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Accurate flood forecasting is a crucial process for predicting the timing, occurrence, duration, and magnitude of floods in specific zones. This prediction often involves analyzing various hydrological, meteorological, and environmental parameters. In recent years, several soft computing techniques have been widely used for flood forecasting. In this study, flood forecasting for the Narmada River at the Hoshangabad gauging site in Madhya Pradesh, India, was conducted using an Artificial Neural Network (ANN) model, a Fuzzy Logic (FL) model, and an Adaptive Neuro-Fuzzy Inference System (ANFIS) model. To assess their capacity to handle different levels of information, three separate input data sets were used. Our objective was to compare the performance and evaluate the suitability of soft computing data-driven models for flood forecasting. For the development of these models, monthly discharge data spanning 33 years from six gauging sites were selected. Various performance measures, such as regression, root mean square error (RMSE), and percentage deviation, were used to compare and evaluate the performances of the different models. The results indicated that the ANN and ANFIS models performed similarly in some cases. However, the ANFIS model generally predicted much better than the ANN model in most cases. The ANFIS model, developed using the hybrid method, delivered the best performance with an RMSE of 211.97 and a coefficient of regression of 0.96, demonstrating the potential of using these models for flood forecasting. This research highlighted the effectiveness of soft computing techniques in flood forecasting and established useful suitability criteria that can be employed by flood control departments in various countries, regions, and states for accurate flood prognosis.

Functional dyspepsia (FD) is commonly known as chronic indigestion and manifests as a group of upper gastrointestinal symptoms such as epigastric pain or burning, postprandial fullness, bloating, belching, nausea, vomiting, and early satiety without any organic or structural cause. In Ayurveda, these symptoms can be considered Ajīrṇa. A 25-year-old male patient with FD, taking on and off conventional treatment for the past few months without much relief, presented to the outpatient department. As the pathophysiology of FD is complex and multifactorial, it is difficult to manage it only with oral medication. Pawanamuktāsana-II and Vajrāsana were advised, along with Hingwāṣṭaka Cūrṇa, to manage the related symptoms. After 4 months of treatment, the signs and symptoms of the disease significantly decreased. The present case report elucidates the role of Yoga as an adjuvant therapy in the Āyurvedic management of FD.

Potassium, a pivotal macronutrient essential for growth, development, and crop yield, serves as a critical determinant of soil productivity. Its depletion disrupts the equilibrium of soil nutrients, prompting an investigation into integrated potassium management strategies to address this challenge. A field experiment was conducted during the winter season of 2020 using a randomized complete block design, with eight treatments, each replicated three times in Chinese cabbage (Brassica rapa L. subsp. chinensis). These treatments comprised standard (100 %) and reduced (75 % and 50 %) rates of the recommended dose of potassium (RDK) via muriate of potash (MOP). Variations in the inclusion and exclusion of plant growth-promoting rhizobacteria (PGPR), farmyard manure (FYM) as 25 % of the potassium recommendation, and foliar spray of nano potash were systematically implemented. Findings unequivocally demonstrated that the treatmentT8, involving 100 % RDK +25 % K through FYM + PGPR + nano K fertilizer spray at 25 and 40 DAS, yielded significant improvements in both green fodder (64.0 t ha−1) and dry fodder (7.87 t ha−1).Moreover, T8 exhibited the highest values for total ash (8.75 %), total ash yield (68.9 ± 2.88 kg ha−1), ether extract (2.85 %), ether extract yield (22.4 ± 0.88 kg ha−1), crude protein (9.71 %), and total crude protein yield (76.4 ± 3.21 kg ha−1). Conversely, a marked reduction was observed in various fiber components and carbohydrate fractions upon application of the T8 treatment. The lowest values of yield, crude protein content, total ash ether extract were recorded in treatment T1 (control) applied with no potassium. This investigation underscores the inadequacy of the recommended potassium dose in achieving optimal productivity, necessitating a re-evaluation of potassium fertilization levels. The integrated approach involving FYM, PGPR, and nano potash, coupled with the recommended potassium dose through MOP, emerges as a promising avenue for augmenting both yield and quality parameters in Chinese cabbage.

To effectively tackle the challenges posed by climate change, it is crucial to enhance the accuracy of rainfall-runoff models to ensure reliability amidst changing climatic conditions. Neural networks, renowned for their ability to capture complex patterns and relationships within uncertain input and output data, offer valuable tools in this pursuit. This study aims to evaluate the efficacy of two neural network (NN) models: the Radial Basis Function Neural Network (RBFNN) and the Model Tree M5 Neural Network (MTM5NN). These models are assessed both individually and in combination with the Wavelet (WT) data processing technique for rainfall-runoff modeling in the Kolar River watershed located in Madhya Pradesh, India. Fifteen models were developed employing four algorithms: RBFNN models, WRBFNN (RBF model integrating wavelet components of rainfall as inputs), MTM5NN, and WMTM5NN (MT model incorporating wavelet components of rainfall as inputs). Initially, rainfall and runoff data underwent normalization and were applied to RBFNN and MTM5NN networks. Subsequently, time series data for both rainfall and runoff were decomposed using wavelet transforms, resulting in various sub-time series signals such as approximations and decompositions. These derived signals were then utilized as input data for RBFNN and MTM5NN, specifically designated as WRBFNN and WMTM5NN. The most effective model identified was Model 8 of WMTM5NN, which demonstrated R2 values close to 0.97, outperforming the other models. These results underscore the superior performance of the WMTM5NN model, highlighting its effectiveness in achieving heightened accuracy in predicting runoff for this specific watershed.

