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Formation of Positive Organizational Climate: Integration of Ubuntu Diversity Management in Banks
A positive organizational climate requires incorporating the notion of Ubuntu into workforce diversity management. Ubuntu in the banking business is essential for improved workforce diversity management and a better workplace climate for women. Employees look for a healthy and positive organizational climate in their workplace settings. Nelson Mandela and Mother Teresa are ideal examples of Ubuntu; they lived a life of selflessness and humanity. In todays competitive world, the concept of Ubuntu is not widely accepted in banking. Banks are vying for new customers, market share, and business loans. Bank services include credit card processing, reconciliation and reporting, cheque collection; payroll; and deposit. They do not have time to think about Ubuntu. The banking sectors operations are tedious, causing tension among employees. Stress is lethal in COVID-19. Using various literature reviews, this study shows how stress can be lethal during COVID-19. Globally, the value system of Ubuntu and Gandhian ideologies are abandoned in favour of monetary gain, resulting in a cruel society. Violence against minority discrimination and black oppression still prevails in our society. Suppression and tyranny have always existed, but COVID-19 triples their impact. One solution is to bring forth Ubuntu. Even if it is too late, we can still revive our society. With Africas great diversity, fostering unity is the key to peace and growth. The best way to achieve peace and progress is to employ Ubuntus values. One continent, one philosophy could be a way out for Africa, but it would necessitate contributions from all African leaders. 2022. Journal of International Womens Studies. -
Irreversibility analysis of the MHD Williamson fluid flow through a microchannel with thermal radiation
The heat transport and non-Newtonian fluid (Williamson fluid) flow through a micro-channel are considered to analyze the entropy generation minimization using the thermodynamic second law. The energy equations have been modeled with the addition of joule heating, heat source, and thermal radiation. The use of suitable dimensionless transformation helps to convert the modeled flow equations into non-dimensional coupled ODEs. The numerical simulations are done via the Finite Element Method. The current outcomes are constructed to examine the behavior of various flow parameters and presented via graphs. It is found that the rise of heat source and Reynolds number Re decays/boosts the entropy rate Ns and the Bejan number Be profile near the left/right plate, and reverse behavior is noticed for the thermal radiation parameter. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Towards Visibility: Subaltern Counterpublics in Paul Chirakkarodes Pulayathara
Christianity has always been celebrated as a catalyst towards modernity for the Dalits of Kerala. Though missionary accounts and ethnographic studies confirm the progress of the community, there was rampant casteism and separatism too. This is succinctly revealed in Dalit Christian texts. Pulayathara by Paul Chirakkarode stands as a testimony to the Dalit Christian dilemma and traces the history of the Kuttanadan Pulaya community in the pre- and post-conversion scenarios. Conversions could not change the existing public sphere of Kerala, where upper castes were the dominant party. They (Dalits) continued to be marginalized and subordinated and lacked a class consciousness. The article highlights the limitations in the public sphere that emerged in Kerala as part of the missionary endeavours in accommodating the converted Dalits. The article attempts to trace the emergence of subaltern counterpublics among the Dalit Christians to oppose the continued oppression and casteism by situating Pulayathara at the centre of the analysis. 2022 Indian Institute of Management, Ahmedabad. -
High-Movement Human Segmentation in Video Using Adaptive N-Frames Ensemble
A wide range of camera apps and online video conferencing services support the feature of changing the background in real-time for aesthetic, privacy, and security reasons. Numerous studies show that the Deep-Learning (DL) is a suitable option for human segmentation, and the ensemble of multiple DL-based segmentation models can improve the segmentation result. However, these approaches are not as effective when directly applied to the image segmentation in a video. This paper proposes an Adaptive N-Frames Ensemble (AFE) approach for high-movement human segmentation in a video using an ensemble of multiple DL models. In contrast to an ensemble, which executes multiple DL models simultaneously for every single video frame, the proposed AFE approach executes only a single DL model upon a current video frame. It combines the segmentation outputs of previous frames for the final