Physical and Psychological Impacts on COVID-19 Hospitalized Patients of 3rd Wave

Background: COVID shelters and many emergency centers were established as a countermeasure to control this pandemic that hit the word by the end of 2019. Due to unavailability of medical care, along with physical health issues, these patients suffer with mental health related issues. Aims: This study aimed to explore the both, physical and psychological impacts upon the patients diagnosed with COVID-19 and admitted in intensive care units of hospitals of Pakistan during the third wave. Methods: This cross-sectional survey was performed during the peak time of COVID-19 for the duration of two months i.e. April & May 2021. After getting ethical approval from Shifa International Hopsital (Ref# 070-021), permission was sorted from public and private hospitals of Pakistan. 183 conscious patients diagnosed with COVID and currently admitted in intensive care units were randomly selected from hospitals of Islamabad and Rawalpindi. Written consent was taken from patients and their caregivers after they were briefed regarding the importance of the study. PHQ-15 was used to assess somatic symptoms related to COVID-19 whereas DASS-21 was used to assess level of depression, anxiety and stress among patients. Results: Of 183 hospitalized patients of COVID-19 in intensive care units, 170 (92.9%) participants showed mild to severe level of somatic symptoms on PHQ-15. Shortness of breath, feeling hearth race, back pain, stomach pain, low energy and sleeping difficulties were the most common somatic complaints reported by patients. The statistics of DASS-21 showed that 51 (27.86%) participants had mild to severe level of depression, 74 (40.4%) had mild to profound level of anxiety and 96 (52.45%) reported mild to profound level of stress. Conclusion: This study portrayed a better understanding and confirms the physical and psychological impacts upon hospitalized COVID-19 patients, therefore highlighting the need of both physical and mental health interventions to minimize these impacts

Wind energy potential in Pakistan : A feasibility study in sindh province

The environment and the economy are negatively impacted by conventional energy sources, such as coal, gasoline, and other fossil fuels. Pakistan’s reliance on these resources has resulted in a catastrophic energy crisis. This has driven the government to make critical decisions such as early retail closures, power outages for the industrial sector, and an increase to two days a week vacations. Wind energy, accessible and affordable, will become a viable option for meeting Pakistan’s present and future energy demands. Approximately 3% of Pakistan’s land can produce nearly 132 GW of power with an installed capacity of 5 MW per km2. In this study, four zones (Karachi, Thatta, Badin, and Jamshoro) in Sindh province are assessed for the feasibility of wind energy generation. The installed capacity, generator types, and detailed specifications are provided for each zone. Moreover, the wind mapping of Pakistan is presented considering the four potential zones. The zones are analyzed using annual wind speed and power output considering wind data measured at 50 m height over one year. The higher mean speed is recorded at Jamshoro compared to other zones. The analysis indicates that all four sites are suitable for large-scale wind power generation due to their energy potential

Predictive Data Analytics for Electricity Fraud Detection Using Tuned CNN Ensembler in Smart Grid

In the smart grid (SG), user consumption data are increasing very rapidly. Some users consume electricity legally, while others steal it. Electricity theft causes significant damage to power grids, affects power supply efficiency, and reduces utility revenues. This study helps utilities reduce the problems of electricity theft, inefficient electricity monitoring, and abnormal electricity consumption in smart grids. To this end, an electricity theft dataset from the state grid corporation of China (SGCC) is employed and this study develops a novel model, a mixture of convolutional neural network and gated recurrent unit (CNN-GRU), for automatic power theft detection. Moreover, the hyperparameters of the proposed model are tuned using a meta-heuristic method, the cuckoo search (CS) algorithm. The class imbalance problem is solved using the synthetic minority oversampling technique (SMOTE). The clean data are trained and then tested with the proposed classification. Extensive simulations are performed based on real energy consumption data. The simulated results show that the proposed theft detection model (CNN-GRU-CS) solved the theft classification problem better than other approaches in terms of effectiveness and accuracy by 10% on average. The calculated accuracy of the proposed method is 92% and the precision is 94%