Flood risk assessment remains a crucial element, particularly within locations highly susceptible to repeated flood occurrences. This study seeks to conduct an elaborate flood risk analysis for Mumbai, India based on an integrated method of geographic information systems and analytic hierarchy process (AHP). In this study, land use/land cover, average annual rainfall, elevation, drainage density, normalized difference vegetation index, distance from rivers as well as distance from roads are identified and considered. For this reason, the expert survey utilizes the AHP weights so as to find out the significance of these factors towards flooding danger. Investigations show a flood risk index and a corresponding map for Mumbai, where all areas are divided into risk zones very low to very high. Very high risks are particularly situated along rivers. Such details offer critical knowledge to policymakers who will undertake informed emergency preparedness measures designed to shield Mumbai's citizens and assets. Therefore, this research is considered as one of the modern techniques for flood risk assessment, which can be applied in other flood-affected areas worldwide. HIGHLIGHTS Merges the geographic information system and the analytic hierarchy process (AHP) for Mumbai flood risk, giving a holistic view.; Evaluate land use, rainfall, elevation, and river proximity, enhancing risk assessments.; Introduces the flood risk index from the AHP, aiding targeted emergency plans.; Detailed flood risk map divides Mumbai into ‘Very Low’ to ‘Very High’ risk zones for precise mitigation strategies.; Offers a modern flood risk model, valuable worldwide amid climate change impacts.;

Rainfall is the major component of the hydrologic cycle and it is the primary source of runoff. The main purpose of this study was to estimate daily discharge by employing an Adaptive Neuro-Fuzzy Inference System (ANFIS) model using rainfall and soil moisture data at three different depths (5 cm, 100 cm and bedrock) for the Damanganga basin. The length of the data for the study period 1983–2022 is 39 years. The model employed nine membership functions for each variable of soil moisture, rainfall, discharge and 30 rules were optimized. The results were compared considering a range of model performance indicators as correlation coefficient (R2) and Nash–Sutcliffe efficiency (NSE) coefficient. The model application results shows that soil moisture at bedrock gives more precise value of daily discharge with (R2) and NSE value as 0.9936 and 0.9981, respectively, as compared to the soil moisture at depths of 5 and 100 cm. The better results obtained for the measurement of soil moisture in the deeper soil layer are consistent with the hydrological behavior anticipated for the analyzed catchment, where the root-zone soil layer is the driver of the runoff response rather than the surface observations. This study can be helpful to hydrologists in selecting appropriate rainfall–runoff models. HIGHLIGHTS To estimate the daily discharge by employing an Adaptive Neuro-Fuzzy Inference System (ANFIS) model using rainfall and soil moisture data.; The model employed nine membership functions for each variable of soil moisture, rainfall, discharge and 30 rules were optimized.; The results were compared considering a range of model performance indicators such as correlation coefficient (R2) and Nash-Sutcliffe efficiency (NSE) coefficient.;

Agricultural management practices alter soil characteristics and influence soil biological properties. Hence, a field trial was carried out to assess the 14-year long-term impact of tillage and residue management practices on soil biological activities and microbial population in a rice-wheat cropping system in two depths viz., 0-15 and 15-30 cm. Soil organic carbon levels differed significantly (p> 0.05) across various treatments. Microbial biomass carbon, Microbial quotient, and soil enzymatic activities were significantly greater (10-82%) in crop residue incorporation/retention treatments. Zero tillage with residue retention (ZT+R) had the greatest bacterial, actinomycetes, and fungi population, next to zero tillage with residue incorporation (RT+R). The ZT+R treatment had the greatest value of K-strategist and r-strategist, and was equivalent to RT+R across both soil depths. When compared to Conventional tillage (CT), zero tillage (ZT) increased wheat yield by 9%. However, compared to CT, rice and rice-wheat systems had lower grain yields, whereas crop residue increased wheat and rice-wheat system yields by 10% and 6%, respectively. The findings of this long-term study show that residue management and tillage practices can enhance soil biological attributes while also supporting microbial diversity.

The objective of this study is to determine the optimum size of stone revetment for different water standing durations and critical drawdown rates for 80% height of flood. The model bank was constructed using soil sourced from the Parlalpur ferry ghat of the Ganga River in Kaliachak, West Bengal, with a field density of 1,500 kg/m3 achieved by maintaining a 10% moisture content (MC) in the model bank soil. The model riverbank was prepared considering Froude (F) similitude having a distorted depth scale of 1/20 and a linear scale of 1/200. In this study, effective stone sizes (D10) – 2.33, 3.22, 4.58, 6.31, and 8.84 mm – were used. These stone sizes were investigated in conjunction with three water standing durations: 15, 30, and 45 min. The bank slope was prepared at 1V:1.5H, and a drawdown ratio of 80% was maintained. The effectiveness of stone revetment size was analysed in terms of the percentage loss of stone revetment and the percentage loss of the model bank's cross-sectional area. The outcomes of this study indicate that the 6.31 mm stone size exhibits optimal performance. HIGHLIGHTS Optimized stone size was determined.; This underscores the importance of careful stone selection, considering soil characteristics.; The findings of this study offer valuable insights into the temporal dynamics of bank stability under real-world hydrological conditions, which are beneficial for engineers.; The importance of synergetic impact of drawdown and velocity for designing the stone revetment for river bank protection.;

The article examines machine learning models for precipitation forecasting in the Ambica River basin, addressing the important requirement for accurate hydrological forecasts in water resource management. Using a comprehensive collection of meteorological variables such as temperature, humidity, wind speed, and precipitation, four separate models are used: Support Vector Regression (SVR), Random Forest (RF), Decision Tree (DT), and Multiple Linear Regression (MLR). These models’ performance is rigorously evaluated using various assessment indicators. The cross-correlation function (XCF) is used in this study to evaluate the correlations between climatic variables and precipitation. The XCF analysis reveals several noteworthy trends, such as a high link between maximum temperature and precipitation, with maxima consistently found at months across all four sites. Furthermore, relative humidity and wind speed have significant connections with precipitation. The findings highlight the value of machine learning approaches in improving precipitation forecast accuracy. The RF and SVR models typically outperform, with values ranging from 0.74 to 0.91. This impressive accuracy underlines their effectiveness in precipitation forecasting, beating competing models in both the training and testing stages. These findings have significant consequences for hydrological processes, notably in the Ambica River basin, where accurate precipitation forecasting is critical for sustainable water resource management. HIGHLIGHTS To create precise precipitation forecasting models for the Ambica River basin and evaluating the efficiency of various machine learning techniques.; To estimate precipitation in the Ambica River basin most accurately, the study will determine the most accurate ML model.;

