Public health emergencies and epidemics shatter the assumptions of the world as a safe place. Healthcare workers are at the forefront of such pressures resulting from a persistent threat to their ...safety and well being. It is therefore important to study such mechanisms that can influence and predict the psychological distress of nurses
While there is an increasing number of studies on positive outcomes of leadership styles, their influence on curbing unwanted adverse outcomes is scarce. This study aims to observe the influence of an inclusive leadership style on psychological distress while assessing the mediating role of psychological safety. It uses the theoretical lens of job demands-resources theory and the theory of shattered assumptions to develop and test hypotheses.
Cross-Sectional Study with Temporal Separation
The researchers recruited 451 on-duty registered nurses from 5 hospitals providing patient care during the highly infectious phase of COVID-19 in January 2020 in Wuhan city, the epicentre of the outbreak in China
After obtaining permission from hospital administration, data were collected through an online questionnaire survey in three stages with temporal separation to avoid common method bias. Partial least square structural equation modelling was used to analyze data. The study controlled for effects of age, gender, experience, working hours and education.
Hypothesized relationships proved significant. Inclusive leadership has an inverse relationship with psychological distress with a strong path-coefficient. Psychological safety mediates the relationship between inclusive leadership and psychological distress while explaining 28.6% variance. Multi-group analysis results indicate no significant differences between respondents based on these control variables
Recurring or prolonged experiences of stress and anxiety at the workplace, without a mechanism to counter such effects, can culminate into psychological distress. Inclusive leadership style can serve as such a mechanism to curb psychological distress for healthcare workers by creating a psychologically safe environment.
A number of image encryption techniques have been proposed in recent years. These techniques use either spatial or transform domain image processing. A major challenge when designing an image ...encryption scheme is to conceal the pixel of the input image, especially when the image has a low texture region. Another problem is the encryption computational time. In this paper, these two issues are addressed. As the use of a single substitution box (S-box) to encrypt digital images does not work well for greater as well as a lower number of gray levels. To solve this problem, a new substitution technique using multiple S-boxes with dynamic substitution is proposed. In the second part of this paper, the proposed discrete wavelet transform based scheme is employed to reduce the encryption computational time. A number of parameters like correlation, entropy, energy, contrast, homogeneity, MSE and PSNR are used to analyze the quality of cipher images.
Traumatic events such as a pandemic shatter the assumption of the workplace as a safe place. Nurses face risks of life-threatening infection, which can create psychological distress. Quality of care ...for infected patients depends on mental well-being of nurses which calls for research on predictors of stress among health care workers. Responding to a call for research on the effects of leadership styles on psychological distress during traumatic events, this paper uses the theoretical lens of social exchange theory and contributes to literature on relationships between inclusive leadership, psychological distress, work engagement, and self-sacrifice. Participants of this cross sectional study included 497 registered nurses from five hospitals in Wuhan. Data were collected with temporal separation through an online questionnaire. Partial least-squares structural equation modeling was used to analyze data. Results show inclusive leadership has a significant negative relationship with psychological distress. Work engagement mediates this relationship, and nurses’ self-sacrificial behavior moderates it. Findings indicate inclusive leadership style serves as a sustainable mechanism to reduce psychological distress during pandemics. It can operationalize the delivery of mental health support in real-time in work settings. Results provide empirical support for social exchange theory through high work engagement to help control psychological distress among nurses.
Recent technological developments pave the path for deep learning-based techniques to be used in almost every domain of life. The precision of deep learning techniques make it possible for these to ...be used in the medical field for the classification and detection of various diseases. Recently, the coronavirus (COVID-19) pandemic has put a lot of pressure on the health system all around the world. The diagnosis of COVID-19 is possible by PCR testing and medical imagining. Since COVID-19 is highly contagious, diagnosis using chest X-ray is considered safe in various situations. In this study, a deep learning-based technique is proposed to classify COVID-19 infection from other non-COVID-19 infections. To classify COVID-19, three different pre-trained models named EfficientNetB1, NasNetMobile and MobileNetV2 are used. The augmented dataset is used for training deep learning models while two different training strategies have been used for classification. In this study, not only are the deep learning model fine-tuned but also the hyperparameters are fine-tuned, which significantly improves the performance of the fine-tuned deep learning models. Moreover, the classification head is regularized to improve the performance. For the evaluation of the proposed techniques, several performance parameters are used to gauge the performance. EfficientNetB1 with regularized classification head outperforms the other models. The proposed technique successfully classifies four classes that include COVID-19, viral pneumonia, lung opacity, and normal, with an accuracy of 96.13%. The proposed technique shows superiority in terms of accuracy when compared with recent techniques present in the literature.
