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Mathematical models are being used to investigate the dynamics of disease dissemination, forecast future trends, and access the most effective preventative measures to minimise the extent of epidemic outbreaks. This study formulates an eight compartmental epidemiological model to analyze the COVID-19 dynamics. The stability analysis of infection-free equilibrium is performed. The parameters are estimated by fitting this model to reported confirmed COVID-19 cases in India for 350 days. Sensitivity analysis is executed to identify the most sensitive parameters in this model. An optimal control analysis for India is implemented by incorporating four controls: 1) Public awareness initiatives using the media and civic society to persuade uninfected people not to interact with infected ones, 2) the effort of vaccinating susceptible individuals by supposing all of the susceptible people who got their vaccination are promptly moved to the recovered class 3) encouraging those who are infected with COVID-19 disease to stay at home or join in quarantine centres, as well as encouraging the severe cases admit in the hospital. The results are demonstrated that employing all four control measures significantly reduced the proportion of COVID-19 infections. [ABSTRACT FROM AUTHOR]

This paper describes a case study of continuous hydrologic modeling in the Brahmani River basin in India using the Hydrologic Engineering Center—Hydrologic Modeling System (HEC–HMS). The soil moisture algorithm (SMA) was employed as loss method for continuous simulations in the thirteen sub-basins. The sensitivity analysis showed that soil storage, tension storage and soil percolation are most sensitive parameters. The model performance was assessed for calibration period of 15 years (1990–2004) and validation period of 12 years (2005–2016), using statistical and visual evaluation techniques. During the calibration period, the observed and simulated values showed a good fit, with R2 ranging from 0.67 to 0.74, NSE from 0.66 to 0.72, and PBIAS from −8.5 to −9.5%. Similar performances are obtained during the validation period, with R2 ranging from 0.62 to 0.70, NSE from 0.60 to 0.68, and PBIAS from −2.5 to 21.4%. The results of the SMA procedure employed in the HEC-HMS model give valuable information for flood control and water supply management in the Brahmani Basin and similar other regions in Eastern India. [ABSTRACT FROM AUTHOR]

Microgrids and hybrid renewable energy systems play a crucial role in today's energy transition. They enable local power generation and distribution, reducing dependence on large centralized infrastructures, can operate independently or connected to a grid, and can provide backup power, thus increasing system resilience. In addition, they combine multiple renewable energy sources, such as solar, wind, hydro, and biomass, to maximize the efficiency and reliability of the supply, and are also adaptable to location-specific conditions, taking advantage of locally available energy resources and reducing the need for energy imports. Moreover, they contribute to decarbonization goals by offering a cleaner and more sustainable alternative. In this article, a documentary review is presented on the interaction of Homer Pro software 3.16.2 (July 2023), used for the design of hybrid renewable energy systems (HRES), with other methods of optimization or sizing. Allusion is made to the type of architecture in the most prominent clean and fossil source configurations, the levelized cost, net annual cost, and maintenance and capital investment cost. A comparison is made among the works reported in the last five years regarding the use of this software tool, based on load demand, geographical area, renewable energy sources, fossil sources, and objective functions, applied to the educational, rural, and industrial sectors. It is shown that India is one of the countries that has reported the most number of HRES techno-economic environmental analysis works, and that the case studies have focused approximately 47% on rural areas, 20% on educational agencies, 14% on commerce and industry, and 29% on urban buildings. [ABSTRACT FROM AUTHOR]

How informative are treatment effects estimated in one region or time period for another region or time? In this article, I derive bounds on the average treatment effect in a context of interest using experimental evidence from another context. The bounds are based on (a) the information identified about treatment effect heterogeneity due to unobservables in the experiment and (b) using differences in outcome distributions across contexts to learn about differences in distributions of unobservables. Empirically, using data from a pair of remedial education experiments carried out in India, I show the bounds are able to recover average treatment effects in one location using results from the other while the benchmark method cannot. [ABSTRACT FROM AUTHOR]

In this study, a sensitivity analysis on a VECTRI dynamical model of malaria transmission is investigated to determine the relative importance of model parameters to disease transmission and prevalence. Apart from being most climatic prone, Odisha is a highly endemic state for malaria in India. The lack in sufficient modeling studies severely impacts the malarial process studies which further hinder the possibility of malaria early warning systems and preventive measures to be undertaken beforehand. Therefore, modeling studies and investigating the relationship between malaria transmission process studies and associated climatic factors are the need of the hour. Environmental conditions have pronounced effects on the malaria transmission dynamics and abundance of the poikilothermic vectors, but the exact relationship of sensitivity for these parameters is not well established. Sensitivity analysis is a useful tool for ascertaining model responses to different input variables. Therefore, in order to perform the requisite study, a dynamical model, VECTRI, is utilized. The study period ranges from 2000 to 2013, where several sensitivity tests are performed using different model parameters such as infiltration and evaporation rate loss of ponds, degree-days for parasite development, threshold temperature for parasite development, threshold temperature for egg development in the vector, and maximum and minimum temperature for larvae survival. The experiments suggest that the lower value of minimum temperature for larvae survival (rlarv_tmin), i.e., 16 °C, provides higher vector density and entomological inoculation rate (EIR) values. EIR reaches its maximum, when the threshold temperature for parasite development (rtsporo) is 22 °C and degree-days for parasite development (dsporo) is 8 degree-days. No change is observed in the vector density; even when rtsporo is 30 °C, values of EIR are close to 0. A successive increment of infiltration and evaporation rate loss of ponds (rwaterfrac evap126) values from 130 to 200 mm/day result in approximately 5% consistent decline in vector density and EIR. The study concludes that the most sensitive parameters are dsporo, rlarv_tmin, and rwaterfrac evap126. The VECTRI model is rather insensitive to maximum temperature for larvae survival (rlarv_tmin) for vector density and EIR variables. Further certain modifications and improvements are required in VECTRI to predict out variables like vector density and EIR more accurately in highly endemic region. [ABSTRACT FROM AUTHOR]

This study employs the Soil and Water Assessment Tool (SWAT) model to evaluate soil loss within the Shilabati and Dwarkeswar River Basin of West Bengal, serving as a pilot investigation into soil erosion levels at ungauged stations during the post-monsoon season. Detailed data for temperature, precipitation, wind speed, solar radiation, and relative humidity for 2000–2022 were collected. A land use map, soil map, and slope map were prepared to execute the model. The model categorizes the watershed region into 19 sub-basins and 227 Hydrological Response Units (HRUs). A detailed study with regard to soil loss was carried out. A detailed examination of soil erosion patterns over four distinct time periods (2003–2007, 2007–2012, 2013–2017, and 2018–2022) indicated variability in soil loss severity across sub-basins. The years 2008–2012, characterized by lower precipitation, witnessed reduced soil erosion. Sub-basins 6, 16, 17, and 19 consistently faced substantial soil loss, while minimal erosion was observed in sub-basins 14 and 18. The absence of a definitive soil loss pattern highlights the region's susceptibility to climatic variables. Reduced soil erosion from 2018 to 2022 is attributed to diminished precipitation and subsequent lower discharge levels. The study emphasizes the intricate relationship between climatic factors and soil erosion dynamics. [ABSTRACT FROM AUTHOR]

