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Comprising the largest population cohort on this planet, Gen Z presents a future-oriented consumer segment driven by climate change and food. This study sought to investigate Gen Z's perceptions toward plant-based foods and diets and explore the relationship that attitude components, meal preparation involvement, personal and lifestyle factors, and perceived barriers in adopting a plant-based diet have with willingness to adopt green-eating practices. Using cross-sectional data from university students in Greece, India, and the UK, various tools were employed to determine the factors influencing youths' consumer behavior toward animal-protein substitutes. PCA indicated the underlying dimensions of students' viewpoints on plant-based foods, whereas hierarchical and k-means clustering provided the cluster structure. An ordered probit model was estimated to delineate Gen Z's willingness to adopt plant-based diets and distinguish among mostly unwilling, somewhat willing, and mostly willing youths. Our findings identified two consumer segments, namely proponents and opponents of plant-based foods and diets, with statistically significant differences in the perceived health benefits of plant-based diets, attachment to animal-based proteins, perceived exclusion of animal-based foods, dissatisfaction with plant-based foods' attributes, and demand for ensuring adequate protein intake. The ordered probit model estimates showed that there is a "homogeneity" in the factors influencing youths' intention to adopt plant-based diets, with attitude components, meal preparation indicators, perceived barriers to eating "green", and personal factors, such as self-assessed knowledge of healthy eating and physical activity, being strongly associated with students' willingness to switch to plant-based diets in all three countries. Mapping potential obstacles and enablers in terms of shifting to more green-eating behaviors, our findings could add information to better understand the factors affecting food choice and youths' transition to a more sustainable lifestyle. [ABSTRACT FROM AUTHOR]

The seasonal water quality of the Surajpur wetland was analysed using 13 physicochemical parameters, including pH, TDS, EC, temperature, total hardness, total alkalinity, DO, BOD, phosphate, fluoride, nitrate, sulphate, and chloride employing standardized procedures. Fifteen samples were collected in triplicate from five distinct locations within the wetland. These samples were then compared to the BIS and WHO standards (2012). The analysis used titrimetric techniques to determine DO, BOD, total hardness, total alkalinity, and chlorides. Moreover, phosphate, sulphate, and nitrate were determined through UV-spectrophotometry. The wetland has an alkaline nature, the hydrogeochemical facies that dominate the surface water are Ca2+, Mg2+, HCO3, and Cl−. The results indicate that TDS, total alkalinity, phosphates, and fluorides exceeded the permissible limits. The evaluation of water quality in the study area was conducted through the implementation of the standard Water Quality Index (WQI), the Canadian Council of Ministers of the Environment Water Quality Index-(CCME-WQI) and the Comprehensive Pollution Index (CPI) and their associated parameters.. The CCME-WQI ranges from 46 to 53, indicating very poor/marginal water quality, the WQI value ranges from 82.80 to 96.18, indicating good water quality, while the CPI ranges from 0.9 to 1.15, indicating very poor/marginal water quality. The pH-EC and pH-TA, TDS-CPI, Cl-TH and the WQI-PO43− are negatively correlated at a significance level p < 0.01. The TA-EC, BOD-DO and TA-PO43− are negatively correlated at a significance level p < 0.05 as per the correlation matrix result. Using hierarchical cluster analysis, 5 sampling sites were divided into two clusters based on the similarity of water quality characteristics: comparatively less polluted and more polluted sites. The four principal components were identified using PCA, principal components contribute to 46.02%, 223.48%, 19.38% and 11.11%, of the total variance of the dataset respectively. The PCA indicate that the variation in water quality is due to anthropogenic activities around the vicinity of the wetland. In order to preserve the wetland's aesthetic value and ecological health, preventive measures such as de-weeding, preventing the infiltration of untreated sewage, and desilting are advised. Incorporating these measures can prevent potential damage to the wetland and ensure the survival of its ecology and aesthetic value. [ABSTRACT FROM AUTHOR]

Morphometric analysis of a river basin, encompassing the assessment of shape, drainage, and terrain characteristics, is crucial for erosional, hydrological, and hydro-geological studies However morphometric analysis of large river basins conventionally poses computational challenges. In this study, Google Earth Engine (GEE), a cloud-based platform was utilized to conduct morphometric modeling and evaluation of geomorphology of river basins using JavaScript Application Programming Interfaces (APIs). Consecutively, soil erosion susceptibility mapping based on morphometric parameters using an ensemble of statistical and machine learning algorithms specifically Principal Component Analysis (PCA), Random Forest (RF), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was performed. These methods were applied to India's largest (Ganga: 834,933.65 Km2) and second-largest (Godavari: 310,000 Km2) river basins. Correlation-based comparative analysis of PCA and RF-derived erosional susceptibility rankings for the Ganga (GARB) and Godavari (GRB) river basins was also performed followed by the identification of highly susceptible zones. Results indicate that 28.50% (86,800 Km2) of GRB and 9.85% (81,823.49 Km2) of GARB are highly susceptible to geological erosion. Highly susceptible GARB sub-watersheds were 2.7 times more susceptible than those in GRB, while less susceptible GARB sub-watersheds were 1.96 times more susceptible than GRB counterparts. Machine learning with RF excelled in river basins with greater morphometric variation (GARB), while PCA proved more effective in homogenous river basins (GRB). This study introduces an alternative method for morphometric modeling based erosional analysis, emphasizing the suitability and constraints of statistical and machine learning algorithms and providing valuable guidance for soil conservation efforts. [ABSTRACT FROM AUTHOR]

Brain tumors are a leading cause of mortality in India, with over 28,000 cases reported annually, resulting in more than 24,000 deaths per year as per the International Association of Cancer Registries. Early detection, segmentation, and accurate classification are crucial in effective tumor analysis, and various algorithms have been developed to achieve this. This study proposes a new approach for the detection and classification of Meningioma and Sarcoma brain tumors using both single slices of MRI and CT, as well as input-level fused images of MRI & CT. Our approach involves the implementation of the PrinciResNet16 model for classification of brain tumors. This model is based on Principal Component Analysis (PCA) and ResNet techniques. We report that our approach significantly improves the accuracy, sensitivity, and specificity parameters to 99%, 95%, and 95%, respectively, based on a dataset of 600 fused slices and 1000 single slices obtained from reputable sources. Our findings hold promise for better brain tumour detection and therapy, which are a significant cause of mortality globally. [ABSTRACT FROM AUTHOR]

The impact of socio-economic factors on the Mutual Funds' investment pattern in India with reference to the frequency and time period of investments of retail investors is attempted. The paper also recognizes the financial objectives and, proposes a model facilitating the investment behavior of the retail investors. The Principal Component Analysis (PCA) and Chi-square test were conducted to evaluate both the significant demographic impact on investment patterns as well as the pertinent perception of the investors towards particular mutual fund/s selection. The study reveals that mutual fund is now being considered as one of the important investment options among the investors irrespective of their gender and age. The findings further support the impact of educational and income levels on the preference of investment in mutual funds. The study also reveals that the investors are inclined more towards this investment avenue mainly due to its ease of investing, higher returns and the minimum risk associated with investing in mutual funds. [ABSTRACT FROM AUTHOR]

A comparative study on the basis of detailed morphological characters of four species of Piper L., viz. - Piper betle L., Piper nigrum L., Piper longum L. and Piper retrofractum Vahl., from southern districts of West Bengal, India was performed to draw a systematic conclusion. Both qualitative and quantitative morphological traits were considered. The usefulness of potential morphometric features was identified. Principle Component Analysis (PCA) and Multivariate Cluster Analysis (UPGMA) were used in establishing a systematic interrelationship among the taxa under study. In this study, potential morphometric traits were identified which were valuable in classifying these taxa. This study found highest level of similarity between P. longum L. and P. retrofractum Vahl., whereas, Pnigrum L. has least affinity with other three taxa. [ABSTRACT FROM AUTHOR]

