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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.

The safe operation of the metro power transformer directly relates to the safety and efficiency of the entire metro system. Through voiceprint technology, the sounds emitted by the transformer can be monitored in real-time, thereby achieving real-time monitoring of the transformer’s operational status. However, the environment surrounding power transformers is filled with various interfering sounds that intertwine with both the normal operational voiceprints and faulty voiceprints of the transformer, severely impacting the accuracy and reliability of voiceprint identification. Therefore, effective preprocessing steps are required to identify and separate the sound signals of transformer operation, which is a prerequisite for subsequent analysis.

This paper proposes an Adaptive Threshold Repeating Pattern Extraction Technique (REPET) algorithm to separate and denoise the transformer operation sound signals. By analyzing the Short-Time Fourier Transform (STFT) amplitude spectrum, the algorithm identifies and utilizes the repeating periodic structures within the signal to automatically adjust the threshold, effectively distinguishing and extracting stable background signals from transient foreground events. The REPET algorithm first calculates the autocorrelation matrix of the signal to determine the repeating period, then constructs a repeating segment model. Through comparison with the amplitude spectrum of the original signal, repeating patterns are extracted and a soft time-frequency mask is generated.

After adaptive thresholding processing, the target signal is separated. Experiments conducted on mixed sounds to separate background sounds from foreground sounds using this algorithm and comparing the results with those obtained using the FastICA algorithm demonstrate that the Adaptive Threshold REPET method achieves good separation effects.

A REPET method with adaptive threshold is proposed, which adopts the dynamic threshold adjustment mechanism, adaptively calculates the threshold for blind source separation and improves the adaptability and robustness of the algorithm to the statistical characteristics of the signal. It also lays the foundation for transformer fault detection based on acoustic fingerprinting.

Users are key stakeholders in event facilities, and therefore facilities management (FM) services must meet their needs and expectations. The paper aims to assess users’ perceptions of FM practices and sustainability in event facilities in higher education institutions.

The study used a descriptive design approach to explore the perceptions of end-users of event facilities. Using Kwame Nkrumah University of Science and Technology as a case study, 384 users of the event facilities were contacted through the email directory of the event facilities. Questionnaires were used to collect data and analysed in descriptive and inferential statistics with the aid of the Statistical Package for Social Sciences (SPSS v22.0).

The study categorised users’ perceptions into customer care, security and safety and service quality dimensions. The results revealed that constituent key important indices of these three dimensions were not significant at both the composite and individual levels. Thus, the users generally perceived FM practices in the event facilities to be below standard. None of the three sustainability constituents was significant, suggesting that users perceived event FM sustainability practices as below standard.

The study presents users’ perceptions of FM practices in event facilities. It has also suggested effective ways of managing event facilities to improve user comfort, safety and satisfaction.

The study provides relevant information on users’ perceptions of FM practices. It specifically has sustainable FM as a key component, which is crucial for achieving sustainable development goals.

This study aims to investigate the impact of changes in audit processes during the pandemic on auditors’ deviant behavior, considering auditors’ personal characteristics, including demographic variables, spirituality levels and personality traits.

A survey consisting of five parts was used to gather data from auditors employed at Big Four audit firms in the Middle East. The questionnaire collected data on auditors’ perceptions of changes in audit processes and deviant audit behavior during the pandemic, as well as information about their personal characteristics.

The findings revealed a significant positive association between changes in audit processes and a heightened perception of deviant audit conduct during the pandemic. Males and extravert auditors expressed less favorable attitudes toward such behavior.

The sample size was limited to 107 auditors due to the challenges of soliciting responses from auditors during the pandemic. The sole focus on the Big Four audit firms limited the generalizability of the results. Upcoming research should integrate qualitative methods alongside surveys and collect data from larger, more diverse samples to enhance the understanding of the pandemic’s impact on audit behavior.

