigation by mitigating risks from geochemical contaminants through appropriate treatment methods. Therefore, the primary objective of this study was to assess the suitability of groundwater collected from Lucknow, India, for both drinking and irrigation. Forty samples were collected from different sites within the study area to evaluate groundwater quality. Various parameters such as pH, turbidity, total dissolved solids (TDS), chlorides (Cl−), total alkalinity, total hardness, sulphate (SO42−), nitrate (NO3−), fluorides (F−), iron (Fe), arsenic (As), magnesium (Mg2+), and calcium (Ca2+) were analyzed. The weighted arithmetic water quality index (WAWQI), a vital rating system representing overall water quality, was employed to classify the water into different categories, such as very good, good, moderate, poor, and unfit for drinking. This classification is invaluable for public awareness and decision-making to make informed decisions regarding effective management, treatment, and sustainable societal development on a broader scale. A correlation matrix was generated and analyzed to observe correlations between the various parameters. Additionally, spatial distribution maps for the analyzed parameters and WQI were prepared using the inverse distance weighted (IDW) method. The study found that WQI values in the area ranged from 2.64 to 168.68, indicating good water quality in most places except for the Kukrail region, where the water quality is unfit for drinking purposes. The water quality map shows that 86% of the area falls under the very good category, 14.63% under good to moderate quality, and 0.37% is categorized as unfit for drinking. Consequently, the findings suggest that the groundwater in the studied area is safe and suitable for drinking and irrigation purposes.

Water is the most precious commodity that human being wanted, nowadays water is depleting due to several human interventions. In Kerala state, even though high rainfall is received, still water scarcity is faced during summer and soil erosion is higher. This watershed area is prone to water scarcity during the summer season and stream network become dry during that period. This clearly indicates that human interventions and unscientific agricultural activities may be the result. In this, study GIS and RS technologies are used to find and map the Soil and water conservation priority zones, also several action Plans where proposed. SOI Topo sheet were used to digitize the contours and DEM was created using that with ArcGIS 10.2.2 software. LULC maps were digitized from satellite image and using other parameters and suitable weight values, the weighted overlay was done to find out the Priority Zones. Cadastral Plot boundaries were overlaid for plot-wise priority zones and several conservation methods like gully plugin, check dams, vegetation bund etc. were proposed.

Background: SARS-CoV-2 or Severe Acute Respiratory Syndrome, is a virus that causes severe respiratory illness, also known as COVID-19, is a global pandemic caused by a novel coronavirus, firstly reported China (Wuhan City) on Dec, 2019. In India, Kerala is the first state in which the COVID-19 first case has registered which has raised to three cases by 3 Feb, 2020. Purpose: The aim of this research is to map the impact and assessment of the COVID 19 pandemic situation using geospatial technology in Rajasthan and suggest various measures to control the pandemic situation. Objectives: (a) Assessing and mapping the affected parts of Rajasthan and evaluating the risk of the pandemic on tourism, and (b) Initially tracking and forecasting the cases of COVID-19 in the study area. Methodology: The methodology consists of four main phases. In the first phase understanding the risk of the pandemic situation at three primary levels i.e. risk analysis, risk evaluation, risk management after that, second step is to identify the target risk zones of COVID-19 cases using geospatial technology based on bulk screening. Assessment and mapping of pandemic risk is the third phase. In the fourth phase, treatment of the risk and evaluation of future occurrence possibilities. Result: COVID 19 pandemic is the greatest threat globally. Geospatial technology provides valuable support in assessing and mapping the risk of COVID 19 cases in Rajasthan at the initial level. Conclusion: This study shows that the geospatial technique contributes very significantly in detecting pre and post COVID 19 pandemic conditions and helps in proper decision-making, not only in Rajasthan but also in the entire world.