Small and medium enterprises (SMEs) in India are suffering from the long-standing challenges related to asymmetric information, high transaction costs, SMEs' opacity and limited access to credit. Blockchain technology, which is still in its infancy in terms of adoption in India, can facilitate SMEs to counter these challenges. Fuelled by this motivation, the study aims to investigate the significant barriers to blockchain adoption in supply chain finance practices by Indian SMEs. Using fuzzy-analytic hierarchy process, sensitivity analysis, and fuzzy-decision-making trial and evaluation laboratory this paper identifies the blockchain barriers, prioritises them and examine their cause and effect relationships. The results of the study indicate that technology barriers are the most influential barriers that impede blockchain adoption. The findings will help the policymakers and practitioners to take suitable measures to overcome these barriers and fuel the adoption of blockchain in Indian SMEs. [ABSTRACT FROM AUTHOR]

In this study, we formulate an eight-compartment mathematical model with vaccination as one of the compartments to analyze the dynamics of COVID-19 transmission. We examine the model's qualitative properties, such as positivity and boundedness of solutions, and stability analysis of the illness-free equilibrium with respect to the basic reproduction number. We estimate ten significant parameters and also compute the magnitude of the basic reproduction number for India by fitting the proposed model to daily confirmed and cumulative confirmed COVID-19 cases in India. Sensitivity analysis with respect to basic reproduction number is conducted, and the main parameters that impact the widespread of disease are determined. We further extend this model to an optimal control problem by including four non-pharmaceutical and pharmaceutical intervention measures as control functions. Our numerical results show that the four control strategy has greater impact than the three control strategies, two control strategies, and single control strategies on reducing the dynamics of COVID-19 transmission. [ABSTRACT FROM AUTHOR]

This study investigates the scour depth of coarser beds constituting boulders up to 100 mm in size. The independent parameters viz. particle size (dm), bed slope (S), and unit discharge (q) were determined experimentally at five bridge sites of Erin Nallah, J&K over a 7 Km stretch, along with a dependent parameter scour depth (D). The relationship between the dependent parameter, scour depth, and independent parameters, namely, particle size, bed slope, and unit discharge were established in the form of an exponential equation through logarithm and matrix method. The established equation was validated using an experimental data set of another site, along with the different models available in the literature. The insignificant variance was observed in reference to available models of estimation of scour depth. A sensitivity analysis is done. Lastly, multiple non-linear regression was also done. The relationship obtained was in corroboration with the relationship derived using the logarithm and matrix method. The coefficient of determination, R2, was found to be 0.99, which signifies relationship is strong. The established empirical equation may be used to estimate scour depth of coarser beds, however further study is needed to make it a common practice. [ABSTRACT FROM AUTHOR]

Aim of the water balance study is to evaluate the distribution of hydrological responses towards water balance components for a watershed. The present study highlights the water balance study for a watershed of Mahanadi river basin of India. Impact estimation of Landuse Land Cover (LULC) on runoff using Soil and Water Assessment Tool (SWAT) model under varying levels of hydrological response unit (HRU) has also been determined. For the purpose, LULC maps are prepared and utilized in decadal intervals for the years 1994, 2004 and 2014. The sensitivity analysis was performed using local and global methods, where eight most sensitive parameters have been utilized for calibration and validation. The model showed good agreement between observed and estimated time scale and showed satisfactory results during calibration and validation. As per impact estimation of LULC during 1994 to 2014, it was noticed that significant percentage of runoff has been increased from 27.6 to 30.95%, respectively. It was also noticed that the annual average ground water contribution decreased with LULC, from 20.1% (1994) to 15.24% (2014). Sub-watershed (SWS-6) and SWS-2 have been identified as high priority for implementation of conservation measures and better water management. Because these sub-watersheds yielded maximum runoff. The HRU role in hydrological modelling revealed that a greater number of HRUs simulate runoff more precisely and more accurately useful for water management studies. [ABSTRACT FROM AUTHOR]

The socio-economic feasibility of biogas as a renewable source of energy has been analyzed for the energy security of India. The impact of Indian government schemes such as the National Biogas and Manure Management Programme (NBMMP) for the implementation of Bioenergy has been discussed in detail. The feasibility of a water-based scrubber (high as well as low pressure) for Bio-methane production in the Indian scenario was analyzed. Theoretical modeling for Steady-State Digester Pressure Water-based Biogas Scrubber (DP-WBS) was performed using the Sum Rate Method. Design parameters for a DP-WBS-based scrubber having a capacity of 60 Nm3/h were optimized at the digester pressure of 110 mm of the Water Column (WC). Modeling for raw biogas (CH4 64 %, CO2 30 %, H2S 1000 ppm) scrubbing was done with and without water recirculation. Sensitivity analysis shows that a 90 m3/h water flow rate and a total of 7 theoretical stages are required to reduce the CO2 concentration in biogas from 30 % to <2.58 % and H2S concentration from 1000 ppm to <20 ppm. H2S removal efficiency in the scrubber was found to be highly dependent on operating conditions at the regeneration section. [ABSTRACT FROM AUTHOR]

Mathematical modeling plays a crucial role in disease modeling. Therefore, in this work, a mathematical model for novel coronavirus is proposed using Caputo-Fabrizio (CF) fractional derivative, while considering the issue of dimensional mismatch for proposed model. This model consists of paramount compartments such as asymptomatic and quarantined. Subsequently, existence and uniqueness of proposed model are shown with the help of fixed-point theorem. The boundedness of solutions is also established using Laplace transform. The local and global stability of disease-free equilibrium point (DFE) of the proposed fractional order model is evaluated. The model is numerically solved using the three-point Adams–Bashforth method (ABM). The predicted results are validated through real data for India. For finding the best-fitted parameters which describe the coronavirus scenario, LSQCURVEFIT function in MATLAB is employed. To determine the reliability of the applied technique, we conduct a comparative analysis of ABM method with Euler method. Notably, the results show the efficiency of ABM method. At the same time, sensitivity analysis for parameters in accordance with reproduction number is conferred. Furthermore, numerical results for distinct values of fractional order derivatives are accomplished. This study also shows the long-term trajectories for recovered, active and death cases. The obtained results of the proposed model are in line with real data for fractional order. Additionally, various control measures are also shown graphically. The simulated results show the effectiveness of fractional order derivatives and control parameters which may help government officials and individuals manage the pandemic in a better way. [ABSTRACT FROM AUTHOR]

Condition assessment is a crucial step in maintaining and managing existing concrete bridges. Most of the studies consider defects and their severity levels in the evaluation process in the literature. As of now, underlying causes, which play an important role in magnifying existing defects, do not get much attention in the assessment process as per the literature. Thus, this study proposes an integrated Analytic Hierarchy Process (AHP) and fuzzy-Technique for Order of Preference by Similarity to Ideal Solution (fuzzy-TOPSIS) approach to rank defects considering underlying causes. Here, causes and sub-causes are considered as criteria and sub-criteria, respectively. AHP is used to determine relative importance weights of causes and sub-causes. The final defects ranking is obtained using fuzzy-TOSIS. The applicability of the model is demonstrated through a case study on a reinforced concrete bridge deck in Gujarat, India, where 'cracks' is obtained as the most critical defect for this specific case. The robustness of the model is examined by a sensitivity analysis. The proposed approach can help bridge owners to enhance the existing decision-making process of concrete bridges. Moreover, this approach can be considered as a first step into a new way of ranking defects of a more comprehensive system. [ABSTRACT FROM AUTHOR]

