Neuroticism, anxiety, and depression in Egyptian atopic bronchial asthma

The association between allergic and psychological disorders had been reported, but whether the key mediating ingredients are predominantly biological, psychological, or mere artifacts remains unknown. We aim to examine the relationship between objectively measured atopic status and anxiety, depression, and neuroticism. Methods: This randomized case controlled trial was conducted on 50 atopic patients and 50 healthy controls. Atopy was determined by skin prick test and allergy related symptoms. Psychological assessment was done using Beck Depression Inventory (BDI) for the level of depression, the State-Trait Anxiety Inventory (STAI) for anxiety level, while Middle Sex Hospital Questionnaire (MHQ) for measurement of neuroticism. Serum total IgE level was detected in both groups. Results: 100 individuals were enrolled (50 atopic patients and 50 healthy control subjects) with a mean age (28.24 ± 9.74) and (32.60 ± 8.23) respectively. The mean STAI score for both state and trait anxiety was significantly higher in atopic versus non-atopic (p = 0.000) while the mean BDI score was higher in atopic than non-atopic patients but without statistical significance. Also, there was no significant difference in the mean MHQ scores (for hysteria, depression, obsession, somatic anxiety, phobic anxiety and free-floating anxiety) in atopic versus non-atopic groups. There was no correlation between the mean STAI, BDI, and MHQ scores and the mean value of total IgE levels in atopic patients. Conclusion: Atopic patients are more likely to have both state and trait anxiety than non-atopic. So, it might be considered in management plan of atopic patients

Improving Power Quality Problems of Isolated MG Based on ANN Under Different Operating Conditions Through PMS and ASSC Integration

Microgrid (MG) technologies assist the power grid in evolving to become more efficient, less polluting, and more resilient by addressing the requirements of energy users. However, several technological issues arise as a result of the unpredictability and difficulty in estimating the efficacy and regulation of the many renewable energy resources (RERs) incorporated in MGs. Two of the most significant of these issues are maintaining system stability and power quality, which necessitate to get better the performance of the MGs. The most difficult challenge, system stability, can be achieved with successful Power Management System (PMS). This paper proposes an effective PMS for an AC MG equipped with a diesel generator (DG), a permanent magnet wind generator (PMWG), and a solar photovoltaic (PV) panel Based on an adaptable Artificial Neural Network (ANN). The ANN weights are properly tuned via the Enhanced Bald Eagle Search (EBES) optimization algorithm to produce a stable system during the whole training period, achieve MG energy balance, reduce the usage of fossil fuel DG and maintain MG voltage stability. In addition, for keeping power quality, an adaptive series shunt compensator (ASSC) is described in this work, along with a developed integrative PID controller, where the latter’s controller gains are ideally set utilizing the EBES optimization algorithm to perform adaptably with self-tuning when the operational circumstances of an MG change. various cases are displayed to test the strong of offered ASSC on harmonic mitigation, dynamic voltage stabilization, reactive power control and power factor correction. Moreover, comprehensive case study based on realistic on-site location for Zafarana region, Suez Gulf region of Egypt is proposed. Taking into account The changing nature of weather-related renewable energy, actual loads states and transient faults