This study examines the relationship between leadership coaching behavior and team knowledge creation, and its subsequent impact on team environmental performance in the tourism sector. Moreover, this study investigates the moderating role of organizational learning culture in the relationship between team knowledge creation and team environmental performance. Data were collected from 356 employees and their immediate supervisors, nested in 78 teams. The analysis employed moderated mediation model utilizing SPSS and AMOS. The findings indicate that leadership coaching behavior exerts a significant (...) direct positive influence on knowledge creation, which, in turn, positively affects team environmental performance. Additionally, the results reveal that the organizational learning culture moderates the relationship between leadership coaching behavior and knowledge creation, as well as the indirect relationship between leadership coaching behavior and team environmental performance via knowledge creation. The theoretical framework of social exchange theory provides support for this study. The insights generated from this research offer valuable guidance for managers and policymakers seeking to enhance their team's environmental performance through leadership coaching and knowledge creation. (shrink)

Quality assurance in higher education is a hot topic with theincreased number of students graduating from postgraduate programmesoffered by the higher education institutions. This qualitative study aimed tolook at the history and the current status of the quality assurance mechanismsin the Maldives; identify its strengths and weaknesses; and make informedrecommendations. Critical event narrative inquiry was used and data werecollected through interviews and analysis of documents. Content analysis ofdata collected revealed that Maldives was one of the very first countries inthe region (...) to establish such a mechanism and it was very similar to that ofAustralia. The most significant finding was that the body mandated to assurehigher education quality and did not have any power or regulatory authority.Therefore, it is important that the authority be an independent body establishedby an act of parliament with the regulatory power. (shrink)

Human gait is a complex activity that requires high coordination between the central nervous system, the limb, and the musculoskeletal system. More research is needed to understand the latter coordination's complexity in designing better and more effective rehabilitation strategies for gait disorders. Electroencephalogram and functional near-infrared spectroscopy are among the most used technologies for monitoring brain activities due to portability, non-invasiveness, and relatively low cost compared to others. Fusing EEG and fNIRS is a well-known and established methodology proven to enhance (...) brain–computer interface performance in terms of classification accuracy, number of control commands, and response time. Although there has been significant research exploring hybrid BCI involving both EEG and fNIRS for different types of tasks and human activities, human gait remains still underinvestigated. In this article, we aim to shed light on the recent development in the analysis of human gait using a hybrid EEG-fNIRS-based BCI system. The current review has followed guidelines of preferred reporting items for systematic reviews and meta-Analyses during the data collection and selection phase. In this review, we put a particular focus on the commonly used signal processing and machine learning algorithms, as well as survey the potential applications of gait analysis. We distill some of the critical findings of this survey as follows. First, hardware specifications and experimental paradigms should be carefully considered because of their direct impact on the quality of gait assessment. Second, since both modalities, EEG and fNIRS, are sensitive to motion artifacts, instrumental, and physiological noises, there is a quest for more robust and sophisticated signal processing algorithms. Third, hybrid temporal and spatial features, obtained by virtue of fusing EEG and fNIRS and associated with cortical activation, can help better identify the correlation between brain activation and gait. In conclusion, hBCI system is not yet much explored for the lower limb due to its complexity compared to the higher limb. Existing BCI systems for gait monitoring tend to only focus on one modality. We foresee a vast potential in adopting hBCI in gait analysis. Imminent technical breakthroughs are expected using hybrid EEG-fNIRS-based BCI for gait to control assistive devices and Monitor neuro-plasticity in neuro-rehabilitation. However, although those hybrid systems perform well in a controlled experimental environment when it comes to adopting them as a certified medical device in real-life clinical applications, there is still a long way to go. (shrink)

The provisions of United Nations’ Convention on the Elimination of All Forms of Discrimination against Women (CEDAW) provisions and the CEDAW Committee’s recommendations expand on the theoretical and practical ways in which countries can combat gender-based discrimination. In Pakistan, the digitisation of women and feminist collectives and their experience of violent misogyny on the internet accentuates the weakness of the country’s internet security mechanisms. This study utilises the human rights framework of CEDAW to assess the performance of Pakistan’s internet security (...) mechanisms, and highlights the state’s obligations towards women’s digital safety. Informed by the CEDAW’s provisions and the Committee’s contributions, this paper provides recommendations to combat gender-based violence and improve Pakistan’s internet security mechanisms. (shrink)

This abstract presents a novel brain-computer interface (BCI) framework to control a prosthetic leg, for the rehabilitation of patients suffering from locomotive disorders, using functional near-infrared spectroscopy (fNIRS). fNIRS signals corresponding to walking intention and rest are used to initiate and stop the gait cycle and a nonlinear proportional derivative computed torque controller (PD-CTC) with gravity compensation is used to control torques of hip and knee joints for minimization of position error. The brain signals of walking intention and rest tasks (...) are acquired from primary motor cortex in the left hemisphere for nine subjects. After acquiring brain signals, in order to remove motion artifacts and physiological noises, the performance of six different filters i.e. Kalman, Wiener, Gaussian, hemodynamic response filter (hrf), Band-pass and finite impulse response, is evaluated. Afterwards, six different features are extracted from oxygenated hemoglobin signals and their different combinations were used for classification. The classification performance of five different classifiers i.e. artificial neural network, quadratic discriminant analysis, linear discriminant analysis (LDA), Naïve Bayes and support vector machine (SVM) is used. The classification accuracies obtained from SVM using hrf filter are significantly higher (p < 0.005) than the other combinations of classifier and filters. These accuracies are 77.5%, 72.5%, 68.3%, 74.2%, 73.3%, 80.8%, 65%, 76.7%, and 86.7% for all nine subjects. The control commands generated using classifier initiate and stop the gait cycle of the prosthetic leg whose knee and hip torques are controlled using PD-CTC to minimize position error. The proposed scheme can be effectively used for neurofeedback training and rehabilitation of lower limb amputation of paralyzed patients. (shrink)