Wind energy has the potential to play a vital role in fulfilling the world's rising energy needs. Due to the cubic relationship between wind speed and power, slight wind speed variations substantially impact power generation. Various wind speed assessment methods are available in the literature, and the Numerical Weather Prediction (NWP) methods are among them. However, its sensitivity analysis is required under the NWP model to estimate wind speed effectively. The planetary boundary layer (PBL) scheme is crucial in the sensitivity analysis. This work used a strategic approach with sensitivity analysis to estimate wind fields in the southern part of Andhra Pradesh, India, using the NWP model. The wind field is simulated using the WRF model, and the best configuration is determined by simulating nine distinct PBL parameterization combinations for four locations. Despite overestimation from 3 am to 8 pm and underestimation from 8 pm to 3 am, the mean absolute percentage errors are determined to be 6%, 9%, 14%, and 16%, respectively. The Weibull distribution mean, shape parameter, and scale parameter all have relative errors of 9.2%, 8.9%, and 15.36%, respectively. The developed methodology would be valuable in assessing wind resources. [ABSTRACT FROM AUTHOR]

The magnetotelluric (MT) investigation carried out in Koyna Seismogenic Zone (KSZ), an intra-plate earthquake region in Western India, along an E–W profile brings out moderately conductive (~ 700–1000 Ωm) near vertical features within the very high resistive (> 20,000 Ωm) granite/granite-gneiss basement. Occurrences of these anomalous moderate conductors are corroborated with sensitivity analysis. The alignment of earthquake hypocenters along the resistive–conductive boundary signifies the moderate conductor as basement fault. The conversion of resistivity values to the ratio of seismic P- to S-wave velocity (vp/vs) suggests that the moderate conductivity of the fault zone (as compared to the surrounding basement) appears due to the presence of fluid in the fault zone. Geophysical evidences reveal ~ 2.5–3.6 vol% fluid in the fault zone with ~ 1.8–2.6% interconnected porosity, which migrates along the structural boundary and invades the mechanically strong basement to nucleate the brittle failure within it. The present study proposes two mechanisms for the seismicity in the Koyna region. First: the meteoric water circulation due to the loading–unloading of nearby Koyna reservoir acts as potential fluid source for this triggered seismicity, which has also been suggested by previous studies. Second: the fluid circulation due to a deep-seated source. The present MT study brings out a conductive feature below 20 km depth which is thought to be emerged due to the dehydration of amphibole bearing rocks. The fluid generated from dehydration might act as a probable source to the triggered seismicity; since the conductive feature has a linkage to the upper crust. [ABSTRACT FROM AUTHOR]

Rapidly increasing population of Kolkata metropolitan city has led to encroachment in the marshy areas, mainly Salt Lake and Rajarhat in the outskirts of the city. Haphazard and unplanned construction in the city and on marshy land-filled regions has rendered the city vulnerable to liquefaction in the event of earthquakes. Liquefaction potential in terms of factor of safety (FS) for silty clay and silty sand has been evaluated by deterministic procedures of Boulanger and Idriss (Evaluating the potential for liquefaction or cyclic failure of silts and clays. Centre for Geotechnical Modelling. Report No. UCD/CGM-04/01, 2004; CPT and SPT based liquefaction triggering procedures. Centre for Geotechnical Modelling. Report No. UCD/CGM-14/01, 2014), respectively, for the study area. Probability of liquefaction (PL) values were determined using first order second moment (FOSM) reliability method and using ArcGIS 10.4.1 software, liquefaction hazard maps were constructed based on factor of safety and probability of liquefaction values at depths 7 m, 15 m and 25 m. Using nonlinear regression analysis, an attempt has been made to correlate PL with its corresponding FS values. Logistic function was utilized and PL–FS relations along with their fitted curves were obtained for silty clay, silty sand as well as for all soils considered in the present study. Sensitivity analysis was conducted on random variables, undrained shear strength (Su), N-value for equivalent clean sand ((N1)60CS), total overburden pressure (σv), effective overburden pressure (σ'v), maximum horizontal acceleration (amax), stress reduction factor (rd) and magnitude scaling factor (MSF), by increasing their coefficient of variation by 10%, 20% and 30%. Sensitivity index for all the parameters were determined which showed that Su and ((N1)60CS are the most sensitive while σv and σ'v being the least sensitive parameters. [ABSTRACT FROM AUTHOR]

Rice arsenic (As) contamination and its consumption poses a significant health threat to humans. The present study focuses on the contribution of arsenic, micronutrients, and associated benefit-risk assessment through cooked rice from rural (exposed and control) and urban (apparently control) populations. The mean decreased percentages of As from uncooked to cooked rice for exposed (Gaighata), apparently control (Kolkata), and control (Pingla) areas are 73.8, 78.5, and 61.3%, respectively. The margin of exposure through cooked rice (MoEcooked rice) < 1 signifies the existence of health risk for all the studied exposed and control age groups. The respective contributions of iAs (inorganic arsenic) in uncooked and cooked rice are nearly 96.6, 94.7, and 100% and 92.2, 90.2, and 94.2% from exposed, apparently control, and control areas. LCR analysis for the exposed, apparently control, and control populations (adult male: 2.1 × 10–3, 2.8 × 10–4, 4.7 × 10–4; adult female: 1.9 × 10–3, 2.1 × 10–4, 4.4 × 10–4; and children: 5.8 × 10–4, 4.9 × 10–5, 1.1 × 10–4) through cooked rice is higher than the recommended value, i.e., 1 × 10–6, respectively, whereas HQ > 1 has been observed for all age groups from the exposed area and adult male group from the control area. Adults and children from rural area showed that ingestion rate (IR) and concentration are the respective influencing factors towards cooked rice As, whereas IR is solely responsible for all age groups from urban area. A vital suggestion is to reduce the IR of cooked rice for control population to avoid the As-induced health risks. The average intake (μg/day) of micronutrients is in the order of Zn > Se for all the studied populations and Se intake is lower for the exposed population (53.9) compared to the apparently control (140) and control (208) populations. Benefit-risk assessment supported that the Se-rich values in cooked rice are effective in avoiding the toxic effect and potential risk from the associated metal (As). [ABSTRACT FROM AUTHOR]

Integrating renewable resources with existing power system are rapidly increasing day by day, becoming an effective way to rural electrification where distribution system extension is not economically feasible. This research aims to design such an optimal grid-connected hybrid renewable energy system (HRES) modelled using solar, wind energy, diesel generator, battery storage, thermal loads, thermal load controller, and boiler. This system is designed to meet the load demand requirement of chintalayapalle village, Andhra Pradesh, India. The techno-economic, sensitivity analysis and cost-effective optimal configuration of this system have been performed using HOMER software. Totally eight configurations have been designed here and the optimal configuration have been identified based on the minimum net present cost (NPC), lowest cost of energy (COE), and the highest renewable fraction (RF). Results obtained shows that the optimally configured system is more environmentally friendly because of less carbon emission. Also, it would be more cost-effective when wind power becomes the main energy source and combined with moderate capacity of solar photovoltaic and battery. [ABSTRACT FROM AUTHOR]