The findings provide guidance and recommendations for audit firms to mitigate deviant behavior during crises while considering auditors’ personal factors. Recommended strategies include the organization of trainings to raise awareness of these risks and the integration of artificial intelligence to modernize audit processes and enhance audit firms’ readiness to confront future crises.

This study offers a novel empirical investigation into how pandemic-driven changes in audit procedures relate to auditors' deviant behavior, while exploring the influence of auditors' individual traits, an unexplored area in the literature. It addresses this gap specifically in the context of the understudied Middle East region.

Social recommendation has been recognized as a kind of e-commerce with large potential, but how social recommendations influence consumer decisions is still unclear. This paper aims to investigate how recommendations from different social ties influence consumers’ purchase intentions through both behavior and brain activity.

Utilizing behavioral (N = 70) and electroencephalogram (EEG) (N = 49) experiments, this study explored participants’ behavior and brain responses after being recommended by different social ties. The data were analyzed using statistical inference and event-related potential (ERP) analysis.

Behavioral results show that social tie strength positively impacts purchase intention, which can be fitted by a logarithmic model. Moreover, recommender-to-customer similarity and product affect mediate the effect of tie strength on purchase intention serially. EEG findings show that recommendations from weak tie strength elicit larger N100, N200 and P300 amplitudes than those from strong tie strength. These results imply that weak tie strength may motivate individuals to recruit more mental resources in social recommendation, including unconscious processing of consumer attention and conscious processing of cognitive conflict and negative emotion.

This study considers the effects of continuous social ties on purchase intention and models them mathematically, exploring the intrinsic mechanisms by which strong and weak ties influence purchase intentions through recommender-to-customer similarity and product affect, contributing to the applications of the stimulus-organism-response (SOR) model in the field of social recommendation. Furthermore, our study adopting EEG techniques bridges the gap of relying solely on self-report by providing an avenue to obtain relatively objective findings about the consumers’ early-occurred (unconscious) attentional responses and late-occurred (conscious) cognitive and emotional responses in purchase decisions.

Owing to highly volatile and chaotic external events, predicting future movements of cryptocurrencies is a challenging task. This paper advances a granular hybrid predictive modeling framework for predicting the future figures of Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Stellar (XLM) and Tether (USDT) during normal and pandemic regimes.

Initially, the major temporal characteristics of the price series are examined. In the second stage, ensemble empirical mode decomposition (EEMD) and maximal overlap discrete wavelet transformation (MODWT) are used to decompose the original time series into two distinct sets of granular subseries. In the third stage, long- and short-term memory network (LSTM) and extreme gradient boosting (XGB) are applied to the decomposed subseries to estimate the initial forecasts. Lastly, sequential quadratic programming (SQP) is used to fetch the forecast by combining the initial forecasts.

Rigorous performance assessment and the outcome of the Diebold-Mariano’s pairwise statistical test demonstrate the efficacy of the suggested predictive framework. The framework yields commendable predictive performance during the COVID-19 pandemic timeline explicitly as well. Future trends of BTC and ETH are found to be relatively easier to predict, while USDT is relatively difficult to predict.

The robustness of the proposed framework can be leveraged for practical trading and managing investment in crypto market. Empirical properties of the temporal dynamics of chosen cryptocurrencies provide deeper insights.

This study aims to investigate the efficiency of Indian commercial banks from 2002 to 2018 using the stochastic frontier analysis.

This study uses the parametric approach of the stochastic frontier to examine the technical efficiency of banks acknowledging exogenous shocks, omitted variables and measurement errors, filling a gap in the existing financial literature. The scope of this study was constrained to 71 scheduled commercial banks to make it manageable and productive with 1,036 observations.

The results show that the mean technical efficiency of new private banks remained constant at 92.7% during the study period because of technology diffusion in banking systems. The technical efficiency of the nationalized, old private and foreign banks has enhanced over the period because of the efficient utilization of various innovative information technology services such as mobile banking, cheque truncation system, magnetic ink character recognition. However, the foreign banks are still laggards with a mean technical efficiency of 81.7%. The empirical findings suggest that new private sector banks depict higher efficiency than nationalized, old private and foreign banks.