The purpose of the present study is to examine the link of Abusive Supervision with Organizational Cynicism i.e. Cognitive, Emotional, or Behavioral Cynicism by focusing on the mediating role of ...Abusive Supervisor's Knowledge Hiding behavior of Playing Dumb in Higher Education Institutions in Pakistan. Data was collected using questionnaire under the survey research design. The participants included 400 faculty and staff members from Higher Education Institutions in Pakistan. Structural Equation Modelling using SmartPLS is used to test the hypothesized relationships of Abusive Supervision and Knowledge Hiding Behavior of Abusive Supervisors with the faculty and staff's Organizational Cynicism behaviors. The results indicate that Abusive Supervision is significantly and positively related with faculty and staff's Cognitive, Emotional, and Behavioral Cynicism. This study also indicates that Knowledge Hiding behavior of Playing Dumb fully mediates the relationship between Abusive Supervision and Cognitive Cynicism and partially mediates the relationship between Abusive Supervision and Behavioral Cynicism. However, Playing Dumb as a knowledge Hiding behaviour does not impact the relationship between Abusive Supervision and Emotional Cynicism. This means that Knowledge Hiding by Playing dumb contributes to the adverse effect of Abusive Supervision, leading to increased Cognitive and Behavioral Cynicism. This study contributes to literature on Organizational Cynicism and Abusive Supervision by studying the link between them and effect of Abusive Supervisor's Knowledge Hiding behavior of Playing Dumb as a mediator. The study also indicates that Abusive Supervision characterized by Knowledge Hiding behavior of Playing Dumb is really a problem in Higher Education Institutions in Pakistan. This study holds importance for top management in Higher Education Institutions in to curb the negative effects of Abusive Supervision by developing a policy framework that ensures prevention of Organizational Cynicism in faculty and staff. Moreover, the policy guidelines should ensure that control of essential resources such as Knowledge in the hands of the Abusive leaders should not be misused, causing Organizational Cynicism and eventually leading to problems such as turnover and psychological and behavioral issues among faculty and staff in Higher Education Institutions of Pakistan.
Effective recognition of fruit leaf diseases has a substantial impact on agro‐based economies. Several fruit diseases exist that badly impact the yield and quality of fruits. A naked‐eye inspection ...of an infected region is a difficult and tedious process; therefore, it is required to have an automated system for accurate recognition of the disease. It is widely understood that low contrast images affect identification and classification accuracy. Here a parallel framework for real‐time apple leaf disease identification and classification is proposed. Initially, a hybrid contrast stretching method to increase the visual impact of an image is proposed and then the MASK RCNN is configured to detect the infected regions. In parallel, the enhanced images are utilized for training a pre‐trained CNN model for features extraction. The Kapur's entropy along MSVM (EaMSVM) approach‐based selection method is developed to select strong features for the final classification. The Plant Village dataset is employed for the experimental process and achieve the best accuracy of 96.6% on the ensemble subspace discriminant analysis (ESDA) classifier. A comparison with the previous techniques illustrates the superiority of the proposed framework.
The COVID-19 outbreak began in December 2019 and has dreadfully affected our lives since then. More than three million lives have been engulfed by this newest member of the corona virus family. With ...the emergence of continuously mutating variants of this virus, it is still indispensable to successfully diagnose the virus at early stages. Although the primary technique for the diagnosis is the PCR test, the non-contact methods utilizing the chest radiographs and CT scans are always preferred. Artificial intelligence, in this regard, plays an essential role in the early and accurate detection of COVID-19 using pulmonary images. In this research, a transfer learning technique with fine tuning was utilized for the detection and classification of COVID-19. Four pre-trained models i.e., VGG16, DenseNet-121, ResNet-50, and MobileNet were used. The aforementioned deep neural networks were trained using the dataset (available on Kaggle) of 7232 (COVID-19 and normal) chest X-ray images. An indigenous dataset of 450 chest X-ray images of Pakistani patients was collected and used for testing and prediction purposes. Various important parameters, e.g., recall, specificity, F1-score, precision, loss graphs, and confusion matrices were calculated to validate the accuracy of the models. The achieved accuracies of VGG16, ResNet-50, DenseNet-121, and MobileNet are 83.27%, 92.48%, 96.49%, and 96.48%, respectively. In order to display feature maps that depict the decomposition process of an input image into various filters, a visualization of the intermediate activations is performed. Finally, the Grad-CAM technique was applied to create class-specific heatmap images in order to highlight the features extracted in the X-ray images. Various optimizers were used for error minimization purposes. DenseNet-121 outperformed the other three models in terms of both accuracy and prediction.
