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A neurological condition of the brain is Parkinson's disease. It causes the body to shake, the hands to shake, and it makes the body stiff. At this advanced level, there is still no viable treatment or cure. Only when treatment is initiated at the earliest stage of the disease is it effective. These could potentially save a life in addition to lowering the cost of the illness. The majority of ways can identify Parkinson's disease after it has advanced, which results in basal ganglia, which regulates movement of the body with a small quantity of dopamine, losing about 60% of their dopamine. The presence of diminutive precursors to chronic diseases, especially neurologically based ailments such as Parkinson’s can indicate and diagnose them in their earliest stages. Parkinson’s disease (PD) is characterized by symptoms such as spasms in the limbs, jaw, and head, rigidity in the limbs and trunk, slow movement, etc. It is important to notice these preliminary symptoms early on to avoid developing Parkinson's disease. This project proposes algorithms used on the dataset to predict PD. Two kinds of datasets are used; one voice dataset and another spiral drawing dataset, and algorithms are used in these datasets to predict the disease and to show the results a user-friendly Web-Application is developed. The algorithms used are K-Nearest Neighbours (KNN) on voice dataset implementation and Random Forest on spiral drawing implementation.

This paper presents a bottom-up methodology to estimate multi-pollutant hourly gridded on-road traffic emission using advanced traffic flow and speed data for Delhi. We have used the globally adopted COPERT (Computer Programme to Calculate Emissions from Road Transport) emission functions to calculate the emission as a function of speed for 127 vehicle categories. At first, the traffic volume and congestion (travel time delay) relation is applied to model the 24 h traffic speed and flow for all the major road links of Delhi. The modelled traffic flow and speed shows an anti-correlation behaviour having peak traffic and emissions in morning–evening rush hours. We estimated an annual emission of 1.82 Gg for PM (particulate matter), 0.94 Gg for BC (black carbon), 0.75 Gg for OM (organic matter), 221 Gg for CO (carbon monoxide), 56 Gg for NOx (oxides of nitrogen), 64 Gg for VOC (volatile organic compound), 0.28 Gg for NH3 (ammonia), 0.26 Gg for N2O (nitrous oxide) and 11.38 Gg for CH4 (methane) for 2018 with an uncertainty of 60 %–68 %. The hourly emission variation shows bimodal peaks corresponding to morning and evening rush hours and congestion. The minimum emission rates are estimated in the early morning hours whereas the maximum emissions occurred during the evening hours. Inner Delhi is found to have higher emission flux because of higher road density and relatively lower average speed. Petrol vehicles dominate emission share (>50 %) across all pollutants except PM, BC and NOx, and within them the 2W (two-wheeler motorcycles) are the major contributors. Diesel-fuelled vehicles contribute most of the PM emission. Diesel and CNG (compressed natural gas) vehicles have a substantial contribution in NOx emission. This study provides very detailed spatiotemporal emission maps for megacity Delhi, which can be used in air quality models for developing suitable strategies to reduce the traffic-related pollution. Moreover, the developed methodology is a step forward in developing real-time emission with the growing availability of real-time traffic data. The complete dataset is publicly available on Zenodo at https://doi.org/10.5281/zenodo.6553770 (Singh et al., 2022).

Our aim is to develop a Machine learning model and testing the model by using the data in relating to previous 6 months payment behaviour which is behavioural data and personal information which is demographic data as input of a client is used for this study. The research study is conducted using Random Forest Algorithm. Our aim is to identify that credit card customer is likely to default in the coming month.

Synovial sarcoma (SS) is a rare soft tissue sarcoma usually arising in the deep soft tissues of the limbs, trunk, and head and neck region. Due to its rarity, diagnosis can be difficult to establish, especially when it involves an uncommon location like the breast. In this case report, we describe a young woman who was found to have primary SS of the breast. Initial immunohistochemistry staining was focally positive for cytokeratin and S100 and she was misdiagnosed with atypical spindle cell carcinoma. Due to the unusual presentation, further testing was performed which showed TLE1 and epithelial membrane antigen positivity, establishing the diagnosis of SS of the breast. A FISH was later sent out and was positive for SS18-SSX fusion transcript. This case highlights the importance of considering rare histopathology in breast lesions and using additional staining and cytogenetics to confirm diagnosis.