In modern era of energy self-sufficiency and its personal preference, an off-grid rooftop solar photovoltaic nanogrid for private use in a single household or among limited number of households poses a promising futuristic prospect. However, several critical aspects need investigation to realize its viability. The present work investigates the techno-economic-space feasibility of an off-grid rooftop photovoltaic nanogrid for residential sector of a cosmopolitan urban locality with unreliable electricity in India. A novel combination of RAMP for load modelling, HOMER PRO for techno-economic analysis and an innovative technique for space feasibility analysis was implemented. 269 houses were surveyed and innovatively classified, based on number of dwelling rooms and demand profile similarity. Correlation and sensitivity analysis were conducted to understand the impact of individual and grouped load profiles, plinth area per household, number of dwelling rooms, climatic conditions, building types and component specification availability in market on the overall cost, configuration and feasibility of the system. Sharing of photovoltaic nanogrids and higher durations of daily occupancy in houses reduce the Levelized Cost of Energy (LCOE) from the system due to reduction in percentage variations of load. The LCOE of shared nanogrids ranged between 0.151 $/kWh to 0.195 $/kWh in India. The study recommends 50 % subsidy on system capital cost against grid prices. Batteries operated at higher depth of discharge (DOD) and having longer life at higher DOD reduce the system LCOE. Apartments in locations with high annual solar radiation, low annual temperatures, large plinth area, low monthly demand and unobstructed rooftops are good candidates for PV nanogrids. In India, high-rise buildings with monthly loads up to 30 kWh/household, mid-rise buildings with loads up to 50 kWh/household, low-rise buildings with loads up to 100 kWh/household and double-storey buildings with loads up to 200 kWh/household can successfully adopt autonomous rooftop PV nanogrids. • Novel combination of RAMP-HOMER PRO-modified GIS-based statistical technique was used. • Sharing of PV nanogrids and higher durations of daily occupancy in houses reduce LCOE. • The LCOE of a shared nanogrid system ranges between 0.151 $/kWh to 0.195 $/kWh in India. • Reduced monthly load/house increases the space viability of rooftop PV nanogrids in buildings. • Higher Depth of Discharge for batteries reduces the LCOE of the system. [ABSTRACT FROM AUTHOR]

Growing organic waste in India is posing a challenge in its effective management. Open degradation of this organic waste pollutes environment, as well as causes a slew of diseases. Situation may be handled effectively using biogas plants employing anaerobic digestion process. However, performance of these plants is dependent upon various environmental factors and feed types. In this paper, the feasibility analysis of a biogas plant for the northern plains of India has been conducted. A new modified anaerobic digestion model (MAD1) has been designed for the analysis. It is evident from the analysis that the northern plains of India have a large potential for biogas production as they generate a large amount of organic waste. Furthermore, analysis reveals that, if the biogas plant is operated at a thermophilic temperature ranging from 44 °C to 55 °C, the profit can be maximised. However, if the biogas plant is operated at 37 °C, the significant profit will begin after 3–4 years, depending on the type of feedstock. Complete analysis has been conducted in MATLAB Simulink environment. • Conducted sensitivity analysis of the parameters of anaerobic digestion process • Conducted feasibility analysis of biogas plant for Northern Indian states. • Conducted economic analysis of biogas plant to maximize profit • Modelled a modified anaerobic digestion model (MAD1) for realistic output [ABSTRACT FROM AUTHOR]

Covid-19 has affected many countries resulting in the loss of many human lives. During the first and second waves of the pandemic, hospitals played a crucial role in saving human lives and hence became the last hope of survival for corona patients. Hence the hospital selection is a crucial decision to be made by corona patients keeping the third wave of the pandemic in view. This work proposes a decision framework for hospital selection considering the essential criteria in the context of providing corona treatment to patients. The proposed framework is based on the Fuzzy Inference System (FIS) and utilizes rules framed by decision-makers for determining the Performance Index (PI) of hospitals. Based on the values of PI patients can select the hospital to which they can get admitted with assurance to get the best possible treatment. Further, the sensitivity analysis has been done to demonstrate the robustness of the framework. The results of the work also provide hospitals a deep insight into their performance and customers' perception of their performance, helping them to initiate appropriate enhancement measures. Six hospitals from central India have been considered as case hospitals to demonstrate the applicability of the proposed framework. [ABSTRACT FROM AUTHOR]

Energy analysis in agriculture sector require modelling technique that can incorporate complex unknown interactions and non-linearity in systems. In this study Artificial neural network technique is used to model and forecast input energy consumed in wheat production in India and is compared for predictive accuracy with linear models. Household data from 256 farmers revealed that the average input energy consumed in region is 29612.43 MJ/ha with urea (47%), diesel (31.5%) and electricity (9.8%) being three main contributors. Multi-layered feed forward model with 2 hidden layers with 8 and 15 neurons respectively and sigmoidal activation function in hidden layers and output layers under gradient descent training algorithm gave the best results. The R2 was 0.99 for training dataset and 0.973 for validation data set, while for MLR model it was 0.95 and 0.73 for respective datasets. The root mean squared error (RMSE) in ANN model was 4779.2 MJ/ha and 2008.96 MJ/ha for training and validation data, respectively. This prediction system could forecast input energy with error margin of ± 7889.83 MJ/ha on training dataset and ± 3298.47 MJ/ha on validation data under various combinations. Sensitivity analysis showed that urea, diesel, and electricity are the important factors in input energy forecasting. [ABSTRACT FROM AUTHOR]

Objectives: This meta-analysis evaluated the Efficacy and Effectiveness of several COVID-19 vaccines, including AstraZeneca, Pfizer, Moderna, Bharat, and Johnson & Johnson, to better estimate their immunogenicity, benefits, or side effects. Methods: Studies reporting the Efficacy and Effectiveness of COVID-19 vaccines from November 2020 to April 2022 were included. The pooled Effectiveness/Efficacy with a 95% confidence interval (95% CI) with Metaprop order was calculated. The results were presented in forest plots. Predefined subgroup analyses and sensitivity analyses were also performed. Results: A total of twenty articles were included in this meta-analysis. After the first dose of the vaccine, the total effectiveness of all COVID-19 vaccines in our study was 71% (95% CI 0.65, 0.78). The total effectiveness of vaccines after the second dose was 91% (95% CI 0.88, 0.94)). The total efficacy of vaccines after the first and second doses was 81% (95% CI 0.70, 0.91) and 71% (95% CI 0.62, 0.79), respectively. The effectiveness of the Moderna vaccine after the first and second dose was the highest among other studied vaccines ((74% (95% CI, 0.65, 0.83) and 93% (95% CI, 0.89, 0.97), respectively). The highest first dose overall effectiveness of the studied vaccines was against the Gamma variant (74% (95% CI, 0.73, 0.75)), and the highest effectiveness after the second dose was observed against the Beta variant (96% (95% CI, 0.96, 0.96)). The Efficacy for AstraZeneca and Pfizer vaccines after the first dose was 78% (95% CI, 0.62, 0.95) and 84% (95% CI, 0.77, 0.92), respectively. The second dose Efficacy for AstraZeneca, Pfizer, and Bharat was 67% (95% CI, 0.54, 0.80), 93% (95% CI, 0.85, 1.00), and 71% (95% CI, 0.61, 0.82), respectively. The overall efficacy of first and second dose vaccination against the Alfa variant was 84% (95% CI, 0.84, 0.84) and 77% (95% CI, 0.57, 0.97), respectively, the highest among other variants. Conclusion: mRNA-based vaccines against COVID-19 showed the highest total efficacy and effectiveness than other vaccines. In general, administering the second dose produced a more reliable response and higher effectiveness than a single dose. [ABSTRACT FROM AUTHOR]

