Explore the vast range of titles available.

Quality of work life (QWL) is important for individual and organizational success.  Post-pandemic times have brought the challenge of declined, yet continued Covid-19 cases among the working Pakistani population. Stigma due to Covid-19 and thus deteriorated QWL in such individuals is plausible. Hence, elucidating factors that enhance the possible damaged quality of work life among Covid-19 stigmatized individuals remains an essential question for managers and practitioners. Drawing on the Social Identity theory, we hypothesize that QWL is damaged in Covid-19 recovered, stigmatized working individuals through deterioration in self-esteem as such individuals are prone to influences from the societal groups that they psychologically identify with. We also argue that self-efficacy buffers this negative effect. Albeit several researches have probed into relations of QWL with stigma-related antecedents and moderation and mediation mechanisms that underlie these effect, but this study investigates self-esteem and self-efficacy as mediator and moderator, respectively, of the Covid-19 related stigma and quality of work life relationship through the lens of the Social Identity Theory. Employing a correlational framework using purposive sampling, we obtained data from 133 working individuals who had been tested positive with Covid-19 in public and private hospitals and health centers of Pakistan during November 2022 – November 2023, had completely recovered and joined their professional lives. Results confirmed study hypotheses, suggesting that stigma related to Covid-19 damages QWL, this relationship is mediated by self-esteem, while self-efficacy buffers the relationship between Covid-19 related stigma and self-esteem. Such individuals and their managers need to collaboratively work in order to reduce effects of this stigma, and undo its damaging effects on self-esteem and QWL. In this regard, boosting self-efficacy plays a supporting role as it acts as a coping mechanism in the inter-play between Covid-19 stigma, self-esteem, and QWL.

Carbon dioxide (CO 2 ) is mainly universal greenhouse gas associated with climate change. However, beyond CO 2 , some other greenhouse gases (GHGs) like methane (CH 4 ) and nitrous oxide (N 2 O), being two notable gases, contribute to global warming. Since 1900, the concentrations of CO 2 and non-CO 2 GHG emissions have been elevating, and due to the effects of the previous industrial revolution which is responsible for climate forcing. Globally, emissions of CO 2 , CH 4 , and N 2 O from agricultural sectors are increasing as around 1% annually. Moreover, deforestation also contributes 12–17% of total global GHGs. Perhaps, the average temperature is likely to increase globally, at least 2 °C by 2100—by mid-century. These circumstances are responsible for climate forcing, which is the source of various human health diseases and environmental risks. From agricultural soils, rhizospheric microbial communities have a significant role in the emissions of greenhouse gases. Every year, microbial communities release approximately 1.5–3 billion tons of carbon into the atmospheric environment. Microbial nitrification, denitrification, and respiration are the essential processes that affect the nitrogen cycle in the terrestrial environment. In the twenty-first century, climate change is the major threat faced by human beings. Climate change adversely influences human health to cause numerous diseases due to their direct association with climate change. This review highlights the different anthropogenic GHG emission sources, the response of microbial communities to climate change, climate forcing potential, and mitigation strategies through different agricultural management approaches and microbial communities.

The World Health Organization declared the outbreak of the novel coronavirus (COVID-19) as a pandemic on March 11, 2020. Due to the global threat, many countries impose immediate lockdown. The impact of lockdown on the environmental pollutants and climate indicators gained considerable attention in the literature. This study aims to describe the variations in the environmental pollutants (CO, NO2, SO2, PM2.5 and PM10) with and without the lockdown period in the majorly hit states and provinces of the USA and China, respectively. Data during the first quarter year of 2019 and 2020 (lockdown period) was used in this study. Moreover, the effect of these pollutants on the pandemic spread was also studied. The results illustrated that the overall concentrations of CO, NO2 and PM2.5 were decreased by 19.28%, 36.7% and 1.10%, respectively, while PM10 and SO2 were increased by 27.81% and 3.81% respectively in five selected states of the USA during the lockdown period. However, in the case of chosen provinces of China, overall, the concentrations of all selected pollutants, i.e., CO, NO2, SO2, PM2.5 and PM10, were reduced by 26.53%, 38.98%, 18.36%, 17.78% and 37.85%, respectively. The COVID-19 reported cases and deaths were significantly correlated with NO2, PM2.5 and PM10 in both China and the USA. The findings of this study concluded that the limited anthropogenic activities in the lockdown situation due to this novel pandemic disease result in a significant improvement of air quality by reducing the concentrations of environmental pollutants. As the trend goes on, the reduction of most pollutant concentrations is expected as long as partial or complete lockdown goes on.