Global challenges, such as population growth, rapid urbanization, and the impacts of climate change, are creating unprecedented demands on water resources in semi-arid regions. Meeting the surging needs for irrigation and water supply requires a departure from the limitations of single reservoir systems. Instead, the construction of multi-reservoir systems within semi-arid river basins is imperative. The research employs an integrated reservoir operation approach that facilitates the controlled release of surplus water from upstream reservoirs to downstream ones. The novel Teaching-Learning-Based Optimization (TLBO) model is utilized to determine optimal irrigation releases, subsequently forming the basis for evaluating reservoir operation performance through the lenses of reliability, resilience, and vulnerability. The findings of this study shed light on the performance of these reservoirs under different models. Notably, the Aji-2 reservoir operation exhibits higher levels of reliability and resilience when the TLBO model is employed, surpassing the outcomes of the Linear Programming (LP) model. Conversely, the vulnerability of the Aji-3 reservoir operation is more pronounced with the TLBO model, albeit reduced when compared to actual release years 2005, 2009, and 2013. This study adds to the development of reservoir operation policies that favor reduced vulnerability and increased reliability. HIGHLIGHTS Integrated reservoir operation measures were taken.; TLBO algorithms were used in integrated reservoir operation.; By recognizing the intricate connections between reliability, resilience, and vulnerability, decision-makers can navigate the complexities of water system planning with greater confidence and effectiveness.;

Urban settlement depends on water distribution networks for clean and safe drinking water. This research incorporates geographic information systems (GIS), remote sensing (RS), and hydraulic modelling software EPANET to analyse and construct water distribution systems in Bota town, India. Satellite images and hydrological data have been utilized for the management of the Bota town's water supply network, sources to cater the demand for urban centres. EPANET simulates hydraulic behaviour in the water distribution system under different operating situations. EPANET simulation shows network leaks, low pressure, and substantial head loss. These findings have advised for water distribution system improvements by analysing network shortcomings. Booster pumps, new pipelines, and repairing of existing leakages are examples of such improvements. GIS, RS, and EPANET provided a comprehensive water distribution system study and more accurate and efficient improvement identification. This study emphasizes the necessity of new technologies in water distribution system analysis and design. The study solves Bota town's water distribution system problems of low pressure, high head loss, and leaks utilizing GIS, RS, and EPANET. The findings of this research can help in enhancing the water delivery systems in other towns with comparable issues. HIGHLIGHTS Novel integration of geographic information system, remote sensing, and EPANET for water distribution system analysis.; Focus on water distribution challenges in Bota town.; Improved system efficiency and equitable water distribution as anticipated outcomes.; Practical applications for water utilities and policymakers in water resource management.; Replicable methodology with potential for global applicability.;

The article provides a comprehensive review of the use of the Internet of Things (IoT) in agriculture, along with its advantages and disadvantages. However, it's important to recognize that IoT holds immense potential for generating new ideas that could drive innovations in modern agriculture and address several challenges faced by farmers today. Applications such as smart irrigation, precision farming, crop and soil tracking, smart greenhouses, supply chain management, livestock monitoring, agricultural drones, pest and disease prevention, and farm machinery are among the areas considered for IoT implementation in agriculture by this paper. These innovative solutions have the potential to revolutionize farming practices, improve efficiency, reduce resource wastage, and ultimately enhance agricultural productivity and sustainability. The analysis examines each application in terms of its utility and outlines measures necessary to enhance its effectiveness. Key considerations include addressing connectivity issues, managing costs, ensuring data security and privacy, scaling solutions appropriately, effectively managing data, and promoting awareness and adoption of IoT tools. Despite these challenges, IoT offers numerous benefits to the agricultural sector. The paper underscores the importance of collaboration among farmers, IoT technology companies, academia, and policymakers to address these issues and fully harness the potential of IoT. To achieve this goal, ongoing research, development, and acceptance of IoT-driven solutions are essential to sustain agriculture as a viable option amidst emerging challenges such as climate change and resource scarcity.

The Narmada River basin, a significant water resource in central India, plays a crucial role in supporting agricultural, industrial, and domestic water supply. Effective management of this basin requires accurate streamflow forecasting, which has become increasingly important. This study delves into streamflow forecasting using historical data from five major river stations, covering the upper reaches in the East and middle sections. The dataset spans from 1978 to 2020 and undergoes rigorous screening and preparation, including normalization using StandardScaler. The research adopts a comprehensive approach, developing models for training on 70 % of historical data, validation on the most current 15 %, and testing against future targets with another 15 % of the data. To achieve precise predictions, a suite of machine learning models is employed, including CatBoost, LGBM (Light Gradient Boosting Machine), Random Forest, and XGBoost. Performance evaluation of these models relies on key indices such as mean squared error (MSE), mean absolute error (MAE), root mean square error (RMSE), root mean square percent error (RMSPE), normalized root mean squared error (NRMSE), and R-squared (R2). Notably, among these models, Random Forest emerges as the most robust for streamflow prediction, showcasing its effectiveness in handling the complexities of hydrological forecasting. This research contributes significantly to the field of streamflow forecasting in the Narmada River basin by providing insights into the performance of various machine learning models. The findings not only enhance our understanding of watershed dynamics but also highlight the pivotal role that machine learning can play in improving hydrological forecasting for sustainable watershed management.

