Redefining governance: a critical analysis of sustainability transformation in e-governance.

With the rapid growth of information and communication technologies, governments worldwide are embracing digital transformation to enhance service delivery and governance practices. In the rapidly evolving landscape of information technology (IT), secure data management stands as a cornerstone for organizations aiming to safeguard sensitive information. Robust data modeling techniques are pivotal in structuring and organizing data, ensuring its integrity, and facilitating efficient retrieval and analysis. As the world increasingly emphasizes sustainability, integrating eco-friendly practices into data management processes becomes imperative. This study focuses on the specific context of Pakistan and investigates the potential of cloud computing in advancing e-governance capabilities. Cloud computing offers scalability, cost efficiency, and enhanced data security, making it an ideal technology for digital transformation. Through an extensive literature review, analysis of case studies, and interviews with stakeholders, this research explores the current state of e-governance in Pakistan, identifies the challenges faced, and proposes a framework for leveraging cloud computing to overcome these challenges. The findings reveal that cloud computing can significantly enhance the accessibility, scalability, and cost-effectiveness of e-governance services, thereby improving citizen engagement and satisfaction. This study provides valuable insights for policymakers, government agencies, and researchers interested in the digital transformation of e-governance in Pakistan and offers a roadmap for leveraging cloud computing technologies in similar contexts. The findings contribute to the growing body of knowledge on e-governance and cloud computing, supporting the advancement of digital governance practices globally. This research identifies monitoring parameters necessary to establish a sustainable e-governance system incorporating big data and cloud computing. The proposed framework, Monitoring and Assessment System using Cloud (MASC), is validated through secondary data analysis and successfully fulfills the research objectives. By leveraging big data and cloud computing, governments can revolutionize their digital governance practices, driving transformative changes and enhancing efficiency and effectiveness in public administration.

Copper phthalocyanines as a mode-locker in an Er-doped fiber laser.

We demonstrate a mode-locked erbium-doped fiber laser (EDFL) utilizing copper phthalocyanines (CuPc) as a saturable absorber (SA) for the first time, to the best of our knowledge. The investigated SA was prepared using a simple, low-cost and straightforward technique, whereby the CuPc powder was embedded into polyvinyl alcohol (PVA) to form a thin film. The thin film acted as a mode-locker when it was incorporated into the EDFL cavity to produce output pulses at a repetition rate of 1.8 MHz with a pulse duration of 1.98 ps. The frequency spectrum showed a signal-to-noise ratio as high as 55 dB, which indicates the stability of the mode-locking operation. To the best of our knowledge, this is the first work to report using CuPc as a mode-locker.

Revolutionizing Urban Mobility: IoT-Enhanced Autonomous Parking Solutions with Transfer Learning for Smart Cities.

Smart cities have emerged as a specialized domain encompassing various technologies, transitioning from civil engineering to technology-driven solutions. The accelerated development of technologies, such as the Internet of Things (IoT), software-defined networks (SDN), 5G, artificial intelligence, cognitive science, and analytics, has played a crucial role in providing solutions for smart cities. Smart cities heavily rely on devices, ad hoc networks, and cloud computing to integrate and streamline various activities towards common goals. However, the complexity arising from multiple cloud service providers offering myriad services necessitates a stable and coherent platform for sustainable operations. The Smart City Operational Platform Ecology (SCOPE) model has been developed to address the growing demands, and incorporates machine learning, cognitive correlates, ecosystem management, and security. SCOPE provides an ecosystem that establishes a balance for achieving sustainability and progress. In the context of smart cities, Internet of Things (IoT) devices play a significant role in enabling automation and data capture. This research paper focuses on a specific module of SCOPE, which deals with data processing and learning mechanisms for object identification in smart cities. Specifically, it presents a car parking system that utilizes smart identification techniques to identify vacant slots. The learning controller in SCOPE employs a two-tier approach, and utilizes two different models, namely Alex Net and YOLO, to ensure procedural stability and improvement.

Testicular Infarction in a Sickle Cell Hemoglobinopathy Patient: A Case Report.

