An analysis of E-governance in Pakistan from the lens of the Chinese governance model.

In this era of digitalization, the role of information and communication technology (ICT) has significantly increased. The integration of ICT into the government system has improved efficiency and working processes. Some countries such as China have successfully integrated ICT into their governance system. However, many other countries especially the developing world are yet to effectively utilize the role of ICT in their governance structure and these countries are struggling to produce a better governance system. It is, therefore, imperative for the developing world to learn from successful nations and devise their governance systems so that ICT can be fully utilized and produce good governance. However, such comparative analyses are not done as such to bring strengths and weaknesses in the integration of ICT into the governance system especially in developing countries' governance systems. This study contributes by conducting a comparative study on the China-Pakistan e-government progress. China has widely implemented e-government, which has helped the country to ensure good governance. Pakistan, on the other hand, is also moving towards digitalization and making efforts to implement e-government. This research examines the United Nations' E-Government Development Index (EGDI) reports and rankings. The findings of our research show that China has significantly improved its ranking, whereas Pakistan's ranking has indicated a gradual decline except for the year 2008. This happened because of a lack of investment in infrastructure, scarcity of financial resources, weak institutional capabilities, and limited access to advanced technologies. Moreover, there is a big gap between public policy and public implementation in Pakistani scenarios. However, it has been dug out in this study that employing the Chinese model and seeking cooperation with China can improve e-governance ranking and overall governance in Pakistan. The study advances the understanding of e-governance and its challenges in Pakistan and the findings of the study will assist researchers, policymakers, and officials in the implementation and development of e-projects in Pakistan.

Complexity reduction of oxygen saturation variability signals in COVID-19 patients: Implications for cardiorespiratory control.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a respiratory illness that leads to severe acute respiratory syndrome and various cardiorespiratory complications, contributing to morbidity and mortality. Entropy analysis has demonstrated its ability to monitor physiological states and system dynamics during health and disease. The main objective of the study is to extract information about cardiorespiratory control by conducting a complexity analysis of OSV signals using scale-based entropy measures following a two-month timeframe after recovery. METHODS: This prospective study collected data from subjects meeting specific criteria, using a Beurer PO-80 pulse oximeter to measure oxygen saturation (SpO2) and pulse rate. Excluding individuals with a history of pulmonary/cardiovascular issues, the study analyzed 88 recordings from 44 subjects (26 men, 18 women, mean age 45.34 ± 14.40) during COVID-19 and two months post-recovery. Data preprocessing and scale-based entropy analysis were applied to assess OSV signals. RESULTS: The study found a significant difference in mean OSV during illness (95.08 ± 0.15) compared to post-recovery (95.59 ± 1.03), indicating reduced cardiorespiratory dynamism during COVID-19. Multiscale entropy analyses (MSE, MPE, MFE) confirmed lower entropy values during illness across all time scales, particularly at higher scales. Notably, the maximum distinction between illness and recovery phases was seen at specific time scales and similarity criteria for each entropy measure, showing statistically significant differences. CONCLUSIONS: The study demonstrates that the loss of complexity in OSV signals, quantified using scale-based entropy measures, has the potential to detect malfunctioning of cardiorespiratory control in COVID-19 patients. This finding suggests that OSV signals could serve as a valuable indicator for assessing the cardiorespiratory status of COVID-19 patients and monitoring their recovery progress.

Correction: Anthropological responses to environmental challenges in SAARC nations: A comparative analysis.

[This corrects the article DOI: 10.1371/journal.pone.0296516.].

Scale based entropy measures and deep learning methods for analyzing the dynamical characteristics of cardiorespiratory control system in COVID-19 subjects during and after recovery.

