A comparative study of advanced evolutionary algorithms for optimizing microgrid performance under dynamic pricing conditions.

The integration of microgrids into the existing power system framework enhances the reliability and efficiency of the utility grid. This manuscript presents an innovative mathematical paradigm designed for the optimization of both the structural and operational aspects of a grid-connected microgrid, leveraging the principles of Demand-Side Management (DSM). The focus of this work lies in a comprehensive exploration of the implications brought about by the Renewable Generation-Based Dynamic Pricing Demand Response (RGDP-DR) mechanism, particularly in terms of its influence on the optimal microgrid configuration, considering perspectives from end-users and the utility entity. This inquiry is rooted in a holistic assessment that encompasses technical and economic performance benchmarks. The RGDP-induced DR framework adeptly addresses the needs of the consumer base, showcasing notable efficiency and economic feasibility. To address the intricate nonlinear optimization challenge at hand, we employ an evolutionary algorithm named the "Dandelion Algorithm" (DA). A rigorous comparative study is conducted to evaluate the efficacy of four optimization techniques, affirming the supremacy of the proposed DA. Within this discourse, the complexity of microgrid sizing is cast as a dual-objective optimization task. The twin objectives involve minimizing the aggregate annual outlay and reducing emissions. The results of this endeavor unequivocally endorse the superiority of the DA over its counterparts. The DA demonstrates exceptional proficiency in orchestrating the most cost-effective microgrid and consumer invoice, surpassing the performance of alternative optimization methodologies.

Assessment of potential risk factors for coronavirus disease-19 (COVID-19) among health care workers.

BACKGROUND: Understanding COVID-19 infection among health workers and the risk factors for adverse outcomes is important not only for characterizing virus transmission patterns and risk factors for infection, but also for preventing the future infection of health workers and other patients and reducing secondary COVID-19 transmission within health care settings. Our aim was to identify risk factors for infection among health care workers to limit adverse events in health care facilities. SUBJECTS AND METHODS: A total of 336 HCWs from COVID-19 treatment hospitals took part in the study with varying COVID-19 exposure risk depending on job function and working site. All participants were asked about risk factors for COVID-19 infection. RESULTS: Among our participants, 42.6% were medical doctors, 28.6% nurses and 7.4% assistant nurses and 21.4% were others. Forty four percent of participants had work experience 5-10 years. More than half of participants received training in Infection Prevention and Control (IPC) (56.8%) about COVID 19; 91% have hand hygiene facilities and 69% admitted availability of PPE. More than half of participants admitted that they always follow IPC measures. Two thirds of participants (66.7%) had close contact with a patient since admission; 42.3% were present in aerosolizing procedures for patients. Forty two percent of participants had respiratory symptom; the most common was sore throat representing (32.4%). The highest frequency of respiratory symptoms was among of nurses and assistant nurses 51%. Frequency of respiratory symptoms was higher among those who contacted the patient directly or for prolonged period compared to those who do not admitted these contacts. CONCLUSIONS: Risk factors for COVID-19 represented by those who were smokers, nurses and assistant nurses were more liable to catch COVID-19 than doctors as they contacted the patient directly for prolonged period or his/her body fluids, materials or surfaces around him.

Fabrication of a novel low-cost superoleophilic nonanyl chitosan-poly (butyl acrylate) grafted copolymer for the adsorptive removal of crude oil spills.

A novel superoleophilic-hydrophobic nonanyl chitosan-poly (butyl acrylate) grafted copolymer was fabricated as a low-cost oil-adsorbent. Chitosan (CS) was coupled with a hydrophobic nonanal (N) to form nonanyl chitosan (NCS) schiff base, and followed by grafting with butyl acrylate monomers (ButA). The grafted copolymer was characterized by FTIR, TGA and SEM tools. The grafting percent was augmented and reached 88.5% with increasing ButA concentration up to 156 mM. Moreover, measurements of contact angle proved the superoleophilic character of NCS-g-poly (ButA) copolymer with an oil-contact angle 31°. Factors affecting the removal process such as contact time, oil type, oil dose, adsorbent dose, temperature and agitation speed were optimized. An increment in the oil removal (%) was observed with increasing the oil viscosity in the order of gasoil < mobil-1 oil < light crude oil < heavy crude oil. Besides, the adsorption process followed the pseudo-second order model and the equilibrium data were sufficiently fitted with the Langmuir model with a maximum adsorption capacity 108.79 g/g at 25 °C. Thermodynamic parameters computed from Van't Hoff plot confirmed the process to be exothermic, favorable and spontaneous. The results nominate the superoleophilic adsorbent as a potential oil- adsorbent for petroleum oil spills removal.

A comparative assessment of heavy metal accumulation in soft parts and byssus of mussels from subarctic, temperate, subtropical and tropical marine environments.

Existing data on metal concentrations in mussels from subarctic, temperate, subtropical and tropical waters were analyzed using multivariate statistics in order to assess regional variations in metal contamination. Potential errors were reduced by only analyzing data from surveys that employed the same protocols, analytical methodologies and analysts. Factor analysis demonstrated that mussels inhabiting extremely contaminated areas (e.g. from Japanese and Swedish metallurgy sources) could be separated from mussels from other contaminated areas, and that metals such as Cd, Pb, Cu and Zn could be used to identify heavily contaminated samples while Co, Fe, Cr and Ni concentrations were good markers for exposure to inputs from different industrial sources. Furthermore byssus, like soft tissue, selectively and sensitively reflects variations of certain metal concentrations in ambient waters and thus serves as a reliable biomonitor for these contaminants in a variety of coastal and estuarine areas.