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This paper aims to propose a machine learning approach-based power theft detection using Garra Rufa Fish (GRF) optimization. Here, the analyzing of power theft is an important part to reduce the financial loss and protect the electricity from fraudulent users.

In this section, a new method is implemented to reduce the power theft in transmission lines and utility grids. The detection of power theft using smart meter with reliable manner can be achieved by the help of GRF algorithm.

The loss of power due to non-technical loss is small by using this proposed algorithm. It provides some benefits like increased predicting capacity, less complexity, high speed and high reliable output. The result is analyzed using MATLAB/Simulink platform. The result is compared with an existing method. According to the comparison result, the proposed method provides the good performance than existing method.

The proposed method gives good results of comparison than those of the other techniques and has an ability to overcome the associated problems.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

With the vast advancement of structural steel properties over the recent decades, structural steel has become the dominate material for the construction of bridges, stadiums, factories and high rise buildings. This paper aims to present the study of structural behaviour and efficiency of tapered steel section with elliptical perforation under shear loading conditions.

The effect of various elliptical perforation configurations such as tapering ratio, perforation size, perforation orientation and perforation layout on the shear behaviour of tapered steel section has been investigated by using finite element method. A total of 112 models are simulated via LUSAS software.

It has been found that the most efficient model is the tapered steel section with tapering ratio of 0.3 and vertical elliptical perforation of 0.2 times the section depths which are arranged in Layout 3. The most efficient model has a shear efficiency of 1,094.35 kN, which is 4.12% less than the tapered steel section without perforation, but it could achieve a 0.32% of weight reduction.

The smaller tapering ratio and perforation size contributed to the higher shear buckling capacity and efficiency for the elliptical perforated tapered steel section.

This paper aims to evaluate the awareness level of university students on energy conservation by focusing on their knowledge, attitude and practice. Energy awareness is a feasible energy conservation measure, but an inappropriate approach can cause the wastage of resources. The current number of reported awareness studies especially among the university student is quite limited, and focus is more given to the awareness on renewable energy, instead of energy conservation.

A paper-based survey using a set of questionnaires that involved 2,857 respondents. There are four sections in this questionnaire, which are basic background information of respondents, knowledge (yes/true or no/false choice of responses), attitude (a scale of 1–10, where 10 indicates “strongly agree” and 1 chooses “strongly disagree”) and practices (a scale of 1–10, where 10 indicates “Always” and 1 selects “Never”). The evaluation includes the correlation analysis of all awareness variables with an educational background to disclose the most critical aspects that should be highly considered in the forthcoming awareness campaign.

This research revealed that university students have a high level of awareness of energy conservation. They hold a high knowledge level and show a positive attitude with very good energy conservation practices. However, some issues require serious attention in preparing energy management plans. Effective approaches should be taken by considering the field of study, gender and family economic status to enhance the awareness level of the university students on energy conservation.

The absence of appropriate references complicates the preparation of energy management plans including the establishment of energy policy and strategies. The findings of this research can contribute some contextual information in the local perspective to ensure the effectiveness of the energy management program and strengthen academic leadership by emphasising the element of sustainability. This is crucial for developing an energy saving culture among the young generation that will be our future scholars and leaders.

Nature and occurrence of food-borne pathogens in raw and processed food products evolved greatly in the past few years due to new modes of transmission and resistance build-up against sundry micro-/macro-environmental conditions. Assurance of food health and safety thus gained immense importance, for which bio-sensing technology proved very promising in the detection and quantification of food-borne pathogens. Considering the importance, different studies have been performed, and different biosensors have been developed. This study aims to summarize the different biosensors used for the deduction of food-borne pathogens.

The present review highlights different biosensors developed apropos to food matrices, factors governing their selection, their potential and applicability. The paper discusses some related key challenges and constraints and also focuses on the needs and future research prospects in this field.

The shift in consumers’ and industries’ perceptions directed the further approach to achieve portable, user and environmental friendly biosensing techniques. Despite of these developments, it was still observed that the comparison among the different biosensors and their categories proved tedious on a single platform; since the food matrices tested, pathogen detected or diagnosed, time of detection, etc., varied greatly and very few products have been commercially launched. Conclusively, a challenge lies in front of food scientists and researchers to maintain pace and develop techniques for efficiently catering to the needs of the food industry.

Biosensors deduction limit varied with the food matrix, type of organism, material of biosensors’ surface, etc. The food matrix itself consists of complex substances, and various types of food are available in nature. Considering the diversity of food there is a need to develop a universal biosensor that can be used for all the food matrices for a pathogen. Further research is needed to develop a pathogen-specific biosensor that can be used for all the food products that may have accuracy to eliminate the traditional method of deduction.

The present paper summarized and categorized the different types of biosensors developed for food-borne pathogens.

This study aims to synthesize pigment and resin from agro-wastes and use them in the formulation of eco-friendly surface coatings.

The pigments and resin were synthesized through a chemical modification of agro-wastes. The pigments were characterized by infrared spectroscopy (FTIR) and were screened for their antimicrobial activities. The physicochemical characteristics of the cashew nutshell liquid (CNSL)-modified resin were evaluated. These precursors and other natural additives were used to formulate surface coatings, and their drying and adhesive properties were evaluated using international testing methods.

It was observed that the curing of the CNSL-modified resin depended on time and temperature. The pigments exhibited antimicrobial activity against E. coli and S. aureus and had high melting points, affirming their stability. The chemically modified precursors successfully yielded surface coatings with acceptable drying times and adhesion to the base substrate.

The use of agro-wastes as the main components of the surface coatings implies waste valorization, a reduction in production costs and the creation of job opportunities for sustainable development. To increase the chemical, physical, corrosion resistance and antimicrobial qualities of paint compositions, chemically modified peanut skin extracts and CNSL can be used as pigments and resins, respectively. This could be a green approach to achieving the targets of Sustainable development goals 11 and 12.