The genus Glochidion, a member of the family Phyllanthaceae, primarily comprises shrub or tree species. It stands out from other genera due to distinctive reproductive features, including prolonged styles in flowers and fruits, apiculate anthers, and lobed and unlobed capsules. This study aimed to compare the morphological characteristics of nine taxa in Assam to facilitate identification and assess ethnobotanical knowledge. Ethnobotanical information was gathered by interviewing the local community, and a taxonomic key was provided for accurate identification. Morphological data underwent principal component analysis (PCA) and cluster analysis using PAST for validation. The comparison revealed distinct characteristics in both vegetative and reproductive traits among Glochidion members. Reproductive features, such as inflorescence, style, ovary, and capsules, were key factors for differentiation and identification. PCA and cluster analysis demonstrated correlation and variation among the taxa, contributing significantly to their demarcation. Ethnobotanical studies indicated the genus's potential medicinal properties, supported by both primary and secondary information. [ABSTRACT FROM AUTHOR]

Horsegram (Macrotyloma uniflorum [Lam.] Verdc.) is an underutilized pulse crop primarily cultivated in South Asian countries like India, Nepal, and Sri Lanka. It offers various nutraceutical properties and demonstrates remarkable resilience to both biotic and abiotic stresses. As a result, it has emerged as a promising crop for ensuring future food and nutritional security. The purpose of this study was to assess the nutritional profile of 139 horsegram germplasm lines obtained from 16 Indian states that were conserved at the National Gene Bank of India. Standard analytical methods, including those provided by the Association of Official Analytical Chemists (AOAC), were used for this investigation. The study revealed substantial variability in essential nutrients, such as protein (ranging from 21.8 to 26.7 g/100 g), starch (ranging from 26.2 to 33.0 g/100 g), total soluble sugars (TSSs) (ranging from 0.86 to 12.1 g/100 g), phenolics (ranging from 3.38 to 11.3 mg gallic acid equivalents (GAEs)/g), and phytic acid content (ranging from 1.07 to 21.2 mg/g). Noteworthy correlations were observed, including a strong positive correlation between sugars and phenols (r = 0.70) and a moderate negative correlation between protein and starch (r = −0.61) among the studied germplasm lines. Principal component analysis (PCA) highlighted that the first three principal components contributed to 88.32% of the total variability, with TSSs, phytates, and phenols emerging as the most significant contributors. The cluster analysis grouped the accessions into five clusters, with cluster III containing the accessions with the most desirable traits. The differential distribution of the accessions from north India into clusters I and III suggested a potential geographical influence on the adaptation and selection of genes. This study identified a panel of promising accessions exhibiting multiple desirable traits. These specific accessions could significantly aid quality breeding programs or be directly released as cultivars if they perform well agronomically. [ABSTRACT FROM AUTHOR]

The study was conducted to find out the morphological variations in tilapia (Oreochrmis mossambicus P. 1852) samples collected from three different water bodies namely Aanasagar lake (S1), Jaisamand lake (S2) and Vallabhsagar dam (S3) during the March 2012 to April 2022 have been studied in the current research. The standard-length and weight ranged between 17.50 to 38.50 cm and 98.50-932.50 g (S1), 15.00 to 24.00 cm and 60.00 to 250.00 g (S2), 15.00 to 33.00 cm and 77.00 to 662.00 g (S3). Key scales were picked out to accomplish the objective of research. The twelve different morpho-parameters (L1, L2,...L12) of Oreochrmis mossambicus P. 1852 scales were measured and analyzed to find presence or absence of morphological variations among the tilapia populations from three habitats. It was found that between these morphometric parameters of Oreochromis mossambicus, L3 and L8 in S1 (0.862), L7 and L12 in S2 (0.830) and L10 and L12 in S3 (0.988) showed very high correlation. Further, the morphological study on measurements was subjected to principal component analysis (PCA) and reported a total 84.98, 89.52 and 93.40 percent of significant variances in scale components (morphometric parameters) for tilapia from S1, S2 and S3 respectively. Variations obtained in morphological (morphometric measurements) data showed four (S1), five (S2) and two (S3) groups of dominant components for the scales of tilapia. It was clearly the formation of disseminated groups in the plot of sheared PCs scores. It is inferred that there was dissimilarity in scale morphological structure of Oreochromis mossambicus collected from different water bodies. [ABSTRACT FROM AUTHOR]

The present study analysed a total of 272 saffron (Crocus sativus L.) genotypes using multivariate analysis. We carefully observed and recorded information about the floral, morphological and corm attributes. Significant variations were observed among the genotypes for all the traits, indicating a high level of variability and suggesting a great potential for saffron improvement. The phenotypic variances were found to be greater than the estimated genotypic variances. Descriptive data on various morphological traits revealed significant differences in the frequency of phenotype classes as well as a wide distribution range. The high heritability estimates were observed in average number of daughter corms per plant (ANDCPP), initial weight of corms (IWC g), no. of buds/corm (NBPC), – no. of leaves in main sprout, (NLMS), number of sprouted buds per corm (NSBpC) and total number of leaves (TNL), whereas average weight of daughter corms per plant (AWBCPP), corm diameter (CDcm), pistal length (PL) cm, style length (STYLcm), fresh weight of pistals per plant (FWOPPPmg) and stigma length (STML cm), revealed medium sense of heritability. The traits dry weight of pistals per plants (DWOPPP mg), inner tepal width (ITW cm), leaf length (LLcm), number of flowers per corm (NFpC), outer tepal length (OTLcm), parianth length with tube (PLWT cm) and weight of stigma (WSTG mg) exhibited low broad-sense heritability. Principal component analysis (PCA) divulged that the first eight component characters had an eigenvalue greater than one with a contributory cumulative variance of 66.15% to the total variance, while as rest of the 16 components contributed 33.85% of total variation in a set of 272 genotypes of saffron. The eigenvalues for yield attributing traits for significant PCs ranged from 5.48 (PC1) to 1.03(PC8). The current study has revealed that there was a sufficient variability in a set of saffron germplasm lines which forms the basis for performance-based clonal selection. Moreover, identified elite genotypes based on saffron yield and corm attributes could be used in the saffron breeding programme for the development of saffron varieties. [ABSTRACT FROM AUTHOR]

Prioritisation of sub-watersheds (SWs) is becoming increasingly important in the conservation of natural resources, particularly in watershed planning. In this study, sub-watershed for the Neyyar basin was prioritised using three methods: morphometric analysis, principal component analysis (PCA) and hypsometric analysis. Morphometric analysis and hypsometric analysis were carried out using remote sensing (RS) and geographic information system (GIS) techniques, while PCA was performed for dimensionality reduction of morphometric parameters. The watershed was divided into 11 sub-watersheds (SW1–SW11), and each sub-watershed was given priority. To rank and prioritise SWs, 15 morphometric parameters were selected from the quantitative measures of morphometric analysis, including linear, relief, and areal. PCA was used to rank and prioritise SWs based on three highly correlated morphometric parameters. The hypsometric integral (HI) values were determined using the elevation relief ratio approach, and HI values were utilised to prioritise SWs. For both methods, such as morphometric analysis and PCA, a higher priority has been given to SW1. Using hypsometric analysis, higher priorities have been assigned to SW1, SW7, SW8, SW9, SW10 and SW11. The most common SWs that belong to the same priority of SWs and have a high correlation between them among the three methods are SW1, SW2, and SW5.The results of this analysis indicate that SW1 is a common high priority area with a significant risk of soil erosion, runoff and peak discharge. Therefore, decision-makers may utilise the high-priority sub-watershed to guide planning and development, measure conservation efforts and manage the land to prevent. [ABSTRACT FROM AUTHOR]

In the present study, a model has been developed for nowcasting using the Automatic Weather Station (AWS) data collected from Thiruvananthapuram, Kerala, India. The Proposed model is based on machine learning techniques: Random Forest (RF), which had been coupled with Principal Component Analysis (PCA). PCA minimizes the presence of multicollinearity issue in the AWS data, which enables the RF to access independent effects of predictors efficiently to predict rainy or non-rainy conditions of the atmosphere for the next 4 hours during the peak summer monsoon of month July. The sensitivity and feasibility of the model were tested for different predictors such as wind speed, temperature, pressure, relative humidity, sunshine, and rainfall, where the demarcation between rainy and non-rainy events was computed using a precision-recall curve. The performance of proposed algorithms for rainfall events is evaluated by using different statistics such as accuracy, precision, recall, probability of detection (POD), and false alarm rate (FAR). The proposed algorithm is found to nowcast with an accuracy rate of 90% and the probability of detection is 68%. The analysis of in-situ observations establishes that the most influential predictors for the nowcasting of rainfall are atmospheric pressure and wind speed. [ABSTRACT FROM AUTHOR]