Utilizing agricultural and industrial wastes, potent reservoirs of nutrients, for nourishing the soil and crops through composting embodies a sustainable approach to waste management and organic agriculture. To investigate this, a 2-year field experiment was conducted at ICAR-IARI, New Delhi, focusing on a pigeon pea–vegetable mustard–okra cropping system. Seven nutrient sources were tested, including a control (T1), 100% recommended dose of nitrogen (RDN) through farmyard manure (T2), 100% RDN through improved rice residue compost (T3), 100% RDN through a paddy husk ash (PHA)–based formulation (T4), 75% RDN through PHA-based formulation (T5), 100% RDN through a potato peel compost (PPC)–based formulation (T6), and 75% RDN through PPC-based formulation (T7). Employing a randomized block design with three replications, the results revealed that treatment T4 exhibited the significantly highest seed (1.89 ± 0.09 and 1.97 ± 0.12 t ha−1) and stover (7.83 ± 0.41 and 8.03 ± 0.58 t ha−1) yield of pigeon pea, leaf yield (81.57 ± 4.69 and 82.97 ± 4.17 t ha−1) of vegetable mustard, and fruit (13.54 ± 0.82 and 13.78 ± 0.81 t ha−1) and stover (21.64 ± 1.31 and 22.03 ± 1.30 t ha−1) yield of okra during both study years compared to the control (T1). Treatment T4 was on par with T2 and T6 for seed and stover yield in pigeon pea, as well as okra, and leaf yield in vegetable mustard over both years. Moreover, T4 demonstrated notable increase of 124.1% and 158.2% in NH4-N and NO3-N levels in the soil, respectively, over the control. The enhanced status of available nitrogen (N) and phosphorus (P) in the soil, coupled with increased soil organic carbon (0.41%), total bacteria population (21.1%), fungi (37.2%), actinomycetes (44.6%), and microbial biomass carbon (28.5%), further emphasized the positive impact of T4 compared to the control. Treatments T2 and T6 exhibited comparable outcomes to T4 concerning changes in available N, P, soil organic carbon, total bacteria population, fungi, actinomycetes, and microbial biomass carbon. In conclusion, treatments T4 and T6 emerge as viable sources of organic fertilizer, particularly in regions confronting farmyard manure shortages. These formulations offer substantial advantages, including enhanced yield, soil quality improvement, and efficient fertilizer utilization, thus contributing significantly to sustainable agricultural practices.

Urban heat islands are hotter than rural places. Sustainable urban growth and improving urban environments need understanding Urban Heat Island (UHI) causes and finding effective mitigation techniques. This research examines the seasonal deviations in surface temperatures for the UHI effect in Pune, India, focusing on land use patterns and water body cooling. Land use categorization included residential, commercial, industrial, vegetation, and open spaces. The research studied the cooling potential and temperature variance by distance from water bodies in the form of lakes, rivers, and ponds. These aquatic bodies have surface and ambient temperature sensors. Roads, soil, commercial areas, residential areas, industrial areas, and vegetation have all shown increases in NDBI, ranging from 15.84 to 36.45%. Urban regions with heat accumulation and dissipation have been revealed by DEM and contour maps. The research found that the water bodies have a cooling effect on LST till the distance of 350 m. The research finds hotter places and shows how natural features mitigate UHI by analyzing land use patterns and water body cooling. The findings emphasize the significance of green areas and water bodies in urban design and development to improve Pune's climate resilience and inhabitability. HIGHLIGHTS Study examines how UHI gets affected by Landuse variation and Water cooling with remote sensing and geospatial analysis.; The findings inform urban planning and climate change adaptation in similar cities.; Urban water bodies can act as natural climate buffers by cooling.; The study illuminates the UHI effects on urban climate resistance.;

Gallic acid (GA) is a well-known herbal bioactive compound found in many herbs and foods like tea, wine, cashew nuts, hazelnuts, walnuts, plums, grapes, mangoes, blackberries, blueberries, and strawberries. GA has been reported for several pharmacological activities, such as antioxidant, inflammatory, antineoplastic, antimicrobial, etc. Apart from its incredible therapeutic benefits, it has been associated with low permeability and bioavailability issues, limiting their efficacy. GA belongs to BCS (Biopharmaceutics classification system) class III (high solubility and low probability). In this context, novel drug delivery approaches played a vital role in resolving these GA issues. Nanocarrier systems help improve drug moiety’s physical and chemical stability by encapsulating them into a lipidic or polymeric matrix or core system. In this regard, researchers have developed a wide range of nanocarrier systems for GA, including liposomes, transfersomes, niosomes, dendrimers, phytosomes, micelles, nanoemulsions, metallic nanoparticles, solid lipid nanoparticles (SLNs), nanoparticles, nanostructured lipid carriers, polymer conjugates, etc. In the present review, different search engines like Scopus, PubMed, ScienceDirect, and Google Scholar have been referred to for acquiring recent information on the theme of the work. Therefore, this review paper aims to emphasize several novel drug delivery systems, patents, and clinical updates of GA.

Landslides are frequent natural hazards in mountainous regions, and harshly upset people's lives and livelihoods. In the present study, we have carried out an analysis of seven GIS-based machine-learning techniques; and asses their performance for landslide susceptibility mapping (LSM) in the Bhilangana Basin, Garhwal Himalaya. A landslide inventory consisting of 423 polygons was prepared using repeated field investigations, and multi-dated satellite images for the periods between 2000 and 2022. The landslide dataset was classified into two groups: training (70%) and test dataset (30%), and 12 predictive variables were used for the LSM. The methods used to produce LSM are boosted regression tree (BRT), Fisher discriminant analysis (FDA), generalized linear model (GLM), multivariate adaptive regression splines (MARS), model-architect analysis (MDA), random forest (RF) and support vector machine (SVM). The sensitivity and performance of these models to predict landslide susceptible areas were carried out using the area under the curve (AUC) method. The RF model (AUC = 0.988) has given the highest precision indicating the best performance. Though MARS (0.974), SVM (0.965) and MDA (0.952) models have also performed adequately for the LSM (all have AUC values above 0.95), however, it is recommended that the RF model is highly suitable for LSM in the mountainous region.

