Impact of Corona Virus Disease in Health Care Professionals in Managing Patients with Positive Disease in Pakistan

ObjectiveThe present was conducted to evaluate psychological impact of COVID-19 infection in health care professionals while treating Corona Virus positive patients.Study and Design Descriptive Observational cross Sectional studyMethods and Materials The Depression, Anxiety and Stress Scale - 21 Items (DASS-21) are a set of three self-report scales designed to measure the emotional states of depression, anxiety and stress. The DASS-21 incorporated in a questionnaire form was distributed among the healthcare professionals that were involved in direct management of COVID-19 patients. 224 consented HCPs participated in this cross sectional study, during MAY 2020, nearing the end of the first wave of this pandemic in Pakistan. Results The results were broken down to scores assigned to: Anxiety (overall mean score 19.01 ± 9.2), with 192 (85.7%) HCPs experiencing moderate to extremely severe anxiety;Depression (overall mean score 18.12 ± 10), with 162 (72.3%) HCPs being moderate to extremely severely depressed;Stress (overall mean score 20.12 ± 12.0), 202 (90.1%) HCPs reporting moderate to extreme stress levels. Conclusions Our study highlights the negative effect of this pandemic, despite the steadfastness to serve, on the psychological well being of our healthcare professionals. As the corona virus pandemic continues, the levels of, anxiety, stress and depression are expected to increase among our major workforce and the main defense against the deadly virus. Hence the mental well being of our doctors and paramedics should be scrutinized often and necessary measures should be taken at a national level to ensure the better functioning of our health care system

Synthesis of Metal Oxide Semiconductor Nanostructures for Gas Sensors

Zinc oxide (ZnO) is a unique and important metal oxide semiconductor for its valuable and huge applications with wide band gap ( 3.37 eV) and most promising candidate for gas sensor due to its high surface-to-volume ratio, good biocompatibility, stability, and high electron mobility. Due these properties, metal oxide shows good crystallinity, higher carrier mobility, and good chemical and thermal stability at moderately high temperatures. In this chapter nanostructures have been investigated, main focus being their synthesis and sensing mechanism of different toxic chemicals, synthesized by thermal evaporation through vapor transport method using vapor-liquid-solid (VLS) mechanism. The doped ZnO nanobelts showed significant enhanced sensing properties at room temperature, indicating that doping is very much effective in improving the methane CH4 sensing of ZnO nanostructures. ZnO nanowires showed a remarkable sensing response toward acetone and CH4 gas

Hill Climbing-Based Efficient Model for Link Prediction in Undirected Graphs

Link prediction is a key problem in the field of undirected graph, and it can be used in a variety of contexts, including information retrieval and market analysis. By “undirected graphs”, we mean undirected complex networks in this study. The ability to predict new links in complex networks has a significant impact on society. Many complex systems can be modelled using networks. For example, links represent relationships (such as friendships, etc.) in social networks, whereas nodes represent users. Embedding methods, which produce the feature vector of each node in a graph and identify unknown links, are one of the newest approaches to link prediction. The Deep Walk algorithm is a common graph embedding approach that uses pure random walking to capture network structure. In this paper, we propose an efficient model for link prediction based on a hill climbing algorithm. It is used as a cost function. The lower the cost is, the higher the accuracy for link prediction between the source and destination node will be. Unlike other algorithms that predict links based on a single feature, it takes advantage of multiple features. The proposed method has been tested over nine publicly available datasets, and its performance has been evaluated by comparing it to other frequently used indexes. Our model outperforms all of these measures, as indicated by its higher prediction accuracy

Comparative Analysis of Machine Learning Algorithms for Author Age and Gender Identification

Author profiling is part of information retrieval in which different perspectives of the author are observed by considering various characteristics like native language, gender, and age. Different techniques are used to extract the required information using text analysis, like author identification on social media and for Short Text Message Service. Author profiling helps in security and blogs for identification purposes while capturing authors’ writing behaviors through messages, posts, comments, blogs, comments, and chat logs. Most of the work in this area has been done in English and other native languages. On the other hand, Roman Urdu is also getting attention for the author profiling task, but it needs to convert Roman-Urdu to English to extract important features like Named Entity Recognition (NER) and other linguistic features. The conversion may lose important information while having limitations in converting one language to another language. This research explores machine learning techniques that can be used for all languages to overcome the conversion limitation. The Vector Space Model (VSM) and Query Likelihood (Q.L.) are used to identify the author’s age and gender. Experimental results revealed that Q.L. produces better results in terms of accuracy