segmentation output when the frame difference is less than a particular threshold. Our method employs the idea of the N-Frames Ensemble (NFE) method, which uses the ensemble of the image segmentation of a current video frame and previous video frames. However, NFE is not suitable for the segmentation of fast-moving objects in a video nor a video with low frame rates. The proposed AFE approach addresses the limitations of the NFE method. Our experiment uses three human segmentation models, namely Fully Convolutional Network (FCN), DeepLabv3, and Mediapipe. We evaluated our approach using 1711 videos of the TikTok50f dataset with a single-person view. The TikTok50f dataset is a reconstructed version of the publicly available TikTok dataset by cropping, resizing and dividing it into videos having 50 frames each. This paper compares the proposed AFE with single models and the Two-Models Ensemble, as well as the NFE models. The experiment results show that the proposed AFE is suitable for low-movement as well as high-movement human segmentation in a video. 2022 Tech Science Press. All rights reserved. -
Frustration Tolerance among Indian Youth: Exploring its relationship with Gratitude and Self Awareness
Introduction: For any person, adulthood is a difficult era of life filled with uncontrollable frustrations. The move from adolescence to young adulthood has reverberation as it marks a shift from adolescent's dependency to the chores and independence of young adulthood (Boll, 2017). Upon review, it was found that many researchers have established the effects of frustration, however, there is little research and evidence-based practice utilizing positive psychology interventions to target low frustration tolerance in youth. A vast body of research have established the positive consequences of gratitude and self awareness in one's life. The present study aims to explore the relationship between gratitude, self awareness and frustration tolerance among young adults. Methodology: Participants were selected through purposive sampling method. Data were collected from 167 young Indian adults (Females-94, Males-73) aged 19-35 years. Participants completed the three inventories measuring the variables of interest using the online survey forms. Data were analyzed by the SPSS software using descriptive analysis, correlation coefficients, and linear regression. Results: Findings show no significant relationship between gratitude and frustration tolerance (r=-0.071). However, there is a significant positive correlation of self awareness with frustration tolerance (r=0.271). The regression model showed that 7.3% of variance in frustration tolerance can be predicted by self awareness. Conclusion: Thus, self-awareness can be viewed as one of the important factors that impact frustration tolerance. The findings are consistent with previous research that has shown that self-awareness has important effects on performance, and emotions. Future implications are discussed. 2022 RESTORATIVE JUSTICE FOR ALL. -
Network Lifetime Enhancement by Elimination of Spatially and Temporally Correlated RFID Surveillance Data in WSNs
In wireless sensor networks (WSNs), radio frequency identification (RFID) plays an important role due to its data characteristics which are data simplicity, low cost, simple deployment, and less energy consumption. It consists of a series of tags and readers which collect a huge number of redundant data. It increases system overhead and decreases overall network lifetime. Existing solutions like Time-Distance Bloom Filter (TDBF) algorithm are inapplicable to the large-scale environment. Received Signal Strength (RSS) used in this algorithm is highly dependent on quality of tag and application environment. In this paper, we propose an approach for data redundancy minimization for RFID surveillance data which is a modified version of TDBF. The proposed algorithm is formulated by using the observed time and calculated distance of RFID tags. To overcome these problems, we design our approach to relevantly reduce the spatiotemporal data redundancy in the source level by adding the Received Signal Strength Indicator (RSSI) concept for energy-efficient RFID data communication in wireless sensor network scenario. We introduce in this paper the new improved idea of an existing algorithm which efficiently reduces the rate of data redundancy spatially and temporally. The implemented results overcome the limitations of existing algorithm for data redundancy reduction. Nevertheless, the performance evaluation shows the efficiency of proposed algorithm in terms of time and data accuracy. Furthermore, this algorithm supports multidimensional and large-scale environment suitable for sensor network nowadays. 2022 Lucy Dash et al. -
Non-destructive silkworm pupa gender classification with X-ray images using ensemble learning