Environmental flows are defined as the flow required into a stream to maintain the river’s ecosystem. The notion of Environmental Flow Allocation (EFA) ensures that a sufficient amount of water is delivered to the stream to maintain ecological integrity. The objective of this study is to examine environmental flows and determine the best acceptable strategy for providing flows into the river in the Lower Tapi Basin. To achieve this objective, daily discharge data from three sites, Ukai (period 1975–2020), Motinaroli (period 1990–2021), and Ghala (period 1995–2005) were collected and analyzed using the Tennant, Tessman, variable monthly flow (VMF), and Smakhtin methodologies. A comparative analysis was carried out on all three sites using the four methodologies. The Tessman and VMF approaches have a strong connection with the computed environmental flow requirements (EFR), according to the results. The calculated EFR was found to be in the range of 30–35% of mean annual flows (MAF). The maximum EFR found at station Ghala is about 54.5% of MAF according to the Tessman method. Such research will help to prevent future degradation of the river by supplying flow in accordance with the EFR, and it will also be used by stakeholders and policymakers to allocate water to preserve the ecosystem.

Multi-objective reservoir operation presents a number of critical challenges that must be overcome for efficient management of water resources. The inherent contradiction between several goals, such as satisfying irrigation demand and maximizing hydropower generation, is one of the major issues. Trade-offs and compromises must be carefully considered to balance these objectives. To solve this problem, a study was carried out to optimize the operation of multi-objective reservoirs with two primary goals: minimizing irrigation deficits and maximizing hydropower generation. This study employs the self-adaptive multipopulation multi-objective Jaya algorithm (SAMP-MOJA), an improved version of the Jaya algorithm, to construct an optimal Pareto Front utilizing an a priori approach. The performance of SAMP-MOJA is compared to that of other algorithms such as multi-objective particle swarm optimization, multi-objective invasive weed optimization, and multi-objective Jaya algorithm. The results of this study demonstrate that the hydropower generated by the developed model surpasses 80% of the actual generation. The study's findings will aid in designing the most effective Pareto front possible. HIGHLIGHTS A multi-objective model is developed to balance irrigation deficiency and maximum hydropower generation.; An improved version of the Jaya algorithm has been implemented over multi-objective reservoir operation.; Self-adaptive optimization method is used in the reservoir operation.; Optimization of multi-objective reservoir operation is carried out using the metaheuristic method.; Developed model outperforms better in comparison to several other algorithms.;

Seepage is the phenomenon of water infiltrating through a gravity dam's foundation, causing erosion and weakening the dam's construction over time. If not properly managed, this can eventually lead to the dam's catastrophic failure, posing a significant danger to public safety and the environment. As a result, precise seepage prediction in gravity dams is essential for ensuring their safety and stability. This review paper looks at the use of artificial intelligence (AI) techniques for predicting seepage in gravity dams, as well as the challenges and possible solutions. The paper identifies and suggests potential solutions to the challenges connected with using AI for seepage prediction, such as data quality and model interpretability. The paper also covers future research paths, such as the creation of advanced machine learning algorithms and the improvement of data collection and processing. Overall, this review gives insight on the current state of the art in using AI to predict gravity dam seepage and recommends methods to improve the accuracy and reliability of such models. HIGHLIGHTS AI methods for predicting gravity dam seepage reviewed, with challenges and solutions.; The review provides an overview of using AI for seepage prediction in gravity dams.; AI challenges addressed with suggested solutions for improved seepage prediction.; Standardizing data collection and improving quality reduces errors in prediction models.; Insights for dam safety practitioners, improving seepage.;

This study addresses the intricate interplay of magnetohydrodynamics (MHD), thermal radiation, and porous media effects, which are crucial in numerous engineering applications, including aerospace, energy systems, and environmental processes. The development of a mass-based hybrid nanofluid model signifies a novel approach, potentially yielding more accurate predictions and insights into the thermal behavior of fluids in diverse scenarios. Thus, the current research explores the heat transfer characteristics of a unique nanofluid known as TiO2 (titania)-CuO (copper oxide)/H2O (water) hybrid nanofluid. This nanofluid flows past a static or moving wedge considering the impact of thermal radiation and magnetic field in the appearance of porous medium. To calculate the effective thermophysical attributions of the hybrid (TiO2-CuO) nanofluid, a mass-based strategy is employed. This approach involves analyzing the masses of both the first and second nanoparticles, along with the mass of the base fluid, as essential input parameters. The proposed mathematical model is modified to a dimensionless form by applying similarity transformations. The numerical solution is obtained by utilizing the bvp4c built-in function within the MATLAB environment. Graphs illustrate the influence of various parameters on temperature and velocity trends, including the magnetic field parameter and heat absorption/generation parameter as well as the thermal radiation parameter. It is noted that along with the enhancement in the values of parameters related to porous medium or magnetic field, the velocity of the hybrid nanofluid improves. This occurs when the moving wedge parameter’s value is below 1. Conversely, when the moving wedge parameter’s value exceeds 1, the velocity of the hybrid nanofluid decreases. The shape factor is more effective in the temperature profile for developed inputs of heat absorption/generation parameter. A juxtaposition of enhancement in heat transfer rate due to nanofluid (TiO2/H2O) and hybrid nanofluid (TiO2-CuO/H2O) is likewise presented. The main outcome indicates that the hybrid nanofluid exhibits superior thermal conductivity relative to the conventional nanofluid.