CRITICAL ANALYSIS AND CHARACTER NARRATION OF SNOW WHITE UNDER PROPP’S MODEL

This abstract provides an overview of a critical analysis that applies Vladimir Propp's narrative theory to dissect and interpret the classic fairy tale "Snow White." Vladimir Propp's model, designed for the analysis of folktales, identifies recurrent narrative functions performed by characters, offering a structural framework to unveil underlying patterns and archetypal elements. In this study, "Snow White" is subjected to a meticulous examination through Propp's Model, unraveling the tale's fundamental narrative components. The analysis encompasses the identification and scrutiny of characters such as the villainous queen, the dwarfs, and Snow White herself, each fulfilling specific functions within Propp's framework. Beyond structural analysis, the study explores how Propp's Model illuminates universal motifs and narrative functions, contributing to a deeper understanding of the fairy tale are enduring resonance. The abstract emphasizes the interdisciplinary nature of this critical analysis, drawing connections between folklore studies and critical analysis of "Snow White.

Lightweight Internet of Things Botnet Detection Using One-Class Classification

Like smart phones, the recent years have seen an increased usage of internet of things (IoT) technology. IoT devices, being resource constrained due to smaller size, are vulnerable to various security threats. Recently, many distributed denial of service (DDoS) attacks generated with the help of IoT botnets affected the services of many websites. The destructive botnets need to be detected at the early stage of infection. Machine-learning models can be utilized for early detection of botnets. This paper proposes one-class classifier-based machine-learning solution for the detection of IoT botnets in a heterogeneous environment. The proposed one-class classifier, which is based on one-class KNN, can detect the IoT botnets at the early stage with high accuracy. The proposed machine-learning-based model is a lightweight solution that works by selecting the best features leveraging well-known filter and wrapper methods for feature selection. The proposed strategy is evaluated over different datasets collected from varying network scenarios. The experimental results reveal that the proposed technique shows improved performance, consistent across three different datasets used for evaluation

Toward Holistic Energy Management by Electricity Load and Price Forecasting: A Comprehensive Survey

Electricity load and price data pose formidable challenges for forecasting due to their intricate characteristics, marked by high volatility and non-linearity. Machine learning (ML) and deep learning (DL) models have emerged as valuable tools for effectively predicting data exhibiting high volatility, frequent fluctuations, mean-reversion tendencies, and non-stationary behavior. Therefore, this review article is dedicated to providing a comprehensive exploration of the application of machine learning and deep learning techniques in the context of electricity load and price prediction. In contrast to existing literature, our study distinguishes itself in several key ways. We systematically examine ML and DL approaches employed for the prediction of electricity load and price, offering a meticulous analysis of their methodologies and performance. Furthermore, we furnish readers with a detailed compendium of the datasets utilized by these forecasting methods, elucidating the sources and specific characteristics underpinning these datasets. Then, we rigorously conduct a performance comparison across various performance metrics, facilitating a comprehensive assessment of the efficacy of different predictive models. Notably, this comparison is carried out using the same datasets that underlie the diverse methodologies reviewed within this study, ensuring a fair and consistent evaluation. Moreover, we provide an in-depth examination of the diverse performance measures and statistical tools employed in the studies considered, providing valuable insights into the analytical frameworks used to gauge forecasting accuracy and model robustness. Lastly, we devote significant attention to the identification and analysis of prevailing challenges within the realm of electricity load and price prediction. Additionally, we delve into prospective directions for future research, thereby contributing to the advancement of this critical field

Mutational Analysis of Myoclonin1 Gene in Pakistani Juvenile Myoclonic Epilepsy Patients

Juvenile myoclonic epilepsy (JME) is the most prevalent and genetically heterogeneous form of epilepsy and accounts for 10–30% of all the cases worldwide. Ef-hand domain- (c-terminal-) containing protein 1 (EFHC1) encodes for a nonion channel protein and mutations in this gene have been extensively reported in different populations to play a causative role in JME. Linkage between JME and 6p11-12 locus has already been confirmed in Mexican and Dutch families. A case-control study was conducted on Pakistani JME patients for the first time, aimed at finding out EFHC1 mutations that have been reported in different populations. For this purpose, 66 clinically diagnosed JME patients and 108 control subjects were included in the study. Blood samples were collected from all the participants, and DNA was isolated from the lymphocytes by the modified organic method. Total 3 exons of EFHC1, harboring extensively reported mutations, were selected for genotypic analysis. We identified three heterozygous variants, R159W, V460A, P436P, and one insertion in the current study. V460A, an uncommon variant identified herein, has recently been reported in public databases in an unphenotyped American individual. This missense variant was found in 3 Pakistani JME patients from 2 unrelated families. However, in silico analysis showed that V460A may possibly be a neutral variant. While the absence of a majority of previously reported mutations in our population suggests that most of the mutations of EFHC1 are confined to particular ethnicities and are not evenly distributed across the world. However, to imply the causation, the whole gene and larger number of JME patients should be screened in this understudied population