Sericulture is the process of cultivating silkworms for the production of silk. High-quality production of silk without mixing with low quality is a great challenge faced in the silk production centers. One of the possibilities to overcome this issue is by separating male and female cocoons before extracting silk fibers from the cocoons as male cocoon silk fibers are finer than females. This study proposes a method for the classification of male and female cocoons with the help of X-ray images without destructing the cocoon. The study used popular single hybrid varieties FC1 and FC2 mulberry silkworm cocoons. The shape features of the pupa are considered for the classification process and were obtained without cutting the cocoon. A novel point interpolation method is used for the computation of the width and height of the cocoon. Different dimensionality reduction methods are employed to enhance the performance of the model. The preprocessed features are fed to the powerful ensemble learning method AdaBoost and used logistic regression as the base learner. This model attained a mean accuracy of 96.3% for FC1 and FC2 in cross-validation and 95.3% in FC1 and 95.1% in FC2 for external validation. 2022 The Authors -
Dirichlet Feature Embedding with Adaptive Long Short-Term Memory Model for Intrusion Detection System
Intrusion Detection System is applied in the network to monitor the network activity and detect the intruder to protect the user data. Various existing models have been applied in the intrusion detection system and have the limitations of high False Alarm Rate (FAR), overfitting problem and data imbalance problem. In this research, Dirichlet Feature Embedding based Adaptive Long Short Term Memory (DFE-LSTM) model is proposed to improve the efficiency of the intrusion detection. The Dirichlet Feature Embedding (DFE) method is applied to effectively represent the feature to analysis the multi-variate of the input data. The enhanced Adaptive Long Short Term Memory (ALSTM) model is applied to select the optimal parameter for the LSTM model to improve the learning rate. The proposed DFE-ALSTM model is compared to three datasets such as UNSW-NB15, NSL-KDD and Kyoto 2006+ for evaluate the efficiency. The proposed DFE-ALSTM model has the accuracy of 94.32 % and existing NB-SVM has 93.75 % accuracy in intrusion detection on UNSW-NB15 dataset. 2022, Success Culture Press. All rights reserved. -
Design of Lowpower 4-bit Flash ADC Using Multiplexer Based Encoder in 90nm CMOS Process
This work describes a 4-bit Flash ADC with low power consumption. The performance metrics of a Flash ADC depend on the kind of comparator and encoder used. Hence open-loop comparator and mux-based encoder are used to obtain improved performance. Simulation results show that the simulated design consumes 0.265mW of power in 90nm CMOS technology using cadence-virtuoso software. The circuit operates with an operating frequency of 100MHz and a supply voltage of 1V. The Author(s). -
Bulirsch-Stoer computations for bioconvective magnetized nanomaterial flow subjected to convective thermal heating and Stefan blowing: a revised Buongiorno model for theranostic applications
Theranostics is a novel procedure that integrates therapy and diagnosis in a single platform. For its application in theranostic and photothermal therapy for melanoma skin cancer, the hydromagnetic bioconvective flow of a nanomaterial over a lengthening surface is investigated. Realistic nanomaterial modeling is achieved by incorporating passive control of the nanoparticles at the boundary. The impact of the Newtonian heating and Stefan blowing constraints are also accounted. Apposite transformations are employed and then transmuted nonlinear ODEs are resolved using the Bulirsch-Stoer and Newton-Raphson methods. The influence of Stefan blowing parameter (Formula presented.), the magnetic field parameter (Formula presented.), and the Biot number (Formula presented.) on the heat transfer rate has been scrutinized and optimized utilizing the response surface methodology (RSM). The sensitivity of heat transport rate is computed. It is found that the Newtonian thermal condition intensifies the nanomaterial temperature that serves asa crucial role in the termination of cancerous cells or tumors. The maximum drag coefficient is experienced for the insignificant intensity of the magnetic field and Stefan blowing. Further, the heat transfer rate is maximum when the Stefan blowing and Biot numbers are at a high level and the Hartmann number is at a low level. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Financial stress, financial literacy, and financial insecurity in India's informal sector during COVID-19
