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ABSTRACTBackground Traumatic brain injury (TBI) is a quite common health problem. A lot of delayed complications are related to inflammatory responses that occurred within the brain itself. Atorvastatin is related to lipid lowering drugs carrying some anti-inflammatory properties and upon this fact this study hypothesis was built.Methods Twenty adult patients with TBI, Glasgow coma scale (GCS) 9–11. Patients were equally and randomly allocated into two groups (group C as control group and group S received atorvastatin 40 mg once daily for 48 h). After 48 h, participants have undergone magnetic resonance imaging brain spectroscopy examination (MRS). The spectral peaks of N-Acetyl aspartate (NAA), Choline, and Creatinine (Cr) were assessed in brain tissue. The primary outcome was presented as ratios of NAA/Cr), Cho/Cr, and NAA/Cho. Other outcomes included GCS and ICU stay.Results There were insignificant variations between groups were found in the MRS results for metabolite alterations (NAA, Cr, and Cho). Contrasted with the control group, the statin group’s Cho/Cr ratio was significantly lower (P = 0.005), and NAA/Cho was significantly greater in the statin group than control group (P = 0.022). Statin group showed higher GCS the 1st day (P = 0.01), and lesser ICU stay (P = 0.04)Conclusion Atorvastatin can be used safely in mild-to-moderate TBI patients with a favourable outcome in the form of decreased Cho/Cr ratio and increased NAA/Cho ratio, higher GCS, and decreased ICU length of stay.

Effective Healthcare Facility Management (HFM) remain a crucial concern for high quality built healthcare sectors, both in the public and private areas. The anticipated resource efficiencies, complex systems, and maintenance are all driving up demand for efficiency and flexibility. However, it has been almost impossible with limited human and machinery resources to cope with the healthcare sector’s demands. The added value of Digitization will assist organizations overcoming the limited resources issue to deal with the increasing healthcare demands. Based on an extensive literature review, the aim of this paper is to suggest innovative digital and facility management methods that improve the healthcare sector experience. The same literature has been used to extract significant variables to build a causal loop diagram (CLD) that assists in better understanding the interrelationship between those variables within healthcare systems. Furthermore, to prepare for the technology adoption, the paper conducted a cross-sectional survey to forecast the acceptance level in 550 participants from Qatar’s healthcare sector. It also discusses applying the most innovative healthcare artificial intelligence techniques through an organized digital transformation process to guarantee delivering the healthcare services efficiently while optimizing resources and costs.

Diverticulitis and ulcerative colitis (UC) are two separate colonic pathologies with different underlying mechanisms. Diverticulosis involves herniation of mucosal and submucosal tissue through muscular tissue in response to increased intraluminal pressure. In contrast, it is believed that the muscular tone in patients with UC is reduced due to chronic inflammatory changes. Thus, it has been reported that there may be an inverse relationship between the presence of diverticulosis in patients with UC, in that UC may possibly be protective against developing diverticular disease. Consequently, the co-presence of both pathologies is uncommon. Here we present a case in which a woman with a history of UC and recurrent diverticulitis after elective partial colectomy was admitted for recurrent acute diverticulitis. It is quite challenging to diagnose diverticulitis in ulcerative colitis patients given the usually similar presentation with abdominal pain, diarrhoea and hematochezia. A level of high suspicion is required for diagnosis.

Recently, building an accurate mathematical model with the help of the experimentally measured data of solar cells and Photovoltaic (PV) modules, as a tool for simulation and performance evaluation of the PV systems, has attracted the attention of many researchers. In this work, Coyote Optimization Algorithm (COA) has been applied for extracting the unknown parameters involved in various models for the solar cell and PV modules, namely single diode model, double diode model, and three diode model. The choice of COA algorithm for such an application is made because of its good tracking characteristics and the balance creation between the exploration and exploitation phases. Additionally, it has only two control parameters and such a feature makes it very simple in application. The Root Mean Square Error (RMSE) value between the data based on the optimized parameters for each model and those based on the measured data of the solar cell and PV modules is adopted as the objective function. Parameters' estimation for various types of PV modules (mono-crystalline, thin-film, and multi-crystalline) under different operating scenarios such as a change in intensity of solar radiation and cell temperature is studied. Furthermore, a comprehensive statistical study has been performed to validate the accurateness and stability of the applied COA as a competitor to other optimization algorithms in the optimal design of PV module parameters. Simulation results, as well as the statistical measurement, validate the superiority and the reliability of the COA algorithm not only for parameter extraction of different PV modules but also under different operating scenarios. With the COA, precise PV models have been established with acceptable RMSE of 7.7547×10-4, 7.64801×10-4, and 7.59756×10-4 for SDM, DDM, and TDM respectively considering R.T.C. France solar cell.