This study’s sample represents all categories of banks (public, private and foreign) including the banks that merged or consolidated during the period of study. To achieve the desired results, the authors incorporate the consolidated and merged banks in their data set. Further, the authors excluded all scheduled small finance banks and scheduled payment banks from their analysis, as these entities commenced operations post-2015. Additionally, the authors also excluded regional rural banks because of their distinct mandate aimed at servicing the rural populace and agricultural sector.

This study contributes to the literature on the performance of conventional banks in general and emerging markets, in particular, using the most recent data and covering a relatively long period using the stochastic frontier approach.

Safety warnings remind construction workers about dangers and guide them to take necessary actions to avoid potential injuries, which could encourage their safe behavior. Workers’ behavior compliance with the safety warnings would be impacted by the risk perception levels induced by the warnings. This study aims to examine whether the design of safety warnings would impact the induced risk perception of workers

This study compared the risk perception levels of construction workers when processing two forms of safety warnings, i.e., safety signs and safety comics, which are commonly used in construction workplaces. Construction workers (n = 20) volunteered for an experiment with an implicit paradigm to probe how they perceive these safety warnings, using event-related potentials (ERPs) features collected by an electroencephalogram (EEG) sensor to indicate the risk perception level

The results demonstrated that the design of safety warnings would impact the induced risk perception. The safety signs and safety comics performed differently in inducing the workers’ risk perception. The safety signs representing prohibition and caution warnings induced significantly higher risk perception than the comics, and there were no significant differences regarding direction warnings

This is the first study to compare the risk perception levels between various forms of safety warnings presenting safety information in different ways. The findings would help to expand the knowledge of the relationship between the design of safety warnings and workers’ safety behavioral compliance

This study aims to explore how the utilization of 3D virtual experiences and social media improve overall gastro-tourism experiences throughout the tourist journeys.

This study employs a mixed-methods approach. By combining self-reported surveys and EEG tracking, this study is able to rigorously unravel Gen Z’s experience and emotions in enjoying tech-enabled gastro-tourism activities.

Showcasing 3D virtual experiences and TikTok heightened customer expectations in the pre-visit stage. The 3D virtual attractions effectively enhanced tourists’ excitement and positive emotions during on-site gastronomic experiences, and subsequently manifested into a long-term impact on future actual visit intention.

This study contributes to the fields of information technology and tourism by examining how digital technologies affect Gen Z’s behavior and enhance the gastro-tourism experience starting from information search, moving to on-site experiences and subsequently affect their post-purchase behavior.

This paper aims to explore recent advances in drift compensation algorithms for Electronic Nose (E-nose) technology and addresses sensor drift challenges through offline, online and neural network-based strategies. It offers a comprehensive review and covers causes of drift, compensation methods and future directions. This synthesis provides insights for enhancing the reliability and effectiveness of E-nose systems in drift issues.

The article adopts a comprehensive approach and systematically explores the causes of sensor drift in E-nose systems and proposes various compensation strategies. It covers both offline and online compensation methods, as well as neural network-based approaches, and provides a holistic view of the available techniques.

The article provides a comprehensive overview of drift compensation algorithms for E-nose technology and consolidates recent research insights. It addresses challenges like sensor calibration and algorithm complexity, while discussing future directions. Readers gain an understanding of the current state-of-the-art and emerging trends in electronic olfaction.

This article presents a comprehensive review of the latest advancements in drift compensation algorithms for electronic nose technology and covers the causes of drift, offline drift compensation algorithms, online drift compensation algorithms and neural network drift compensation algorithms. The article also summarizes and discusses the current challenges and future directions of drift compensation algorithms in electronic nose systems.