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Statistical optimization of process variables for enhanced serratiopeptidase production from soil Serratia marcescens VS56
Identification and classification method of landslide pattern in the soil water index-based early warning system
The contribution of integrated and non-integrated pig farms to epidemiological dynamics of porcine reproductive and respiratory syndrome virus in Italy
Progress of chronic kidney disease and associated predictors among patients under treatment at Gambi and Felege-Hiwote hospitals
Using five exposure metrics to explore the association between ambient PM2.5 and the hospital admissions for COPD in Tianshui city, China
Dentists’ role in oral care for head and neck cancer patients in GCC countries
UPLC/MSn analysis of Bougainvillea glabra leaves and investigation of antioxidant activities and enzyme inhibitory properties
Abstract Bougainvillea glabra is a well-known and well-documented ornamental plant belonging to family Nyctaginaceae, always planted for its beautifully colored flowers. The current study was performed for profiling the metabolites of B. glabra leaf extract. Further, the leaf extract was evaluated using the total phenolic and total flavonoid assays and profiling using UPLC/MSn for the leaf extract’s secondary metabolites. The leaf extract was also subjected to several antioxidant assays, viz. DPPH, ABTS, CUPRAC, FRAP, metal chelating, and phosphomolybdenum tests, as well as enzyme inhibition assays such as α-amylase, α-glucosidase, acetylcholinesterase (AChE), and butyrylcholinesterase (BChE). The results showed that the total phenolic and flavonoid contents were 27.68 mg GAE/g and 31.76 mg RE/g, respectively. Besides, twenty-one metabolites were tentatively identified and quantified, where flavonoids and phenolic acids constituted the most abundant classes of compounds. The molecular docking experimentss showed that the most abundant components, namely rhamnocitrin-O-rutinoside, sagerinic acid, tri-O-caffeoyl-shikimic acid, and chlorogenic acid, had the best scores when docked in the vicinity of the selected enzyme targets. The extract recorded a potentially powerful antioxidant activity, compared to the used standards, while it showed a good inhibitory effect against AChE (2.40 mg GALAE/g) and BChE (1.95 mg GALAE/g). The tyrosinase-inhibiting effect was 48.23 mg CAE/g. The amylase and glucosidase inhibitory effects were 0.30 mmol ACAE/g and 0.03 mmol ACAE/g, respectively. Thus, this study suggests that B. glabra may not only act as an ornamental plant, but also it may be a promising source for effective phytochemicals that act as antioxidants and enzyme inhibitors, which may play a role in reversing the aging process and age-related ailments like diabetes.
Assessing carbon–neutral supercapacitors in renewable energy systems with self-improving agent-based molecular fuzzy intelligent algorithms
Acoustic standing wave driven bubble dynamics in Oldroyd-B fluids using a semi analytical approach
Application of concentrated solar energy in postprocessing of selective laser melted Ti6Al4V alloy through simultaneously gas nitriding and heat treatment
The impact of embedded global innovation networks on manufacturing value chains
Using machine learning to forecast conflict events for use in forced migration models
Abstract Forecasting the movement of populations during conflict outbreaks remains a significant challenge in contemporary humanitarian efforts. Accurate predictions of displacement patterns are crucial for improving the delivery of aid to refugees and other forcibly displaced individuals. Over the past decade, generalized modeling approaches have demonstrated their ability to effectively predict such movements, provided that accurate estimations of conflict dynamics during the forecasting period are available. However, deriving precise conflict forecasts remains difficult, as many existing methods for conflict prediction are overly coarse in their spatial and temporal resolution, rendering them inadequate for integration with displacement models. In this paper, we propose a hybrid methodology to enhance the accuracy of conflict-driven population displacement forecasts by combining machine learning-based conflict prediction with agent-based modeling (ABM). Our approach uses a coupled model that combines a Random Forest classifier for conflict forecasting with the Flee ABM—a model of the movements of refugees and internally displaced persons (IDPs). The coupled model is validated using case studies from historical conflicts in Mali, Burundi, South Sudan, and the Central African Republic. Our results demonstrate comparable predictive accuracy over traditional methods without the need for manual conflict estimations in advance, thus reducing the effort and expertise needed for humanitarian professionals to provide urgent displacement forecasts.