Noise annoyance is recognized as an expression of physiological and psychological strain in acoustical environment. The studies on prediction of noise annoyance and parametric sensitivity analysis of factors affecting it have been rarely reported in India. A hybrid ConvLSTM technique was developed in the study to predict traffic-induced noise annoyance in 484 people based on ambient noise levels, as well as survey information. Ambient noise levels were obtained at different locations of Dhanbad city using sound level meter at varying intervals, viz. 09AM–12PM, 03PM–06PM, and 08PM–11PM. The proposed method was compared with some well-known neural network techniques such as K-nearest neighbors (KNN), artificial neural network (ANN), recurrent neural network (RNN), and long-short-term memory (LSTM). The experimental results indicate that the proposed method outperforms other techniques and can be a reliable approach for prediction of noise annoyance with an accuracy of 93.8%. It can be concluded from noise maps that the noise levels in all locations of the Dhanbad city were higher than 70 dB(A) and noise sensitivity is the most important input variable of traffic-induced noise annoyance, followed by honking noise, education, exposure hours, LAeq, sleeping disorder, and chronic disease. The study shall facilitate in developing a decision support tool for prediction of noise annoyance and promoting implementation of suitable public policy in urban cities. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed an SVEIQR compartmental model modifying the classical SEIR model with inclusion of vaccinated and quarantined classes to explain the COVID-19 outbreak mathematically. We have calibrated our model with the daily COVID-19 data reported by the WHO coronavirus dashboard. To observe the disease dynamics of COVID-19, a detailed stability analysis of the proposed SVEIQR model is carried out. Our results show that the disease free equilibrium (DFE) is stable if the basic reproduction number is less than unity and unstable otherwise. Moreover, endemic equilibrium (EE) is found to be stable when certain restrictions hold. The expression for effective reproduction number has been derived analytically and its value is calculated based on the reported cases. Sensitivity analysis of effective reproduction number is performed employing PRCCs and Latin hypercube scheme. We have compared short-term and long-term transmission dynamics of COVID-19 for India with different levels of vaccination and without control strategies. The impact of different degrees of control interventions is ascertained with the numerical simulation of the model. [ABSTRACT FROM AUTHOR]

• Several potential barriers to the implementation of solar drying technology in India are identified. • The DEMATEL scheme is utilized to prioritize the identified barriers. • The sensitivity analysis is performed to test the robustness of the proposed methodology. • Political instability is identified as the most significant barrier to the implementation of solar drying technology in India. • Several potential recommendations for the removal of these barriers are also suggested to ensure the contribution towards the achievement of UN sustainable development goals. In the present scenario, food security is one of the major concerns in the Indian context; engendered due to increased population and other associated anthropological activities. Despite high levels of food production, the current poor ranking of the country in the Global Hunger Index (GHI) has put an alarming trend for policy-makers as well as the contributors working towards food security improvement. The significant post-harvest losses in several durables and perishable food products affect society adversely. The use of solar dryers is one of the most efficient, cost-effective, and sustainable techniques for various food-processing and preservation applications; without any compromise with their morphological structure. Besides having the pre-eminent product quality along with several economic, environmental, and social impacts, the adoptability of solar dryers has not touched a benchmark; as per expectation due to various associated potential barriers. In this work, a systematic framework, utilizing the MCDM tools; has been suggested for the identification and prioritization of several barriers responsible for such a hampered adoption rate. The study comprises three major sections- (a) Identification and categorization of potential barriers, (b) Prioritization of identified barriers using DEMATEL scheme based priority weights, followed by (c) the sensitivity analysis to confirm the robustness of the proposed methodology. The findings suggested that political instability and interference have a significant impact on the adoptability of solar-thermal-based drying technology. Several potential recommendations for the removal of these barriers are also suggested to ensure the contribution towards the achievement of UN sustainable development goals. [ABSTRACT FROM AUTHOR]

Integration of a grid with an under-developed remote hilly area faces various technical and geographical challenges. Thus, generation of power from renewable resources in off-grid conditions has become one of the most cost-effective and reliable solutions for such areas. The present research deals with the possible application of an integrated solar/hydro/biomass/battery-based system to generate power in autonomous mode for a remote hilly town of a northeastern Indian state. Four different cases of the integrated energy system (IES) were designed using the hybrid optimization model for electric renewable (HOMER Pro), examining the performance of each case. The best combination of the integrated system was chosen out of several cases depending upon the optimized solution that can meet the load demand of the proposed hilly town sustainably, reliably and continuously. The simulation results show that the integrated battery/biomass/hydro/solar-based system is the best optimized, cheapest and most suitable solution to generate renewable-based power for the specified location, having the lowest net present cost (NPC) of USD 644,183.70 with a levelized cost of energy (COE) of 0.1282 USD/kWh. Further, the result also indicates that the optimized configuration reduces the emission of CO2 gas in the environment compared to the battery/biomass/hydro system having the worst emission rate. A sensitivity study was also carried out with variation in load, hydro stream flow and solar irradiation, respectively that may largely affect the technical as well as economical aspect of an integrated energy system. [ABSTRACT FROM AUTHOR]

This paper focus on the drought monitoring and forecasting for semi-arid region based on the various machine learning models and SPI index. Drought phenomena are crucial role in the agriculture and drinking purposes in the area. In this study, Standardized Precipitation Index (SPI) was used to predicted the future drought in the upper Godavari River basin, India. We have selected the ten input combinations of ML model were used to prediction of drought for three SPI timescales (i.e., SPI -3, SPI-6, and SPI-12). The historical data of SPI from 2000 to 2019 was used for creation of ML models SPI prediction, these datasets was divided into training (75% of the data) and testing (25% of the data) models. The best subset regression method and sensitivity analysis were applied to estimate the most effective input variables for estimation of SPI 3, 6, and 12. The improved support vector machine model using sequential minimal optimization (SVM-SMO) with various kernel functions i.e., SMO-SVM poly kernel, SMO-SVM Normalized poly kernel, SMO-SVM PUK (Pearson Universal Kernel) and SMO-SVM RBF (radial basis function) kernel was developed to forecasting of the SPI-3,6 and 12 months. The ML models accuracy were compared with various statistical indicators i.e., root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), root relative squared error (RRSE), and correlation coefficient (r). The results of study area have been showed that the SMO-SVM poly kernel model precisely predicted the SPI-3 (R2 = 0.819) and SPI-12 (R2 = 0.968) values at Paithan station; the SPI-3 (R2 = 0.736) and SPI-6 (R2 = 0.841) values at Silload station, respectively. The SMO-SVM PUK kernel is found that the best ML model for the prediction of SPI-6 (R2 = 0.846) at Paithan station and SPI-12 (R2 = 0.975) at the Silload station. The compared with SVM-SMO poly kernel and SVM-SMO PUK kernel was observed, these models are best forecasting of drought (i.e. SPI-6 and SPI-12), while SVM-SMO poly kernel is good for SPI-3 prediction at both stations. The results have been showed the ability of the SVM-SMO algorithm with various kernel functions successfully applied for the forecasting of multiscale SPI under the climate changes. It can be helpful for decision making in water resource management and tackle droughts in the semi-arid region of central India. [ABSTRACT FROM AUTHOR]

Mathematical modeling is one of the most used techniques for analyzing and preventing the transmission of COVID-19. To control this pandemic, it is essential to classify the infected population. So in this article, a new SEAIQHRDP model was formulated to investigate the transmittal dynamics of COVID-19. This model contains nine compartments Susceptible(S) class, Exposed(E) class, Asymptomatic(A) class, Infected(I) class, Quarantined(Q) class, Hospitalized(H) class, Recovered(R) class, Death(D) class, and Insusceptible (P) class. This model was fitted to the daily and cumulative confirmed COVID-19 cases in the period between 30th January 2020 and 13th January 2021 in India. Sensitivity analysis concerning R0 was performed to classify the significance of parameters. Contour plots for R0 were executed and the effect of various parameters on the infected classes had shown graphically. The necessity of stringent face mask usage and social seclusion is highlighted by optimal control analysis as a key factor in the dramatic reduction of infection rates. So the optimal control technique was adopted to lessen the disease mortality by taking both nonpharmaceutical and pharmaceutical intervention strategies as control functions and comparing infectives and recoveries with and without controls. [ABSTRACT FROM AUTHOR]