Diagnosing chronic kidney disease (CKD) accurately and quickly is a big challenge in healthcare today. In a region called Uddanam in Andhra Pradesh, India, there's a common kidney disease called Uddanam nephropathy. In the 1990s, Andhra Pradesh was the first place to find this kind of kidney disease. In our research, we gathered data about personal and clinical of 1,055 individuals living in the Uddanam area (Srikakulam district of Andhra Pradesh, India). Among these, 656 had CKD, and 399 did not. We looked at 37 different aspects of their health. We apply for feature reduction Principal component Analysis and for classification uses MLP and Intelligent GA-MLP Hybrid model. This method combines the power of Genetic Algorithms (GA) and Multi-Layer Perceptron neural networks (MLP) to make CKD diagnosis more accurate. By using GA's ability to find the best solutions and MLP's skill in recognizing patterns, this new method can overcome the problems with older ways of diagnosing CKD. We tested our method thoroughly, comparing it to other methods. Our results showed that our approach can find CKD early and accurately. By combining GA and MLP, we're not only helping improve medical diagnosis but also giving hope for better CKD management and patient care. Our hybrid model, which uses PCA (20 dimensions) along with GA and MLP, performed the best with an accuracy of 98.34% during training and 98.54% during testing. [ABSTRACT FROM AUTHOR]

Simple Summary: Angel sharks (genus Squatina) are small- to medium-sized sharks with flattened bodies, that live on the seafloor. Until now, 23 valid species of angel sharks have been identified around the world, of which over half are thought to be facing a moderate to severe risk of extinction. Several juvenile angel sharks were collected by researchers working on the Mascarene Plateau, an elevated area of seabed in the Indian Ocean, in 1988 and 1989. They appeared different in coloration and in body shape and structure to a species known from East Africa and Madagascar, the African angel shark. Additional angel sharks were caught off the western coast of India in 2016 and in the central western Indian Ocean in 2017, including adult individuals. Information on body measurements and skeleton structure were collected, and genetic analyses were also conducted on these sharks and on museum specimens previously identified as African angel sharks. The results indicated that the specimens collected from the Mascarene Plateau and off southwestern India were a species that is new to science. It is genetically and morphologically distinct from the African angel shark; is smaller when born and when fully grown; and lives in a distinctly different area. The newly described species has been named Lea's angel shark. Sampling efforts on the Saya de Malha Bank (part of the Mascarene Plateau, western Indian Ocean) unveiled three unusual small juvenile angel shark specimens, that were a much paler color than the only known western Indian Ocean species, Squatina africana Regan, 1908. However, it took many years before further specimens, including adults of both sexes, and tissue samples were collected. The present manuscript contains a redescription of S. africana based on the holotype and additional material, as well as the formal description of the new species of Squatina. All specimens of the new species, hereafter referred to as Squatina leae sp. nov., were collected in the western Indian Ocean off southwestern India and on the Mascarene Plateau at depths of 100–500 m. The new species differs from S. africana in a number of characteristics including its coloration when fresh, smaller size at birth, size at maturity, and adult size, genetic composition, and distribution. Taxonomic characteristics include differences in the morphology of the pectoral skeleton and posterior nasal flap, denticle arrangement and morphology, vertebral counts, trunk width, pectoral–pelvic space, and clasper size. A key to the species of Squatina in the Indian Ocean is provided. [ABSTRACT FROM AUTHOR]

Devanagari script is used by most of the people in India. There are some scriptspecific structural characteristics of Devanagari script which makes the character recognition problem more challenging. Many OCR tools are available for printed or handwritten Devanagari script recognition. In these systems the input is given in the form of images of the script which can be scanned or photographed. But the existing systems are not robust. They give unexpected results when the input to the system is not ideal that is the image is rotated or tilted or has illumination variance. Our goal is to build a robust OCR system for printed Marathi highlighted text where the variation with respect to font, size, orientation and illumination are allowed. This paper proposes appropriate image transformation techniques to get a robust Devanagari Character Recognition System. [ABSTRACT FROM AUTHOR]

Streams are pristine natural life-thriving water sources for people living in the mountainous proximity of NE India, where water scarcity is a common occurrence in most villages and towns. In the last few decades, factors like coal mining had drastically reduced the usability of stream water in the region; as such, an attempt has been made to assess the spatiotemporal variation of stream water chemistry affected by acid mine drainage (AMD) at Jaintia Hills, Meghalaya. The water variables were subjected to a multivariate statistical technique of principal component analysis (PCA) to understand their condition at each sampling point while comprehensive pollution index (CPI) and water quality index (WQI) was incorporated to assess the quality status. Maximum WQI was recorded in S4 (541.14) during summer, while minimum value was estimated in winter at S1 (14.65). Throughout the seasons, the WQI revealed "Good" quality in S1 (unimpacted stream), while the impacted streams (S2, S3, and S4) exhibited a "Very poor" to "Water unsuitable for drinking" status. Similarly, in S1, the CPI showed a ranged value of 0.20 to 0.37, presenting a water quality status of "Clean to Sub-Clean," whereas, CPI of the impacted streams indicated "Severely polluted" status. In addition, PCA bi-plot presented higher affinity of free CO2, Pb, SO42−, EC, Fe, and Zn in AMD-impacted streams than in unimpacted streams. The result demonstrates the environmental issues induced by coal mine waste and in particular, stream water being severely affected by AMD in mining areas of Jaintia Hills. Thus, measures to stabilized the mine repercussions and cumulative effects on the water bodies need to be formulated by the government, as stream water will remain the primary water source for the tribal communities in this region. [ABSTRACT FROM AUTHOR]

Accuracy in groundwater quality monitoring plays a vital and decisive role in environment eco-system, human health and agriculture practices. The present study aims to identify the relationship between the water quality parameters and sources of contamination using radial basic function (RBF) networks. The primary focus of the study is to understand the prioritisation of ionic concentration in terms of groundwater quality. Additionally, the multivariate statistical analysis of groundwater for 30 sample locations was carried out in the present study. Piper and Gibbs plot divulged that, rock-water interaction, ion exchange process, weathering of host rocks are the primary process that influences the groundwater quality in the study area. The principal component analysis (PCA) of groundwater confirmed that two phenomena such as geogenic and anthropogenic activities govern the chemical composition of groundwater. These major phenomena are validated by Hierarchical Cluster Analysis (HCA) and K-mean cluster analysis. The RBF network reveals that anion (R2 = 0.95) plays a vital role in the quality of groundwater than cations (R2 = 0.901). The major sources of contamination observed in the study area are weathering of host rocks, ion exchange process, excess utilisation of synthetic fertilisers. The leachate of dumping yards and agriculture field are might be the source of higher concentration of anions in the groundwater chemistry, and it leads to an increase the value of pH in groundwater. The integrated approach of the present study suggests that remedial measure to be followed to avoid the contamination of groundwater in the study area. The research findings may provide useful information to the decision-maker regarding the groundwater pollution risk management mainly in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

Diwali has become an occasion of air and noise pollution, and the release of particulate matter and toxic gases has chronic and acute effects on people and their environment. Thus, an air quality assessment study was done by CSIR-CSMCRI covering the pre-to-post Diwali 2021 period (5 days) in the three locations (traffic, residential, and control) of Bhavnagar. The average 24-h concentration of PM10 (380 µg/m3), PM2.5 (182.2 µg/m3), and SPM (403 µg/m3) was significantly higher during Diwali, exceeding the National Ambient Air Quality Standards (NAAQS). The concentrations of SO2 and NO2 were 121.8 µg/m3 and 102.1 µg/m3. Metals like Zn, Al, Pb, and Mn were found in higher concentrations during the study. The air quality index (AQI) was maximum on Diwali, resembling very poor air quality. More elements and oxides were detected in PM2.5 (S, Al, Mg, Ba, and Zn and their oxides) than in PM10 (Fe and S) through WDXRF. Water-soluble anions like SO42−, Cl−, and NO3− were observed during the study, with a higher SO42− (64%) on Diwali. The PM10 morphology and mapping of elements were done using SEM–EDX. Emerging contaminants, specifically phthalate groups, were detected through GCMS. The enrichment factor (EF) showed Zn and Pb originating from anthropogenic activities. The air quality data was validated using a variance test, least significance difference (LSD), correlation, and principal component analysis (PCA). This paper is the first to highlight the air quality assessment during Diwali for a western coastal place in India. It is time to implement regulations on burning firecrackers for pollution reduction, aiming to achieve a sustainable atmosphere. [ABSTRACT FROM AUTHOR]