The purpose of the study was to use hierarchical clustering and Thiessen polygon algorithms to identify the significant rain gauge stations for flood forecasting at Sardar Sarovar Dam. Rainfall data from 2010 to 2018 was utilized to analyze the catchment region between Omkareshwar Dam and Sardar Sarovar Dam. The study identified two clusters of rain gauge stations with similar rainfall patterns and divided the study area into seventeen regions using Thiessen polygons. The land use map showed that the study area was mostly covered by crop lands, and the soil map divided the area into three types of sedimentary claystone soil. A hydrological model, Hydrologic Engineering Center – Hydrologic Modelling System (HEC-HMS), was used for rainfall-runoff modeling, and the computed runoff was compared with observed gauge discharge and inflow of Sardar Sarovar Dam. Regression analysis was performed to assess the performance of the model, and the results showed a good correlation between observed rainfall and estimated runoff values for 2012 and 2016. The study concludes that the existing rain gauge network is sufficient for flood forecasting, and the developed model along with the Thiessen polygon method can provide more accurate predictions of flow. The study highlights the importance of selecting suitable rain gauge stations for reliable flood forecasting in flood-prone basins. The study findings can be useful for future runoff prediction and flood forecasting in the study area.

A catastrophic flood occurred on 7 February 2021 around 10:30 AM (local time) in the Rishiganga River, which has been attributed to a rockslide in the upper reach of the Raunthi River. The Resourcesat 2 LISS IV (8 February 2021) and CNES Airbus satellite imagery (9 February 2021) clearly show the location of displaced materials. The solar radiation observed was higher than normal by 10% and 25% on 6 and 7 February 2021, respectively, however, the temperature shows up to 34% changes. These conditions are responsible for the sudden change in instability in glacier blocks causing deadly rock-ice slides that led to the collapse of the hanging glacier as a wedge failure. The displaced materials mixed with ice, snow, and debris caused catastrophic floods downstream within no time that destroyed critical infrastructure and killed human lives. The hydrodynamic modelling (HEC-RAS software) shows mean flow velocity up to 22.4 ± 8.6 m/s with an average depth of 16.3 ± 6.5 m that caused deadly devastation in the source region and along the rivers due to the flow of water in the valley.

Background: Neonatal sepsis is responsible for about 30%–50% of the total neonatal deaths in developing countries. Thyroid hormone changes are highly common in hospitalized patients or with critical illnesses. The aim of the study is to compare thyroid hormone profile in term neonates with early-onset sepsis (EOS) and healthy controls and their association with outcome of sepsis. Materials and Methods: A total of 150 neonates, 75 cases with EOS and 75 healthy controls, were included in this study. EOS was defined according to the European Medical Agency Sepsis Criteria of neonatal sepsis. Results: The mean free triiodothyronine and mean thyroid-stimulating hormone level in neonates with EOS were significantly lower than in control group. Conclusion: Neonatal sepsis causes significant alteration in thyroid hormone profile in term neonates with EOS.

Water resource management is a complex engineering problem, due to the stochastic nature of inflow, various demands and environmental flow downstream. With the increase in water consumption for domestic use and irrigation, it becomes more challenging. Many more difficulties, such as non-convex, nonlinear, multi-objective, and discontinuous functions, exist in real life. From the past two decades, heuristic and metaheuristic optimization techniques have played a significant role in managing and providing better performance solutions. The popularity of heuristic and metaheuristic optimization techniques has increased among researchers due to their numerous benefits and possibilities. Researchers are attempting to develop more accurate and efficient models by incorporating novel methods and hybridizing existing ones. This paper's main contribution is to show the state-of-the-art of heuristic and metaheuristic optimization techniques in water resource management. The research provides a comprehensive overview of the various techniques within the context of a thorough evaluation and discussion. As a result, for water resource management problems, this study introduces the most promising evolutionary and swarm intelligence techniques. Hybridization, modifications, and algorithm variants are reported to be the most successful for improving optimization techniques. This survey can be used to aid hydrologists and scientists in deciding the proper optimization techniques. HIGHLIGHTS This paper reviews the different evolutionary algorithms and their applications.; This paper reviews the different swarm intelligence algorithms and their applications.; It identifies the importance and demerits of different heuristic and meta-heuristic algorithms.; It highlights some of the observations on the challenges and needs.; It highlights the state-of-the-art of water resources management and planning.;

This paper introduces an effective and reliable approach based on a multi-population approach, namely the self-adaptive multi-population Jaya algorithm (SAMP-JA), to extract multi-purpose reservoir operation policies. The current research focused on two goals: minimizing irrigation deficits and maximizing hydropower generation. Three different models were formulated. The results were compared with those for an ordinary Jaya algorithm (JA), particle swarm optimization (PSO), and an invasive weed optimization (IWO) algorithm. In Model-1, the minimum irrigation deficit obtained by SAMP-JA and JA was 305092.99 . SAMP-JA was better than JA, PSO and IWO in terms of convergence. In Model-2, the maximum hydropower generation achieved by SAMP-JA, JA and PSO was 1723.50 . When comparing the average hydropower generation, SAMP-JA and PSO performed better than JA and IWO. In terms of convergence, SAMP-JA was better than PSO. In Model-3, a self-adaptive multi-population multi-objective Jaya algorithm (SAMP-MOJA) was better than multi-objective particle swarm optimization (MOPSO) and multi-objective Jaya algorithm (MOJA) in terms of maximum hydropower generation, and MOPSO was better than SAMP-MOJA and MOJA in terms of minimum irrigation deficiency. While comparing convergence, SAMP-MOJA was found to be better than MOPSO and MOJA. Overall, SAMP-JA was found to outperform JA, POS and IWO. HIGHLIGHTS Advanced optimization algorithm free from internal parameters.; Introduces an effective and reliable approach based on a multi-population approach.; An improved Jaya algorithm was used for single and multi-objective optimization models for reservoir operation.; Both hydropower benefits and irrigation deficiencies are considered.; Proposed algorithms can generate greater hydropower with lower inflow than the actual inflow.;

