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Customers response to online food delivery services during COVID-19 outbreak using binary logistic regression
This study aims to empirically measure the distinctive characteristics of customers who did and did not order food through Online Food Delivery services (OFDs) during the COVID-19 outbreak in India. Data are collected from 462 OFDs customers. Binary logistic regression is used to examine the respondents characteristics, such as age, patronage frequency before the lockdown, affective and instrumental beliefs, product involvement and the perceived threat, to examine the significant differences between the two categories of OFDs customers. The binary logistic regression concludes that respondents exhibiting high-perceived threat, less product involvement, less perceived benefit on OFDs and less frequency of online food orders are less likely to order food through OFDs. This study provides specific guidelines to create crisis management strategies. 2020 John Wiley & Sons Ltd -
Inclusion of Sexual Health Education for the Wellbeing and Dignity of Secondary School Children: An Indian Rural Perspective
The study investigates students perspectives on incorporating sexual health education into the curriculum of secondary schools in rural Bangalore. Focused on assessing how such education impacts students physical and psychological well-being, confidence, and ability to make informed decisions, the research collected data from 981 students across 6th to 10th grades. A structured questionnaire, measured on a five-point Likert scale, explored students perceptions of sexual health education and its outcomes. After a meticulous data cleaning process, which included outlier removal, the study utilized a final sample of 900 students. IBM SPSS 25 and AMOS 25 facilitated the statistical analysis. The findings underscore the significant positive effect of sexual health education on students confidence levels. It highlights how this form of education aids in maintaining personal hygiene and fosters balanced decision-making skills among students. The studys results advocate for the implementation of sexual health education in schools, emphasizing its role in enhancing student wellbeing and confidence. Additionally, it contributes to defining the scope and framework of a sexual health education curriculum from the students perspective in rural Bangalore schools, aligning educational objectives with the actual needs and perceptions of the student body. 2023 Indian Institute of Health Management Research. -
Hope, Belief in Just World and Trust in Government: An Interaction Amidst Covid-19 Pandemic in India
The outbreak of COVID 19 has brought about changes in all spheres of human life. In the present times of pandemic, human life has suffered not only from physical stresses but also encountered and endured several mental stresses. In recent times people adopted several measures to bring positivity to their life. The present study explores the relationship between- Hope, Belief in Just World, Covid ?19, and Trust in the Government in India, during the Covid-19 Pandemic. Data was collected online from young adults, via Google forms, using the tools- Adult Hope scale, Covid Anxiety scale, Belief in Just world scale, and Trust in Government. Results showed a significant correlation between the three variables. Hope, Belief in Just World, and Trust in government. Regression analysis found these three variables to significantly impact Covid anxiety. Further, Belief in Just World was found to mediate the relationship between Hope and Covid anxiety. During challenging times, it is important to boost mental health in the right direction. Implications have been further discussed in the article. The Author(s) 2023. -
CRM Practices in Private Commercial Banks, Influencing Long Term Relationship and Customer Centric Holistic Approach
Purpose: The exigent purpose of the research is to find out whether socio-economic characteristics impress the study on CRM in private banks and to study CRM practices, factors sway long term relationship between customer and banks, and to know CRM as a customer central holistic approach. CRM gaining more attention as it is attracting and retaining the customers. CRM technology is used to organize, mechanized and integration of sales, marketing, support service and technical support (Robertshaw, 1999). There is a tremendous changes in market, innovation of technology, regional integration increasing competition and especially moderating customers. Approach: A well structure questionnaire was recommended for data collection in order to avoid delay, non-response and incompleteness. Respondents were met while they approached the bank. Either before or after their work respondents were appealed to provide the suitable consumer. A total of 220 questionnaires were in the hand and out of this 200 were usable and this forming 91% success rate. Findings: There is a significant variation in socio economic uniqueness except the demography account at different bank branches and all the factors shows high relationship sums account at different banks. The CRM practices ranked by respondents in the rank-wise are providing security of funds, providing greater value for money and transparency in banking services. Factors like customer satisfaction, well developed privacy policy and quick service are influencing better forever relationship between private sector banks and customers. The measurement of CRM a customer centric approach reveals that CRM protects data privacy, establishes and maintains strong relationship and CRM anticipates anticipates needs of customers. Further factors like data privacy, retention of existing customers and establish and maintain strong relationships are the impressing factors of customer centric approach. 2024, Collegium Basilea. All rights reserved. -