Purpose Cucumber fermentation is traditionally done using lactic acid bacteria. The involvement of probiotic cultures in food fermentation guarantees enhanced organoleptic properties and protects food from spoilage. Methods Autochthonous lactic acid bacteria were isolated from spontaneously fermented cucumber and identified to species level. Only strains adjudged as safe for human consumption were examined for their technological and functional characteristics. Strain efficiency was based on maintaining high numbers of viable cells during simulated GIT conditions and fermentation, significant antioxidant activity, EPS production, nitrite degradation, and antimicrobial ability against Gram-positive and Gram-negative foodborne pathogens. Result Two strains, Lactiplantibacillus plantarum NPL 1258 and Pediococcus pentosaceus NPL 1264, showing a suite of promising functional and technological attributes, were selected as a mixed-species starter for carrying out a controlled lactic acid fermentations of a native cucumber variety. This consortium showed a faster lactic acid-based acidification with more viable cells, at 4% NaCl and 0.2% inulin (w/v) relative to its constituent strains when tested individually. Sensory evaluation rated the lactofermented cucumber acceptable based on texture, taste, aroma, and aftertaste. Conclusion The results suggest that the autochthonous LAB starter cultures can shorten the fermentation cycle and reduce pathogenic organism’ population, thus improving the shelf life and quality of fermented cucumber. The development of these new industrial starters would increase the competitiveness of production and open the country’s frontiers in the fermented vegetable market.

This study aimed at investigating whether job insecurity and turnover intentions are outcomes of workplace bullying, and whether psychological capital is a moderator of relationships of workplace bullying with job insecurity and with turnover intentions among telecom, banking and healthcare sectors in Pakistan. Employing snowball sampling method, we drew a sample of 300 respondents from various firms of Pakistan using a cross-section study design. Study instruments included the Negative Acts Questionnaire, (Einarsen et al., 2009) the Psychological Capital Questionnaire, (Luthans et al., 2007) the Job Insecurity Scale, (Ashford et al., 1989) and three items each from Singh et al. (1996) and Camman et al. (1979) quitting intentions scales. Data were analysed using correlation, regression, and moderation techniques. Results showed that workplace bullying prompts job insecurity and quitting intentions in bullied employees and psychological capital acts as an important resource by offering a buffering mechanism that offsets the undesirable impact of workplace bullying on job insecurity and quitting intentions. This study mainly highlights the instrumentality of psychological capital as a positive psychological resource to the negative impact of work-place bullying on job insecurity and quitting intentions. This study makes a novel contribution to literature by testing for buffering effect of psychological capital within bullying prone work contexts in Pakistan, and offers psychological capital as a preemptive individual-level coping mechanism bullying-prone work settings.

Judging from persistent changes, drive for performance and widespread uncertainty that characterize the Pakistani higher education system, this study sought to confirm whether workplace bullying – a by-product of relentless change – triggers job insecurity and counterproductive work behaviours in the bullied faculty, and whether these damaging outcomes are moderated by work engagement. Using convenience sampling, we sought data from 337 faculty members from the higher education sector. Analysis confirmed that bullying triggers job insecurity and counterproductive work behaviours while mixed findings emerged for the hypothesized moderation effects of work engagement. The study mainly stresses infusing work engagement within a work environment where bullying prevails. Managers may design jobs to augment engagement in a pressurized work environment with an aim to curtail job insecurity and counterproductive work behaviours for sustained performance in a changing work environment.

Monopolar transurethral resection of the prostate (TURP) is a common surgical procedure for benign prostatic hyperplasia, often associated with significant intraoperative blood loss. Dutasteride, a 5-alpha reductase inhibitor, has been recommended to reduce perioperative bleeding by decreasing vascularity within the prostate. The purpose of this study was to investigate the impact of pre-operative administration of dutasteride for a duration of two weeks on the reduction of intra-operative blood loss in patients undergoing monopolar TURP. This prospective, two-armed, quasi-experimental study enrolled 132 patients based on the specified inclusion criteria. Patients who fulfilled the criteria for monopolar TURP were administered 0.5mg dutasteride for two weeks prior to their TURP procedure. Afterward, these patients were admitted to the hospital ward and underwent the necessary preparations for the surgery. During the surgical procedure, the intra-operative irrigation fluid was quantified and collected and the hemoglobin (Hb) was tested. The amount of blood loss was then determined using an appropriate equation. The assessment of blood loss was conducted using several indicators, including the analysis of irrigation fluid, the measurement of Hb levels in the irrigation fluid, the preoperative Hb levels, and the weight of the resected tissue. A significant decline in blood loss was observed in the interventional group in comparison to the control group. The average blood loss observed in Group A was 296ml, whereas in Group B it was 370ml. Furthermore, the blood loss per gram was found to be 11.7ml/g in Group A and 14.7ml/g in Group B. The mean operative time for Group A was recorded as 42 minutes, while Group B had a mean operative time of 49 minutes. The study's findings indicate a significant superiority of administering dutasteride before surgery for a duration of two weeks. A substantial reduction in both intraoperative blood loss and blood loss per gram is seen in patients who underwent monopolar TURP with dutasteride.