The present study was undertaken in Imphal East district of Manipur to know the marketing cost, marketing margin, price spread, producer’s share in consumer rupee, marketing efficiency of High Yielding Variety (HYV) paddy and Black aromatic paddy. A total of 120 sample respondents are analysed. Among these 42 are marginal farmers,36 are small farmers,26 are semi medium and 16 are medium farmers .For High yielding variety paddy, in channel I, overall average net price receive by producer is Rs.1885 per quintal and the consumer purchase price is Rs.2000 per quintal. In channel-II, overall average net price receive by producer Rs.1609 per quintal and the consumer purchase price is Rs.3000 per quintal. For Black Aromatic paddy, in channel-I, overall net price received by producer is Rs.6185 per quintal, and the consumer purchase price is Rs.6300 per quintal. In channel-II, overall net price received by producer is Rs.5109 per quintal and the consumer purchase price is Rs.12000 per quintal. Marketing efficiency revealed that for High Yielding Variety paddy (HYV) marketing efficiency of channel-I is high with 16.39. For Black Aromatic Paddy, Marketing efficiency of channel-I is high with 53.78.Farmer should chose for those marketing channels which have lesser intermediaries to get the maximum profits. Price fluctuation, lack of suitable govt. Policy like procurement and market regulation are the major marketing constraints to the sample farmer in study area.

The present study investigate the efficiency of marketing of Apple in Kullu district of Himachal Pradesh. Kullu district was purposively selected for the study as apple is primarily grown crop in the district. The sample drawn for the study comprised of 100 apple growers of Kullu and Naggar blocks. The various analytical tools were used to analyse the efficiency of marketing channels of apple. The marketing channels involves in the study area is three in number, channel I (Producer– Retailer–Consumer) was the most effective channel as it had the highest marketing efficiency index of 2.28. Majority 18 per cent of the growers marketed apple through channel II (Producer– Wholesaler–Retailer–Consumer). The price spread was the lowest (Rs. 397.9) in channel I (Producer–Retailer–Consumer); and accordingly, the producer’s share in final consumer rupee was the highest (69.57 %) in channel I.

The study pertains to the study of impact of SHGs on women’s Behaviour and standard of living before and after joining the SHGs in Satna District of Madhya Pradesh reveals that Women always remained in backstage because of their passive involvement in decision making. In rural areas women can’t get as much as freedom as compared to men, they are always suppressed and men are always dominant. But, after the introduction of SHGs rural women are involving in it. being an effective medium for community involvement in developmental activities, can be a powerful tool for alleviating rural poverty through the empowerment of women, by freeing themselves from the clutches of usurious moneylenders. Due to the existing patriarchal norms, they have excluded from the decision- making the process even at their households. Self-help groups brought a whole new platform for women empowerment. It covered almost all aspects of empowerment (social, political, economic). Self-help group improved the qualitative and quantitative participation of women in decision making. This study will help to know the real impact of SHGs and grassroots level development is needed to make our women empowered.

The control and monitoring system presented is based on Supervisory Control and Data Acquisition concept. It provides GUI service for operators and data visualization capability to display data trends using an appropriate DBMS (database management system). The SCADA system also incorporates Alarm and PAS (public address system) for operational and functional safety. The remote control of the gates is carried out through PC based SCADA software from a centrally located control room. The SCADA system consists of a Master Terminal Unit (MTU) located in the control room, MicroLogix 1400 PLC-based Remote Terminal Units (RTUs) located in the control panels of dam gates, and ultrasonic sensors equipped with MODBUS/ RTU interface to measure upstream and downstream water levels. RTUs-MTU communication is realized using a dedicated LAN. This paper presents the system design along with hardware layout and software programs for both, RTUs and MTU. The program for PLC has been written in ladder logic using RSLogix 500, a powerful development software from Rockwell Automation.

The cloth and loom were a long history since the emergence of human beings on the earth. The Hindu epics, the Mahabharata and the Ramayana, the Vedas and the sacred books of Hindu mythology speak of the art of weaving. Loom work is proving a large-scale employment in rural and urban places and it is an ancient industry. Weaving is the traditional occupation in India as well as in the Bhagalpur district of Bihar. Even though weavers form an integral part of the Indian loom industry, there is not enough recognition of their contribution to the sector due to various production and marketing constraints. It leads to poor economic conditions which casts doubt on their social security and future aspirations. Bhagalpur district of Bihar state was selected for this study.120 respondents were selected from 3 villages based on the population of weavers in villages. The present study was conducted in the years 2021-2022.The main constraints in production and marketing are revealed by respondents as scarcity of raw materials, insufficient weaving facility, COVID- 19 lockdown, lack of cooperative support, high commission charges, less transportation etc.

Electrical energy is playing an important role in our day-to-day life. The burden on utility is increasing continuously due to huge utilization of electrical energy thereby utilities are suffering from peak shortage. The concept of demand side management can be applied to relieve the utilities from suffering peak load burden. In this paper, a Lowest Tariff Load Shifting (LTLS) approach of DSM is suggested to flatten the load curve as desired by the utilities. Residential and commercial loads are considered for validation of proposed algorithm. The use of DSM techniques can delay the expansion of power system for short duration such as few months or years. This paper produces a flatten load curve by applying LTLS technique of DSM and results demonstrates cost saving and peak load reduction in residential as well as commercial area.