Background Child malnutrition remains a matter of concern in India as the current levels are high and the decline is slow. National Family Health Survey (NFHS–4, 2015-16) data, for the first time, provides credible, good quality data at district level on social, household and health characteristics. Methods Techniques of spatial analysis on data in respect of 640 districts were used to identify spatial characteristics of the nutrition levels for children in the 0–60-month age group. Further, the principal component analysis (PCA) was used to identify 7 important correlates of the malnutrition out of 21 relevant components provided in the NFHS-4. The paper further uses three techniques, ordinary least squares (OLS), spatial lag model (SLM) and spatial error model (SEM) to assess the strength of correlation between the malnutrition levels and the shortlisted correlates. Results The use of SLM and SEM shows improvement in the strength of the association (high R -square) compared to OLS. Women's height and Iodized salt in stunting, child anaemia in wasting, women's height and child anaemia in underweight were found to be significant factors (P  < 0.01) along with spatial autoregressive constant. Conclusions Such analysis, in combination with PCA, has shown to be more effective in prioritizing the programme interventions for tackling child malnutrition. [ABSTRACT FROM AUTHOR]

Fly ash is an inevitable by-product from the coal-fired power plants in many developing countries including India that needs safe, timely and productive disposal. The addition of fly ash alters physicochemical properties of soil and hence could be used as a soil conditioner or modifier along with the appropriate level of vermicompost to support plant growth. Several studies have focalized sole use of fly ash and vermicompost in agricultural production systems lacking information on combined application effects. This work was carried out at Chiplima in the district of Sambalpur, Odisha, India, to ascertain the best suited combination of native soil, fly ash and vermicompost (from farmyard manure) for rice nursery based on the changing physicochemical properties and seedling growth. The experiment consisting of 21 treatment combinations of soil, fly ash and vermicompost at 0%, 20%, 40%, 60%, 80% and 100% by weight was laid out in a factorial complete randomized design with three replications. Fly ash and vermicompost at moderate concentrations significantly ameliorated the physical properties, viz., porosity, bulk and particle densities, water holding capacity, infiltration rate and the capillary rise of water in rice nursery soil that ultimately resulted in vigorous rice seedlings at 40 DAS through beneficial soil biota as well as better root and shoot development. The porosity, water holding capacity and infiltration rate significantly increased with the addition of vermicompost while fly ash addition substantially reduced them. Fly ash and vermicompost in moderate quantities smothered soil chemical properties like electrical conductivity and organic carbon that increased the availability of N, P, K, B, S and Zn. The pH did not differ significantly due to treatment effects owing to a marginal difference in pH of the substrates, whereas electrical conductivity increased significantly with only marginal addition of fly ash to vermicompost. Considering the economic feasibility and environmental impacts, 40% soil + 20% fly ash + 40% vermicompost may be recommend to the farmers for wet rice nursery raising and also for remediating the coal fly ash in agricultural production system. [ABSTRACT FROM AUTHOR]

The principal aim of the present study is to comprehensively investigate the effects of economic growth and financial development on income inequality in India. This study uses the autoregressive distributed lag (ARDL) model to examine the variables' long and shortrun relationships. This study also employs principal component analysis (PCA) to construct a comprehensive financial development index. In addition, this study utilised the unit root test developed by Ng-Perron to analyse the order of integration of variables used. The study finds a long-run relationship between economic growth, financial development, and income inequality in India for the period under consideration. The results suggest that per capita growth and government expenditure for most of the estimated models (both short and longrun) are inequality-narrowing factors in India, while inflation worsens the income distribution. The study confirms an inverted U-shaped relationship between financial sector development and income inequality, thus validating the Greenwood-Jovanovic hypothesis for the Indian economy. The findings of this study can aid concerned quarters and policymakers in framing appropriate policies for economic growth and financial sector development to improve the income inequalities in India. [ABSTRACT FROM AUTHOR]

In the present study, principal component analysis (PCA) is used to investigate the processes controlling groundwater salinity in the Mewat (Nuh) district, Haryana, India. Twenty groundwater samples were collected from salinity-affected areas in the March–April months of years 2018 and 2019 and were analyzed for chemical variables pH, EC, Ca2+, Mg2+, Na+, K+, HCO 3 - , Cl−, SO42−, NO 3 - , TDS, and total hardness. Three principal components were selected based on the eigen value, which explains 79.58% and 85.08% of the total variation in the years 2018 and 2019, respectively. The first principal component (PC-1) is identified with salinity, the second principal component (PC-2) with alkalinity, and the third principal component (PC-3) described the pollution. When the yearly comparison was made, the samples collected in 2019 were found to have an increased salinity compared to 2018, which shows an increased vulnerability to the aquifer of Mewat on account of the decline in rainfall recharge. It was also evident that declining recharge also triggered the recharge from other sources; thus, the impact of pollution is more pronounced in 2019 compared to 2018. [ABSTRACT FROM AUTHOR]

The study aimed to assess the spatial and seasonal variation of water chemistry along the Doyang river. Data of sixteen physicochemical parameters of water quality collected from eight sampling stations during 2016–2017 were subjected to multivariate statistical techniques like principal component analysis (PCA) and cluster analysis (CA) to address the objectives of the study. Based on the similar characteristics of water qualities, CA differentiated the study area into three different clusters or groups. The result of PCA obtained for pre-monsoon, monsoon and post-monsoon showed three PCs each explaining 94.75%, 93.63%, and 93.91% of the total variance. In each of the seasons, PC1 produced the maximum explanatory variance with over 60%. The present study found midstream stations (S2, S3, S4, and S5) experiencing much of the seasonal influence (rainfall), and parameters like WT, CO2, pH, DO and BOD were found to accord the seasonal cycle. Anthropogenic activities were found to be the main cause of pollution along the Doyang river. Accordingly, the river is segregated into three different zones based on the nature of the pollution load, i.e., mineral loading at the upstream zone, organic loading at the midstream zone, and nutrient loading at the downstream zone. Finally, the use of PCA and CA has proven effective in evaluating the complex datasets and demonstrates its usefulness in assessing the spatio-temporal variations of surface water chemistry in the Doyang river. [ABSTRACT FROM AUTHOR]

Buckwheat (Fagopyrum spp.) is an important crop in the high-altitude regions of the Northwest Indian Himalayas. The agro-climatic heterogeneity of this region offers a great deal of diversity in the agro-morphology of buckwheat species. In this study, a total of 61 accessions of Fagopyrum esculentum and Fagopyrum tataricum were characterized for 17 morphological (8 qualitative and 9 quantitative) traits. Significant differences (P < 0.0001) among all the traits were revealed by one-way analysis of variance. Further, significant phenotypic variability in both qualitative as well as quantitative traits was also observed. Both positive and negative correlations were observed between the traits of agronomic relevance. The principal component analysis (PCA) reveals about 69% variability among the first six components. The accessions were divided into two key clusters with numerous subclusters by considering the unweighted pair group method with arithmetic mean dendrogram. A cluster of 19 accessions was formed utilizing a PCA scatter plot indicating accessions with maximum values for important quality traits like plant height, leaf blade width, stem colour (red), primary branches, inflorescence length, flower colour (greenish-yellow), seed anthocyanin colour (green), seed shape (ovate) and seed weight. These accessions can be of vital significance for future buckwheat breeding programmes. The findings from the current study will form a favourable base for genetic resource management, improved cultivation and applications of buckwheat at the commercial level in the northwestern Himalayas of India. [ABSTRACT FROM AUTHOR]