The 2004 Indian Ocean earthquake and tsunami significantly impacted the coastal shoreline of the Andaman and Nicobar Islands, causing widespread destruction of infrastructure and ecological damage. This study aims to analyze the short- and long-term shoreline changes in South Andaman, focusing on 2004–2005 (pre- and post-tsunami) and 1990–2023 (to assess periodic changes). Using remote sensing techniques and geospatial tools such as the Digital Shoreline Analysis System (DSAS), shoreline change rates were calculated in four zones, revealing the extent of the tsunami’s impact. During the pre- and post-tsunami periods, the maximum coastal erosion rate was −410.55 m/year, while the maximum accretion was 359.07 m/year in zone A, the island’s east side. For the 1990–2023 period, the most significant coastal shoreline erosion rate was also recorded in zone A, which was recorded at −2.3 m/year. After analyzing the result, it can be seen that the tsunami severely affected the island’s east side. To validate the coastal shoreline measurements, the root mean square error (RMSE) of Landsat-7 and Google Earth was 18.53 m, enabling comparisons of the accuracy of different models on the same dataset. The results demonstrate the extensive impact of the 2004 Indian Ocean Tsunami on South Andaman’s coastal shoreline and the value of analyzing shoreline changes to understand the short- and long-term consequences of such events on coastal ecosystems. This information can inform conservation efforts, management strategies, and disaster response plans to mitigate future damage and allocate resources more efficiently. By better understanding the impact of tsunamis on coastal shorelines, emergency responders, government agencies, and conservationists can develop more effective strategies to protect these fragile ecosystems and the communities that rely on them.

Illegal sand mining has been identified as a significant cause of harm to riverbanks, as it leads to excessive removal of sand from rivers and negatively impacts river shorelines. This investigation aimed to identify instances of shoreline erosion and accretion at illegal sand mining sites along the Chambal River. These sites were selected based on a report submitted by the Director of the National Chambal Sanctuary (NCS) to the National Green Tribunal (NGT) of India. The digital shoreline analysis system (DSAS v5.1) was used during the elapsed period from 1990 to 2020. Three statistical parameters used in DSAS—the shoreline change envelope (SCE), endpoint rate (EPR), and net shoreline movement (NSM)—quantify the rates of shoreline changes in the form of erosion and accretion patterns. To carry out this study, Landsat imagery data (T.M., ETM+, and OLI) and Sentinel-2A/MSI from 1990 to 2020 were used to analyze river shoreline erosion and accretion. The normalized difference water index (NDWI) and modified normalized difference water index (MNDWI) were used to detect riverbanks in satellite images. The investigation results indicated that erosion was observed at all illegal mining sites, with the highest erosion rate of 1.26 m/year at the Sewarpali site. On the other hand, the highest accretion was identified at the Chandilpura site, with a rate of 0.63 m/year. We observed significant changes in river shorelines at illegal mining and unmined sites. Erosion and accretion at unmined sites are recorded at −0.18 m/year and 0.19 m/year, respectively, which are minor compared to mining sites. This study’s findings on the effects of illegal sand mining on river shorelines will be helpful in the sustainable management and conservation of river ecosystems. These results can also help to develop and implement river sand mining policies that protect river ecosystems from the long-term effects of illegal sand mining.

The immense dependence of the growing population on groundwater has resulted in depletion at a fast pace can be seen nowadays. Identifying a groundwater potential zone can be proved as an aid to provide insight to the decision-makers and local authorities for planning purposes. This study evaluated the delineation of groundwater potential zones using integrated remote sensing and GIS approach. Various thematic layers such as geology, geomorphology, lineament, slope, drainage, soil, land use/land cover, and rainfall were considered in this study as these have influence on the occurrence of groundwater and its cycle, and maps have been prepared in GIS domain. Afterward, appropriate weights were assigned to these layers based on multi-criteria decision analysis, i.e., Analytical Hierarchy Process (AHP). Groundwater potentiality has been delineated in different zones (low, moderate, high, and very high) in the study region based on weighted overlay analysis. The study reveals zones with different groundwater prospects viz. low (1.27%), moderate (15.65%), high (75.54%), and very high (7.29%). The ground survey data provided by CGWB (Central Ground Water Board) of nearly 100 wells/dug wells/borewells/piezometers have been used for validation purposes, showing comparable results with the groundwater prospects zones. It also confirms that the majority of these wells fall under very high or high groundwater potential zones. They were also found to be thereby indicating that there is the existence of a permeable reservoir with considerable water storage in the subsurface. One of the most important issues for users and governments is groundwater depletion. Planning for the available groundwater resource is made easier by identifying the potential for groundwater (low to high).