The study the constraints faced by the women associated with SHGs in Satna district of Madhya Pradesh was conducted in agriculture year 2021-22.mutlisatge sampling technique was used. The study pertains to the major constraints faced by the womens are gender dominance, Lack of men confidence to women, Responsibility of family, being much busy to work at home, Less literacy, Lack of technical knowledge and Lack of guidance for group formation.

Acute decompensated heart failure (ADHF) is a challenging medical emergency with high mortality and its prevalence is increasing in India. There is paucity of data on ADHF in the country.Indian College of Cardiology National Heart Failure Registry (ICCNHFR) is an on-going observational registry on ADHF contributed by 22 hospitals across India; and we present the in-hospital and 30-day outcomes of ADHF patients enrolled from August 2018 to July 2019. Major objective included capturing demographics, comorbid conditions, aetiology, prescription patterns and assessing clinical outcomes.Of 5269 patients (mean age: 61.90 ± 13.85 years) enrolled in this study, males were predominant (67.09%). Mean duration of hospitalization was 5.74 ± 4.74 days. Ischemic heart disease was the most common (75.44%) aetiology. Abnormal electrocardiogram readings were found in most patients (89.86%). LVEF of ˂40% was found in 68.29% of patients. In-hospital mortality rates were 6.98%. The 30-day cumulative mortality was 12.35% and 30-day rehospitalization rate was 7.98%. At discharge, all guideline-based medical therapy (GDMT) were prescribed only to 24.99% of patients and 23.72% adhered to the prescription until 30 days. Older age, high serum creatinine levels and poor LVEF contributed to high mortality and rehospitalization.Patients with ADHF were younger and predominantly males. Usage of GDMT in ADHF patients was low (24.99%) and the in-hospital mortality was high. Older age, high serum creatinine levels, poor LVEF contributed for 30-day mortality and rehospitalization. This data on ADHF, could help in developing strategies to improve outcomes for HF patients in India.

Road traffic emission is considered to be the major source of pollution exposure in megacities around the globe. Traffic emission makes urban air pollution to be highly spatially heterogeneous with sharp concentration gradients that can vary substantially within a few meters near the road. The spatially heterogeneous and temporally varying emissions are required to account for concentration gradients that have a direct impact on the population exposure to outdoor air pollution. However, estimating such a detailed emission is very complex as it requires multi-category emission factors and a huge amount of georeferenced detailed traffic activity data such as traffic volume and speed, distance traveled, vehicle category share, fuel share, engine share, technology share etc. In the absence of detailed data, emission estimations have been limited to coarser resolution which may not be suitable for high resolution air quality modeling, exposure assessment and management..Here we present an emission model to estimate multi-pollutant hourly gridded on-road traffic emission over Delhi. The model uses the globally adopted COPERT (Computer Programme to Calculate Emissions from Road Transport) emission functions to calculate the emission as a function of speed for 127 vehicle categories. For traffic activity, the emission model uses advanced traffic volume and speed data for Delhi obtained from TRIPP (Transportation Research and Injury Prevention Programme, IIT Delhi). Further the model considers the congestion (travel time delay based on TOMTOM) and speed-volume relation for different road categories to estimate hourly traffic volume and speed for each road link in Delhi that is used to calculate the hourly emissions using the COPERT emission functions Further, the emissions are gridded at 100 m × 100 m resolution to generate high-resolution spatio-temporal emission maps for Delhi and shown in Fig. 1 for four different hours of the day.We analyzed the modeled emissions to identify peak emission hours, pollution hotspots and most polluting vehicles. The hourly variation of emissions show distinct bimodal distribution with morning and dominant evening peaks for almost all pollutants linked with congestion and peak traffic.Figure 1. Estimated gridded NOx emission at 100m × 100m spatial resolution at different time of the day; the time is displayed in the upper-right corner of each subplot.The emissions are high near the busy roads and traffic junctions. The emission flux in the central areas of Delhi (Fig. 1) is 40-50% higher than mean emission flux due to the higher road and traffic density and lower average speed. Diesel vehicles have been found to be the dominant contributor to PM, BC and NOx emission. Our results suggest that the top 5 polluting vehicle categories account for more than half (55% - 91%) of the emissions. This study provides very detailed spatio-temporal emission maps for megacity Delhi, which can be used in air quality models for developing suitable strategies to reduce the traffic related pollution. The developed model can be applied for developing emission inventory and real-time emission with the growing availability of real-time traffic data.