The lockdowns and restrictions imposed to control COVID-19 have made life miserable for people, especially those involved in informal economic activities. The pandemic induced financial hardships, caused financial anxiety and financial stress among informal sector participants. This study aimed to measure and analyze the financial stress and financial insecurity of one of the important informal sector elements (street vendors) in India. Street vendors in Bangalore were interviewed in this descriptive research through personal interaction and telephonic interviews. The collected primary data were processed using SPSS statistical package. The results have indicated that the pandemic inflicted financial stress on street vendors irrespective of their gender, marital status, age, education, monthly income, and type of product dealt. Financial stress levels varied depending on the number of dependents of street vendors and their business nature. Financial literacy differed according to street vendors' marital status. A person becomes extremely sensitive and cautious in personal finance matters on getting married. Financial stress and financial literacy correlated negatively. 89.5% of street vendors perceived that they had financial insecurity in the future due to this pandemic. The results indicated that financial stress and financial literacy did not affect financial insecurity perceptions of street vendors. The predictors of financial insecurity have been marital status and the number of dependents of the street vendors (r2: 16.6%). However, marital status alone impacted the 6% variance in financial insecurity. This study concluded that the pandemic caused financial stress and financial insecurity among street vendors, but not financial stress and financial literacy. Thangaraj Ravikumar, Mali Sriram, S Girish, R Anuradha, M Gnanendra, 2022. -
Numerical study of Reiner-Rivlin nanoliquid flow due to a rotating disk with Joule heating and non-uniform heat source using Bulirsch-Stoer algorithm
The flow of a Reiner-Rivlin hydromagnetic nanoliquid due to rotating disk in the presence of Joule heating and a non-uniform heat source is investigated. To control the volume fraction of nanoparticles on the surface of a disk, a realistic passive control strategy is used, taking the thermal jump condition into account. Nonlinear governing differential equations are solved numerically using the Bulirsch-Stoer technique and a parametric analysis is performed using graphical representations. Using the Response Surface Methodology (RSM), the interaction effects of the influential parameters on the rate of heat transfer are visualized via three-dimensional surface graphs and contours. Further, the optimum rate of the heat transfer is estimated through the RSM analysis. It is found that the surface drag demotes due to enhancement in the cross-viscosity coefficient. A rise in the space-dependent heat source augments the temperature profile. The heat transfer rate is negatively influenced by the Eckert number. Further, when thermal slip is augmented, the sensitivity of the heat transfer towards the Hartmann number decreases at the rate of 0.2267%, and the sensitivity towards the Reiner-Rivlin fluid parameter decrements at the rate of 0.0554%. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
?-Ni(OH)2 supported over g-C3N4: A novel catalyst for para-nitrophenol reduction and supercapacitor electrode
?-Ni(OH)2 supported over g-C3N4 was synthesized by two simple approaches. Its catalytic activity in reducing hazardous para-nitrophenol (PNP) to the industrially important para-aminophenol (PAP) was studied in detail. The applicability of this material as a supercapacitor electrode was also studied. The material shows 100% conversion efficiency with a rate constant of 65.4410 10?3 s?1. The catalytic efficiency and the supercapacitor electrode behaviour of the material can be explained based on the structure of ?-Ni(OH)2 nanoparticle and the synergy between ?-Ni(OH)2 and g-C3N4. 2022 The Author(s) -
Enhanced Secure Technique for Detecting Cyber Attacks Using Artificial Intelligence and Optimal IoT
The Internet of Things (IoT) is a broad term that refers to the collection of information about all of the items that are linked to the Internet. It supervises and controls the functions from a distance, without the need for human interaction. It has the ability to react to the environment either immediately or via its previous experiences. In a similar vein, robots may learn from their experiences in the environment that is relevant to their applications and respond appropriately without the need for human interaction. A greater number of sensors are being distributed across the environment in order to collect and evaluate the essential information. They are gaining ground in a variety of industries, ranging from the industrial environment to the smart home. Sensors are assisting in the monitoring and collection of data from all of the real-time devices that are reliant on all of the different types of fundamental necessities to the most advanced settings available. This research study was primarily concerned with increasing the efficiency of the sensing and network layers of the Internet of Things to increase cyber security. Due to the fact