The analysis of geological samples that have several chemically diffused zones which formed under certain physico-chemical condition is difficult to achieve. The quantitative estimations of the minerals in such samples are tedious. The present work demonstrates the application of LIBS for qualitative and quantitative analyses of a quartz-fluorite composite which was procured from an amygdaloidal basalt from Deccan Traps, India. The presence of weak emission lines of F in the spectral range of 200–900 nm makes it challenging to quantify the fluorine. This study has addressed a promising alternative to quantify the fluorine using electronic bands of CaF molecules observed in the Laser-induced Breakdown Spectroscopy (LIBS) spectrum. In addition to this spectroscopic technique, the authors also have used Photoacoustic Spectroscopy (PAS) and UV-VIS spectroscopy technique to obtain molecular information from the geological sample. Principal Component Analysis (PCA) was applied to a truncated spectral region of the CaF molecule, and it showed 99% variance. Further, the obtained results with these spectroscopic techniques were compared with the results that were obtained from X-ray diffraction and Electron Probe Micro Analyzer, and they show good agreement. Thus, the LIBS technique can be promising for in situ profile section (varies from few microns to centimeters size) studies without the sample's destruction using the point detection capability of LIBS. [ABSTRACT FROM AUTHOR]

Pb–Zn mineralization at Rajpura‐Dariba and Zawarmala, India, is hosted in the Palaeoproterozoic meta‐volcano‐sedimentary sequence of Aravalli‐Delhi Belt. We have studied different generations of pyrite (Py‐I, II and III) and sphalerite (Sph‐I, II and III), associated with ore mineralization and remobilization stages, from both the deposits. In Rajpura‐Dariba, high concentrations of the majority of trace elements in syngenetic Py‐I and Sph‐I suggest their formation from trace element enriched hydrothermal fluid in SEDEX environment. Enrichment of lattice bound elements in Py‐II (As, Co, Ni) and Sph‐II (Mn, Hg and Cd) during metamorphic recrystallization of early pyrite and sphalerite indicates their local derivation from host rocks. The Pb, Tl, Ag and Sb rich Py‐III and Sph‐III in discordant ore indicate their crystallization from low melting‐point chalcophile elements enriched sulfide melt. In Zawarmala, the trace element poor natures of diagenetic banded Py‐I and Sph‐I suggest that the initial ore‐forming fluid was depleted in trace elements. Enrichment of trace elements in Py‐II (As, Mn, Tl, Cu) and Sph‐II (Mn, Hg, As, Ga) in sphalerite‐pyrite vein ore signify their derivation from carbonate host rocks. The high abundance of trace elements in Py‐III and Sph‐III indicates their precipitation at lower temperatures from a trace element enriched hydrothermal fluid during the massive galena‐sphalerite ore formation. Pyrite and sphalerite from global SEDEX are trace elements enriched as compared to MVT deposits, where their compositions have remarkably correlation with host rocks. Our study highlights the influence of host rock in pyrite and sphalerite compositions and their ore genetic implications. [ABSTRACT FROM AUTHOR]

The digitalization process has been increasing rapidly worldwide today. There is various technologies developed and continue developing tremendously in the world, such as mobile payment systems. Such type of emerging technologies has made a very significant transformation in people lives everywhere. Technologies such as mobile devices facilitate people to finish their work in less time and effort required usually. That is why, these innovations and advancements in technologies led people to be more inclined toward their use. Furthermore, in the current scenario, mobile payments have become an integral part of the daily lives of people due to the impact of the novel coronavirus (COVID-19) disease on society. The presence of mobile payment systems can be considered for years, but the use of mobile payment systems has increased in the current situation of the COVID-19 pandemic, which is the core basis of this research paper. However, the user's acceptance of mobile payment systems is still low due to some pros and cons of this technology which are examined in this paper by applying a hybrid machine learning model. We collected primary data through Google Form from the institute in the Meerut region and other sources from India with a total of 222 respondents. In this study, the data is processed and applied on to the machine learning models. Data analysis and findings of the research showed that the proposed ensemble of classifier algorithms outperforms other exiting machine models in terms of accuracy, precision, recall and F1-score. The research and findings will help the organizations have a clear and correct analysis of the scenario of the adoption of mobile payments in India and also design better mobile payment systems to curb the situation like the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

A well‐developed 86 m‐thick Cenozoic sedimentary succession in the Matanomadh area, western Kachchh, was studied to assess the stratigraphic positions of different units, ascertain depositional environments, and the degree of climatic controls over time depositional processes. Age‐diagnostic nannoplankton species Cyclicargolithus floridanus (46.29–12.1 Ma), Reticulofenestra dictyoda (53–30.8 Ma), Reticulofenestra minuta (50.5–2.59 Ma) and foraminiferal species, for example, Orbulinoides beckmanni (41–39.5 Ma) found across studied units envisage the stratigraphic age and palaeoenvironment. CaO with strong negative elemental correlation, binary/ternary discriminant tectonic and depositional regimes cross‐plot from units indicate the biogenic origin of limestones and shales were formed in typical passive, marginal, shallow littoral settings with plausible detritus influx from Deccan basaltic provenance. Total organic carbon with TE‐REE respectively connotes a sedimentation process in a nearly consistent open shallow marine, locally transient semi‐restricted marginal basin in a tropical‐equatorial region. Furthermore, principal component analysis (PCA) of weathering indices based on elemental abundance and X‐ray diffraction data of clays deterministically suggest with empirical relation between humid tropical climate‐induced weathering and fluctuant terrestrial influx. Palaeoredox indices with biological data demonstrate localized redox‐stratified conditions. However, coherent signatures of the global events such as Eocene Thermal Maximum‐2, Middle Eocene Climatic Optimum, and Mid Miocene Climatic Optimum can be traced and validated by synthesizing geochemical data for direct/indirect estimation of sediment routing, rainfall (αAlMg and αAlCa) and climate proxies (PCA), productivity indices (Babio, Cubio and Nibio), and localized periodic oxic‐dysoxic environment (V/Cr, U/Th, Ni/Co) from core samples. [ABSTRACT FROM AUTHOR]

The present manuscript explores a spectroscopic technique to select turmeric powder, free from impurities, and has compounds of medicinal importance among the tainted and natural turmeric. Six Curcuma longa (turmeric powder) samples, named S1, S2, S3, S4, S5, and S6, were analyzed to discriminate between tainted and natural turmeric using the LIBS and multivariate technique. Other techniques such as UV–Vis, FTIR, and EDX are also used to ascertain the elements/compounds showing the medicinal properties of C. longa. Spectral lines of carbon, sodium, potassium, magnesium, calcium, iron, strontium, barium, and electronic bands of CN molecules were observed in the LIBS spectra of turmeric samples. Spectral signatures of toxic elements such as lead and chromium are also observed in the LIBS spectra of all samples except S6. Adulteration of metanil yellow, a toxic azo dye, is used to increase the appearance of curcumin when the actual curcumin content is low. The presence of spectral lines of lead and chromium in the LIBS spectra of S1 to S5 suggested that it may be adulterated with lead chromate which is used for coloring turmeric. Further, the presence of sulfur in EDX analysis of sample S5 indicates that it may also have been adulterated with metanil (C18H14N3NaO3S). The concentration of samples' constituents was evaluated using CF-LIBS, and EDX was used to verify the results obtained by CF-LIBS. The principal component analysis applied to the LIBS data of the turmeric samples has been used for instant discrimination between the sample based on their constituents. We also analyzed antioxidant activity and total phenolic and flavonoid content of different turmeric samples and found a negative Pearson correlation with heavy metals. The presence of curcumin in turmeric is confirmed using LIBS and UV–Vis, which have medicinal properties. [ABSTRACT FROM AUTHOR]

Sunflower ( Helianthus annuus L.), known for its adaptability and high yield potential, is vital in global edible oil production. Estimating genetic diversity is a key pre-breeding activity in crop breeding. The current study comprised of 48 genotypes which were assessed for their biometrical traits at department of Oilseeds, Tamil Nadu Agricultural University, during the rainy season of 2022. The lines were subsequently characterised using 103 simple sequence repeat (SSR) markers for molecular diversity analysis. The results indicated that the net nucleotide distances indicated varying genetic divergence, with subpopulations II and V showing the highest (0.056) and I and IV the lowest (0.014). Subpopulation IV exhibited the highest heterozygosity (0.352), while subpopulation III had the lowest heterozygosity and a low Fst (0.173). Principal components analysis (PCA) and hierarchical cluster analysis were employed for assessing the morphological diversity, facilitating genotype grouping and parent selection for breeding programs. The first four components cumulatively accounted for 86.72% of the total variation. Cluster Analysis grouped 48 sunflower genotypes into three clusters based on genetic diversity. COSF 13B stands out for its high head diameter, oil content, seed yield, and oil yield based on mean performance of morphological data. Principal coordinate analysis (PCoA) mirrored the groupings from the Neighbor Joining method, with the first three components explaining 27.24% of the total variation. Molecular data analysis identified five distinct clusters among the germplasm. By integrating morphological and molecular marker data with genetic distance analysis, substantial diversity was revealed with the genotypes RHA 273 and GMU 325 consistently demonstrated high oil yield per plant. The genotypes GMU 477, GMU 450, COSF 13B, RHA 102, CMS 1103B, and RHA GPR 58 have been identified as suitable parents for enhancing oil content in sunflower breeding programs. These findings also aid in selecting SSR markers for genotype characterization and in choosing diverse parents for breeding programs., Competing Interests: Sushil Kumar is an Academic Editor for PeerJ., (©2024 Lavudya et al.)