Leveraging Financial Data to Optimize Automation: An Industry 4.0 approach
Industry 4.0 is a transformative approach that leverages advanced technologies to enhance business efficiency and productivity. Automation is a crucial aspect of next-generation industry, and leveraging financial data is essential to optimizing the automation process. This chapter discusses the role of financial data in optimizing automation processes using an I-4.0 approach. Financial data is derived from various sources and can be collected through different methods, such as automated data collection, manual entry, or using sensors and Internet of Things (IoT) devices. The integration of these sources can pose challenges for businesses. The chapter outlines techniques for automation optimization, such as machine learning, predictive analytics, and business process reengineering. Optimizing automation using financial data offers various benefits for businesses, including cost savings, improved quality, and increased profitability. However, there are challenges that businesses face in leveraging financial data, including the integration of various data sources and formats and the need for skilled personnel to analyze and interpret the data. The successful implementation of automation and optimization of processes can lead to sustainable growth and enhanced operations, making it crucial for businesses to remain competitive in the I-4.0 era. By leveraging financial data to optimize automation processes, businesses can maximize their potential and drive growth. Overall, this chapter highlights the significance of financial data in automation optimization and provides insights into the benefits and challenges that businesses must consider when leveraging financial data for optimization. 2024 selection and editorial matter, Nidhi Sindhwani, Rohit Anand, A. Shaji George and Digvijay Pandey; individual chapters, the contributors. -
Deep learning approaches to understanding psychological impacts on vulnerable populations
This chapter investigates the psychological effects on vulnerable groups, with a particular emphasis on the relationship between deep learning techniques and the impact of climate. Vulnerable groups confront particular problems, which might lead to negative psychological results. Investigating this complexity is critical to designing effective intervention techniques. Using sophisticated deep learning techniques, this study seeks to find subtle patterns and correlations in a variety of datasets, including psychological markers, socioeconomic characteristics, and climatic variables. The work employs a comprehensive technique that includes deep learning models, feature extraction, and interpretability analysis to untangle complicated relationships. Preliminary findings imply that deep learning approaches might uncover previously unknown links between climate change and psychological effects on vulnerable groups. This insight adds to a more comprehensive understanding of the difficulties. This understanding contributes to a more holistic grasp of the challenges faced by these groups. By including climate-related factors into the deep learning framework, this study hopes to close the gap between environmental impacts and psychological 2024, IGI Global. All rights reserved. -
Enhancing Movie Genre Classification through Emotional Intensity Detection: An Improvised Machine Learning Approach
Movie Genre Classification through Emotion Intensity is a computer vision technique used to identify facial emotion through a sequential neural network model and to get the genre of the movie with it. This paper delves into latest advancements in Emotion Detection, particularly emphasizing neural network models and leveraging face image analysis algorithms for emotion recognition. Grenze Scientific Society, 2024. -
How Are We Surviving the Pandemic, COVID-19?: Perspectives from Hospitality Industry Workers in Australia
The COVID-19 pandemic has been disastrous and has affected the hospitality industry worldwide, and the people working in the sector were impacted immensely. The purpose of this study is to understand the viewpoints of hospitality workers in Australia on how lockdowns have impacted professional and personal well-being. The case study methodology is adopted for this study. Viewpoints from Australian hospitality workers were collected through semi-structured interviews. With the pandemic taking surprising turns with the rise of new infections and in turn new pandemic waves, the industry is facing a constant lurking fear of lockdowns. Changing variants of COVID-19 creates a profound effect on the psychological and personal well-being of the people employed in the hospitality sector. This chapter would reflect upon the viewpoints of hospitality workers in Australia after two years of the COVID-19 crisis. A real-time assessment is required to understand the vulnerability of hospitality industry workers in a developed country. 2023 Priyakrushna Mohanty, Anukrati Sharma, James Kennell and Azizul Hassan. -
DAWM: Cost-Aware Asset Claim Analysis Approach on Big Data Analytic Computation Model for Cloud Data Centre