Background To study incidence of sinonasal mucormycosis in active and post COVID-19 patients in a district-level hospital in India and develop a simplified screening and referral protocol for use at peripheral centres to aid rapid diagnosis/treatment. Methods Study design: A prospective, interventional cohort study conducted from April 2021 to January 2022. Setting: Secondary level hospital in North India. Inclusion criteria: COVID-19 positive patients with diabetes mellitus as co-morbidity and with at least one of the following: received steroid therapy and/or on high flow oxygen therapy and/or had prolonged hospital stay (> 7 days). Exclusion criteria: Patients already immunocompromised/having malignancy/organ transplant recipients. Clinical workup: History, examination, imaging (CECT/MRI nose and paranasal sinuses if indicated), diagnostic nasal endoscopy + Nasal scrapings for KOH mount to detect fungal elements. STROBE guidelines were followed in the study. Results Fourteen out of 250 patients tested positive for mucormycosis (incidence 5.6%). Thirteen were symptomatic, one patient was asymptomatic and detected on screening. No significant difference was found in mucormycosis versus non-mucormycosis group with respect to HbA1c status, vaccination status or steroid + oxygen treatment (p > 0.05 in all scenarios). Patients were treated with intravenous liposomal amphotericin B and surgical debridement when indicated. Two succumbed to disease (survival 85.7%). A clinical screening protocol was thus developed which can be used as an effective tool even at far-flung and remote healthcare facilities for diagnosis and timely referral of patients. Conclusions Mucormycosis is a potentially lethal disease which needs rapid diagnosis and timely action to decrease morbidity and mortality.

Background Both Alzheimer’s disease (AD) genetic risk factors and indices of cognitive reserve (CR) influence risk of cognitive decline, but it remains unclear whether they interact. This study examined whether a CR index score modifies the relationship between AD genetic risk factors and long-term cognitive trajectories in a large sample of individuals with normal cognition. Methods Analyses used data from the Preclinical AD Consortium, including harmonized data from 5 longitudinal cohort studies. Participants were cognitively normal at baseline (M baseline age = 64 years, 59% female) and underwent 10 years of follow-up, on average. AD genetic risk was measured by (i) apolipoprotein-E (APOE) genetic status (APOE-ε2 and APOE-ε4 vs. APOE-ε3; N = 1819) and (ii) AD polygenic risk scores (AD-PRS; N = 1175). A CR index was calculated by combining years of education and literacy scores. Longitudinal cognitive performance was measured by harmonized factor scores for global cognition, episodic memory, and executive function. Results In mixed-effects models, higher CR index scores were associated with better baseline cognitive performance for all cognitive outcomes. APOE-ε4 genotype and AD-PRS that included the APOE region (AD-PRSAPOE) were associated with declines in all cognitive domains, whereas AD-PRS that excluded the APOE region (AD-PRSw/oAPOE) was associated with declines in executive function and global cognition, but not memory. There were significant 3-way CR index score × APOE-ε4 × time interactions for the global (p = 0.04, effect size = 0.16) and memory scores (p = 0.01, effect size = 0.22), indicating the negative effect of APOE-ε4 genotype on global and episodic memory score change was attenuated among individuals with higher CR index scores. In contrast, levels of CR did not attenuate APOE-ε4-related declines in executive function or declines associated with higher AD-PRS. APOE-ε2 genotype was unrelated to cognition. Conclusions These results suggest that APOE-ε4 and non-APOE-ε4 AD polygenic risk are independently associated with global cognitive and executive function declines among individuals with normal cognition at baseline, but only APOE-ε4 is associated with declines in episodic memory. Importantly, higher levels of CR may mitigate APOE-ε4-related declines in some cognitive domains. Future research is needed to address study limitations, including generalizability due to cohort demographic characteristics.

Reliability of the Power System dictates system adequacy and security. Generally, power system networks reliability is estimated based on its service availability from the customer's viewpoint of satisfaction. Parameters in this analysis interpret the service uptime and downtime frequencies, duration and count of affected customers, etc. Corresponding to these indices expected losses in terms of service, load, and revenues are estimated. But to work upon very fundamental issues for overall improvement of power system networks performance, assessment of systems effectiveness remains a rarely studied area. For example, although it is understood that a radial network configuration has low reliability, still major primary distribution structures and rural power distribution networks of (5KV -35KV) are mostly configured in radial structure only. Because it remarkably reduces the cost involved in network duplication especially the cost incurred for vital equipment. For this significant analysis, authors have extended the concept of Barringer for analysis of Dual generator fed radial power system networks Reliability, Availability, Maintainability, and Capability based Effectiveness (RAMCE) measure. This paper demonstrates here the technique for analysis of a Dual Generator set feeding to 5-load bus radial systems is considered here.