COVID-19, known as Coronavirus Disease 2019 primarily targets the respiratory system and can impact the cardiovascular system, leading to a range of cardiorespiratory complications. The current forefront in analyzing the dynamical characteristics of physiological systems and aiding clinical decision-making involves the integration of entropy-based complexity techniques with artificial intelligence. Entropy-based measures offer promising prospects for identifying disturbances in cardiorespiratory control system (CRCS) among COVID-19 patients by assessing the oxygen saturation variability (OSV) signals. In this investigation, we employ scale-based entropy (SBE) methods, including multiscale entropy (MSE), multiscale permutation entropy (MPE), and multiscale fuzzy entropy (MFE), to characterize the dynamical characteristics of OSV signals. These measurements serve as features for the application of traditional machine learning (ML) and deep learning (DL) approaches in the context of classifying OSV signals from COVID-19 patients during their illness and subsequent recovery. We use the Beurer PO-80 pulse oximeter which non-invasively acquired OSV and pulse rate data from COVID-19 infected patients during the active infection phase and after a two-month recovery period. The dataset comprises of 88 recordings collected from 44 subjects(26 men and 18 women), both during their COVID-19 illness and two months post-recovery. Prior to analysis, data preprocessing is performed to remove artifacts and outliers. The application of SBE measures to OSV signals unveils a reduction in signal complexity during the course of COVID-19. Leveraging these SBE measures as feature sets, we employ two DL techniques, namely the radial basis function network (RBFN) and RBFN with dynamic delay algorithm (RBFNDDA), for the classification of OSV data collected during and after COVID-19 recovery. To evaluate the classification performance, we employ standard metrics such as sensitivity, specificity, false positive rate (FPR), and the area under the receiver operator characteristic curve (AUC). Among the three scale-based entropy measures, MFE outperformed MSE and MPE by achieving the highest classification performance using RBFN with 13 best features having sensitivity (0.84), FPR (0.30), specificity (0.70) and AUC (0.77). The outcomes of our study demonstrate that SBE measures combined with DL methods offer a valuable approach for categorizing OSV signals obtained during and after COVID-19, ultimately aiding in the detection of CRCS dysfunction.

Anthropological responses to environmental challenges in SAARC nations: A comparative analysis.

The purpose of the study is to investigate the relationships and potential impacts of environmental pollutants, human resources, GDP, sustainable power sources, financial assets, and SAARC countries from 1995 to 2022. Board cointegration tests, D-H causality, cross-sectional reliance (CSD), Saville and Holdsworth Restricted (SHL), and the DSK Appraisal Strategy were among the logical techniques employed to discover long-term connections between these components. Results demonstrate that GDP growth, renewable energy sources (REC), and environmental pollution (ENP) all contribute to SAARC countries' progress. However, future opportunities and HR are negatively impacted by increased ecological pollution. The results of the two-way causality test demonstrate a strong correlation between HR and future possibilities. Opportunities for the SAARC countries are closely related to the growth of total national output, the use of green electricity, and public support sources. Ideas for tackling future projects are presented in the paper's conclusion. These include facilitating financial development, reducing ecological pollution, financing the progress of human resources, and promoting the use of sustainable power sources.

Telehealth-Enabled In-Home Elbow Rehabilitation for Brachial Plexus Injuries Using Deep-Reinforcement-Learning-Assisted Telepresence Robots.

Due to damage to the network of nerves that regulate the muscles and feeling in the shoulder, arm, and forearm, brachial plexus injuries (BPIs) are known to significantly reduce the function and quality of life of affected persons. According to the World Health Organization (WHO), a considerable share of global disability-adjusted life years (DALYs) is attributable to upper limb injuries, including BPIs. Telehealth can improve access concerns for patients with BPIs, particularly in lower-middle-income nations. This study used deep reinforcement learning (DRL)-assisted telepresence robots, specifically the deep deterministic policy gradient (DDPG) algorithm, to provide in-home elbow rehabilitation with elbow flexion exercises for BPI patients. The telepresence robots were used for a six-month deployment period, and DDPG drove the DRL architecture to maximize patient-centric exercises with its robotic arm. Compared to conventional rehabilitation techniques, patients demonstrated an average increase of 4.7% in force exertion and a 5.2% improvement in range of motion (ROM) with the assistance of the telepresence robot arm. According to the findings of this study, telepresence robots are a valuable and practical method for BPI patients' at-home rehabilitation. This technology paves the way for further research and development in telerehabilitation and can be crucial in addressing broader physical rehabilitation challenges.

Exact solutions of the (3+1)-generalized fractional nonlinear wave equation with gas bubbles.

In this manuscript, we implement the travelling wave solutions of the fractional (3+1) generalized computational nonlinear wave equation with gas bubbles via application of five mathematical methods. Liquids with gas bubbles primarily arise in various applications like science, engineering, and mathematical physics. The obtained solitary waves solutions have fruitful applications in engineering, science, life, nature and physics. Several novel soliton solutions of concerned model are established in the form of hyperbolic, trigonometric, exponential and rational functions. To handle all calculations and verification of obtained results, computational software Mathematica 12.1 is used. For the demonstration of the physical behaviour of concern model, some solutions are plotted graphical in 2-dimensional and 3-dimensional by imparting specific values to the parameters under constrain conditions. Finally, we intrigue both two and three dimensional to explain the physical behavior of the model.

Potential impacts of Russo-Ukraine conflict and its psychological consequences among Ukrainian adults: the post-COVID-19 era.