Road detection technology plays an essential role in a variety of applications, such as urban planning, map updating, traffic monitoring and automatic vehicle navigation. Recently, there has been ...much development in detecting roads in high-resolution (HR) satellite images based on semantic segmentation. However, the objects being segmented in such images are of small size, and not all the information in the images is equally important when making a decision. This paper proposes a novel approach to road detection based on semantic segmentation and edge detection. Our approach aims to combine these two techniques to improve road detection, and it produces sharp-pixel segmentation maps, using the segmented masks to generate road edges. In addition, some well-known architectures, such as SegNet, used multi-scale features without refinement; thus, using attention blocks in the encoder to predict fine segmentation masks resulted in finer edges. A combination of weighted cross-entropy loss and the focal Tversky loss as the loss function is also used to deal with the highly imbalanced dataset. We conducted various experiments on two datasets describing real-world datasets covering the three largest regions in Saudi Arabia and Massachusetts. The results demonstrated that the proposed method of encoding HR feature maps effectively predicts sharp segmentation masks to facilitate accurate edge detection, even against a harsh and complicated background.
Transition metal dichalcogenides such as molybdenum disulfide (MoS2) were classified as excellent solid self-lubricant materials in vacuum or dry conditions. However, their poor mechanical and ...oxidation resistance properties limited their applications when used in humid air or at elevated temperature. In this paper, nitrogen was incorporated into the molybdenum disulfide matrix to synthesize the Mo–S–N films using RF magnetron sputtering system to improve above mentioned properties. The results showed the deposited nitrogen-free film was MoSx (x < 2), and due to its substoichiometry with respect to sulfur, both amorphous and crystalline state co-existed in MoSx film. Incorporation nitrogen into MoSx matrix induced the poor crystallinity and the formation of Mo–N, S–N and N–N bonds. All Mo–S–N films exhibit higher hardness and oxidation resistance temperature than that of MoSx mainly due to the Mo–N bonds. By comparison with MoSx film, all Mo–S–N films also had excellent tribological properties in the air at room temperature, 200 and 400 °C.
Display omitted
•All Mo–S–N films exhibited higher hardness and oxidation resistance temperature than that of MoSx film.•Addition of nitrogen into MoSx matrix could improve room temperature tribological properties in humid air significantly.•Addition of nitrogen broadened the film's effective service temperature range from 200 to 400 °C.
•The corrosion characteristics of liquid metal flow were simulated on the DCLL blanket.•Wall corrosion declined employing nano-coatings in comparison to that of uncoated one.•FCI utilization causes a ...sharp deterioration on the corrosion of the DCLL blanket.
The corrosive behaviors of liquid metal fluid flow on the channel wall of Dual Coolant Liquid (DCLL) blanket of fusion reactor were simulated by imposing the discrete phase model (DPM) under different magnetic field strength, inlet velocities and neutron heat sources while considering blanket channel walls with and without non-wetting nanomaterial coating conditions. Results showed that regardless of the external environment of the blanket (except in the absence of magnetic field), the corrosion of the inner walls of the blanket channel declined significantly after employing nano-coatings in comparison to that of uncoated wall. For the uncoated condition, channel wall corrosion rate increased exponentially with time, various parameters have significant influence on channel corrosion, and the effect of magnetic field intensity is highest among them. In contrast, the corrosion rate of the channel walls with nanomaterial coating increased linearly with time, and unlike uncoated wall there is little difference on the corrosion rate of the channel walls for different parameters. Hence, these minute alterations in the corrosion of the channel walls with varying parameters indicate that the nanomaterial coating layer has a substantial protective effect on the blanket wall structure.
PDF
