A mass vaccination site selection problem: An application of GIS and entropy-based MAUT approach

Climate change impact on green spaces planning in an urban area using a hybrid approach

This study presents a hybrid methodology for planning green spaces to enhance urban sustainability and livability, evaluating the impacts of climate change on cities. Cities, once accommodating a small population, have become major centers of migration and development since the eighteenth century. Rapid urban growth intensifies infrastructure, environmental, and social challenges. Fossil fuel reliance and deforestation increase greenhouse gas emissions, exacerbating climate change, impacting ecosystems, and urban livability. This research assesses green spaces in Izmir's Bayraklı district by assigning weights to site selection criteria via the analytic hierarchy process (AHP), entropy weight method (EWM), and Game Theory. The weighted linear combination (WLC) method integrates these weights to produce decision maps. Game Theory harmonizes discrepancies between AHP and EWM, influencing the decision maps. Notably, incorporating climate change criteria reduced highly suitable areas from 50.3 to 41.5%, stressing climate considerations in planning. Grey relational analysis (GRA) prioritizes investment areas, showing objective, criterion-based planning's importance in sustainable urban development.

Anti-prostate cancer mechanism of black ginseng during the "nine steaming and nine sun-drying" process based on HPLC analysis combined with vector space network pharmacology

HPLC analysis determined six small-molecule organic acids, maltol, 5-hydroxymethylfurfural (5-HMF), 17 ginsenosides, four oligosaccharides, and 20 amino acids in black ginseng samples with different processing times. Based on the content determination results, the differential ingredients in the processing of black ginseng were screened by multivariate statistical analysis. Network pharmacological methods obtained the core targets and pathways of the above ingredients against prostate cancer. Finally, the entropy weight method was used to assign values to the above ingredients, targets, and pathways, and the vector space network pharmacology method was established to study the anti-prostate cancer mechanism of black ginseng in the process of "nine steaming and nine sun-drying". Based on principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA), fructose, glucose, dencichin, glutamate, ginsenoside 20 (S)-Rg3, 20 (R)-Rg3, 20 (S)-Rh2, Rg1, Re, and Rc were the main differential ingredients in various steaming and sun-drying cycle periods of black ginseng. The results of vector space network pharmacology showed that the main reason for the change in the anti-prostate cancer pathway of black ginseng with the number of steaming and sun-drying was the different regulatory ability of black ginseng on the PI3K-Akt signaling pathway and chemical carcinogenesis-receptor activation pathway. It gave researchers a fresh perspective for exploring the anti-prostate cancer active components of black ginseng and the change in the mechanism of the effect of traditional Chinese medicine in processing.

Resilient and social health service network design to reduce the effect of COVID-19 outbreak

With the severe outbreak of the novel coronavirus (COVID-19), researchers are motivated to develop efficient methods to face related issues. The present study aims to design a resilient health system to offer medical services to COVID-19 patients and prevent further disease outbreaks by social distancing, resiliency, cost, and commuting distance as decisive factors. It incorporated three novel resiliency measures (i.e., health facility criticality, patient dissatisfaction level, and dispersion of suspicious people) to promote the designed health network against potential infectious disease threats. Also, it introduced a novel hybrid uncertainty programming to resolve a mixed degree of the inherent uncertainty in the multi-objective problem, and it adopted an interactive fuzzy approach to address it. The actual data obtained from a case study in Tehran province in Iran proved the strong performance of the presented model. The findings show that the optimum use of medical centers' potential and the corresponding decisions result in a more resilient health system and cost reduction. A further outbreak of the COVID-19 pandemic is also prevented by shortening the commuting distance for patients and avoiding the increasing congestion in the medical centers. Also, the managerial insights show that establishing and evenly distributing camps and quarantine stations within the community and designing an efficient network for patients with different symptoms result in the optimum use of the potential capacity of medical centers and a decrease in the rate of bed shortage in the hospitals. Another insight drawn is that an efficient allocation of the suspect and definite cases to the nearest screening and care centers makes it possible to prevent the disease carriers from commuting within the community and increase the coronavirus transmission rate.