The body conformations of 262 adult Ganjam goats were subjected to principal component analysis (PCA) with 11 morphometric variables. The results were then used to predict the mature body weight of the goats. Most of the traits were positively correlated, and the correlations were statistically significant. The three main components that the PCA recovered explained 76.12% of the variation in body morphometry overall. The first component accounted for approximately 54.74% of the overall variation and described almost all the traits except ear length and tail length, as indicated by high component loadings. The second component accounted for approximately 11.48% of the variation, mostly accounting for the variation in tail length. The principal component accounted for 9.89% and mostly explained the variation in ear length. The communalities ranged between 0.557 (horn length) and 0.848 (chest circumference) for the first three extracted components. The highest percentage of variability in chest girth was explained by the first three principal components, whereas it was the lowest for the horn length. In the context of predicting body weight through stepwise regression analysis, nine primary variables accounted for 57.3% of the total variance in body weight. Conversely, utilizing the first principal component alongside six additional principal components as independent variables resulted in capturing 56.3% of the variation in the adult live weight of goats while maintaining model comparability with other pertinent parameters. PCA was used efficiently for body weight prediction from major morphometric traits of Ganjam goats addressing the multicollinearity issue., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

Drought assessment in any region primarily hinges on precipitation deficiency, which is subsequently propagated to various components and sectors, leading to different drought types. In countries such as India, an intricate relationship between various governing factors, drought types, and their quantification methodologies make it elusive to timely initiate government relief measures. This also prevents comprehensive inclusion of the integrated effect of the principal drivers of drought, resulting in ambiguous categorization of severity, where groundwater storage variability is often neglected despite its significant role in irrigation. Here, we developed a multivariate Joint Drought Index (JDI) combining satellite and model-based standardized indices of precipitation and evapotranspiration (SPEI), soil moisture (SSI), groundwater (SGI), and surface runoff (SRI) with different temporal scales by employing two robust methods, principal component analysis (PCA) and Gaussian copula, and applied the index to highly drought-prone Marathwada region from central India. Our novel approach of using different scale combinations of integrated indices for two primary seasons (Kharif and Rabi) provides more realistic drought intensities than multiple univariate indices, by incorporating the response from each index, representing the seasonal drought conditions corroborating with the seasonal crop yields. JDI, with both methods, successfully identified two major drought events in 2015 and 2018, while effectively capturing the groundwater drought. Moreover, despite the high correlation between JDI using PCA and copula, we observed a significant difference in the intensities reported by these methods, where copula detected exceptional drought conditions more frequently than PCA. JDI effectively detected the onset, duration, and termination of drought, where the improved accuracy of drought detection can play a critical role in policy formation and socioeconomic security of the related stakeholders. Seasonal agriculture drought categorization for holistic quantification of drought conditions as presented in this study should provide broad methodological implications on drought monitoring and mitigation measures, especially for agriculture-dominated regions in semiarid climates. [ABSTRACT FROM AUTHOR]

Soil erosion is common and has a wide range of spatiotemporal variability. It is crucial in determining sediment output, which is essential for proper watershed management. In this research, we propose morphometric deterministic models (MDM) for prediction of sediment yield index using morphometric parameters of 49 watersheds from Upper Narmada Basin of Madhya Pradesh state, India. For this purpose, Shuttle Radar Topography Mission generated Digital Elevation Model was used to extract and analyze 12 morphometric parameters including linear, aerial, and relief parameters. Principle Component Analysis has been applied for the most effective parameter estimation. The linear and nonlinear MDM were discovered to be suitable for the field of sediment research due to the high value of R2 (over 70%). The sediment yield forecasting is critical for taking the appropriate management measures in the watershed to reduce the sediment load in the reservoir and extend the life of the structure. [ABSTRACT FROM AUTHOR]

We investigated the hydrogeochemical regime of an AMD-affected coal mining province. 98 water samples were collected over two seasons and analysed for 14 parameters. We attempted to discriminate the sources of variation of water quality using select multivariate techniques: display methods (principal component analysis) and unsupervised pattern recognition (cluster analysis). Most of the groundwater and river water were characterised by shallow freshwater facies (Ca–Mg–HCO3 type), whereas the samples representative of mine water were of the Ca–Mg–SO4 type. The mines of the area annually discharge 2901 t of solute loads, ranging from 91 to 1030 t/year. Various molar ratios suggest that dissolution of the silicates associated with the mixing process is the predominant solute acquisition processes that govern the water chemistry of the region besides AMD. The chemometric results indicated that only a few groundwater and river water samples had low pH and elevated total dissolved solids, and these were near the three mines that were affected by AMD. These results substantiate the effectiveness of the mine water treatment measures implemented at the mine sites. [ABSTRACT FROM AUTHOR]

Aulacoseira granulata (Ehrenberg) Simonsen 1979 are considered as the eco-variable species which varies in density and diversity along with their morphological traits with the interference of environmental changes, so it is considered as one of the major ecological indicators of the water quality of lotic as well as lentic aquatic ecosystems. To assess major environmental factors which contribute to A. granulata bloom in the riverine system, a study was carried out from 2018 to 2019 comprising four different seasons at 11 sampling sites of river Ganga in the middle and lower stretch of river Ganga comprising freshwater and estuarine zones. For the analysis, different univariate, as well as multivariate, analytical tools such as principal component analysis (PCA) and water pollution index (WPI) were used. In the finding, it was observed that the average abundance of A. granulata was found maximum during the winter season. Among all the studied sites, the maximum average abundance was at Balagarh (71,576 cell l−1) and minimum at Diamond Harbour (68 cell l−1). The environmental factors such as dissolved oxygen, depth, and altitude showed a significant influence on the growth of A. granulata, while the water temperature negatively influenced the growth rate of A. granulata. The WPI showed a significantly negative correlation with cell length. Finally, the study concludes that the blooming of A. granulata is highly influenced by varied environmental conditions along the river Ganga, suggesting possible eutrophication. Therefore, a certain minimum flow and depth especially during the lean season have to be maintained for the sustenance of planktonic biota in the river Ganga. [ABSTRACT FROM AUTHOR]

The study investigates the effect of various existing hill land-use practices on soil quality. The land-use types selected for the analysis was based on common land-use types practice in the area. Soil samples were collected from different land-use types and analysed for bulk density (BD), clay, sand, silt content, soil organic carbon, total nitrogen, available phosphorus, microbial biomass carbon and nitrogen (MBC and MBN), and exchangeable cations. Further, the soil fertility index, soil evaluation factor and Principal Component (PC) Analysis suggested that PC1 accounts maximum variance (41.6%) followed by PC2 (19.7%), PC3 (13.5%), PC4 (12.4%) and PC5 has the least variance (10.4%). Weighted soil quality index (SQIw) was computed based on SQIw. The majority of land-use samples showed a moderate quality of soil except for samples of current jhum (CJ) (SQIw = 0.75) which is in the good category. Almost all the soil parameters except BD have a higher concentration in the settled agricultural farm owing to the integration of sustainable approaches (viz., integration of livestock manure, conservation tillage, mixed cropping and terracing). The present study indicates that long term settled sustainable land-uses agricultural practices are the key to maintain soil organic carbon, and soil health in the sloping hilly region of tropics. [ABSTRACT FROM AUTHOR]