Aquaculture can be considered as a rapidly growing sector in many Indian states including Odisha. The impressive overall upward trend in fish production is likely to continue in future years as there are plenty of unutilized or underutilized fishery resources in the state. In spite of the growing popularity of aquaculture in the state, fish farmers have been experiencing financial, social and technical constraints in fish farming and in marketing practices. Cuttack district of Odisha was selected for the study.98 respondents from 4 villages were selected based on the population of the villages. The present study was conducted in the year 2021-2022 years.This paper examines the constraints faced by the fish farmers while producing and marketing of the freshwater fish Rohu (Labeo rohita) which reveals that damage due to natural calamities in production and frequent price fluctuations in marketing are the major constraints expressed by the farmers.

Background. Body balance is among the most important aspects for achieving athletic excellence in competitive sports. Body balance helps in the development of proper body posture control, which supports high performance and skill demonstration across all sports. Body balance is known to be positively correlated to competitive athletic performance. In sports, good balance implies moving more efficiently and with better body control. Yoga is one of the means of improving body balance, and it has also been linked to enhancing athletic performance. Vinyasa yoga is a powerful method that targets the entire body. Navigate challenging poses to improve your strength, flexibility, and balance, especially if you want to target muscle groups that aren’t used in your preferred sport. The focus of this study. To analyse the efficacy of a 6-week Vinyasa Yoga Training Programme (VYTP) in improving dynamic body balance of male national level yoga players. Materials and methods. Twenty male national level yoga players (18-25 years) from the Lakshmibai National Institute of Physical Education, Gwalior, India, participated in the study. The participants were then randomly allocated into two groups, one experimental (Vinyasa) and the other control. The Sensbalance machine was used to measure the dynamic body balance. Results. ANCOVA indicates a significant difference in the selected variable of dynamic body balance in the experimental group compared to the control group. When compared to the control group which did not receive any training, the post-test found that the experimental yoga group significantly improved in dynamic body balance. Conclusions. VTYP was found to be helpful in enhancing dynamic body balance. In the current study after six weeks of vinyasa training it was observed that the male national yoga players had a substantial difference in dynamic body balance compared to the control group.

Generative adversarial networks (GANs) have emerged as a powerful generative model in computer vision. Given their impressive abilities in generating highly realistic images, they are also being used in novel ways in applications in the life sciences. This raises an interesting question when GANs are used in scientific or biomedical studies. Consider the setting where we are restricted to only using the samples from a trained GAN for downstream group difference analysis (and do not have direct access to the real data). Will we obtain similar conclusions? In this work, we explore if “generated” data, i.e., sampled from such GANs can be used for performing statistical group difference tests in cases versus controls studies, common across many scientific disciplines. We provide a detailed analysis describing regimes where this may be feasible. We complement the technical results with an empirical study focused on the analysis of cortical thickness on brain mesh surfaces in an Alzheimer’s disease dataset. To exploit the geometric nature of the data, we use simple ideas from spectral graph theory to show how adjustments to existing GANs can yield improvements. We also give a generalization error bound by extending recent results on Neural Network Distance. To our knowledge, our work offers the first analysis assessing whether the Null distribution in “healthy versus diseased subjects” type statistical testing using data generated from the GANs coincides with the one obtained from the same analysis with real data. The code is available at https://github.com/yyxiongzju/GLapGAN.

Diabetes Mellitus is a public health problem worldwide due to high morbidity and mortality rate associated with it. Diabetes can be managed by synthetic hypoglycemic drugs, although their persistent uses have several side effects. Hence, there is a paradigm shift toward the use of natural products having antidiabetic potential. Seaweeds, large marine benthic algae, are an affluent source of various bioactive compounds, including phytochemicals and antioxidants thus exhibiting various health promoting properties. Seaweed extracts and its bioactive compounds have antidiabetic potential as they inhibit carbohydrate hydrolyzing enzymes in vitro and exhibit blood glucose lowering effect in random and post prandial blood glucose tests in vivo. In addition, they have been associated with reduced weight gain in animals probably by decreasing mRNA expression of pro-inflammatory cytokines with concomitant increase in mRNA expression levels of anti-inflammatory cytokines. Their beneficial effect has been seen in serum and hepatic lipid profile and antioxidant enzymes indicating the protective role of seaweeds against free radicals mediated oxidative stress induced hyperglycemia and associated hyperlipidemia. However, the detailed and in-depth studies of seaweeds as whole, their bioactive isolates and their extracts need to be explored further for their health benefits and wide application in food, nutraceutical and pharmaceutical industries.