Post-pandemic times have brought the challenge of declined, yet continued Covid-19 cases among the working Pakistani workforce. Social stigma related with Covid-19 and deteriorated employee engagement in such individuals is plausible. As employee engagement is an important indicator of individual and organizational success, elucidating factors that enhance the possible damaged engagement among socially stigmatized Covid-19 individuals remains an enigma for managers and practitioners. Drawing on the Social Determination Theory, (SDT) the present study hypothesizes that employee engagement in Covid-19 recovered, socially stigmatized working individuals is buffered by (employee) confidence. Several earlier studies that have probed into antecedents of employee engagement have largely focused on work-related aspects, while social aspects including social stigmatization and buffering mechanisms that underlie these effects have been overlooked. To address these literature gaps, this study investigates employee engagement as an outcome of Covid-19 related stigma and also investigates confidence as a moderator of the Covid-19 related stigma- employee engagement relationship. Employing a correlational framework using purposive sampling, data were obtained from 133 working individuals who had been tested positive with Covid-19 in public and private hospitals and health centers of Pakistan during November 2022 till March 2024, had completely recovered and joined their professional lives. Results confirmed study hypotheses, suggesting that stigma related to Covid-19 damages employee engagement, and that confidence is a moderator of this relationship. Such individuals and their managers need to collaboratively work in order to reduce effects of this social stigma and undo its negative effects on employee engagement. Theoretical and practical implication, future research suggestions are also offered at the end.

In recent years, deep convolutional neural network (DCNN) models have shown great promise in the automated detection of brain stroke from CT scan images. However, existing DCNN models may not be optimized for early detection of stroke. In this study, we present a novel DCNN model for the early detection of brain stroke using CT scan images. The proposed DCNN model consists of three main components: a feature extractor, a feature fusion module, and a stroke detection module. The feature extractor consists of multiple convolutional and pooling layers that extract high-level features from the input CT scan images. The feature fusion module combines the features extracted from the different layers of the feature extractor to create a more informative representation of the input image. The stroke detection module uses the fused features to detect the presence of stroke in the image. To evaluate the proposed model, we used a dataset of CT scan images from stroke patients and healthy controls. The dataset was divided into training and testing sets, and the model was trained using a combination of supervised and unsupervised learning techniques. We compared the performance of the proposed model to several state-of-the-art DCNN models for stroke detection, including VGG16, ResNet50, and InceptionV3. The results of our experiments showed that the proposed model achieved a higher accuracy, sensitivity, and specificity than the other DCNN models for stroke detection. The proposed model also outperformed existing methods for early detection of stroke, achieving an accuracy of 96.5% in detecting stroke within 6 hours of onset. Our code is available at (“ https://github.com/FatimaAyub12/DCNN ”).

The cell phone is assuming a crucial job in present day life. It offers types of assistance and applications, for example, location tracking, medical applications, and human activity examination. All android smartphones have motion sensors i.e. Accelerometer, gyroscope, in order to detect motion of a user in a very precise way. In early conditions, committed sensors were utilized for activity acknowledgment. Different techniques are developed for distinguishing normal or human activities scenes in the crowd by processing the video or an image. A novel KNN-SVM human activity detection method is proposed to detect human activities in the UCI dataset for complex multi-process physical activities. Model trained with machine learning algorithms to capture the temporal dependency, normal sequences with high dimension is uniformly utilized to train the model to discriminate each activity. In the classification process, 2 different efficient classifiers are applied to identify the types of human activities in the UCI dataset. Support Vector Machine and K-Nearest Neighbour are applied in the proposed method for the classification. The efficiency of each classifiers is about 85% to 87%. The classification efficiency is comparable with existing literature after applying the majority decision in these classification techniques.