that sensors are resource-constrained devices, it is vital to provide a method for reacting, analysing, and transmitting data collected from the sensors to the base station as efficient as possible. Resource requirements, such as energy, computational power, and storage, vary depending on the kind of sensing devices and communication technologies that are utilised to link real-world objects together. Sensor networks' physical and media access control layers, as well as their applications in diverse geographical and temporal domains, are distinct from one another. Transmission coverage range, energy consumption, and communication technologies differ depending on the application requirements, ranging from low constraints to high resource enrich gadgets. This has a direct impact on the performance of the massive Internet of Things environment, as well as the overall network lifetime of the environment. Identifying and communicating matching items in a massively dispersed Internet of Things environment is critical in terms of spatial identification and communication. 2022 Anand Kumar et al. -
Exploring the challenges and prospects of healthcare reporting in Indias Hinterland
Differential access and utilisation of healthcare services are caused by multiple Social Determinants of Health (SDHs), which requires adequate and informed policy intervention. The mass media, mainly the news media, have been seen as a practical approach in communicating the health anomalies at the policy level. The comprehensive coverage of factors associated with the healthcare system can further lead to addressing inequalities in health. The study was aimed to identify the factors that act against effective reporting of healthcare news from peripheral regions of India. The qualitative method was employed to examine media professionals' persistent challenges and experiences in covering health-related disparities. Sixteen media professionals were interviewed during October 2019 employed in vernacular news agencies all across the north-eastern state of Assam, India. Three themes have emerged from the analysis covering health-related news and barriers to healthcare in the peripheral region and identifying health-related disparities. It is found that an effective reporting mechanism of the health news will positively influence the policymakers and undertake efforts to address the health-related disparities. Copyright 2022 (Jyoti Nath, Tamuli). -
Social Network User Profiling With Multilayer Semantic Modeling Using Ego Network
Social and information networks undermine the real relationship between the individuals (ego) and the friends (alters) they are connected with on social media. The structure of individual network is highlighted by the ego network. Egocentric approach is popular due to its focus on individuals, groups, or communities. Size, structure, and composition directly impact the ego networks. Moreover, analysis includes strength of ego alter ties degree and strength of ties. Degree gives the first overview of network. Social support in the network is explored with the gap between the degree and average strength. These outcomes firmly propose that, regardless of whether the approaches to convey and to keep up social connections are evolving because of the dispersion of online social networks, the way individuals sort out their social connections appears to remain unaltered. As online social networks evolve, they help in receiving more diverse information. 2022 IGI Global. All rights reserved. -
Classification of Silicon (Si) Wafer Material Defects in Semiconductor Choosers using a Deep Learning ShuffleNet-v2-CNN Model
The silicon wafer is one of the raw materials used to make semiconductor chipsets. Semiconductor failure or dysfunction could be the result of defects in the layers of this material. As a result, it is essential to work toward the development of a system that is both quick and precise in identifying and classifying wafer defects. Wafer map analysis is necessary for the quality control and analysis of the semiconductor manufacturing process. There are some failure patterns that can be displayed by wafer maps. These patterns can provide essential details that can assist engineers in determining the reason of wafer failures. In this research, a deep-learning-based silicon wafer defect identification and classification model is proposed. The main objective of this research is to identify and classify the silicon wafer defects using the wafer map images. This proposed model identifies and classifies the defects based on the wafer map images from the WM-811K dataset. The proposed model is composed of a pretrained deep transfer learning model called ShuffleNet-v2 with convolutional neural network (CNN) architecture. This ShuffleNet-v2-CNN performs the defects identification and classification process following the workflow of data preprocessing, data augmentation, feature extraction, and classification. For performance evaluation, the proposed ShuffleNet-v2-CNN is evaluated with performance metrics like accuracy, recall, precision, and f1-score. The proposed model has obtained an overall accuracy of 96.93%, 95.40% precision, 96.26% recall, and 95.75% F1-score in classifying the silicon wafer defects based on the wafer map images. 2022 Rajesh Doss et al. -