Vaso-occlusive phenomena in sickle cell disease lead to ischemia and possible infarction of the affected organ. We report a case of a 20-year-old Saudi male known to have homozygous sickle cell hemoglobinopathy who was admitted to our institution with abdominal pain. One day post admission, the patient developed left testicular pain. Ultrasound showed decreased echogenicity, and Doppler examination showed absent blood flow in the left testicle. Left radical orchidectomy was done, and histopathological assessment revealed ischemic necrosis with sickled red blood cells (RBCs). A few studies have been reported worldwide suggesting that a vaso-occlusive event is the mainstay mechanism in such cases. This is the first case reported in the Eastern Province of Saudi Arabia.

Influence of Interfering Ions and Adsorption Temperature on Radioactive Iodine Removal Efficiency and Stability of Ni-MOF-74 and Zr-UiO-66.

Metal-organic frameworks (MOFs) often exhibit an exceptional adsorption-based separation performance for a variety of gases, ions, and liquids. While most radioactive iodine removal studies focus on the capture of radioactive iodine from off-gas streams, few studies have systematically investigated the effect of structure-property relationships of MOFs on iodine removal performance in the presence of interfering ions in liquid solutions. Herein, we investigated the iodide ion (I-) adsorption performance of two model MOFs (e.g., Ni-MOF-74 and Zr-UiO-66) in liquid phase as a function of iodine concentration (e.g., 0.125 to 0.25 and 0.50 mmol/L) and adsorption temperature (e.g., 25 to 40 and 60 °C), and in the presence of interfering ions such as Cl- and CO32- through batch-mode experiments. Under identical experimental conditions, Ni-MOF-74 outperformed Zr-UiO-66 in immobilizing iodine from the solution by achieving a maximum iodine removal efficiency of 97% at 60 °C. The results showed that the presence of other interfering ions marginally affects the iodine removal efficiency (e.g., capacity and rate of iodine capture) over both MOF adsorbents. The adsorption kinetics was found to be controlled by multiple transport processes encompassing external surface adsorption, intraparticle diffusion, and final equilibrium. Moreover, the leach test results revealed 8 and 12% iodine release from Ni-MOF-74 and Zr-UiO-66, respectively, at 25 °C after 48 h aging. This study establishes guiding principles for sustainable removal of iodine in the presence of Cl- and CO32- species in cyclohexane.

COVID-19 vaccine had a significant positive impact on patients with SARS-COV-2 during the third (Omicron) wave in Saudi Arabia.

INTRODUCTION: The third (Omicron) wave had caused significant increase in the number of COVID-19 cases around the globe. The severity of the disease dependeds on the extent of the vaccination status. METHODS: This is a retrospective study of infected COVID-19 patients during the third (Omicron) wave in a hospital in Saudi Arabia. RESULTS: A total of 400 patients were included with 220 (55 %) males and 180 (45 %) females, and a mean age (+/- SD) of 36.34 + 16.47 years. The most common presenting symptoms were: sore throat 159 (39.8 %), cough 158 (39.5 %), fever 132 (33 %), headache 122 (30.5 %), and muscle ache 124 (31%). There was no difference in underlying conditions, signs and symptoms between males and females apart from the occurrence of sore throat with an OR of 2.014 (95 % CI: 1.103-3.677, P = 0.023) and need of hospitalization OR 2.457 (95 % CI: 1.168-5.167, P value =.018) in a binary logistic regression comparison. The need for hospitalization was inversely related to the number of COVID-19 vaccination doses. The rate of admission was 8 (72.7 %), 34 (12 %), 4 (5.4 %) for one, two, and three doses of COVID-19 vaccine, respectively (P < 0.0001). Of all the patients, 14 (3.5 %) and 8 (2 %) required intensive care (ICU) admission and mechanical ventilation, respectively. The median Ct-value of SARS-CoV-2 was higher in those who had 2 or 3 doses compared to those who had one dose of the COVID-19 vaccine, but the difference did not reach statistical significance. None of the included patients died during the study period. CONCLUSION: Omicron variant symptoms among infected patients are generally milder compared to other variants. Prior COVID-19 vaccination may limit disease severity and need for hospitalization.

Multifactor Authentication for Smart Emergency Medical Response Transporters.