Given the concern for maintaining voltage stability in power systems integrated with renewable power systems due to a mismatch in generation and demand, there remains a need to invoke flexible alternating current transmission system (FACTS) devices in the distribution network. The present paper deals with identifying the locations of placement of a static var compensator in an experimental IEEE 14-bus system; the voltage drop in different buses in an IEEE 14-bus system is calculated by the standard formula. The total voltage drop in the network (TVDN) is also calculated as a reference. The ranking of buses in order of decreasing voltage drop is made, and the weak buses are identified as those showing the maximum or near-maximum voltage drop for the installation of a Static Var Compensator (SVC). The optimum usable size is calculated using a Genetic Algorithm approach to optimize the installation cost. After size optimization, installing a 2 MW solar generator is considered for the weak and most potential bus, which suffers from voltage drops or power loss. Based on the generator at the weakest bus, the total power loss in the network is calculated and compared with a similar method to assess the efficiency of the proposed model. Thus, the voltage stability enhancement problem is solved by applying a Genetic algorithm (GA) to optimize SVC size and using the Total Voltage Drop in Network (TVDN) method to identify weak buses in the systems. It is found that the performance of the proposed system is comparable with another existing system. It is further observed that a gain in power loss to 6.56% is achievable by adopting the proposed strategy, and the gain is better than the other system.

Groundwater resources are increasingly important as the main supply of fresh water for household, industrial, and agricultural activities. However, overuse and depletion of these resources can lead to water scarcity and resource deterioration. Therefore, assessing groundwater availability is essential for sustainable water management. This study aims to identify potential groundwater zones in the Bilaspur district of Himachal Pradesh using the Multi Influencing Factor (MIF) technique, a modern decision-making method widely used in various sectors. Geospatial models were integrated with the MIF technique to evaluate prospective groundwater areas. Grid layouts of all underground water influencing variables were given a predetermined score and weight in this decision-making strategy. The potential groundwater areas were then statistically assessed using graded data maps of slope, lithology, land-use, lineament, aspect, elevation, soil, drainage, geomorphology, and rainfall. These maps were converted into raster data using the raster converter tool in ArcGIS software, utilizing Survey of India toposheets, SRTM DEM data, and Resourcesat-2A satellite imageries. The prospective groundwater zones obtained were classified into five categories: nil–very low, covering 0.34% of the total area; very low–low (51.64%); low–moderate (4.92%); moderate–high (18%) and high–very high (25%). Scholars and policymakers can collaborate to develop systematic exploration plans for future developments and implement preservative and protective strategies by identifying groundwater recharge zones to reduce groundwater levels. This study provides valuable insights for long-term planning and management of water resources in the region.

Bank erosion hazard is a frequent occurrence that poses threats to floodplain ecosystems. This analysis examined changes to the Jia Bharali River channel in India using the GIS-based Digital Shoreline Analysis System [DSAS]. The Jia Bharali’s future channel was predicted so as to identify the most erosion-susceptible zones. The rate of bankline movement was calculated using remotely sensed data collected over a period of 45 years (1976–2021). The results show that the river’s erosion and deposition rates were higher in the early years than towards the later part of the period under analysis. On the right and left banks of the river, the average shift rate was −9.22 and 5.8 m/y, respectively, which is comparatively high. The chosen portion of the river was evenly divided into three zones, A, B, and C. The most positively affected zone was zone A. The left bank of zone B exhibited a higher rate of erosion than the right bank, indicating that the river was moving to the left [eastward] in this zone. At the same time, the right bank was being eroded faster than the left, indicating a westward thrust at zone C. The predicted result demonstrates that the left bank of zone B and the right bank of zone C would have a higher average migration rate. Therefore, these banks were identified as being the most susceptible to bank erosion. The study evaluates the spatio-temporal change of the river in sensitive regions where neighboring settlements and infrastructure were at risk of changing channel dynamics. Using the actual and forecasted bankline, the degree of accuracy was confirmed. The results of the automated prediction approach could be useful for river hazard management in the Jia Bharali and in similar environmental settings with tropical high precipitation zones.