The heterogeneous resource-required application tasks increase the cloud service provider (CSP) energy cost and revenue by providing demand resources. Enhancing CSP profit and preserving energy cost is a challenging task. Most of the existing approaches consider task deadline violation rate rather than performance cost and server size ratio during profit estimation, which impacts CSP revenue and causes high service cost. To address this issue, we develop two algorithms for profit maximization and adequate service reliability. First, a belief propagation-influenced cost-aware asset scheduling approach is derived based on the data analytic weight measurement (DAWM) model for effective performance and server size optimization. Second, the multiobjective heuristic user service demand (MHUSD) approach is formulated based on the CPS profit estimation model and the user service demand (USD) model with dynamic acyclic graph (DAG) phenomena for adequate service reliability. The DAWM model classifies prominent servers to preserve the server resource usage and cost during an effective resource slicing process by considering each machine execution factor (remaining energy, energy and service cost, workload execution rate, service deadline violation rate, cloud server configuration (CSC), service requirement rate, and service level agreement violation (SLAV) penalty rate). The MHUSD algorithm measures the user demand service rate and cost based on the USD and CSP profit estimation models by considering service demand weight, tenant cost, and energy cost. The simulation results show that the proposed system has accomplished the average revenue gain of 35%, cost of 51%, and profit of 39% than the state-of-the-art approaches. 2021 M. S. Mekala et al. -
Nonlinear analysis of the effect of viscoelasticity on ferroconvection
Thispaper concerns a nonlinear analysis of the effects of viscoelasticity on convection in ferroliquids. We consider the Oldroyd model for the constitutive equation of the liquid. The linear stability analysis yields the critical value of the Rayleigh number for the onset of oscillatory convection in Maxwell and Jeffrey ferroliquids. The use of a minimal mode double Fourier series in the nonlinear perturbation equations yields a KhayatLorenz model for the ferromagnetic liquid, and that is scaled further to get the classical Lorenz model as a limiting case. The scaled KhayatLorenz model thus obtained is solved numerically and the solution is used to compute the time-dependent Nusselt number, which quantifies the heat transport. The results are analyzed for the dependence of the time-averaged Nusselt number on different parameters. 2021 Wiley Periodicals LLC -
Brain Tumor Detectin Using Deep Learning Model
Brain tumor is a life-threatening disease that can disrupt normal brain functioning and have a significant impact on a patient's quality of life. Early detection and diagnosis are crucial for effective treatment. In recent years, deep learning techniques for image analysis and detection have played a vital role in the medical field, supplying more accurate and reliable results. Segmentation, the process of distinguishing between normal and abnormal brain cells or tissues, is a critical step in the detection of brain tumors. In this research, we aim to investigate various techniques for brain tumor detection and segmentation using Magnetic Resonance Imaging (MRI) images. The detection process begins by analyzing the symmetric and asymmetric shape of the brain to identify abnormalities. We will then classify the cells as either Tumored or non-Tumored. This research is aimed at finding a more accurate and efficient method for detecting brain tumors. Four Keras models are compared side by side to find out the best deep learning model for providing a suitable outcome. The models are ResNet50, DenseNet201, Inception V3 and MobileNet. These models gave training accuracy of 85.30%, 78%, 78%, and 77.12% respectively. 2023 IEEE. -
Development and characterization of carbon fiber reinforcement in Aluminium metal matrix composites
Carbon fibers (CF) possess exceptional mechanical properties and the highest degree of chemical stability. However, carbon reinforcement in metal matrix composites is extremely scarce due to production difficulties, particularly in obtaining a uniform distribution. Carbon fiber reinforced composites are typically made using high temperature processing processes. However, the fibers must be coated with Ni or Cu in order to achieve effective particle dispersion; otherwise, there is a larger likelihood of intermetallic compound formation, which reduces the chances for enhanced properties. In this work, the metallurgical, mechanical, and tribological characteristics of the carbon fiber reinforcement in AA 7050 are examined. Uncoated carbon fibers are reinforced into the Aluminium matrix using a low temperature processing technique known as powder metallurgy. The AA 7050 matrix reinforced with carbon fibers at various weight percentages between 0 and 1.5. The samples undergone mechanical and metallurgical testing in accordance with ASTM guidelines. The findings indicate that the 0.25 weight percent carbon fiber reinforcement in the matrix increased the material's hardness by 30% over the monolithic alloy, making it an excellent alternative for structural applications. Published under licence by IOP Publishing Ltd. -
Experimental Investigation of Nano Hexagonal Boron Nitride Reinforcement in Aluminum Alloys Through Casting Method
Aluminum metal matrix composites (AlMMCs) have a significant impact on a variety of industries that seek for innovation, efficiency, and sustainability. AlMMCs are substantial because of the special combination of properties that make them an essential part of contemporary production and design. Custom made properties of the AlMMCs can be obtained by the reinforcing different ceramic particles. Among the reinforcements, nano hexagonal boron nitride were rarely used. Hexagonal boron nitride particles have self-lubrication properties and it is one of the promising substitutes of graphite. The incorporation of hexagonal boron nitride (hBN) as a reinforcement material in aluminum alloys has garnered significant attention in recent years. This paper provides an overview of the reinforcement of nano hBN in aluminum alloys through casting method and highlights the mechanical and thermal properties of these alloys. The results show that the wear rate of the composite at 2wt.% is 9.91% lower for a load of 40 N when compared to unreinforced composite. Furthermore, the impact of hBN content, dispersion, and processing parameters on the properties of the composites is analyzed. The unique structural and thermal properties of hBN, along with excellent lubricating abilities, make it a promising candidate for reinforcing aluminum composites. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Processing of nanoreinforced aluminium hybrid metal matrix composites and the effect of post-heat treatment: a review