Utjecaj COVID-a na funkcionalni status hospitaliziranih bolesnika

Originating with unexplained symptoms from Wuhan, city of China, COVID-19 being a global pandemic causing tremendous morbidity and mortality, has proved to be the biggest challenge of the 20th century. This study aimed to explore the functional impacts of COVID-19 upon those patients who were diagnosed with this disease and were admitted in hospitals. This cross-sectional survey included 183 COVID-19 diagnosed patients from COVID-19 isolation wards of public and private hospitals of Islamabad and Rawalpindi. After getting ethical permission from Institutional Review Board of Shifa International Hospital (Ref # 070-21), this survey was conducted for the time period of 6 months from December 2020 to May 2021. Through convenient sampling, 183 patients with the age range of 25 to 55 years with no already diagnosed psychological complaints were assessed for eligibility briefed regarding the study purpose and then were asked for their voluntary participation. The Functional Status Scale for the Intensive Care Unit (FSS-ICU) was used to assess the functional status impacted due to COVID-19 during hospitalization. Frequencies and percentages were calculated through SPSS-21. On FSS-ICU, out of 183 COVID-19, 11 (6%) patients reported that they were dependent, 18 (9.8%) required maximum assistance, 32 (17.5%) required moderate assistance, 27 (14.8%) required minimal, 24 (13.1%) required supervision to complete their tasks, 28 (15.3%) required assistive devices, whereas 43 (23.5%) were totally independent. Results indicated a temporal impact of COVID-19 upon functional status of hospitalized patients in intensive care units, therefore highlighting the need of physiotherapeutic and psychotherapeutic interventions.Pojavom neobjašnjivih simptoma u Wuhanu, Kini, globalna pandemija COVID-19, pokazala se najvećim izazovom 20. stoljeća uzrokujući ogroman pobol i smrtnost. Cilj istraživanja bio je istražiti funkcionalne učinke COVID-19 na bolesnike kojima je dijagnosticirana ova bolest i koji su hospitalizirani u zdravstvenim ustanovama. Ovo poprečno istraživanje obuhvatilo je 183 bolesnika s dijagnozom COVID-19, a koji su smješteni u izolacijske odjele javnih i privatnih bolnica u Islamabadu i Rawalpindiju. Nakon dobivanja dopusnice Etičkog povjerenstva Bolnice Shifa (br. # 070-21), istraživanje je provedeno u razdoblju od 6 mjeseci, od prosinca 2020. do svibnja 2021. Prikladnim uzorkovanjem, ukupno je 183 bolesnika u dobi od 25 do 55 godina bez prethodno dijagnosticiranih psiholoških tegoba, analizirano radi ispunjavanja kriterija istraživanja. Nakon što su ispitanici bili upoznati sa svrhom istraživanja, zamoljeni su za dobrovoljno sudjelovanje. Skala za određivanje funkcionalnog statusa bolesnika u jedinici intenzivnog liječenja (engl. Functional Status Scale - Intensive Care Unit, FSS-ICU) korištena je za procjenu utjecaja COVID-19 na funkcionalni status bolesnika tijekom hospitalizacije. Frekvencije i postoci izračunati su koristeći SPSS-21. Na FSS-ICU ljestvici, od 183 bolesnika s COVID-19, 11 (6%) je ovisno o tuđoj pomoći, 18 (9,8%) treba maksimalnu pomoć, 32 (17,5%) treba umjerenu pomoć, 27 (14,8%) treba minimalnu pomoć, 24 (13,1%) treba nadzor kako bi izvršili svoje zadatke, 28 (15,3%) treba pomoćna sredstva, dok je 43 (23,5%) bilo potpuno neovisno. Dobiveni rezultati ukazali su na vremenski utjecaj COVID-19 na funkcionalni status hospitaliziranih bolesnika u jedinicama intenzivnog liječenja te stoga naglašavaju potrebu fizioterapeutskih i psihoterapijskih intervencija