Background: There are increasing concerns about the adverse effects of highly active antiretroviral therapy (HAART) on lipid profiles. Limited studies reported the adverse effects of HAART on lipid profiles from the Indian subcontinent. Methods: This observational study was conducted at a tertiary care hospital in North Western India. The objective of the study was to find out the adverse effects on lipid profiles after HAART initiation in newly diagnosed HIV-infected children of immune stage III and IV in the 18 months to 16 year age group. These 43 children were evaluated every 3 months for lipid profile for 1 year. Results: There was a nonsignificant increase in total lipid and phospholipid levels and a statistically significant increase in triglycerides (TGs), cholesterol, low-density lipoprotein (LDL), and very low-density lipoprotein (VLDL) levels after initiation of HAART therapy and a statistically significant decline in high-density lipoprotein (HDL) level after initiation of HAART therapy in immune phase III. The mean level of total lipid profile, phospholipids, cholesterol, HDL, LDL, VLDL, and TGs before starting of treatment was 474 mg/dl, 147 mg/dl, 126.8 mg/dl, 44.7 mg/dl, 94.3 mg/dl, 22.9 mg/dl, and110 mg/dl in immune stage IV, respectively. After starting treatment, the mean level of total lipid profile, phospholipids, cholesterol, HDL, LDL, VLDL, and TGs was 495.7 mg/dl, 164 mg/dl, 146.5 mg/dl, 38.9 mg/dl, 108.5 mg/dl, 28.9 mg/dl, and 127.3 mg/dl in immune stage IV, respectively. There was a statistically significant increase in total lipid, TG, phospholipid, cholesterol, LDL, and VLDL levels. There was a statistically significant decline in HDL level after initiation of HAART therapy in immune stage IV. Conclusions: We concluded that there was a significant alteration in lipid profile in HIV-infected children of immune stage III and IV with HAART.

Introduction: Although ventilator-associated pneumonia (VAP) mostly has bacterial etiology, many cases have viral etiology. The present study aimed at determining the proportion of viral VAP and any associated factors and identifying the viral isolates in pediatric VAP. Materials and Methods: A descriptive observational study was conducted at pediatric intensive care unit of tertiary referral center including 120 VAP cases aged 1–18 years. Endotracheal aspiration samples from these patients were tested for viral etiology. Results: Most VAP cases were males (66.7%). Virus was isolated in 14.2% of cases of VAP. Human adenovirus (29%), respiratory syncytial virus (29%), and herpes simplex virus (24%) were the most common viruses identified. Viral VAP was significantly associated with age, primary disease, and total leukocyte count. Neutrophil count decreased and lymphocyte count increased significantly after viral VAP. Conclusion: Viral etiology should be considered in VAP patients, especially in patients not responding to antibiotics as proper diagnosis and timely initiation of appropriate antiviral could be lifesaving. Prevalent viruses may vary geographically; hence, hospitals should try to identify the common viruses causing VAP in their settings to guide appropriate battery of tests and antiviral drugs.

Extreme weather events such as droughts are catastrophic and can have serious consequences for people and the environment. Drought may be managed if measures are taken in advance. The success of this endeavor depends on a number of factors, not the least of which is accurate descriptions and measurements of drought conditions. Reducing the negative consequences of droughts requires an early forecast of drought conditions. The primary objective of this research is, hence, to establish a process for the assessment and prediction of drought. The drought evaluation was carried out using the standards established by the SPI and the Indian Meteorological Department. Maps of drought severity were generated using severe drought data. Thirty years’ worth of SPI readings was analyzed. Fuzzy-based drought forecasting model parameters were determined during a 25-year period, and the model was validated throughout the remaining years. The findings of this study can be used by the community to help combat the drought. Before the drought worsens, the local government can implement lifesaving mitigating measures.

The management of water resources depends heavily on hydrological prediction, and advances in machine learning (ML) present prospects for improving predictive modelling capabilities. This study investigates the use of a variety of widely used machine learning algorithms, such as CatBoost, ElasticNet, k-Nearest Neighbors (KNN), Lasso, Light Gradient Boosting Machine Regressor (LGBM), Linear Regression (LR), Multilayer Perceptron (MLP), Random Forest (RF), Ridge, Stochastic Gradient Descent (SGD), and the Extreme Gradient Boosting Regression Model (XGBoost), to predict the river inflow of the Garudeshwar watershed, a key element in planning for flood control and water supply. The substantial engineering feature used in the study, which incorporates temporal lag and contextual data based on Indian seasons, leads it distinctiveness. The study concludes that the CatBoost method demonstrated remarkable performance across various metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2) values, for both training and testing datasets. This was accomplished by an in-depth investigation and model comparison. In contrast to CatBoost, XGBoost and LGBM demonstrated a higher percentage of data points with prediction errors exceeding 35% for moderate inflow numbers above 10,000. CatBoost established itself as a reliable method for hydrological time-series modelling, easily managing both categorical and continuous variables, and thereby greatly enhancing prediction accuracy. The results of this study highlight the value and promise of widely used machine learning algorithms in hydrology and offer valuable insights for academics and industry professionals.

As one of nature’s most destructive calamities, floods cause fatalities, property destruction, and infrastructure damage, affecting millions of people worldwide. Due to its ability to accurately anticipate and successfully mitigate the effects of floods, flood modeling is an important approach in flood control. This study provides a thorough summary of flood modeling’s current condition, problems, and probable future directions. The study of flood modeling includes models based on hydrologic, hydraulic, numerical, rainfall–runoff, remote sensing and GIS, artificial intelligence and machine learning, and multiple-criteria decision analysis. Additionally, it covers the heuristic and metaheuristic techniques employed in flood control. The evaluation examines the advantages and disadvantages of various models, and evaluates how well they are able to predict the course and impacts of floods. The constraints of the data, the unpredictable nature of the model, and the complexity of the model are some of the difficulties that flood modeling must overcome. In the study’s conclusion, prospects for development and advancement in the field of flood modeling are discussed, including the use of advanced technologies and integrated models. To improve flood risk management and lessen the effects of floods on society, the report emphasizes the necessity for ongoing research in flood modeling.

