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This study aims to investigate the influence of wall curvature in a semicircular thermal annular system on magneto-nanofluidic flow, heat transfer and entropy generation. The analysis is conducted under constant cooling surface and fluid volume constraints.

The mathematical equations describing the thermo-fluid flow in the semicircular system are solved using the finite element technique. Four different heating wall configurations are considered, varying the undulation numbers of the heated wall. Parametric variations of bottom wall undulation (f), buoyancy force characterized by the Rayleigh number (Ra), magnetic field strength represented by the Hartmann number (Ha) and inclination of the magnetic field (γ) on the overall thermal performance are studied extensively.

This study reveals that the fluid circulation strength is maximum in the case of a flat bottom wall. The analysis shows that the bottom wall contour and other control parameters significantly influence fluid flow, entropy production and heat transfer. The modified heated wall with a single undulation exhibits the highest entropy production and thermal convection, leading to a heat transfer enhancement of up to 21.85% compared to a flat bottom. The magnetic field intensity and orientation have a significant effect on heat transfer and irreversibility production.

Further research can explore a wider range of parameter values, alternative heating wall profiles and boundary conditions to expand the understanding of magneto-nanofluidic flow in semicircular thermal systems.

This study introduces a constraint-based analysis of magneto-nanofluidic thermal behavior in a complex semicircular thermal system, providing insights into the impact of wall curvature on heat transfer performance. The findings contribute to the design and optimization of thermal systems in various applications.

By positioning this study within the theoretical lenses of Bourdieu’s practice theory, this paper aims to provide a more contextual understanding of training transfer (TT) with a particular focus on the duality between objectivity and subjectivity that characterize social structures within two different fields (a public post office company and a multinational evolving in the ICT high-tech sector).

Multiple case study.

The findings demonstrate that TT cannot be dissociated from social interaction dynamics in the workplace, where objective and subjective structures play a strategic role. In fact, capital dispatching, power disparities and cultural imperatives influence TT practice in both cases. However, this influence differs from one field to another.

Top management team should pay more attention to power and particularly to symbolic power as it can influence TT intentions and effectiveness. They must be aware that not only the economic capital is sought after but also the cultural and the symbolic capital.

This study aims at lessening the gap between theory and practice on the TT problem, in an effort to increase comprehension of the social roots of the transferring process. This research deepens the analysis of the complexity of socialization structuring TT practice in two different fields.

This study aims to examine the role of hope and hate in political leaders’ messages in influencing liberals versus conservatives’ social-distancing behavior during the COVID-19 pandemic. Given the increasing political partisanship across the world today, using the appropriate message framing has important implications for social and public policy.

The authors use two Natural Language Processing (NLP) methods – a pretrained package (HateSonar) and a classifier built to implement our supervised neural network-based model architecture using RoBERTa – to analyze 61,466 tweets by each US state’s governor and two senators with the goal of examining the association between message factors invoking hate and hope and increased or decreased social distancing from March to May 2020. The authors examine individuals’ social-distancing behaviors (the amount of nonessential driving undertaken) using data from 3,047 US counties between March 13 and May 31, 2020, as reported by Google COVID-19 Community Mobility Reports and the New York Times repository of COVID-19 data.

The results show that for conservative state leaders, the use of hate increases nonessential driving of state residents. However, when these leaders use hope in their speech, nonessential driving of state residents decreases. For liberal state leaders, the use of hate displays a directionally different result as compared to their conservative counterparts.

Amid the emergence of new analytic techniques and novel data sources, the findings demonstrate that the use of global positioning systems data and social media analysis can provide valuable and precise insights into individual behavior. They also contribute to the literature on political ideology and emotion by demonstrating the use of specific emotion appeals in targeting specific consumer segments based on their political ideology.

The findings have significant implications for policymakers and public health officials regarding the importance of considering partisanship when developing and implementing public health policies. As partisanship continues to increase, applying the appropriate emotion appeal in messages will become increasingly crucial. The findings can help marketers and policymakers develop more effective social marketing campaigns by tailoring specific appeals given the political identity of the consumer.

Using Neural NLP methods, this study identifies the specific factors linking social media messaging from political leaders and increased compliance with health directives in a partisan population.