Muskmelon, which is an important cucurbit of the tropical and subtropical region of the world, shows great diversity, with six different botanical or horticultural groups and fifteen varietal groups. In this study, a total of fifty (50) simple sequence repeat (SSR) markers were used to investigate the genetic diversity and population structure of 46 muskmelon accessions of different horticultural or varietal groups. Thirty-eight (38) of the fifty SSR markers were highly polymorphic. A total of 99 alleles were generated by the polymorphic markers, with an average of 2.06 alleles per loci. Heterozygosity among accessions for individual loci varied from 0.00 to 0.21, with the highest (0.21) reported for the CMCTN71 marker. The gene diversity and polymorphism information content (PIC) values varied between 0.08 to 0.72 and 0.07 to 0.67, with an average of 0.47 and 0.38, respectively. The primer that showed the highest gene diversity and PIC values was DM0913. The unweighted pair-group method for arithmetic average (UPGMA)-based dendrogram classified all the 46 accessions into two major clusters. Population structure analysis classified 46 muskmelon accessions into 2 subpopulations. The subpopulation I contained 29 accessions from the cantalupensis group, and subpopulation II contained 17 accessions from momordica , inodorus and conomon groups, respectively. Analysis of variance indicated that 18 and 68% of variance was due to subpopulations and differences among individuals, respectively. The present study supports the existence of sufficient variation among musk melon genetic resources in India, and their classification based on molecular markers will be helpful to accelerate the breeding programme for specific traits. [ABSTRACT FROM AUTHOR]

Regular monitoring and precise mapping of urban environment is required by various applications. In this paper, a new method is proposed, in which different combinations of feature bands have been utilized for extraction of built-up surfaces, sub-surfaces and materials in different levels (Level-1, 2 and 3) using AVIRIS-NG hyperspectral imagery of Jodhpur, Rajasthan region of India. Features identified in this study are based on spectral indices, major principal components and fractional abundances, in which first combination is developed using spectral indices and fractional abundances while second is made using spectral indices and major principal components and finally third using combination of all the aforesaid features. It is observed that the combined form of all the aforementioned features produces better extraction results than the other two while combination of spectral indices and fractional abundances may be more useful than the combined form of spectral indices and major principal components. [ABSTRACT FROM AUTHOR]

Yellow vein mosaic virus (YVMV) is the most destructive viral disease of okra, has become a limiting factor in the successful cultivation and production of okra in India, resulting in yield losses ranging from 17.09 to 96.49 per cent. As a result, it is critical that breeders continue to develop superior varieties or hybrids with long-lasting resistance to overcome this major devastating disease. In this context present investigation was carried with 74 elite okra lines to discover potential parents for a resistance breeding programme. On the basis of D² values the 74 genotypes were divided into seven groups. Cluster I constituted the highest number of genotypes followed by cluster II, III, IV, V, VI and VII. The traits per cent disease index of YVMV contributed maximum towards divergence followed by total phenolic content, peroxidase activity, fruit yield per plant, number of primary branches and number of fruit per plant. PCA showed four principal components with Eigen values more than one viz., 3.87, 3.21, 1.89 and 1.64 and accounted 81.56 per cent of the total genetic variation. Principal component analysis revealed that PC1 captured potential traits viz., total phenolic content, peroxidase activity, polyphenol oxidase, number of fruits per plant, number of primary branches per plant and fruit yield per plant, which could be used in future breeding programmes for high yield and YVMV resistance. The present study it was revealed that, eleven accessions viz., AE-65, AE-66, AE-CBE-921, Pusa Bhendi-5, SB-2, IC112449, AE-CBE-94, AE-CBE-943, AE-CBE-934, AE-CBE-92 and AE-CBE-93 appeared to be very promising lines for future use in resistant breeding programmes. [ABSTRACT FROM AUTHOR]

National river of India, Ganga River, nurturing ecological, economic, and socio-cultural aspects defining the last long history of country. Forming the largest catchment, fulfil the needs of millions of people for fresh water used in domestic, agriculture, commercial and industrial sectors. Therefore, River Ganga is always the center of attraction to the administrative authorities, institutions, academicians and researchers for its quality issues. The periodically examination of the river water quality for its conservation, restoration or rejuvenation is essential and conducted by many researcher on different quality aspects. The aim of the present investigation is to estimates the spatio-temporal variability in the Ganga River System's hydrology in upstream regions in the Himalayan Region of Uttarakhand state, India. The result clearly explains considerable variation in water quality index (WQI: 29.39–71.60) screening seasonal variation, defining the deterioration of water quality (WQ) of Ganga River from good to moderate polluted level through the overall index of pollution (OIP). Moreover, principal component analysis (PCA) has implied to identify the pollution sources. The PCA generates seven components and contributes (85.1%) to influencing river water's hydrochemistry. These outcomes give detailed information to understand the water quality affecting factors in the Ganga River system. The management and remedial practices are an urgent task required to conserve WQ in the upstream region to check WQ's further deterioration in the future. [ABSTRACT FROM AUTHOR]

A cistellata on mango is one of the major problem throughout northern India. It is a devastating pest which led to the formation of galls in place of apical and axillary buds, later resulted into the inhibition of inflorescence and drying up of affected branches. The study was carried out during the year 2016 to 2018 at Horticulture Research Centre, Patharchatta, G. B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand. This study aimed to examine the life processes and the impact of climatic variables on population fluctuations of psylla, that will help in the execution of management practices during critical stages. Principal Component Regression Analysis (PCRA) was used to establish relationship between the weather variables and the abundance of psylla population. Biological attributes of A. cistellata such as pre-oviposition (14.94 ± 0.62 h), oviposition (2.94 ± 0.26 h), post-oviposition (1.70 ± 0.22 h), fecundity (121.90 ± 7.63 eggs), incubation (156.10 ± 1.36 days) and nymphal period (187.66 ± 2.76 days) were studied. Sex ratio favored the females over males; females lived for significantly longer period as compared to male, (when provided with honey solution). Correlation coefficient stated that egg count was positively significantly correlated to gall and adult count, whereas significantly negatively associated to panicle number at 0.05 level of significance. Egg hatching initiated with the sudden increase in rainfall (102.00 mm) and continued till the period of monsoon season i.e.33 SMW to 40 SMW. Reduction in temperature, Tma (18.2–29.0 °C) and Tmi (6.5–11.5 °C), led to the formation of galls in axillary and apical bud positions. Prediction model for psylla was designed based on explanatory variables such as Tma, Tmi, R.Hm and Ev using Principal Component Regression Analysis Technique to forecast A. cistellata population on mango. This investigation delivers appropriate understandings of variables which control the psylla population in mango and the decision‐making method regarding IPM tactics against shoot gall psylla. [ABSTRACT FROM AUTHOR]

This study is based on integrative taxonomy and reports a new fish species Dussumieria modakandai sp. nov. from India. The new species differs from three valid species within the genus by a combination of characters such as longer maxilla (9.1%–9.9% standard length vs. 8.7% in Dussumieria elopsoides, 6.3%–8.5% in Dussumieria acuta) and one or two rows of small conical teeth on palatine (vs. several rows in D. elopsoides and Dussumieria albulina). It also differs by the absence of longitudinal striae on the posterior side of body scales (vs. presence in D. acuta and D. albulina) and the absence of parasphenoid teeth (vs. presence in D. acuta). The maxilla length of D. modakandai sp. nov. is greater than snout length, which distinguishes it from other congeners. The multivariate analysis of morphometric characters using PCA differentiated the new species from D. elopsoides and D. acuta samples collected in this study. The molecular analysis, based on cytochrome c oxidase I, distinguished the new species from D. acuta, D. albulina and D. elopsoides with a high genetic distance of 13.73%, 12.22% and 12.74%, respectively. The maximum‐likelihood phylogenetic tree and automatic barcode gap discovery analysis showed the existence of six putative species in Dussumieria. Even the exhaustive sub‐clade formation within species and high intra‐species genetic distance in D. acuta (1.59) and D. modakandai (1.95) indicate the possibility of a few more cryptic species. This warrants comprehensive sample collection across the distribution range and integrative taxonomic study of the genus Dussumieria. [ABSTRACT FROM AUTHOR]