A closer look at industry-associated value premium: evidence from India
This paper examines whether the academic literature-promised value premium has any industry association in the Indian equity market and the relationship between stock returns, value, and size within and across industries. We examine all listed firms trading at BSE India between 1999-2020, using CAPM and Fama-French three-factor models on each firm-levels and industry-level portfolio. The positive and significant value effect was found to exist in 17 out of 21 industry groups. Both industry and firm-level value effects are identified; however, the firm-level effect seems more prominent. Furthermore, the value effect is most substantial in small-cap value stocks of value- and growth-oriented industries, large-cap value stocks of value-oriented industry groups, then small-cap growth stocks of value- and growth-oriented industries and large-cap growth stocks of value- and growth-oriented industries. We also show evidence confirming the claim that value premium results from investors challenging higher returns from firms and industries operating in higher risk and distressing constraints. Copyright 2022 Inderscience Enterprises Ltd. -
A Scoping Review of Formal Care to Children with Special Needs during the Covid-19 Pandemic
The Covid-19 pandemic caused an unprecedented closure of direct service for children with special needs (CSNs), which shifted service to remote mode. This scoping review analyzed the strategies adopted by different formal care services for CSNs, their strengths and weaknesses, and the challenges faced by the formal care providers (FCPs). This study identified relevant articles through academic databases and Google searches using appropriate search strings and keywords. It included ten journal articles (n=10) and eight pieces (n=8) of grey literature through a meticulous selection process and extracted data. This review drew results by collating the descriptive numerical data analysis and qualitative thematic analysis and interpreting them. Reporting incor-porated all the possible items recommended by the PRISMA-ScR guidelines. This review demonstrated that pediatric rehabilitation adopted the telehealth approach and that special education changed to remote learning. When childcare programs in the USA functioned according to specific guidelines, residential care in South Asian countries faced a financial crunch. FCPs faced personal and professional challenges that required systematic training to deal with pandemic situations. This scoping review made suggestions for relevant policy formulations for equitable and effective service delivery to CSNs during pandemic situations, and it exposed new avenues for research. 2022 Authors. -
The Influence of Cartoons Soundscape Irrelevant Sound Effects on young children's Auditory Processing and Working memory skills
Background: Irrelevant sound or speech effect (ISE) affects an individual's serial recall task of visual and auditory presentations. Cartoon soundscape mimics irrelevant sound effect hypothesis. A constant and repeated exposure to cartoons in early childhood should influence children's auditory learning or recall performance. Purpose: To investigate the effects of cartoons' soundscape irrelevant sound effects on young children's auditory processing and working memory skills. Research Design: A cross-sectional study was used to observe the influence of the cartoon soundscape irrelevant sound effects on children. Study sample: Sixty young children with normal hearing in the age range 5-6years were exposed to cartoons (Indian plus Non-Indian) considered for the study. Data Collection and analysis: Pitch Pattern Test (PPT), Duration Pattern Test (DPT), and Corsi-Block working memory apparatus were applied to the participants exposed to cartoons. The data obtained were compared statistically in terms of the groups' performances. Results: There was a significant difference in PPT (p=.023) and DPT (p=.001) between the cartoon exposed and non-exposed groups. In contrast, there was no significant difference between the two groups in Corsi-Block working memory(p>0.05). Conclusion: Cartoon soundscape irrelevant sound or speech affects young children's auditory processing skills. The visual-spatial recall follows a different developmental pattern in young children without recoding to phonological aspects. It is predicted that our study findings might help determine the ill effects of cartoons on the auditory and language development process. 2022 American Academy of Audiology. All rights reserved.