To identify genomic segments associated with days to flowering (DF) and leaf shape in pigeonpea, QTL-seq approach has been used in the present study. Genome-wide SNP profiling of extreme phenotypic bulks was conducted for both the traits from the segregating population (F2) derived from the cross combination- ICP 5529 × ICP 11605. A total of 126.63 million paired-end (PE) whole-genome resequencing data were generated for five samples, including one parent ICP 5529 (obcordate leaf and late-flowering plant), early and late flowering pools (EF and LF) and obcordate and lanceolate leaf shape pools (OLF and LLS). The QTL-seq identified two significant genomic regions, one on CcLG03 (1.58 Mb region spanned from 19.22 to 20.80 Mb interval) for days to flowering (LF and EF pools) and another on CcLG08 (2.19 Mb region spanned from 6.69 to 8.88 Mb interval) for OLF and LLF pools, respectively. Analysis of genomic regions associated SNPs with days to flowering and leaf shape revealed 5 genic SNPs present in the unique regions. The identified genomic regions for days to flowering were also validated with the genotyping-by-sequencing based classical QTL mapping method. A comparative analysis of the identified seven genes associated with days to flowering on 12 Fabaceae genomes, showed synteny with 9 genomes. A total of 153 genes were identified through the synteny analysis ranging from 13 to 36. This study demonstrates the usefulness of QTL-seq approach in precise identification of candidate gene(s) for days to flowering and leaf shape which can be deployed for pigeonpea improvement.

The present study was conducted for the period 2020-21 and primary data were used for The analysis. Further, stratified random sampling technique was followed to select the respondents. The district of Varanasi in eastern Uttar Pradesh has been deliberately chosen as the district with the highest horticultural production. Two blocks, namely Arajiline and Pindra, from Varanasi district with maximum horticultural production were purposefully selected.Two hundred and thirty farmers were selected from 15 villages randomly. The study concluded that most of the respondents belong to the middle age group. The literacy level of the farm families were classified into seven groups. 84.78 percent of the respondents have a high level of literacy. The respondents of farmers’ families belong to the nuclear family system, followed by 18.26 percent of farmers to the extended family system (57.40) and 24.34 percent in the joint family system. The average land under cash crop growers (chilli and tomato) in marginal, small, semi-medium, and medium-sized farm groups were 0.68 ha, 1.58 ha, 2.38 ha, and 4.15 ha respectively. Agriculture was the most common major occupation, accounting for 55.66 per cent of all occupations. The farming experience of cash vegetable crop growers showed that the majority of the growers (60.86 %) had farming experience of less than 20 years.

In this paper a system is developed for monitoring the crops in green house using solar energy. India is an agricultural country and the agricultural sector accounts for 18% of GDP of India. This sector provides 50% employment to Indian workforce. As per 2011 census of India, almost 65% of people are fully dependent on agriculture. In spite of fertile land, crops yields are not more than compared to developed countries. Therefore, it is not wrong to say that from time to time there has been less development and research in agriculture sector. Technology may play a vital role in agricultural sector, because it reduces human effort and time. The developed system is a combination of power electronics and power system in which the system uses GSM technique (Global System for Mobile communication), Arduino, and various sensors to sense the real time data in greenhouse. Using sensor, the system obtains the real time data of the crops and then transmits it on the user mobile. The user can monitor and control the temperature, watering and light through mobile.

The demand and supply gap of energy is large and the people are pressured to bear increasing hours of load losing, unnecessary intake of energy makes subjects even worse. So, the importance and requirement for energy conservation is increased exponentially. This paper outlines a step towards the conservation of electricity in standard and electricity especially with the aid of employing efficient constructing system with automation and installation of renewable based Micro Distributed Generator (MDG). The Smart Grid (SG) is a technological transformation from conventional electric grid, electro-mechanically controlled system, to smart, intelligent, and electronically controlled system. There are about 20–30% losses present in the conventional electric grid due to substandard operations at generation, transmission and distribution side. MDG play important role to build an efficient and autonomous micro-grid. In this paper importance of microgrid and some renewable energy sources utilized in formation of microgrid are discussed.

This article describes the velocity-based motion and orientation control method for a differential-driven two-wheeled E-puck Robot (DDER) using the Multi-Objective Particle Swarm Optimization (MPSO) algorithm in the Virtual Robot Experimentation Platform (V-REP) software environment. The wheel velocities data and Infra-Red (IR) sensors reading make the multi-objective fitness functions for MPSO. We use front, left, and right IR sensors reading and right wheel velocity data to design the first fitness function for MPSO. Similarly, the front, left, and right IR sensors reading, and left wheel velocity data have been taken for making the second fitness function for MPSO. The multi-objective fitness functions of MPSO minimize the motion and orientation of the DDER during navigation. Due to the minimization of motion and orientation, the DDER covers less distance to reach the goal and takes less time. The Two-Dimensional (2D) and Three-Dimensional (3D) navigation results of the DDER among the scattered obstacles have been presented in the V-REP software environment. The comparative analysis with previously developed Invasive Weed Optimization (IWO) algorithm has also been performed to show the effectiveness and efficiency of the proposed MPSO algorithm.