Accurate skin lesion segmentation is an important task in dermatology for facilitating early diagnosis and treatment planning. The challenges in skin lesion segmentation comprehend the variability in lesion, low contrast, heterogeneous backgrounds, overlapping or connected lesions, noise and certain artifacts. Despite of these challenges, Deep learning models accomplish remarkable results for skin lesion segmentation by automatically learning discriminative features. The current research introduces a novel approach utilizing the ASSP-based Deeplabv3+ for skin lesion segmentation along with other UNET-based learners while employing (...) VGG-16, VGG-19 and Dense nets as encoders. In addition, an analysis is conducted on GAN-UNET to evaluate the potential of Generative Artificial Intelligence in generating segmented images of skin lesions. Three benchmark medical image datasets, namely ISIC-2016, ISIC-2018, and HAM10000 Lesion Boundary Segmentation, are used to evaluate all five models. The models are trained exclusively on the ISIC-2018 dataset. A comparative analysis is performed, comparing the performance of these models against state-of-the-art segmentation methods, focusing on standard computer vision metrics. The proposed Deeplabv3+ model outperforms by showcasing its ability to accurately delineate skin lesions and surpassing existing techniques in terms of segmentation accuracy as 0.97, Jaccard coefficient as 0.84 and dice coefficient as 0.91. (shrink)

A training physician has his first interaction with a pharmaceutical representative during medical school. Medical students are often provided with small gifts such as pens, calendars and books, as well as free lunches as part of drug promotion offers. Ethical impact of these transactions as perceived by young medical students has not been investigated in Pakistan before. This study aimed to assess the association of socio-demographic variables with the attitudes of medical students towards pharmaceutical companies and their incentives.

Although the role of leadership in fostering employee creativity has been extensively studied, it is still unclear whether and how leader humor affects employee creativity. Drawing upon cultural representation theory, we examined creative self-efficacy as a mediator and traditionality as a situational factor in the relationship between leader humor and employee creativity by analyzing a sample of 306 employees and 88 leaders collected through survey questionnaire from firms based in Hubei Province, China, covering the industries of automobile, IT, and medicine. (...) Following the multi-level examination, leader humor was positively related to employee creativity, and creative self-efficacy was found to mediate the impact of leader humor on employee creativity. Furthermore, traditionality moderated the effect of leader humor on creative self-efficacy, as well as the indirect effect of leader humor on employee creativity through creative self-efficacy. This study provides a social psychological explanation for the association between humor and employee creativity, deepens the current understanding of the influence process of leader humor. Finally, the theoretical and practical implications of the study are discussed at the end alongside limitations and recommendations for future research. (shrink)

A passive brain–computer interface based upon functional near-infrared spectroscopy brain signals is used for earlier detection of human drowsiness during driving tasks. This BCI modality acquired hemodynamic signals of 13 healthy subjects from the right dorsolateral prefrontal cortex of the brain. Drowsiness activity is recorded using a continuous-wave fNIRS system and eight channels over the right DPFC. During the experiment, sleep-deprived subjects drove a vehicle in a driving simulator while their cerebral oxygen regulation state was continuously measured. Vector phase analysis (...) was used as a classifier to detect drowsiness state along with sleep stage-based threshold criteria. Extensive training and testing with various feature sets and classifiers are done to justify the adaptation of threshold criteria for any subject without requiring recalibration. Three statistical features along with six VPA features were used. The average accuracies for the five classifiers are 90.9% for discriminant analysis, 92.5% for support vector machines, 92.3% for nearest neighbors, 92.4% for both decision trees, and ensembles over all subjects’ data. Trajectory slopes of CORE vector magnitude and angle: m and m are the best-performing features, along with ensemble classifier with the highest accuracy of 95.3% and minimum computation time of 40 ms. The statistical significance of the results is validated with a p-value of less than 0.05. The proposed passive BCI scheme demonstrates a promising technique for online drowsiness detection using VPA along with sleep stage classification. (shrink)

This paper analyzes the impact of the COVID-19 pandemic on the mental health of the teachers, specifically the techno stress arising in them as a result of issues faced by them in the use of technology when they conduct the online academic activities. It aims to assess the major factors related to the online teaching that specifically adds to techno stress on the teachers during the COVID-19 outbreak. Finally, the study aims to provide suggestions to the policymakers and the management (...) of the universities so that the effect of the COVID-19's on teachers' mental health and the related techno stress can be reduced. This paper is a literature review of the articles on the notion of techno stress on teachers and their mental health by searching the related articles with these terminologies using the renowned search engines of Google Scholar and Web of Science. A combination of the terms such as Coronavirus, COVID-19, mental health, psychological distress, techno stress, and online teaching were used in the article search for the review. The literature has suggested that the COVID-19 outbreak has significantly affected the mental health of the employees in general and specifically, the teachers who are engaged in online academic activities and teaching in the universities. The paper has identified a few factors that are the cause of the techno stress and provides recommendations for the university management and the policy makers for minimizing their negative impact on the teachers, in terms of the techno stress and their mental health. Coronavirus is a new strain of the viruses that has badly engulfed the entire population of the world. It is even now badly rising and causing deaths while this article is in the writing phase. The article has addressed the mental health concerns of the university teachers as they are now working from home using ICT for delivering the lectures and conducting the online teaching and learning activities for the students at their universities. This is a matter of grave importance now and requires immediate attention. Hence, this article broadens the scope of the research on the corona virus and its impact on the university teachers. (shrink)