Positive Stereotyping Could Be Reasoned to Workplace Intergenerational Retention: A Study of Three Generations in the Health Sector of Pakistan

Purpose: The research on intergenerational work environment has attracted researchers in past decades variously and seems valuable in the present time era. The purpose of the present research is to examine the effect of positive stereotyping on intergenerational retention while organizational commitment plays mediating role in this relationship. Methodology: The sample consisted of 206 nurses from hospitals operating under the Punjab health department and the convenience sampling technique was used based on the cross-sectional design. The quantitative survey was conducted to assess the role of organizational commitment between positive stereotypes and workplace inter-generational retention. Findings: The results of the current study were analyzed on SMART PLS 3.2.2 software to predict reliability, assess the structural model, and hypothesized relationships between variables. Obtained results show that positive stereotyping has a significant direct effect on intergenerational retentions. Further organizational commitment significant positively mediates this relationship. Conclusion: Drawing upon generational cohort theory the research highlights the positive role of stereotyping among various generations at the workplace and recommends to the retention of educators is more positive stereotyping among various age group employee

Positive Stereotyping Could Be Reasoned to Workplace Intergenerational Retention: A Study of Three Generations in the Health Sector of Pakistan

Purpose: The research on intergenerational work environment has attracted researchers in past decades variously and seems valuable in the present time era. The purpose of the present research is to examine the effect of positive stereotyping on intergenerational retention while organizational commitment plays mediating role in this relationship. Methodology: The sample consisted of 206 nurses from hospitals operating under the Punjab health department and the convenience sampling technique was used based on the cross-sectional design. The quantitative survey was conducted to assess the role of organizational commitment between positive stereotypes and workplace inter-generational retention. Findings: The results of the current study were analyzed on SMART PLS 3.2.2 software to predict reliability, assess the structural model, and hypothesized relationships between variables. Obtained results show that positive stereotyping has a significant direct effect on intergenerational retentions. Further organizational commitment significant positively mediates this relationship. Conclusion: Drawing upon generational cohort theory the research highlights the positive role of stereotyping among various generations at the workplace and recommends to the retention of educators is more positive stereotyping among various age group employee

MAESTROS: multi-agent simulation of rework in Open Source software

Rework Management in software development is a challenging and complexissue. Defined as the effort spent to re-do some work, rework implies big costsgiven the fact that the time spent on rework does not count to the improvement of theproject. Predicting and controlling rework causes is a valuable asset for companies,which maintain closed policies on choosing team members and assigning activitiesto developers. However, a trending growth in development consists in Open SourceSoftware (OSS) projects. This is a totally new and diverse environment, in the sensethat not only the projects but also their resources, e.g., developers change dynamically.There is no guarantee that developers will follow the same methodologiesand quality policies as in a traditional and closed project. In such world, identifyingrework causes is a necessary step to reduce project costs and to help projectmanagers to better define their strategies. We observed that in real OSS projectsthere are no fixed team, but instead, developers assume some kind of auction inwhich the activities are assigned to the most interested and less-cost developer. Thislead us to think that a more complex auctioning mechanism should not only modelthe task allocation problem, but also consider some other factors related to reworkcauses. By doing this, we could optimise the task allocation, improving the developmentof the project and reducing rework. In this paper we presented MAESTROS,a Multi-Agent System that implements an auction mechanism for simulating taskallocation in OSS. Experiments were conducted to measure costs and rework withdifferent project characteristics. We analysed the impact of introducing a Q-learningreinforcement algorithm on reducing costs and rework. Our findings correspond to a reduction of 31 % in costs and 11 % in rework when compared with the simpleapproach. Improvements to MAESTROS include real projects data analysis and areal-time mechanism to support Project Management decisions