Background: The process of radiotherapy treatment planning and delivery involves multiple steps and professionals causing it to be prone to errors. Radiotherapy centers equipped with old telecobalt machines have certain peculiar challenges to workflow. We designed and tested a checklist for radiotherapy technicians (RTTs) to reduce chances of error during treatment delivery on a telecobalt machine. Materials and Methods: A physical checklist was designed for RTTs to use in the pretreatment pause using a template advocated by the American Association of Physicists in Medicine. It was tested on 4 RTTs over 1000 radiotherapy delivery sittings. Results: The checklist helped to rectify 41 documentary lapses and 28 errors in radiotherapy treatment parameters while also identifying 12 instances where treatment plan modifications were due and 30 where the patient was due for review by the radiation oncologist. The average time to go through the checklist was between 2.5 and 3 min. Conclusions: The development and use of the checklist has helped in reducing errors and also improving workflow in our department. It is recommended to utilize such physical checklists in all radiotherapy centers with telecobalt machines. The success of the checklist depends upon leadership, teamwork, acceptance of a need to inculcate a “safety culture,” with voluntary error-reporting and a willingness to learn from such errors.

Craniopharyngiomas (CPs) are histological benign lesions that can develop at different positions along the pituitary–hypothalamic axis from the sella to the third ventricle. A common characteristic of pediatric CPs is the presence of a large suprasellar cystic component and symptoms are mainly caused by direct compression over adjacent neurovascular structures and obstruction of the Cerebrospinal Fluid (CSF) flow pathway. They represent a distinctive management challenge, and aggressive surgical resection can have major postoperative complications and adverse outcomes. The transcranial endoscopic decompression of cysts followed by the placement of an Ommaya reservoir is a safe, minimally invasive technique used to abolish symptoms caused by the pressure effect. In this study, we present our experience of managing cystic CPs of pediatric age using the transcranial endoscopic approach.Six consecutive cystic CP cases operated on using the transfrontal transventricular approach between 2015 and 2018 were retrospectively reviewed. An endoscope was introduced through a Kocher’s burr hole, the cyst fenestrated, and the contents gently aspirated using an angiography catheter and a 5-ml syringe. The cyst wall, free from surrounding structures, was excised, and the remnants coagulated. An Ommaya reservoir was placed in all cases.Headache and hydrocephalus were relieved in all cases (6/6), and visual improvement was observed in 5/6 cases. Post-operative diabetes insipidus and hypothalamic dysfunction were not observed in any patient. One patient with cyst recurrence at follow-up underwent subtotal resection followed by adjuvant radiotherapy.Our experience with limited number of cases demonstrates that the transcranial endoscopic approach is a minimally invasive and safe technique for the decompression of cystic CPs. It provides acceptable long-term disease control and circumvents the complications associated with radical surgery.

Background: There is lack of information regarding pattern of distribution of pediatric cancers in Bihar. Aim: The aim of this study is to identify the pattern of distribution of pediatric cancers. Objectives: To analyze demographic data, type, and pattern of pediatric cancers in Bihar by retrospective clinical audit. Materials and Methods: All individual consecutive patients between ages 0 and 18 years registered in the Department of Medical and Pediatric Oncology from January 1, 2018 till December 31, 2018, were enrolled in this study. Data pertaining to age, sex, and type of cancer were retrieved from clinical database by retrospective audit and stratified into hematolymphoid and solid pediatric cancer cohorts. Frequency distribution and descriptive statistics were used to analyze the data using SPSS version 17.0. Results: A total of 247 pediatric cancers were registered, of which 142/247 (57%) and 15/247 (43%) were pediatric hematolymphoid and solid cancers, respectively. The median age was 9 years, while male-to-female ratio was 2.26. Acute lymphoblastic leukemia (ALL), 76/247 (31%) was the most common pediatric cancer overall. Hodgkin's lymphoma, 27/142 (19%) was the second most common hematolymphoid malignancy, after ALL was 76/142 (54%). Among solid tumors, Wilms' tumor was the most common, 28/105 (27%) followed by Ewing's sarcoma, 16/105 (15%), and germ cell tumor, 15/105 (14%). Central nervous system malignancies were among the least common solid tumor cancers, 3/105 (3%). Conclusion: ALL and Hodgkin's lymphoma are the most common pediatric cancers. Among solid malignancies, Wilms tumor, Ewing's sarcoma, and Germ cell tumor are predominant.

Gallic acid (GA) is a well-known herbal bioactive compound found in many herbs and foods like tea, wine, cashew nuts, hazelnuts, walnuts, plums, grapes, mangoes, blackberries, blueberries, and strawberries. GA has been reported for several pharmacological activities, such as antioxidant, inflammatory, antineoplastic, antimicrobial, etc. Apart from its incredible therapeutic benefits, it has been associated with low permeability and bioavailability issues, limiting their efficacy. GA belongs to BCS (Biopharmaceutics classification system) class III (high solubility and low probability). In this context, novel drug delivery approaches played a vital role in resolving these GA issues. Nanocarrier systems help improve drug moiety's physical and chemical stability by encapsulating them into a lipidic or polymeric matrix or core system. In this regard, researchers have developed a wide range of nanocarrier systems for GA, including liposomes, transfersomes, niosomes, dendrimers, phytosomes, micelles, nanoemulsions, metallic nanoparticles, solid lipid nanoparticles (SLNs), nanoparticles, nanostructured lipid carriers, polymer conjugates, etc. In the present review, different search engines like Scopus, PubMed, ScienceDirect, and Google Scholar have been referred to for acquiring recent information on the theme of the work. Therefore, this review paper aims to emphasize several novel drug delivery systems, patents, and clinical updates of GA. [ABSTRACT FROM AUTHOR]