The demand for cutting-edge materials with a high strength-to-weight ratio and economic considerations is steadily increasing. Lightweight materials such as aluminium (Al) and its alloys are attractive, but some properties such as low thermal stability and high wear rate limit the application of aluminium alloys (AA) to some extent. Many researchers have developed various composites to get around these restrictions and increase the performance of aluminium and its alloy. Metal matrix composites (MMCs) with nanoparticles have revealed greater mechanical and tribological properties compared with micron-sized reinforcements. Most engineering applications require materials with excellent multidimensional properties, which are difficult to achieve using single reinforced MMCs. Hybrid metal matrix composites (HMMCs) with superior properties are the latest trends in composite technology. The choice of reinforcement selection has a vibrant role in the manufacturing of hybrid metal matrix composites. Researchers face a major challenge in finding optimum reinforcement combinations and their corresponding concentrations. The manufacturing of nanocomposites is difficult due to their high surface area and energy. To determine the most effective reinforcement combinations for hybrid composites, this article addresses several nanoreinforcements, their effects, and the appropriate processing methods for aluminium and its alloys. Researchers have paid less attention to the impact of precipitation hardening in aluminium and its alloys; thus, this paper also considers the effect of post-heat treatment ofaluminium composites. 2022, King Abdulaziz City for Science and Technology. -
MR Brain Tumor Classification and Segmentation Via Wavelets
Timely, accurate detection of magnetic resonance (MR) images of brain is most important in the medical analysis. Many methods have already explained about the tumor classification in the literature. This paper explains the method of classifying MR brain images into normal or abnormal (affected by tumor), abnormality segments present in the image. This paper proposes DWT-discrete wavelet transform in first step to extract the image features from the given input image. To reduce the dimensions of the feature image principle component Analysis (PCA) is employed. Reduced extracted feature image is given to kernel support vector machine (KSVM) for processing. The data set has 90 brain MR images (both normal and abnormal) with seven common diseases. These images are used in KSVM process. Gaussian Radial Basis (GRB) kernel is used for the classification method proposed and yields maximum accuracy of 98% compared to linear kernel (LIN). From the analysis, compared with the existing methods GRB kernel method was effective. If this classification finds abnormal MR image with tumor then the corresponding part is separated and segmented by thresholding technique. 2018 IEEE. -
Multiway Relay Based Framework for Network Coding in Multi-Hop WSNs
In todays information technology (IT) world, the multi-hop wireless sensor networks (MHWSNs) are considered the building block for the Internet of Things (IoT) enabled communication systems for controlling everyday tasks of organizations and industry to provide quality of service (QoS) in a stipulated time slot to end-user over the Internet. Smart city (SC) is an example of one such application which can automate a group of civil services like automatic control of traffic lights, weather prediction, surveillance, etc., in our daily life. These IoT-based networks with multi-hop communication and multiple sink nodes provide efficient communication in terms of performance parameters such as throughput, energy efficiency, and end-to-end delay, wherein low latency is considered a challenging issue in next-generation networks (NGN). This paper introduces a single and parallels stable server queuing model with a multi-class of packets and native and coded packet flow to illustrate the simple chain topology and complex multiway relay (MWR) node with specific neighbor topology. Further, for improving data transmission capacity in MHWSNs, an analytical framework for packet transmission using network coding at the MWR node in the network layer with opportunistic listening is performed by considering bi-directional network flow at the MWR node. Finally, the accuracy of the proposed multi-server multi-class queuing model is evaluated with and without network coding at the network layer by transmitting data packets. The results of the proposed analytical framework are validated and proved effective by comparing these analytical results to simulation results. 2023 Tech Science Press. All rights reserved. -
Islanding detection technique of distribution generation system
Islanding is a condition in which the micro grid is disconnected from the main grid which consists of loads and distribution generation. Islanding is required whenever there is a fault and whenever the maintenance is required. Under normal condition or stable condition, the system works under constant current control mode. After islanding the system switched to voltage controlled mode. There are different methods that can be used to detect islanding situation such as active and passive methods. In this paper DQ-PLL detection technique used for detecting islanding condition is carried out. This paper also explains in detail the advantages of DQ-PLL method for islanding detection The implementation is validated by using MATLAB/SIMULINK software. 2016 IEEE. -