Background: Since February 2022, the nation of Ukraine has become entangled in an escalating conflict that erupted after coronavirus outbreak fostering a situation of indeterminacy and precariousness, which adversely affected several facets, especially psychological well-being. However, there is a lack of empirical evidence on the psychological well-being of Ukrainians during the Russo-Ukraine war, as well as their coping strategies in response to the war's repercussions. Consequently, this investigation endeavors to explore the prevalence of symptoms associated with depression, anxiety, stress, and insomnia and to correlate these symptoms with Ukrainians' effective coping mechanisms during the ongoing war. Methods: An online survey was administered in Ukraine from June to August 2022 due to the ongoing Russo-Ukraine conflict. The survey employed a quota sampling technique, targeting 2,664 individuals (≥18 years). Out of the total sample, 1,833 valid responses were obtained, yielding a response rate of 68. 81%. Depression, anxiety, and stress were measured using the depression, anxiety, and stress scale-21 (DASS-21), while the Pittsburgh sleep quality index (PSQI) was utilized to evaluate insomnia symptoms. In addition, Brief-COPE was adopted to evaluate the coping mechanisms of the selected study participants. Results: Of 1,833 Ukrainian adults, 60.5% had symptoms of stress; 62.4% of them reported symptoms of anxiety; and 58.2% reported symptoms of depression. Symptom criteria for insomnia were found in about 21.8% of the study sample. The factors of sex, living area, area occupied by Russian forces, and having older adults and children in the house were statistically significant with symptoms of depression, anxiety, stress, and insomnia. The productive coping strategies of self-distraction, using instrumental support, planning, and behavioral disengagement, were observed as statistically significant with four psychological constructs. Conclusion: The study outcomes highlight a substantial prevalence of symptoms related to depression, anxiety, stress, and insomnia attributed to the accumulated consequences of ongoing conflict and the COVID-19 outbreak. The aforementioned findings emphasize the imperative of providing healthcare services and facilitating effective coping strategies among Ukrainians amid the ongoing war.

Bismuth-Nanoparticles-Embedded Porous Carbon Derived from Seed Husks as High-Performance for Anode Energy Electrode.

In energy application technology, the anode part of the electrode is typically composed of carbon-coated materials that exhibit excellent electrochemical performance. The carbon-coated electrodes facilitate electrochemical reactions involving the fuel and the oxidant. Energy electrodes are used in stationary power plants to generate electricity for the grid. These large-scale installations are known as distributed generation systems and contribute to grid stability and reliability. Understanding the practical applications of energy materials remains a significant hurdle in the way of commercialization. An anode electrode has one key limitation, specifically with alloy-type candidates, as they tend to exhibit rapid capacity degradation during cycling due to volume expansion. Herein, biomass-derived carbon from sunflowers (seeds husks) via pyrolysis and then bismuth nanoparticles are treated with carbon via a simple wet-chemical method. The electrode Bi@C offers several structural advantages, such as high capacity, good cycling stability, and exceptional capability at the current rate of 500 mA g-1, delivering a capacity of 731.8 mAh g-1 for 200 cycles. The biomass-derived carbon coating protects the bismuth nanoparticles and contributes to enhanced electronic conductivity. Additionally, we anticipate the use of low-cost biomass with hybrid composition has the potential to foster environment-friendly practices in the development of next-generation advanced fuel cell technology.

Studying Dynamical Characteristics of Oxygen Saturation Variability Signals Using Haar Wavelet.

An aim of the analysis of biomedical signals such as heart rate variability signals, brain signals, oxygen saturation variability (OSV) signals, etc., is for the design and development of tools to extract information about the underlying complexity of physiological systems, to detect physiological states, monitor health conditions over time, or predict pathological conditions. Entropy-based complexity measures are commonly used to quantify the complexity of biomedical signals; however novel complexity measures need to be explored in the context of biomedical signal classification. In this work, we present a novel technique that used Haar wavelets to analyze the complexity of OSV signals of subjects during COVID-19 infection and after recovery. The data used to evaluate the performance of the proposed algorithms comprised recordings of OSV signals from 44 COVID-19 patients during illness and after recovery. The performance of the proposed technique was compared with four, scale-based entropy measures: multiscale entropy (MSE); multiscale permutation entropy (MPE); multiscale fuzzy entropy (MFE); multiscale amplitude-aware permutation entropy (MAMPE). Preliminary results of the pilot study revealed that the proposed algorithm outperformed MSE, MPE, MFE, and MMAPE in terms of better accuracy and time efficiency for separating during and after recovery the OSV signals of COVID-19 subjects. Further studies are needed to evaluate the potential of the proposed algorithm for large datasets and in the context of other biomedical signal classifications.