This paper mainly dealt with the technical and economic feasibility of an off-grid hybrid power generation system for a remote rural Turtuk village of Ladakh, located in the northern part of India. The study showed that the proposed configured renewable integrated hybrid system, using Hybrid Optimization of Multiple Energy Resources (HOMER) software, efficiently met the energy demand, exhibiting optimum performance with low investment. The proposed PV(115 kW)/Wind(1 kW)/Battery(164 strings of 6 V each)/DG(50 kW) hybrid system was a highly commendable, feasible solution preferred from a total of 133,156 available solutions resulting from HOMER simulations. The net present cost and energy cost of the proposed configuration were $278,176 and $0.29/kWh, respectively. The proposed hybrid configuration fulfilled local load, with 95.97% reduced dominant harmful carbon dioxide emission, as compared to the sole us of a diesel generator power supply system. The technical performance of the hybrid system was ensured, with advantages including the highest renewable penetration and least unmet load. Furthermore, the analysis exclusively evaluated the impact of the system's economic parameters (namely, its expected inflation rate, nominal discount rate, and project lifetime) on the net present cost and cost of energy of the system using a noble single fix duo vary approach. [ABSTRACT FROM AUTHOR]

In rural areas of Ethiopia, India, Lesotho, and Senegal, men working in agriculture have modestly lower hemoglobin levels than men with other occupations. One explanation could be lower kidney function, due to undetected chronic kidney disease, among agricultural workers. More population-based surveys could consider adding kidney function assessments by occupation. Objective: Estimate agricultural work's effect on hemoglobin (Hgb) level in men. A negative effect may indicate presence of chronic kidney disease of uncertain etiology. Methods: We use Demographic and Health Surveys data from seven African and Asian countries and use matching to control for seven confounders. Results: On average, Hgb levels were 0.09 g/dL lower among agricultural workers compared with matched controls. Significant effects were observed in Ethiopia, India, Lesotho, and Senegal, with effects from 0.07 to 0.30 g/dL lower Hgb level among agricultural workers. The findings were robust to multiple control groups and a modest amount of unmeasured confounding. Conclusions: Men engaged in agricultural work in four of the seven countries studied have modestly lower Hgb levels. Our data support integrating kidney function assessments within Demographic and Health Surveys and other population-based surveys. [ABSTRACT FROM AUTHOR]

To analyze randomized trials with imperfect compliance, a standard approach is to estimate the local average treatment effect in the sub-population of compliers using randomization status as an instrumental variable. Though quantile analysis has been popular in general, the local (or complier) quantile treatment effect (cQTE) as a causal estimand has received insufficient attention. In this paper, we map out the details for the estimation, inference, and sensitivity analysis of the cQTE in a completely nonparametric setting. We propose to estimate the cQTE using nonparametric plug-in estimators of the cumulative distribution functions for the potential outcomes of the compliers. The cQTE estimator is shown to be asymptotically normal, with asymptotic variance estimated through kernel-smoothed density estimators. The procedure is easily extended to adjust for discrete covariates for gains in statistical efficiency. Moreover, by exploiting the stochastic monotonicity of the quantile functional, we develop sensitivity bounds for the cQTE when key assumptions such as exclusion restriction and instrument monotonicity are violated. Extensive simulations show that the proposed methods provide valid inference of the target local estimand and outperform standard intent-to-treat tests, especially under low compliance rates and/or heterogeneous treatment effects. A recent study on a government-funded health insurance program in India is analyzed as an illustration. [ABSTRACT FROM AUTHOR]

Suitable management and treatment of healthcare waste (HCW) have become a key issue due to its potential risk to human health and the environment, predominantly in emerging nations. The selection of an optimal HCW treatment (HCWT) option is a complicated multicriteria decision-making (MCDM) problem that includes several incompatible qualitative and quantitative attributes. This article presents an extended MCDM methodology for assessing and choosing the HCWT options using Pythagorean fuzzy stepwise weight assessment ratio analysis (PF-SWARA) and additive ratio assessment (PF-ARAS) approaches. To do this, attribute weights are estimated by the SWARA model and the ARAS framework decides the preference order of the options on Pythagorean fuzzy sets (PFSs). Furthermore, a selection problem of HCWT options in India is presented within PFSs to illustrate the efficiency and practicality of the introduced framework. Comparative discussions and sensitivity analysis are presented to demonstrate the rationality and stability of the developed approach for prioritizing HCWT options. [ABSTRACT FROM AUTHOR]

Professional driving involves sitting in uncomfortable positions, navigating difficult terrain and roads, and occasionally conducting small repairs and other auxiliary transportation duties while at work for long periods. Drivers who engage in these activities may develop a variety of musculoskeletal disorders (MSDs). MSDs in professional drivers are accompanied by several risk factors. In this study, the various risk factors for MSD have been identified through the literature reviews, discussions with professional drivers, and consultations with ergonomics specialists. This study employed the ordinal priority approach (OPA), a multi-criteria decision-making (MCDM) technique, to rank the identified risk variables for MSD in order of importance. The same OPA method has also been used to identify the group of professional drivers who use eight different types of vehicles and are more likely to develop MSDs. The analyses findings show that the five main risk factors for MSDs among drivers are prolonged sitting, restricted posture, working hours, alcohol consumption, and uncomfortable seating. Additionally, among all drivers regarded as professionals, truck drivers are found to be the most at risk. For the study's conclusions to be validated, a sensitivity analysis was also carried out. The results of this study are anticipated to help formulate strategies for lowering these hazards through the ergonomic design of drivers' cabins by automobile OEMs (Original Equipment Manufacturers) and vehicle scheduling by concerned transportation organizations to reduce driver tiredness. [ABSTRACT FROM AUTHOR]

Nowadays, preparing natural hazard maps has become essential to protect society and its infrastructure from future natural disasters. In this context, the multi-criteria semi-quantitative technique, the analytical hierarchy process (AHP) was integrated into the Geographic Information System (GIS) environment to produce a flood hazard map. The Lower Mahananda basin of India was chosen as the study area that annually suffers from damage from floods. The produced map was categorized into five classes as the zones of flood susceptibility, namely very high, high, moderate, low and very low. The corresponded zones were then examined using sensitivity analysis, and the results show there are very small differences in the spatial distribution of flood susceptible zones. The high flood hazard zones (18%) are located generally along river tracts in the lower portion of the study area that gets flooded more or less every year. The comparison found between the various flood susceptible zones, and the rise in the water level at the Mahananda conveys the accuracy and reliability of the applied methodology. Moreover, the settlement zones were identified within the high flood probable category and the sub-districts situated along the western margin have been found with inadequate medical facilities to combat the flood events. Therefore, the proposed methodology and resultant analysis of the work can be useful for local government authorities to prepare flood mitigation plans and strategies. [ABSTRACT FROM AUTHOR]

Idukki district faced adverse mishappenings during the 2018 Kerala landslides due to incessant torrential rainfall. This study emphasizes developing an efficient and accurate ANN model to integrate the data, process and generate landslide susceptibility maps. Fifteen conditioning factors that influence landslides' occurrence opted in the study constitutes 49 input neurons to the ANN model (L49). Seven inputs with high robustness were identified using the sensitivity analysis approach and were adopted to generate a new ANN model (L7). Both ANN models were processed to obtain an optimal output with lesser cross-entropy error. The landslide susceptibility maps derived from these ANN models show similar trends with the region's observed landslide locations. The ANN models were validated using ROC, and it provided a very good fit with AUC values of 0.91 and 0.83 as prediction rate for ANN models L49 and L7, respectively. [ABSTRACT FROM AUTHOR]