Negotiating Inclusion: Minority Institutions and Constitutional-Legal Dimensions in India
The chapter Negotiating Inclusion: Minority Institutions and Constitutional and Legal Dimensions in India is based on the premise that special provisions for inclusion of minority groups were one of the contested topics that have been negotiated in India since independence. The present chapter critically explores the two main sites of negotiation: Constituent Assembly Debates and the cases involving the question of minority rights to culture and education as adjudicated by the Indian courts. In doing so, the paper undertakes an examination of the logic of state recognition and reservations, voiced by nationalist leaders and members of the Constituent Assembly, who were apprehensive that the provisions on minority accommodation may not be compatible with Indias secular credentials. Constitutional provisions, specifically the fundamental rights embodied in Articles 29 and 30 were further debated and re-interpreted by the High Courts and Supreme Court. Further on, the issue of minority accommodation led to the establishment of institutional mechanisms in India, one such institution being the National Commission for Minority Educational Institutions (NCMEI)-a recent addition in the series of negotiating spaces of the religious and minority communities in India. A thorough examination of the functioning of the NCMEI, an institution which remains understudied, may inform new avenues into thinking about the sites of minority rights negotiations in India, given the shifting ideological positions at the national level. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Understanding Startup Valuation and its Impact on Startup Ecosystem
Startups play a substantial role in the economic growth of a nation, by introducing new technologies, ground-breaking innovation, creating jobs, etc. A couple of decades back, it was extremely difficult to start a business, but today new businesses pop up every day, all around the world. Recognizing the importance of a startup, governments across the globe are doing their best to provide an atmosphere where startups can bloom. Despite its importance and all the support, the startup failure rate is at 90%; about 10% of startups fail in the first year and 70% fail in two to five years. The startup boom saw the emergence of alternative sources of funding like Venture Capitalist, Angel Investors, etc. These investors (Venture Capitalist, Angel Investors, etc.) played a crucial role in startup success by providing easy access to funds which is a critical and scarce resource for any founder. Traditionally business success is linked with sustainable profitability but in the startup world most used method to define success is valuation. Based on CB Insights research, as of January 2022, there are more than 900 unicorns (startup with a valuation of over $1 billion) around the world and of these unicorns less than 10% are profitable. It's difficult to explain/comprehend how startups' which are neither profitable nor foresee profitability in near future are valued higher than traditional business with stable profitability. Current valuation methods have impacted the startup ecosystem. Today, founders start their business with exit in mind, the focus of founders is on growth/scale rather than profitability. There is a school of thought that believes that such valuations will soon result in the bursting of the startup bubble just like the dotcom bubble seen in late 1990s. The focus of this paper is to investigate the techniques used by investors for startup valuation and how these techniques are impacting the startup ecosystem and its founders. The paper looks at all stages of the investment cycle, from seed to IPO or takeover and understands the process of valuation at each stage and how it impacts all stakeholders in the ecosystem. 2022 Walter de Gruyter GmbH, Berlin/Boston. -
Buffer zones in Wayanad: A social constructivist exploration into farmers mental health
Buffer zones are regions set aside to border protected areas to preserve biodiversity, control interactions between people and wildlife, and foster sustainable development. The majority of research on buffer zones focuses on ecological issues, and little is known about how they affect local communities mental health. This study explores buffer zones potential consequences on farmers mental health in Wayanad. Through purposive sampling, eleven participants residing in Wayanad were recruited for the study. The socio-demographics of participants were collected through printed translated questionnaires. The qualitative exploration of their lived experiences, perceptions, and coping strategies was conducted using semi-structured, in-depth interviews. Thematic analysis by Braun and Clarke was used to gain a clearer understanding of the data collected. Through in-depth analysis of the data, it was identified that Mental Health Factors, Communication Factors, Financial Impact, Operational Stress, Interference of Judiciary and Legislature, and Seclusion of the Tribal Community were the issues the farmers faced in Wayanad. The results will contribute to the expanding mental health field and give policymakers, conservationists, and mental health professionals information about the potential psychological effects of buffer zones and guide them in creating suitable interventions and support systems to improve mental health. The Author(s) 2024.