This chapter aims to draw readers' attention on the challenges of modeling in finance. The new quantitative methods offer extraordinary capabilities with the latest algorithms using AI, ML, etc., aided by high technological computational power. However, the adoption of the latest tools and techniques comes with many challenges that are limited by human resources and nuances of financial industries. Unlike the recommendation-based models in the technology industry, real money is at stake in the financial industry. Hence, it is not very prudent to accept the result of quantitative methods without understanding the inherited risks. Despite the hype created by data scientists, the financial industry cautiously adopted the highly complicated learning tools after due diligence because investor and shareholder money is at stake and experts want to strategize financial decisions based on the data model outputs. Further, the chapter brings the key highlights of financial models.

Sericulture is one of the most agro-based industries in World. From time, sericulture practices have undergone changes to improve productivity. Disease development and mortality are ever present phenomena in the silkworms as in other living organism. Among silkworm disease, Grasseric, a viral disease of silkworm, Bombyx mori L. has been causing great economic loss to sericulturist. Perhaps a good understanding of this disease, its mode of transmission and recent developments are of great relevance for combating the disease. The present study review deals with a detailed account of the LC50 for larval mortality during fifth instar was 0.096 X 106. Nucleopolyherosis virus inclusion bodies ml-1 in Pure Mysore and 0.218 X 106 PIBs ml4 in NB4D2 silkworms. Besides mortality of silkworms, there was a significant reduction in the cocoon weight, shell weight and shell ratio in the surviving silkworms. The F1 progeny obtained by selfing the moths emerged from the silkworms inoculated with BmNPV exhibited interiority in fecundity, hatchability, larval weight, cocoon yield, cocoon weight shell weight, shell ratio, filament length, denier and effective rate of rearing when compared to control batches.

Having psychological advantage that enables athletes to perform at their best regardless of challenges and adversity is what is meant by having mental toughness (MT). In order to better understand the mental toughness of elite Indian standing and sitting para-thrower athletes, this study looked at a variety of mental sub - scales. Elite para-throwers who competed at the international level, consider making up the sample of the current study; which includes a total of nine elite para-throwers [5 Standing Para-throwers (StPT) and 4 Seated Para-throwers (SePT)]. The researcher used the Psychological Performance Inventory (PPI), designed by Loehr, and the Personal Demographic Information form to gather data in order to ascertain the participants’ attitudes of mental toughness. An independent T-test with 0.05 significance level was performed for the research’s findings, there are no statistical significant difference between the two groups’ levels of motivation, self-confidence, ability to control negative energy, attention, visualisation, ability to control positive energy, and attitude control factors of MT. Elite Indian para-thrower athletes’ mental toughness is similar in both groups, with no discernible differences.

Few studies have looked at the psychological mechanisms that govern soccer officials’ performance to this point. The main goal of the study is to identify age-related changes in mental toughness among officials. Sixty active national officials were chosen at random from the All-India Football Federation’s national officials’ roster (AIFF). Group A is 25-30 years old, with a mean and SD of 27.4±3.39 (lower age group); Group B is 31-35 years old, with a mean and SD of 31.8±1.28 (middle age group); and Group C is 36-40 years old, with a mean and SD of 37.6±1.98 (higher age group). The Psychological Performance Inventory (PPI), developed by James E. Loehr in 1982, was chosen as the research’s test item. This instrument evaluates seven aspects of mental toughness, including selfconfidence, negative energy control, attention control, visualisation and imagery control, motivation, positive energy control, and attitude control. One-way analysis of variance (ANOVA), with a significance threshold of 0.05, was used as the statistical approach to assess the study’s hypothesis. The finding of the research paper shows no significant difference among various groups.

The degree of valuation and competency that we give to ourselves determines our self-esteem. We may boost our self-esteem by having a good perception of our bodies as well as the physical talents and abilities we develop through sports. We gain a sense of positive self-worth as a consequence of the praise we receive through family and friends, as well as the social bonds we form as a result of our participation in sports and physical activity. Self-esteem plays a key role in athletic performance and sporting success. In the past, studies have focused on the self-esteem concept of sports psychology in sports in elite athletes; however, there is little research on self-esteem in para-sports. This study aimed to examine the self-esteem of elite Indian para-throwers who participated at the international level. Based on this, the aim of this study was to investigate the mental self-esteem levels of elite Indian. The study group of the research consisted of 11 para-throwers (6 = standing thrower and 5 = seated thrower) who participated at the international level (Paralympic, World Championship, World Grand Prix) aged 18–40 years. Purposeful sampling approaches and easily accessible sampling methods were used in the selection of the study group. The scale form was used to collect research data. The study consisted of two parts. In the first part, of this form, there was a personal information form developed by the investigator to collect demographic information of athletes, and in the second part, there was the “Rosenberg Self-Esteem Scale” developed by Rosenberg (1965). After analysis of the data, it was found that there were no significant differences in self-esteem level at level 0.05 in both groups of para-throwers and most of the para-athletes found average on self-esteem concept.