Identification of hospital sites and their ranking is important for the planning and development of any country's health infrastructure. The site selection problem is a typical multi-criteria decision making problem involving multiple stakeholders and their interests. Multi-Criteria Decision Analysis (MCDA) is a promising approach to solve a location-based problem due to the constitution of various criteria involved in decision making. In this research, eleven criterion are chosen which are classified under three main criteria; socio-economic, geographical and environmental. This research aims to identify the appropriate MCDA method for the selection of a new hospital sites. Here, two MCDA methods named Analytical Hierarchy Process (AHP) and Fuzzy AHP (FAHP) are used. Further, Geographical Information System (GIS) based MCDA methodology is proposed in this paper. The results obtained with both AHP and FAHP methods are compared. This comparison is based on criterion rankings, proposed hospital locations and sensitivity analysis. The main difference in results is shown in the result of sensitivity analysis in which constant variation in site ranking is obtained when weight change analysis is performed using AHP. The FAHP result shows only one variation in site ranking after a change in weight from +10 to +20%. The result suggests that FAHP may be a better approach to the hospital site selection problem. [ABSTRACT FROM AUTHOR]

India, unlike several other countries, has witnessed a greater challenge in overcoming the pandemic during the second wave crisis. The strategies on lockdown imposition during the first wave of the pandemic were well implemented due to which the year-long wave did not witness uncontrollable surges in the infections. In this study, we present a detailed study on the disease spread during the first and second wave of COVID-19, by performing numerical simulation in a phase-wise manner as per the lockdown and unlock phases implemented in India. The inclusion of a piecewise treatment function in the framed epidemiological model is a noteworthy aspect of the study since this function takes into consideration the availability of medical equipment, based upon the threshold number of infections. This function framed in this model first grows linearly reaching a peak, then it declines due to shortage of medical resources and finally gets saturated. Analysis of the impact of lockdown is presented for each phase of the pandemic in India. Though the data considered for the study is for a period that marked the beginning of the pandemic, major analysis and predictions are presented based on the second wave data in terms of sensitivity analysis and time series behavior. A comparison of deterministic and stochastic differential equations is presented with simulation results on certain parameter set to examine variation in treatment and recovery. The simulations are performed using MATLAB and R softwares. The work is validated with the real data and model fitting is done applying the Maximum Likelihood method. The study implies that, under accurate lockdown strategies and sufficient medical care, the peak in cases would be attained by 16 May 2021, after which a decline in the cases could be observed. [ABSTRACT FROM AUTHOR]

This paper delineates the groundwater potential zones (GWPZ) within the Tadri River Basin in the Western Ghats of India, using analytical hierarchical process (AHP) and geoinformatics-based techniques. Eight discrete parameters (slope, rainfall, drainage density, lineament density, lithological units, geomorphological units, soil types and land use, and land cover classes) were selected and weighted according to their influence on groundwater availability and combined in a hierarchical manner to obtain the resulting GWPZ map. This was classified into five classes (very good, good, moderate, low and very low) depending upon the relative groundwater availability within each. Excellent GWPZ were present in the lower part of the Tadri Basin and along the Western Ghats' foothills beside the coast while the pediment-pediplain complex had less viability. The GWPZ map was validated using the area's average groundwater depth and well density, via the receiver operating characteristic (ROC) curve and the area under curve (AUC), which elicited an accuracy of 79% and 82.1%, respectively. Sensitivity analysis revealed that the basin's lithology, land use/land cover, drainage density and lineament density had the most impact in GWPZ delineation. [ABSTRACT FROM AUTHOR]

The aim of this article is to select the optimum green energy sources for sustainable planning for a region. This research presents an integrated model based on theoretical base of benefit, opportunities, costs, risks and a well-known multicriteria decision-making technique, i.e., the analytical hierarchy process, to evaluate the green energy sources from northeast India along with 16 local factors. The analyzed result shows that solar photovoltaic is the optimum green energy source having the highest score value followed by other sources, appraised by the integrated model. Based on the results this article, we suggest some policies for the energy managers, policymaker, and decision makers. This article has both theoretical and practical implications. Theoretically, it contributes holistic measures for designing and managing the green energy sources selection framework for sustainability, and, practically, it helps various organizations operating in the green energy sources selection sector to improve their sustainability dimension for the cleaner future. The proposed article considers not only various cost criteria, but also all other criteria, such as power generation, implementation period, and useful life, that are considered to select the optimum green energy sources for the better future. The findings of this article can provide useful information to energy decision makers and serve as a reference for Tripura's energy policy. [ABSTRACT FROM AUTHOR]

This study has estimated returns accrued from a potato variety called Kufri Pukhraj developed by ICAR-CPRI in 1998. This variety is most popular in North Indian Plains and covers about 33 % of total potato area in India. Timeseries data collated from published records have been used to assess benefits during the period 1978-2021. The economic surplus model is employed to measure benefits in a closed economy framework at all-India level. Results have shown that adoption of Kufri Pukhraj provided gross returns of Rs 1,03,585 crore during the period 1978-2021, and large benefits have gone to consumers (73 %) and remaining to producers. The average annual return was assessed as Rs 2,354 crore. The NPV from spending in potato research was observed Rs 20,073 crore, and Rs 7,975 crore at 5 % and 8 %, respectively, during the study period. The IRR from monetary benefits was 49 %, a good rate of returns. Sensitivity analyses have provided substantial returns, and IRR between 46-51 %. The study infers that spending on R&D on potato has proved financially beneficial and suggests that other major technologies (varieties/ production practices/ others) having special traits may be studied for justifying past funding and make the strong case for future funding. [ABSTRACT FROM AUTHOR]

A comprehensive seasonal assessment of groundwater vulnerability was conducted in the weathered hard rock aquifer of the upper Swarnrekha watershed in Ranchi district, India. Lineament density (Ld) and land use/land cover (LULC) were integrated into the conventional DRASTIC and Pesticide DRASTIC (P-DRASTIC) models and were extensively compared with six modified models, viz. DRASTIC-Ld, DRASTIC-Lu, DRASTIC-LdLu, P-DRASTIC-Ld, P-DRASTIC-Lu, and P-DRASTIC-LdLu, to identify the most optimal model for vulnerability mapping in hard rock terrain of the region. Findings were geochemically validated using NO 3 - concentrations of 68 wells during pre-monsoon (Pre-M) and post-monsoon (Post-M) 2022. Irrespective of the applied model, groundwater vulnerability shows significant seasonal variation, with > 45% of the region classified as high to very high vulnerability in the pre-M, increasing to ̴67% in post-M season, highlighting the importance of seasonal vulnerability assessments. Agriculture and industries' dominant southern region showed higher vulnerability, followed by regions with high Ld and thin weathered zone. Incorporating Ld and LULC parameters into DRASTIC-LdLu and P-DRASTIC-LdLu models increases the 'Very High' vulnerability zones to 17.4% and 17.6% for pre-M and 29.4% and 27.9% for post-M, respectively. Similarly, 'High' vulnerable zones increase from 32.5% and 25% in pre-M to 33.8% and 35.3% in post-M for respective models. Model output comparisons suggest that modified DRASTIC-LdLu and P-DRASTIC-LdLu perform better, with accurate estimations of 83.8% and 89.7% for pre-M and post-M, respectively. However, results of geochemical validation suggest that among all the applied modified models, DRASTIC-LdLu performs best, with accurate estimations of 34.4% and 20.6% for pre-M and post-M, respectively., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The focus of this study is to examine the service quality of unsignalized intersections based on the perception of automobile drivers. This was achieved by developing Automobile Level of Service (ALOS) models using two computational intelligence methods – Functional Linked Artificial Neural Network (FLANN) and Differential Evolution (DE). The required data were collected at 47 unsignalized intersections in India with widely varying driving environments. Traffic simulation models were developed to estimate parameters that could not be directly measured in the field. DE model exhibited higher prediction efficiencies with coefficient of determination (R2) of 0.94 and 0.93 for training and testing datasets, respectively. Applying sensitivity analysis, significant parameters affecting ALOS were arranged in descending rank of their relative influence. Pavement condition is the most significant parameter influencing ALOS of unsignalized intersections. The proposed model will help the transportation administrators in prioritizing the key factors for investment in infrastructural development. [ABSTRACT FROM AUTHOR]