The present research focuses on the identification of stable trait specific genetic resources across the five semiarid environments located in the four states of India. The study illustrated the existence of a wide range of variations for most of the characters among the sunflower genotypes, which provides opportunities for genetic gain through selection or hybridization. Genotype x Environment (G x E) interaction was significant for all traits except for days to 50 % flowering, maturity days and 100-seed weight which means different genotypes responded differently to environments for the rest of the traits. Based on per se performance across the locations GMU 296 and HOHAL 30 can be considered as high yielding and high oil content accessions. Based on biplot, stability and per se performance genotypes GMU 806, GMU 635, GMU 296, GMU 802 and check DRSF113, can be considered as a medium to high yielding with medium oil content and medium maturity genotypes across the environments and can be considered as an ideal genotype across semi-arid environments. Character association indicated that among the seven traits, seed yield per plant had a significant negative association with oil content. The traits 50 % flowering and maturity days fall under one group and showed the more or less similar type of pattern in the expression of the traits by heat map approach. Based on diversity analysis identified genotypes from the second cluster can be utilized for the development of high yielding, medium oil content and medium duration diverse gene pool in sunflower for semi-arid environments. [ABSTRACT FROM AUTHOR]

Soil contamination by various heavy metals is a significant global environmental problem. The problem is more severe in the rapidly industrializing and developing countries like India, posing significant health risks. This study was undertaken to measure heavy metals contamination of soil in industrial clusters developed by Haryana State Industrial and Infrastructure Development Corporation (HSIIDC) and various traffic junctions of Rohtak City. Risk assessment and characterization of heavy metals in soil was carried out using contamination factor (CF), pollution load index (PLI), and potential ecological risk index (RI). A total of 32 soil samples were collected from various locations and heavy metals were analyzed using atomic absorption spectrophotometer (AAS). The average value of heavy metals like Cd, Fe, Zn, Ni, and Cr in soil was 7.54 ± 5.89, 209.80 ± 137.44, 127.39 ± 80.43, 21.57 ± 24.02, and 17.05 ± 10.73 mg/kg, respectively. The mean values of contamination factor (CF) for Cd, Zn, Ni, Cr, and Fe were 37.68, 1.00, 0.44, 0.24, and 0.005, respectively. The values of the potential ecological risk index (RI) were 1130.4, 2.20, 1.00, and 0.48 for Cd, Cr, Zn, and Ni, respectively. Cadmium (Cd) presented the highest ecological risk index in comparison to other heavy metals. The mean geo-accumulation (Igeo) indices for heavy metals were ranked as follows: Cd > Zn > Ni > Cr > Fe. Principal component analysis (PCA) showed that 78.27% variation was caused by the first five principal components (PCs). The interpolation of spatial distributions of soil heavy metals highlighted that contamination values for most metals increased towards the western side of the city. The present study reveals that a higher concentration of cadmium was found in soils of Rohtak urban area. It could be due to the presence of metal processing and plating industries in the surveyed area and a lack of strict industrial norms. [ABSTRACT FROM AUTHOR]

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Deficiency of nutrients in agricultural soils of the world is one of the major factors of soil degradation. The deficiency of nutrients especially sulfur (S) and micronutrients in different soils affect global crop production. Delineation of soil management zones (MZs), by understanding spatial distribution of soil parameters, could be an efficient way for devising strategies for effective nutrient management and sustainable crop production. In this study, we aimed at creation of soil MZs for ameliorating S and micronutrients deficiencies in the Narmada River basin (NRB), an important agricultural area of central India. We collected 5,984 geo‐referenced top layer (0–15 cm) soil samples from the NRB, India, and analyzed for soil acidity (soil‐water suspension), electrical conductivity (soil‐water suspension), soil organic carbon (Walkley and Black carbon), phyto‐available S (0.15% calcium chloride [CaCl2] extractable), and phyto‐available (diethylene triamine penta acetic acid extractable) micronutrients namely zinc (Zn), copper (Cu), manganese (Mn), iron (Fe), and boron (B) (hot‐water‐soluble). The values of studied soil parameters varied widely with coefficient of variation values ranging from 11.0 to 74.9%. Pearson's correlation coefficient analysis revealed significant correlations among the soil parameters. Geostatistical analysis revealed exponential, pentaspherical, K‐Bessel, and circular best‐fit semi‐variogram models for different soil parameters with moderate to strong spatial dependence. About 41.2, 78.6, 10.1, 2.70, and 32.6% of the area of the NRB had a deficit concentration of phyto‐available S, Zn, Fe, Mn, and B, respectively. The principal component analysis and fuzzy c‐means clustering produced five MZs. The produced MZ maps could be utilized for prioritization of nutrients supply and adoption of zone‐specific soil nutrient management strategies in order to achieve sustainable crop production in the NRB of India. [ABSTRACT FROM AUTHOR]

This study investigates three modeling approaches based on Xtreme Boosting Machine (XGBoost), Genetic Algorithm-optimized Emotional Neural Network (GA-EmNN) and Group Method of Data Handling-Neural Network (GMDH-NN) for rainfall-runoff modeling. The redundancy capability of Principal Component Analysis (PCA) was applied to solve the problems of input selection in these machine learning models. Hence, the objective of this study is to develop an efficient approach to pre-process the data structure and ensemble it with machine learning models that produce a higher predictive accuracy. The three XGBoost, GA-EmNN and GMDH-NN models are trained and validated for streamflow forecasting using monthly rainfall and monthly runoff data of the Jhelum basin, India. Statistical fitness indices, like correlation (r), Root Mean Square Error (RMSE), relative Nash-Sutcliffe Efficiency coefficient (r NSE), Percent Bias (PBias) and Kling-Gupta Efficiency (KGE), were used for model performance assessment. The analysis of results revealed that GA-EmNN model was well capable (Training: R=0.999, RMSE = 345.48 cumec, Testing: R=0.997, RMSE = 428.35 cumec) of predicting the streamflow, followed by GMDH-NN and XGBoost. The results of this approach would benefit future modeling efforts, by underlining the application of these methods in hydrological modeling. [ABSTRACT FROM AUTHOR]

The mafic and ultramafic rocks of Mettupalayam belong to the southern granulite terrain of India, which is concomitant with vital economic resources. The advantage of Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) data for mapping the litho units are exploited well here for differentiating the rock units with the aid of band combination (1, 3, 6), principal component analysis (5, 1, 6) and band ratioed band combination (2/3, 3/2, 1/5 and (9-8)/1, (8-6)/2, and (9-6)/3). As part of the field study, the collection of samples and ground control points were carried out and in addition to that, the generation of laboratory reflectance spectra for samples was achieved. The Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) were performed using ASTER data with the aid of spectra obtained from the laboratory conditions to demarcate the abundance of mafic and ultramafic rocks of the area. The XRF method was used to retrieve the major oxides of the field-collected samples and the spectral absorption characters are validated with it. The results show a vibrant interpretation of the litho units. [ABSTRACT FROM AUTHOR]

Apricot (Prunus armeniaca L.) is a world-wide highly appreciated fruit, with its attractive colour, soft texture and typical flavour. In the current investigation, 68 apricot accessions collected from Jammu and Kashmir, India were analysed to determine the measure of variation using 12 qualitative and 16 quantitative traits with an aim to identify superior apricot accessions with excellent fruit quality traits. High phenotypic variability was observed among the studied apricot accessions with significant differences among most of the qualitative and quantitative traits. Fruit-related characteristics including fruit weight, fruit length, fruit ratio, fruit firmness, fruit shape, fruit suture, stone weight, kernel weight and stone shape were the most diverse with a high coefficient of variation (>30%). One way analysis of variance showed significant differences (P < 0.0001) among all the quantitative traits. Significant positive and negative correlations were observed between all the agronomically important fruit quality traits. The principal component analysis (PCA) revealed that 75.34% of the variability was defined by the first eight components. The unweighted pair group method with arithmetic mean (UPGMA) dendrogram, based on all traits measured grouped the accessions into two main clusters with several sub-clusters. Both UPGMA dendrogram and PCA scatter plot formed a cluster of 14 accessions, having the highest values regarding most important fruit quality traits such as fruit weight, fruit length and width, fruit ratio, stone weight and kernel weight can be treated as potentially superior accessions. These accessions can be used directly for cultivation and in future apricot breeding programmes. The present findings are promising for genetic resource management, cultivar improvement and commercial applications of apricot in Jammu and Kashmir, India. [ABSTRACT FROM AUTHOR]