The paper outlines a prospective approach to use unwanted waste (peanut skin, cashew nutshells) and other natural additives as industrial raw materials. These novel surface coating precursors are cost-effective, readily available, eco-friendly and could replace conventional precursors.

This paper has used the well-known machine learning (ML) computational algorithm with Internet of Things (IoT) devices to predict the COVID-19 disease and to analyze the peak rate of the disease in the world. ML is the best tool to analyze and predict the object in reasonable time with great level of accuracy. The Purpose of this paper is to develop a model to predict the coronavirus by considering majorly related symptoms, attributes and also to predict and analyze the peak rate of the disease.

COVID-19 or coronavirus disease threatens the human lives in various ways, which leads to deaths in most of the cases. It affects the respiratory organs slowly and this penetration leads to multiple organ failure, which causes death in some cases having poor immunity system. In recent times, it has drawn the international attention because of the pandemic threat that is harder to control the spreading of infection around the world.

This proposed model is implemented by support vector machine classifier and Bayesian network algorithm, which yields high accuracy. The K-means algorithm has been applied for clustering the data set models. For data collection, IoT devices and related sensors were used in the identified hotspots. The data sets were collected from the selected hotspots, which are placed on the regions selected by the government agencies. The proposed COVID-19 prediction models improve the accuracy of the prediction and peak accuracy ratio. This model is also tested with best, worst and average cases of data set to achieve the better prediction rate.

From that hotspots, the IoT devices were fixed and accessed through wireless sensors (802.11) to transfer the data to the authors’ database, which is dedicated in data collection server. The data set and the proposed model yield good results and perform well with expected accuracy rate in the analysis and monitoring of the recovery rate of COVID-19.

The study aims to investigate the potential of green immature soybean among Nigeria soybean varieties as human food.

Five Nigeria soybean varieties were harvested at 90 days old. The fresh green immature soybean seed were evaluated for chemical composition, physical and sensory characteristics. The physical characteristics looked into seed size (breadth and length), weight, seed colour, hull thickness and percentage of hydration. The chemical composition was compared to mature soybean seeds while the sensory attributes were compared to fresh green peas.

The raw mature soybean (RMS) was significantly higher and lower in chemical composition and anti‐nutritional factors respectively. The highest moisture content ranges from 62.8 per cent in TGX 1019‐2^EB to 65.4 per cent in TGX 1485‐1^D. The protein content (15.3 per cent) was highest in TGX1485‐1^D and lowest value in TGX1448‐2^E. The level of tannin was significantly higher in TGX1448‐2^E and 923‐2^E while trypsin inhibitor was significantly (p<0.05) higher in TGX1440‐1^E and TGX1485‐1^D. TGX1485‐1^D had superior physical characteristics to other immature varieties with significant (p<0.05) higher value for breadth, length and height. The hull thickness of the seeds was within 0.01 to 0.05 and the percentage of hydration ranges within 5.5 to 6.8 per cent. All varieties had a green colour for the seed coat. Overall sensory acceptability of TGX TGX1440‐1^E, 1485‐1^D and 1019‐2^EB compared favourably well with green peas used as control for sensory evaluation.

This study could help to identify the potential of some Nigerian soybean cultivars for production for use as a source of vegetable in the diet and also provided valuable information for further improvement of soybean for food uses.

This study aims to understand the influence of R&D intensity and ownership concentration on performance of Indian technology SMEs, at the intersection of “value creation” perspective of corporate governance and country cultural context in innovation.

Cross-sectional data of 264 Indian technology SMEs have been employed to probe the impact of ownership and R&D intensity on market performance of the technology SMEs.

This study does not find support of individual influence of R&D intensity on SME performance. The authors find support for the “value creation” hypothesis of corporate governance in Indian technology SME context. This study finds that interaction of promoter's ownership concentration and R&D intensity has a positive influence on the performance of Indian technology SMEs.

This study has deployed cross-sectional data. Future studies can examine the “value creation” hypothesis based on panel data for a long-run understanding. Ownership can be further segregated into different categories of ownership in future studies.

This study underscores on distinct necessity in the concentrated ownership in the context of Indian technology SMEs. The findings of the study may encourage policymakers to focus on the “value creation” of the technology SMEs than “value protection.”

This study aims to understand the market value of R&D practice of SMEs. The findings of this study establish that R&D intensity individually may not have any significant influence on SME performance. R&D intensity coupled with concentrated ownership can significantly increase SME performance. Thus, this study identifies factors that can help in SME innovation and growth options. Additionally, this study advocates for the fact concentrated ownership in technology SMEs of India by establishing the link with SME performance.

Absorbent disposable products, such as diapers and sanitary napkins, are mostly one-time use items and designed to absorb and retain body fluids and solid waste. The present research work deals with the development and characterization of baby diapers made from four different fibrous compositions namely, pure bamboo, pure cotton, bamboo/cotton (70/30) and bamboo/cotton (50/50). Antibacterial activity tests have been carried out on baby diapers against S.aureus and E.coli. The strongest antibacterial activity in terms of reduction of the organism is found in diapers produced from pure bamboo fibre and the weakest antibacterial activity is found in cotton diapers. Superabsorbent polymer (SAP), namely, sodium polyacrylate is incorporated into the diapers to enhance their absorption capacity. The diapers are subjected to tests such as absorption capacity, liquid strike-through, acquisition time under load and diaper rewet under load to study their performance. Upon an analysis of the results, it is found that the performance of diapers produced from a bamboo/cotton (70/30) fibre blend is superior in comparison to the other ones.