The purpose of this paper is to use the Black-Scholes-Merton option pricing model to evaluate the incremental performance of an eChannel addition.

Data were collected from 53 Taiwan financial services firms. In total, 33 of them introduced their online services, whereas the other 20 firms did not introduce their online services during the period under examination.

The research findings show that firm asset values increase following eChannel additions. Thus, eChannel additions enhance firm financial performance. A further analysis comparing the performance between firms with and without eChannel additions also shows that firms with eChannel additions have higher asset value growth rates, which further validates the capacity of eChannel additions to enhance financial performance.

Managers and shareholders in firms making eChannel additions are not required to be concerned regarding stock price volatility, and managers in firms without any eChannel investment could use eChannels to enhance their stock price and seize future opportunities. Using eChannel is a valid approach for firms to provide enhanced services to current customers, access new markets, and extend market coverage, thus enhancing overall financial performance. Investors could confide those firms implementing eChannel additions.

Studies investigating whether eChannel additions enhance firm financial performance are scant. No study has evaluated performance from a long-term perspective or from a volatility aspect (both are important considerations in eChannel performance evaluation). The research represents a pioneering work that empirically investigates these issues.

The current study employs text mining and sentiment analysis to identify core banking service attributes and customer sentiment in online user-generated reviews. Additionally, the study explains customer satisfaction based on the identified predictors.

A total of 32,217 customer reviews were collected across 29 top banks on bankbazaar.com posted from 2014 to 2021. In total three conceptual models were developed and evaluated employing regression analysis.

The study revealed that all variables were found to be statistically significant and affect customer satisfaction in their respective models except the interest rate.

The study is confined to the geographical representation of its subjects' i.e. Indian customers. A cross-cultural and socioeconomic background analysis of banking customers in different countries may help to better generalize the findings.

The study makes essential theoretical and managerial contributions to the existing literature on services, particularly the banking sector.

This paper is unique in nature that focuses on banking customer satisfaction from online reviews and ratings using text mining and sentiment analysis.

The purpose of this paper is to present a broad historical review of critical wisdom literature of ancient Indian tradition and examine how these precepts can complement and enrich the contemporary managerial frames.

The paper attempts to critically review remarkably deep religious and secular traditions of India and integrate them in a conceptual model.

The paper findings point towards the need for a holistic frame in overcoming fragmented viewpoints of contemporary management by strengthening the reflective domains of the managerial world.

The limitation of the paper lies in its didactic nature and the specificity of the contextual boundary limiting its ready transferability.

The paper provides a pointer in extending horizons of business or non‐business organizations in opening up their possibilities for achieving holistic managerial perspectives by combining economic, social and other higher order sustainable goals.

The paper's contribution is in its integrative value of some of the key themes of Indian wisdom literature and demonstrating their relevance to the modern management.

Social media channels provide an avenue for expressing views about different services/products. However, unlike merchandise/company websites (where users can post both reviews and ratings), it is not possible to understand user's ratings for a particular service-related comment on social media unless explicitly mentioned. Predicting ratings can be beneficial for service providers and prospective customers. Additionally, predicting ratings from a user-generated content can help in developing vast data sets for recommender systems utilizing recent data. The aim of this study is to predict ratings more accurately and enhance the performance of sentiment-based predictors by combining it with the emotional content of textual data.

This study had utilized a combination of sentiment and emotion scores to predict the ratings of Twitter posts (3,509 tweets) in three different contexts, namely, online food delivery (OFD) services, online travel agencies (OTAs) and online learning (e-learning). A total of 29,551 reviews were utilized for training and testing purposes.

Results of this study indicate accuracies of 58.34%, 57.84% and 100% in cases of e-learning, OTA and OFD services, respectively. The combination of sentiment and emotion scores showed an increase in accuracies of 19.41%, 27.83% and 40.20% in cases of e-learning, OFD and OTA services, respectively.

Understanding the ratings of social media comments can help both service providers as well as prospective customers who do not spend much time reading posts but want to understand the perspectives of others about a particular service/product. Additionally, predicting ratings of social media comments will help to build databases for recommender systems in different contexts.

The uniqueness of this study is in utilizing a combination of sentiment and emotion scores to predict the ratings of tweets related to different online services, namely, e-learning OFD and OTAs.