The fault monitoring and diagnosis of industrial machineries are very significant in Industry 4.0 revolution but are often complicated and labour intensive. The application of artificial intelligence (AI) techniques have now been an important part of condition monitoring of the mechanical and electrical machines because of its fast computation, higher accuracy, and robustness in performance, reducing the dependence on experienced personnel with expert knowledge. This paper presents a review of applications of AI-based fault diagnosis techniques that have had demonstrated success when applied to various industrial machineries. The important literature published in the last twenty years (i.e., 2000 to 2020) have been reviewed and added. In this work, first, a brief of various AI techniques such as artificial neural networks (ANN), deep learning (DL), fuzzy logic (FL), and support vector machine (SVM) are added. The literature on AI-based diagnostics used for various industrial machines, such as induction motor, bearing, gear, and centrifugal pump, are added and discussed in detail. The observation, research gap, and new ideas have been discussed, followed by a conclusion.

Traditional machine learning methods assume that training and testing data must be from the same machine running condition (MRC) and drawn from the same distribution. However, in several real-time industrial applications, this assumption does not hold. The traditional methods work satisfactorily in steady-state conditions but fail in time-varying conditions. In order to utilize time-varying data in variable MRCs, this article proposes a novel low-level knowledge transfer framework using a deep neural network (DNN) model for condition monitoring of machines in variable running conditions. The low-level features have been extracted in time, frequency, and time–frequency domains. These features are extracted from the source data to train the DNN. The trained DNN-based parameters are then transferred to another DNN, which is modified according to the low-level features extracted from the target data. The proposed approach is validated through three case studies on: 1) the air compressor acoustic data set; 2) the Case Western Reserve University bearing data set; and 3) the intelligent maintenance system bearing data set. The prediction accuracy obtained for the above case studies is as high as 100%, 93.07%, and 100%, respectively, with fivefold cross-validation. These real-time results show considerable improvement in the prediction performance using the proposed approach. Note to Practitioners —Condition-based monitoring schemes are widely applicable to rotating machines in various industries since they operate in tough working situations, and consequently, unpredicted failures occur. These unpredicted failures may cause perilous accidents in the industries. CBM systems prevent such failures, which results in the reduction of equipment damage and, hence, increases machinery lifetime. Modern industries are so complex and generating huge data, and these data can be collected using sensors, but placing a large number of sensors is difficult and expensive for different but similar kinds of faults in industries. This also increases the cost due to additional sensors and circuits. In this article, the authors have proposed a novel low-level knowledge transfer framework using the deep neural network (DNN)-based method for condition monitoring of machines in variable running conditions. Low-level features have been extracted to reduce the computations of DNN drastically with improved performance. This article also considered additional faults in the target domain, which is more practical in real-time applications. The proposed scheme has been validated with three case studies on acoustic and vibration signatures.

A highly selective, specific, precise sensitive and reliable ICPMS method has been developed and validated by using ICP-MS for the determination of multielement in Sucroferric oxyhydroxide. The described ICP-MS method provides specific detection and quantification of minor and trace elements from 0.3J(30%) to 2J(200%) of its individual specification of each element i.e Ag, As, Au, Ba, Cd, Co, Cr, Cu, Hg, Ir, Li, Mo, Ni, Os, Pb, Pd, Pt, Rh, Ru, Sb, Se, Sn, Tl, and V.The analytical method found to be Linear for each individual element with working concentration range from 30%, 50%, 100%, 150% and 200% i.e 0.3J, 0.5J, 1J, 1.5J and 2J with correlation coefficient not less than 0.990.The % recoveries of elemental impurities of each individual elements at three different concentrations with spiking in samples were found to be an acceptable range as 70% to 150%.The method was found to be precise and robust and its relative standard deviation was below 20%.The actual observed relative standard deviation in Precision was found to be in an acceptable range. Therefore developed method can be use for routine quantitative analysis of elemental impurities like Ag, As, Au, Ba, Cd, Co, Cr, Cu, Hg, Ir, Li, Mo, Ni, Os, Pb, Pd, Pt, Rh, Ru, Sb, Se, Sn, Tl, and V to ensure the quality of drug product.

Nutritionally rich cucumber seeds remain in demand in the agricultural, health and cosmetic sectors as they are essential for a successful crop stand establishment and seed-based products. However, the production of cucumber seeds is impeded by source limitation and nutrient deficiency. The foliar application of micronutrients can supplement this deficiency and overcome the physiological setback. An experiment was undertaken to compare the impacts of the foliar application of Fe and Zn, as nanoparticles and fertilizers, on the yield and seed quality of cucumber under open and protected environments. A foliar spray of nano-ZnO (ZnNPs) and nano-Fe