Molecular epidemiology of hcv among health care workers of khyber pakhtunkhwa

<p>Abstract</p> <p>Background</p> <p>Studies of the molecular epidemiology and risk factors for hepatitis C virus (HCV) in health care workers (HCWs) of Peshawar, Khyber Pakhtunkhwa region are scarce. Lack of awareness about the transmission of HCV and regular blood screening is contributing a great deal towards the spread of hepatitis C. This study is an attempt to investigate the prevalence of HCV and its possible association with both occupational and non-occupational risk factors among the HCWs of Peshawar.</p> <p>Results</p> <p>Blood samples of 824 HCWs, aged between 20-59 years were analysed for anti-HCV antibodies, HCV RNA and HCV genotypes by Immunochromatographic tests and PCR. All relevant information was obtained from the HCWs with the help of a questionnaire. The study revealed that 4.13% of the HCWs were positive for HCV antibodies, while HCV RNA was detected in 2.79% of the individuals. The most predominant HCV genotype was 3a and 2a.</p> <p>Conclusion</p> <p>A program for education about occupational risk factors and regular blood screening must be implemented in all healthcare setups of Khyber Pakhtunkhwa province in order to help reduce the burden of HCV infection.</p

Rising burden of Hepatitis C Virus in hemodialysis patients

<p>Abstract</p> <p>Aim</p> <p>High prevalence of Hepatitis C virus (HCV) has been reported among the dialysis patients throughout the world. No serious efforts were taken to investigate HCV in patients undergoing hemodialysis (HD) treatment who are at great increased risk to HCV. HCV genotypes are important in the study of epidemiology, pathogenesis and reaction to antiviral therapy. This study was performed to investigate the prevalence of active HCV infection, HCV genotypes and to assess risk factors associated with HCV genotype infection in HD patients of Khyber Pakhtunkhwa as well as comparing this prevalence data with past studies in Pakistan.</p> <p>Methods</p> <p>Polymerase chain reaction was performed for HCV RNA detection and genotyping in 384 HD patients. The data obtained was compared with available past studies from Pakistan.</p> <p>Results</p> <p>Anti HCV antibodies were observed in 112 (29.2%), of whom 90 (80.4%) were HCV RNA positive. In rest of the anti HCV negative patients, HCV RNA was detected in 16 (5.9%) patients. The dominant HCV genotypes in HCV infected HD patients were found to be 3a (n = 36), 3b (n = 20), 1a (n = 16), 2a (n = 10), 2b (n = 2), 1b (n = 4), 4a (n = 2), untypeable (n = 10) and mixed (n = 12) genotype.</p> <p>Conclusion</p> <p>This study suggesting that i) the prevalence of HCV does not differentiate between past and present infection and continued to be elevated ii) HD patients may be a risk for HCV due to the involvement of multiple routes of infections especially poor blood screening of transfused blood and low standard of dialysis procedures in Pakistan and iii) need to apply infection control practice.</p

Genomic investigations of unexplained acute hepatitis in children

Since its first identification in Scotland, over 1,000 cases of unexplained paediatric hepatitis in children have been reported worldwide, including 278 cases in the UK1. Here we report an investigation of 38 cases, 66 age-matched immunocompetent controls and 21 immunocompromised comparator participants, using a combination of genomic, transcriptomic, proteomic and immunohistochemical methods. We detected high levels of adeno-associated virus 2 (AAV2) DNA in the liver, blood, plasma or stool from 27 of 28 cases. We found low levels of adenovirus (HAdV) and human herpesvirus 6B (HHV-6B) in 23 of 31 and 16 of 23, respectively, of the cases tested. By contrast, AAV2 was infrequently detected and at low titre in the blood or the liver from control children with HAdV, even when profoundly immunosuppressed. AAV2, HAdV and HHV-6 phylogeny excluded the emergence of novel strains in cases. Histological analyses of explanted livers showed enrichment for T cells and B lineage cells. Proteomic comparison of liver tissue from cases and healthy controls identified increased expression of HLA class 2, immunoglobulin variable regions and complement proteins. HAdV and AAV2 proteins were not detected in the livers. Instead, we identified AAV2 DNA complexes reflecting both HAdV-mediated and HHV-6B-mediated replication. We hypothesize that high levels of abnormal AAV2 replication products aided by HAdV and, in severe cases, HHV-6B may have triggered immune-mediated hepatic disease in genetically and immunologically predisposed children

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Deep convolutional neural network and IoT technology for healthcare