Integrating wind power plants (WPPs) into power systems are increasing dramatically now a day. However, the dynamic performance of power systems will be affected by the large penetration level of such renewable sources of energy. From this context power system operators and transmission system operators have put regulation rules to keep pushing wind power plants to safeguard limits that keep power system more stable and reliable. One of these rules is providing a low voltage ride through (LVRT) for wind farms without disconnecting it from the power system. The current paper implements the STATCOM as a LVRT for a 9 MW wind farm connected to the grid through transmission system of 120 kV. For enhancing the dynamic performance of STATCOM, two types of optimization methodologies: ant colony (ACO) and particle swarm optimization (PSO), are proposed to fine tune the coefficients of PI controllers to optimally manage the STATCOM dynamics.

The voltage quality (VQ) index has become a significant measure of recent power system stability. The integration of photovoltaic (PV) systems plus smart home loads (SHLs) at low voltage levels (LVLs) has resulted in various issues such as harmonics rise and voltage instabilities as a result of faults and systems nonlinearity. In this work, a dynamic voltage resistor (DVR) is implemented to enhance VQ, and its dynamic performance hinges on its control system ability. To enhance the DVR’s control system, for surpassing nonstandard voltage with a quick response and harmonics reduction at LVL under harsh operating events, an optimal controller design using the Harris Hawks algorithm (HHA) is proposed. To verify the value of the suggested solution, the hard operating events (voltage sag, voltage swell, fluctuating voltage, and imbalanced voltage) are examined and assessed. To show the effectiveness of the HHA technique, a comparison of the % total harmonic distortion (THD) reductions achieved by the suggested and conventional controllers of DVR is conducted for the scenarios under study. Moreover, the suggested controller stability is analyzed and assessed using Lyapunov’s function. The benefits of the optimized controller system are inferred from the results, including their robustness, simplicity, efficient harmonic rejection, minimal tracking error, quick response, and sinusoidal reference track. The results of the simulation show that the DVR’s optimized controller is efficient and effective in maintaining a voltage at the needed level with low THD, safeguarding the sensitive load as expected, and showing a noticeable improvement in voltage waveforms. The mathematical modeling of HHA, PV system, DVR, and SHLs are all verified using MATLAB\Simulink.

Modern electrical power systems now require the spread of microgrids (MG), where they would be operating in either islanded mode or grid-connected mode. An inherent mismatch between loads and sources is introduced by changeable high renewable share in an islanded MG system with stochastic load demands. The system frequency is directly impacted by this mismatch, which can be alleviated by incorporating cutting-edge energy storage technologies and FACTS tools. The investigated islanded MG system components are wind farm, solar PV, Electric vehicles (EVs), loads, DSTATCOM, and diesel power generator. An aggregated EVs model is connected to the MG during uncertain periods of the generation of renewable energy (PV and wind) to support the performance of MGs. The ability to support ancillary services from the EVs is checked. DSTATCOM is used to provide voltage stability for the MG during congestion situations. The MG is studied in three scenarios: the first scenario MG without EVs and DSTATCOM, the second scenario MG without DSTATCOM, and the third scenario MG with all components. These scenarios are addressed to show the role of EVs and DSTATCOM, and the results in the third scenario are the best. The system voltage and frequency profile is the best in the last scenario and is entirely satisfactory and under the range of the IEEE standard. The obtained results show that both EVs and DSTATCOM are important units for improving the stability of modern power grids. The Matlab/Simulink program is considered for checking and validating the dynamic performance of the proposed configuration.

Objective: The purpose of this study was to determine the prevalence of diabetes, impaired glucose tolerance (IGT), impaired fasting glycemia (IFG), and associated risk factors in a rural South African black community.Research Design and Methods: This was a cross-sectional survey conducted by random cluster sampling of adults aged >15 years. Participants had a 75-g oral glucose tolerance test using the 1998 World Health Organization criteria for disorders of glycemia.Results: Of 1,300 subjects selected, 1,025 subjects (815 women) participated (response rate 78.9%). The overall age-adjusted prevalence of diabetes was 3.9%, IGT 4.8%, and IFG 1.5%. The prevalence was similar in men and women for diabetes (men 3.5%; women 3.9%) and IGT (men 4.6%; women 4.7%) but higher in men for IFG (men 4.0%; women 0.8%). The prevalence of diabetes and IGT increased with age both in men and women, with peak prevalence in the 55- to 64-year age-group for diabetes and in the >or=65-year age-group for IGT. Of the cases of diabetes, 84.8% were discovered during the survey. In multivariate analysis, the significant independent risk factors associated with diabetes included family history (odds ratio 3.5), alcohol ingestion (2.8), waist circumference (1.1), systolic blood pressure (1.0), serum triglycerides (2.3), and total cholesterol (1.8); hip circumference was protective (0.9).Conclusions: There is a moderate prevalence of diabetes and a high prevalence of total disorders of glycemia, which suggests that this community, unlike other rural communities in Africa, is well into an epidemic of glucose intolerance. There is a low proportion of known diabetes and a significant association with potentially modifiable risk factors. [ABSTRACT FROM AUTHOR]