Rainfall and runoff are the significant hydrologic component in the water resources assessment. Numerous methods are available to estimate runoff from rainfall; however, the SCS-CN method remains the most popular and frequently used method as runoff curve number (CN) is a crucial factor of the SCS-CN method and depends on land use/land cover (LULC), soil type, and antecedent soil moisture (AMC). Besides this, various parameters, such as Hydrological Soil Characteristics (HSG), precipitation (P), Potential Maximum Retention (PMR), Antecedent Moisture Condition (AMC), Weighted Curve Number (WCN), are the mandatory inputs to the SCS-CN model. In the results, the daily runoff from the Sind river basin for ten years, i.e., 2005 to 2014, has been used. As a result, the average annual surface runoff calculated for the Sind river basin is 133.71 mm. The total average volume of runoff is 35.04 × 10 8 m 3, representing 17.21% of the total average annual rainfall.

Rainbow trout, a salmonid species has an economic importance worldwide and contributes to the indigenous food security. The present study was conducted in complete randomized design (CRD) to evaluate the dietary inclusion of citric acid with phytase as supplement on growth responses of rainbow trout. There were four treatments of diets with 0.5% citric acid (T1), 1% citric acid (T2), 1.5% citric acid (T3) and 0% control (T4) of citric acid incorporated with 1200FYT phytase as supplement. The treatments were replicated three times. Total harvest weight of fish in T3 (5720.2±105.6g) was significantly higher and different than T2 (4595.9±434.9g), T1 (4589.8±240.2g) and T4 (4567.5±217.3g). The highest daily growth rate was seen in treatment T3 (1.1±0.0) which was significantly different than T2 (0.9±0.0), T1 (0.9±0.0) and T4 (0.8±0.0). The highest specific growth rate was seen in the treatment T3 (0.8±0.01) which was significantly different than T1 (0.7±0.0), T2 (0.7±0.1) and T4 (0.7±0.0). The lowest food conversion ratio was seen in T3 (2.2±0.1) which was significantly different with T2 (2.6±0.1), T1 (2.7±0.1) and T4 (2.8±0.1). Similarly, the highest protein efficiency ratio was seen in treatment T3 (1.0±0.1) which was not significantly different with T2 (0.8±0.0) and T1 (0.8±0.1) but was significantly different than T4 (0.7±0.0). No significant changes were observed in water temperature, dissolve oxygen and pH under different treatments. The present findings demonstrated that dietary inclusion of citric acid with phytase as supplement enhance growth by reducing the pH in the gut which increases the phytate hydrolysis, kills the pathogens, decreases the rate of gastric emptying, improves mineralization and nutrient absorption.

Three-dimensional in vitro spheroids are a reliable model to study tumor biology and drug toxicity. However, inconsistencies exist in terms of seeding cell density that governs spheroid size and shape, influencing the experimental outcome. We investigated the effect of varying cell densities using glioblastoma cells on tumorsphere formation and their responsiveness to drug treatment. Our results demonstrated that in comparison with spheroids formed with lower cell density, spheroids formed with higher cell density were not only larger in size but also had a larger necrotic core surrounded by a higher number of quiescent cells and were irresponsive to drug treatment. Our study highlights the importance of predetermination of cell density to obtain desired/appropriate spheroid size to produce consistent and reliable data on drug toxicity studies in tumor cells.