Securing telehealth IoT infrastructure is essential to provide high-level medical care and prevent cyberattacks. A vulnerable stage in IoT telehealth is while the patient is being transported to a healthcare facility, the transporter could be an ambulance or an air ambulance. In this paper, we propose a multifactor authentication scheme to secure the system when the patient is in transit to the healthcare facility. We apply this scheme to an ambulance, using physical unclonable functions (PUFs) embedded in the ambulance to facilitate authentication and secure key exchange. We validated the security of the proposed scheme using formal and informal security analysis. The analysis supports our claim that the proposed scheme protects against many types of attacks.

Workplace bullying in surgical environments in Saudi Arabia: A multiregional cross-sectional study.

BACKGROUND: Workplace bullying (WPB) is any sort of repeated and unjustified verbal, sexual, or physical intimidation that a person is exposed to by a group or another person in the workplace. In healthcare environments, practitioners are occasionally victims of WPB incidents. Bullying in surgical environments is an important issue that needs attention as it could affect patient care either directly or indirectly. The objective of this study was to assess the prevalence of bullying in surgical environments in multiple regions in Saudi Arabia. MATERIALS AND METHODS: This cross-sectional study was conducted among surgeons, trainees, interns, nurses, and students in surgical specialties in multiple Saudi regions. The survey was designed by Survey Monkey and posted online. The data were analyzed through SPSS Version-21 by computing descriptive statistics as frequency and percentages with graph construction. RESULTS: About two-thirds (65.2%) of the 788 study participants were male and were between the age of 20 and 29 years (67.8%). Consultants came first as perpetrators of bullying in the past 12 months (44.3%) and residents and interns were the major victims. The NAQ-R score ranged from 22.00 to 110.00, with a mean score of 42.47 (SD=17.9). Statistically significant association was found between mean NAQ-R score and age (P = 0.007), specialty (P = 0.002), and position (P < 0.001). CONCLUSION: WPB is a pervasive problem in surgical environments in multiple regions of Saudi Arabia. Consultants and specialists are the primary offenders in bullying, which makes the hospital an environment for bullying behaviors.

The Long-Term Impact of COVID-19 Pneumonia on the Pulmonary Function of Survivors.

BACKGROUND: The sequelae of COVID-19 pneumonia on pulmonary function and airways inflammation are still an area of active research. OBJECTIVE: This research aimed to explore the long-term impact of COVID-19 pneumonia on the lung function after three months from recovery. METHODS: Fifty subjects (age 18-60 years) were recruited and classified into two groups: the control group (30 subjects) and the post-COVID-19 pneumonia group (20 patients). Pulmonary function tests, spirometry, body plethysmography [lung volumes and airway resistance (Raw)], diffusion capacity for carbon monoxide (DLCO), and fractional exhaled nitric oxide (FeNO), were measured after at least 3 months post-recovery. RESULTS: Significant reduction in total lung capacity (TLC), forced vital capacity (FVC), forced expiratory volume (FEV1), FEV1/FEV, and diffusing capacity for carbon monoxide (DLCO) was observed in post-COVID-19 subjects compared to controls. Restrictive lung impairment was observed in 50% of post-COVID-19 cases (n = 10) compared to 20% in the control group (n = 6, P = 0.026). In addition, mild diffusion defect was detected in 35% (n = 7) of the post-COVID-19 group compared to 23.3% (n = 7) in the controls (P = 0.012). CONCLUSION: COVID-19 pneumonia has an impact on the lung functions in terms of restrictive lung impairment and mild diffusion defect after three months from recovery. Therefore, a long-term follow-up of the lung function in post-COVID-19 survivors is recommended.

On Reliable and Efficient Data Gathering Based Routing in Underwater Wireless Sensor Networks.

This paper presents cooperative routing scheme to improve data reliability. The proposed protocol achieves its objective, however, at the cost of surplus energy consumption. Thus sink mobility is introduced to minimize the energy consumption cost of nodes as it directly collects data from the network nodes at minimized communication distance. We also present delay and energy optimized versions of our proposed RE-AEDG to further enhance its performance. Simulation results prove the effectiveness of our proposed RE-AEDG in terms of the selected performance matrics.