Cachar district of Assam, India has less cultivated land due to waterlogging of agricultural land in the monsoon period and shortage of surface water in the dry season. But, the area has untapped enormous groundwater which can be extracted from the aquifer to increase cultivation in the dry season. In this regard, a detailed analysis of the groundwater system and a feasible plan for sustainable groundwater management are required. Therefore, a transient state groundwater flow model was developed for the region using MODFLOW. The model was calibrated and validated using the observed groundwater level (1996–2015). The groundwater recharge was estimated using GEC 1997. The results from the model study indicated that the total inflow to the aquifer is 28.47 million cubic meters (mcm) and the outflow is 27.45 mcm. Hence, the rising trend of water level and more waterlogged area were observed. A high hydraulic gradient was visualized in the area vicinity to river Barak, where the flow direction is towards the aquifer in monsoon, and vice-versa in the nonmonsoon period. The model was further predicted for a hypothetical scenario by proposing additional pumping wells for groundwater utilization in agriculture and to reduce the waterlogged areas. Sensitivity analysis shows that model is more sensitive in recharge than hydraulic conductivity. [ABSTRACT FROM AUTHOR]

Purpose: This paper demonstrates a multi-criteria analytic hierarchy process (AHP) framework for evaluating and benchmarking maintenance performance in the select agro-based industry. Design/methodology/approach: Initially, 20 maintenance practices (criteria) have been identified after a detailed literature review and discussion with the agro-based industry (sugar, textile and dairy industry) executives. These are then grouped into six maintenance management areas referred to as group criteria. The multi-criteria methodology consists of three steps: criteria identification, hierarchical modeling and data collection and maintenance performance evaluation, and benchmarking. The multi-criteria methodology proposed in this work facilitates two ways of carrying out benchmarking: (1) within the agro-based industry and (2) between the agro-based industry. The methodology has been explained by taking a case example of 45 agro-based industries (18 dairy, 13 sugar and 14 textile) from the western region of India. The sensitivity analysis of the model has been performed to ascertain the robustness of the results. Findings: There is a difference in the maintenance performance across the agro-based industries due to different maintenance practices perceived differently. Research limitations/implications: The outcome of the model is mainly given by the judgments of the agro-based industry executives. It is also sensitive to any change in the relative importance to the evaluation criteria or the perception about the maintenance performance. Practical implications: The study contributes in identifying the weakness, if any, by comparing the agro-based industry under investigation with the benchmark factory at three levels, namely, overall performance (factory level), group criteria (maintenance management area level) and criteria (maintenance practice level) allowing further improvement. Originality/value: The methodology assists in better decision-making and in improving maintenance performance. [ABSTRACT FROM AUTHOR]

The accurate and efficient monitoring, assessment, and prediction of water resources is crucial for the sustenance of life and health of the environment. Traditional methods for assessing water quality can be laborious and time-consuming, but the use of machine learning algorithms can improve the speed and precision of these predictions. The study aimed to evaluate the quality of surface water in the lower Thoubal river using a comprehensive approach that analyzed 12 physico-chemical properties of the water samples collected from 16 different sites. Four machine learning algorithms namely, deep neural network (DNN), gradient boost model (GBM), generalized linear model (GLM), and random forest (RF) were applied and compared for the prediction efficiency of a water quality index (WQI). Sensitivity and uncertainty analysis of the parameters were carried out using a DNN-based model. The study incorporated model evaluation using learning curve along with five key performance assessors. The findings revealed marked variation in the WQI in the watershed. Of the samples tested, about 70 % were found to have good water quality, 18 % had excellent water quality, 11 % had poor water quality, 1 % had very poor water quality, and less than 1 % were deemed unsuitable. Turbidity, BOD, and COD were found to be the most influential parameter towards WQI prediction. The effectiveness of the predictive models ranked as GLM>DNN>RF>GBM, GLM>GBM>DNN>RF, and GLM>DNN>RF>GBM; based on testing, training and 5-fold cross-validation. The GLM model consistently showed superior performance with the lowest RMSEs of 0.14783 in training, 0.15936 in validation, and 0.26115 in 5-fold cross-validation. Conversely, during the training, RF displayed the worst RMSEs of 5.38354, 4.79754 by GBM in validation, and 7.04887 by GBM in 5-fold cross-validation. The analysis using the Taylor diagram and learning curve further supported that GLM was the most effective model for predicting surface water quality. Biological oxygen demand, chemical oxygen demand, electrical conductivity, turbidity and total hardness were found to be the key parameters influencing the water quality. Unregulated and illegal sand mining from the riverbed, garbage dumping into the water bodies, runoffs from agricultural fields have significantly deteriorated the water quality in the study area. The scalable approach of the study and its results can benefit the local water managers and water research community. [Display omitted] • Traditional water quality assessment methods are tedious. • Machine learning and deep learning can predict water quality with precision. • Generalized linear model was found to be the most effective predictive model. • Turbidity, conductivity and biological oxygen demand affect water quality negatively. • Unregulated sandmining and garbage dumping are the major water pollution source. [ABSTRACT FROM AUTHOR]

Advanced oxidation processes (AOPs) in water treatment industries can simultaneously offer disinfection and eradicate organic contaminants. The present work examined the direct exposure impact of various oxidative (UV, O3, and H2O2) and their eclectic combinations (UV/O3, H2O2/O3, and UV/H2O2) for the abolition of Trihalomethane (THMs) precursors. The application of the Fenton process proved to be the most effective technique for reducing TOC (85.23%) and UV254 (91.11%) than other used AOPs. Higher removal of THMs precursors found in the Fenton process may be attributed to the higher degradation potential of hydroxyl radical generated from this process than other AOPs. The deformed ferrous iron salts (Fe2+) in the Fenton reagent catalyzed the hydrogen peroxide (H2O2) and produced highly reactive radicals. Seasonal probabilistic lifetime cancer and non-cancer risks of THMs in males and females were also investigated in the drinking water supplies of five major cities in India. Comparative risk analysis through different pathways revealed significant risk from oral ingestion (99%), followed by inhalation exposure (0.77%) and dermal absorption (0.002%), where females were found at higher risk of cancer. Sensitivity analysis using a radar plot was also performed to identify the most influential parameter affecting the total risk of cancer. The Risk value of chloroform (CF) was highest among the THMs compound in all the WTPs for both seasons. Seasonal risk assessment revealed a higher risk of cancer in the pre-monsoon season. [ABSTRACT FROM AUTHOR]