Efficient Link Prediction Model For Real-World Complex Networks Using Matrix-Forest Metric With Local Similarity Features

Link prediction in a complex network is a difficult and challenging issue to address. Link prediction tries to better predict relationships, interactions and friendships based on historical knowledge of the complex network graph. Many link prediction techniques exist, including the common neighbour, Adamic-Adar, Katz and Jaccard coefficient, which use node information, local and global routes, and previous knowledge of a complex network to predict the links. These methods are extensively used in various applications because of their interpretability and convenience of use, irrespective of the fact that the majority of these methods were designed for a specific field. This study offers a unique link prediction approach based on the matrix-forest metric and vertex local structural information in a real-world complex network. We empirically examined the proposed link prediction method over 13 real-world network datasets obtained from various sources. Extensive experiments were performed that demonstrated the superior efficacy of the proposed link prediction method compared to other methods and outperformed the existing state-of-the-art in terms of prediction accuracy

A Systematic Analysis of Community Detection in Complex Networks

Numerous techniques have been proposed by researchers to uncover the hidden patterns of real-world complex networks. Finding a hidden community is one of the crucial tasks for community detection in complex networks. Despite the presence of multiple methods for community detection, identification of the best performing method over different complex networks is still an open research question. In this article, we analyzed eight state-of-the-art community detection algorithms on nine complex networks of varying sizes covering various domains including animal, biomedical, terrorist, social, and human contacts. The objective of this article is to identify the best performing algorithm for community detection in real-world complex networks of various sizes and from different domains. The obtained results over 100 iterations demonstrated that the multi-scale method has outperformed the other techniques in terms of accuracy. Multi-scale method achieved 0.458 average value of modularity metric whereas multiple screening resolution, unfolding fast, greedy, multi-resolution, local fitness optimization, sparse Geosocial community detection algorithm, and spectral clustering, respectively obtained the modularity values 0.455, 0.441, 0.436, 0.421, 0.368, 0.341, and 0.340.

FOREST COVER CHANGE DETECTION IN PAKTIA PROVINCE OF AFGHANISTAN USING REMOTE SENSING AND GIS: 1998-2018

Monitoring changes in forest cover is important to address issues like biodiversity conservation, sustainable management, and climate change. The study has been conducted in the Paktia province of Afghanistan to assess the changes in different forest classes which occurred together with political changes by using Remote Sensing and Geographic Information System (GIS). The change was analyzed for a period of two decades, i.e., 1998 to 2018. Landsat TM and OLI satellite images of 30m resolution for the years 1998 and 2018 were used respectively. The overall classification accuracy of the mapping was estimated as 82.67% and the kappa coefficient was estimated as 0.8081. The study area was delineated via visual image interpretation technique into 11 LULC classes’ viz., closed forest, open forest, forest scrub, grassland other classes (Agriculture, Agroforestry, horticulture, habitation, waterbody, wasteland, and snow). The comparison of maps 1998 and 2018 revealed that the total area under closed forest, open forest, showed an increase of 0.43%, 0.73%, respectively. While the areas under forest scrub, showed a decline of 0.30%, during the study period (1998-2018)

Real-World Protein Particle Network Reconstruction Based on Advanced Hybrid Features

Biological network proteins are key operational particles that substantially and operationally cooperate to bring out cellular progressions. Protein links with some other biological network proteins to accomplish their purposes. Physical collaborations are commonly referred to by the relationships of domain-level. The interaction among proteins and biological network reconstruction can be predicted based on various methods such as social theory, similarity, and topological features. Operational particles of proteins collaboration can be indirect among proteins based on mutual fields, subsequently particles of proteins involved in an identical biological progression be likely to harbor similar fields. To reconstruct the real-world network of proteins particles, some methods need only the notations of proteins domain, and then, it can be utilized to multiple species. A novel method we have introduced will analyze and reconstruct the real-world network of protein particles. The proposed technique works based on protein closeness, algebraic connectivity, and mutual proteins. Our proposed method was practically tested over different data sets and reported the results. Experimental results clearly show that the proposed technique worked best as compared to other state-of-the-art algorithms