Depression is a mental health problem characterized by a persistent sense of sadness and loss of interest. EEG signals are regarded as the most appropriate instruments for diagnosing depression because they reflect the operating status of the human brain. The purpose of this study is the early detection of depression among people using EEG signals.

(i) Artifacts are removed by filtering and linear and non-linear features are extracted; (ii) feature scaling is done using a standard scalar while principal component analysis (PCA) is used for feature reduction; (iii) the linear, non-linear and combination of both (only for those whose accuracy is highest) are taken for further analysis where some ML and DL classifiers are applied for the classification of depression; and (iv) in this study, total 15 distinct ML and DL methods, including KNN, SVM, bagging SVM, RF, GB, Extreme Gradient Boosting, MNB, Adaboost, Bagging RF, BootAgg, Gaussian NB, RNN, 1DCNN, RBFNN and LSTM, that have been effectively utilized as classifiers to handle a variety of real-world issues.

1. Among all, alpha, alpha asymmetry, gamma and gamma asymmetry give the best results in linear features, while RWE, DFA, CD and AE give the best results in non-linear feature. 2. In the linear features, gamma and alpha asymmetry have given 99.98% accuracy for Bagging RF, while gamma asymmetry has given 99.98% accuracy for BootAgg. 3. For non-linear features, it has been shown 99.84% of accuracy for RWE and DFA in RF, 99.97% accuracy for DFA in XGBoost and 99.94% accuracy for RWE in BootAgg. 4. By using DL, in linear features, gamma asymmetry has given more than 96% accuracy in RNN and 91% accuracy in LSTM and for non-linear features, 89% accuracy has been achieved for CD and AE in LSTM. 5. By combining linear and non-linear features, the highest accuracy was achieved in Bagging RF (98.50%) gamma asymmetry + RWE. In DL, Alpha + RWE, Gamma asymmetry + CD and gamma asymmetry + RWE have achieved 98% accuracy in LSTM.

A novel dataset was collected from the Central Institute of Psychiatry (CIP), Ranchi which was recorded using a 128-channels whereas major previous studies used fewer channels; the details of the study participants are summarized and a model is developed for statistical analysis using N-way ANOVA; artifacts are removed by high and low pass filtering of epoch data followed by re-referencing and independent component analysis for noise removal; linear features, namely, band power and interhemispheric asymmetry and non-linear features, namely, relative wavelet energy, wavelet entropy, Approximate entropy, sample entropy, detrended fluctuation analysis and correlation dimension are extracted; this model utilizes Epoch (213,072) for 5 s EEG data, which allows the model to train for longer, thereby increasing the efficiency of classifiers. Features scaling is done using a standard scalar rather than normalization because it helps increase the accuracy of the models (especially for deep learning algorithms) while PCA is used for feature reduction; the linear, non-linear and combination of both features are taken for extensive analysis in conjunction with ML and DL classifiers for the classification of depression. The combination of linear and non-linear features (only for those whose accuracy is highest) is used for the best detection results.

Self-care is defined as the ability to take care of one’s body and health with or without the help of healthcare personnel. The purpose of this paper is to explore the opportunities for self-care among imprisoned women within the constraints of a confined life, which, in turn, affect their health.

Primary data have been collected through semi-structured interviews with 90 women in three prisons in the Indian state of West Bengal.

Findings reveal that a majority of the women cited the inability to self-care was due to factors such as constricted architecture, specific penal policies that thwarted relational contexts in prisons and also the loss of control over their consumptive choices. However, it was found that coping mechanisms also existed among some women who actively constituted penal spaces for self-care. Many long-term imprisoned women tried to actively engage themselves in daily activities such as the “labour” allotted to them.

The paper concludes that abilities to self-care have a deep impact on the health of women, which if not facilitated will lead to a health depleting experience. At a time when Indian prisons are focussing on rehabilitation, the recommendations for providing opportunities for self-care in prisons can minimize the “pains” of imprisonment and pave the way for rehabilitation.

The research is based on data collected during original fieldwork conducted in three prisons in West Bengal, India. It provides valuable insights on how penal environments affect self-care opportunities of imprisoned women.