A common phenomenon associated with alluvial rivers is their meander evolution, eventually forming cutoffs. Point bar deposits and ox-bow lakes are the products of lateral bend migration and meander cutoff. The present study focuses on identifying the meanders of River Manu and their cutoffs. Moreover, this study compares the temporal evolution and predicts the progress of selected meanders of River Manu. In the present research, the Survey of India topographical map, satellite imagery, and geographic information system (GIS) technique were used to examine the evolution of the Manu River meander. Subsequently, a field visit was done to the selected cutoffs and meanders of River Manu to ascertain the present status and collect data. It has been observed that many cutoffs have undergone temporal changes, and their sizes have decreased. Some have become dried or converted to agricultural fields. The width of River Manu has decreased in all the selected bends from 1932 to 2017. The sinuosity index has changed from 2.04 (1932) to 1.90 (2017), and the length of the river has decreased by 7 km in 85 years (1932-2017). The decrease in length is evident from lowering the number of meanders. Uniformity coefficient and coefficient of curvature of the bank soil samples were calculated, indicating that the soil is poorly graded and falls under the cohesionless category. Based on cross-section analysis, sediment discharge, grain-size analysis of the bank material, channel planform change, and radius of curvature, it can be stated that almost all the selected bends have the probability of future cutoff. The highest probabilities were observed in bend 3 (Jalai) and bend 4 (Chhontail). This work is aimed to provide planners with decisions regarding the construction of roads and bridges in areas that show the huge dynamicity of river meandering.

Wound healing responses play a major role in chronic inflammation, which affects millions of people around the world. One of the daunting tasks of creating a wound-healing drug is finding equilibrium in the inflammatory cascade. In this study, the molecular and cellular mechanisms to regulate wound healing are explained, and recent research is addressed that demonstrates the molecular and cellular events during diabetic wound healing. Moreover, a range of factors or agents that facilitate wound healing have also been investigated as possible targets for successful treatment. It also summarises the various advances in research findings that have revealed promising molecular targets in the fields of therapy and diagnosis of cellular physiology and pathology of wound healing, such as neuropeptides, substance P, T cell immune response cDNA 7, miRNA, and treprostinil growth factors such as fibroblast growth factor, including thymosin beta 4, and immunomodulators as major therapeutic targets.

The Kaziranga Eco-Sensitive Zone is located on the edge of the Eastern Himalayan biodiversity hotspot region. In 1985, the Kaziranga National Park (KNP) was declared a World Heritage Site by UNESCO. Nowadays, anthropogenic interference has created a significant negative impact on this national park. As a result, the area under natural habitat is gradually decreasing. The current study attempted to analyze the land use land cover (LULC) change in the Kaziranga Eco-Sensitive Zone using remote sensing data with CA-Markov models. Satellite remote sensing and the geographic information system (GIS) are widely used for monitoring, mapping, and change detection of LULC change dynamics. The changing rate was assessed using thirty years (1990–2020) of Landsat data. The study analyses the significant change in LULC, with the decrease in the waterbody, grassland and agricultural land, and the increase of sand or dry river beds, forest, and built-up areas. Between 1990 and 2020, waterbody, grassland, and agricultural land decreased by 18.4, 9.96, and 64.88%, respectively, while sand or dry river beds, forest, and built-up areas increased by 103.72, 6.96, and 89.03%, respectively. The result shows that the area covered with waterbodies, grassland, and agricultural land is mostly converted into built-up areas and sand or dry river bed areas. According to this study, by 2050, waterbodies, sand or dry river beds, and forests will decrease by 3.67, 3.91, and 7.11%, respectively; while grassland and agriculture will increase by up to 16.67% and 0.37%, respectively. The built-up areas are expected to slightly decrease during this period (up to 2.4%). The outcome of this study is expected to be useful for the long-term management of the Kaziranga Eco-Sensitive Zone.