Background Deep Learning is an AI technology that trains computers to analyze data in an approach similar to the human brain. Deep learning algorithms can find complex patterns in images, text, audio, and other data types to provide accurate predictions and conclusions. Neuronal networks are another name for Deep Learning. These layers are the input, the hidden, and the output of a deep learning model. First, data is taken in by the input layer, and then it is processed by the output layer. Deep Learning has many advantages over traditional machine learning algorithms like a KA-nearest neighbor, support vector algorithms, and regression approaches. Deep learning models can read more complex data than traditional machine learning methods. Objectives This research aims to find the ideal number of best-hidden layers for the neural network and different activation function variations. The article also thoroughly analyzes how various frameworks can be used to create a comparison or fast neural networks. The final goal of the article is to investigate all such innovative techniques that allow us to speed up the training of neural networks without losing accuracy. Methods A sample data Set from 2001 was collected by www.Kaggle.com . We can reduce the total number of layers in the deep learning model. This will enable us to use our time. To perform the ReLU activation, we will make use of two layers that are completely connected. If the value being supplied is larger than zero, the ReLU activation will return 0, and else it will output the value being input directly. Results We use multiple parameters to determine the most effective method to test how well our method works. In the next paragraph, we'll discuss how the calculation changes secret-shared Values. By adopting 19 train set features, we train our reliable model to predict healthcare cost's (numerical) target feature. We found that 0.89503 was the best choice because it gave us a good fit (R2) and let us set enough coefficients to 0. To develop our stable model with this Set of parameters, we require 26 iterations. We use an R2 of 0.89503, an MSE of 0.01094, an RMSE of 0.10458, a mean residual deviance of 0.01094, a mean absolute error of 0.07452, and a root mean squared log error of 0.07207. After training the model on the train set, we applied the same parameters to the test set and obtained an R2 of 0.90707, MSE of 0.01045, RMSE of 0.10224, mean residual deviation of 0.01045, MAE of 0.06954, and RMSE of 0.07051, validating our solution approach. The objective value of our secured model is higher than that of the scikit-learn model, although the former performs better on goodness-of-fit criteria. As a result, our protected model performs quite well, marginally outperforming the (very optimized) scikit-learn model. Using a backpropagation algorithm and stochastic gradient descent, deep Learning develops artificial neural systems with several interconnected layers. There may be hidden layers of neurons in the network that have the tanh, rectification, and max-out hyperparameters. Modern features like momentum training, dropout, active learning rate, rate annealed, and L1 or L2 regularization provide exceptional prediction performance. The worldwide model's parameters are multi-threadedly (asynchronously) trained on the data from that node, and the model-based data is then gradually augmented by model averaging over the entire network. The method is executed on a single-node, direct H2O cluster initiated by the operator. The operation is parallel despite there just being a single node involved. The number of threads may be adjusted in the settings menu under Preferences and General. The optimal number of threads for the system is used automatically. Successful predictions in the healthcare data sets are made using the H2O Deep Learning operator. There will be a classification done since its label is binomial. The Splitting Validation operator creates test and training datasets to evaluate the model. By default, the settings of the Deep Learning activator are used. To put it another way, we'll construct two hidden layers, each containing 50 neurons. The Accuracy measure is computed by linking the annotated Sample Set with a Performer (Binominal Classification) operator. Table 3 displays the Deep Learning Model, the labeled data, and the Performance Vector that resulted from the technique. Conclusions Deep learning algorithms can be used to design systems that report data on patients and deliver warnings to medical applications or electronic health information if there are changes in the patient's health. These systems could be created using deep Learning. This helps verify that patients get the proper effective care at the proper time for each specific patient. A healthcare decision support system was presented using the Internet of Things and deep learning methods. In the proposed system, we examined the capability of integrating deep learning technology into automatic diagnosis and IoT capabilities for faster message exchange over the Internet. We have selected the suitable Neural Network structure (number of best-hidden layers and activation function classes) to construct the e-health system. In addition, the e-health system relied on data from doctors to understand the Neural Network. In the validation method, the total evaluation of the proposed healthcare system for diagnostics provides dependability under various patient conditions. Based on evaluation and simulation findings, a dual hidden layer of feed-forward NN and its neurons store the tanh function more effectively than other NN. To overcome challenges, this study will integrate artificial intelligence with IoT. This study aims to determine the NN's optimal layer counts and activation function variations