General phytochemical screening of the leaves of Saurauia roxburghii (Actinidiaceae) revealed the presence of alkaloids, flavonoids, glycosides, O-glycosides, terpenoids, carbohydrates, steroids, reducing sugar, tannins, phlobatannins and saponin are present in this plant whereas cardiac glycosides are absent. Two steroid compounds were isolated from the n-hexane extract of the leaves from S. roxburghii. Based on the spectral evidence IR, 1H-NMR and 13C-NMR, structures were determined to be stigmasterol (1) and β-sitosterol (2) This is the first report so far of occurrence and details spectroscopic description of these compounds from S. roxburghii.
This study intended to validate customer inspiration (CI)in Malaysian/developing country context. Data were collected from two different respondents for two studies - from Millennial customers of the auto industry and Generation Z customers of the smartphone industry. The survey conducted through a standardized and structured questionnaire. The variables of the both studies were customer-defined market orientation (MO) (customer orientation, competitor orientation, and interfunctional coordination), CI (inspired-by and inspired-to), and customer loyalty (CL). This research strategy, in terms of quantity, is descriptive and correlational. Statistical analysis of the data was carried out, using ADANCO 2.0. The finding of the study suggests all results of data 1 and data 2 were significant, and CI mediates the sub-constructs of MO with CL.
In this study, the extraction conditions extracted maximize amounts of phenolic and bioactive compounds from the fruit extract of Ficus auriculata by using optimized response surface methodology. The antioxidant capacity was evaluated through the assay of radical scavenging ability on DPPH and ABTS as well as reducing power assays on total phenolic content (TPC). For the extraction purpose, the ultrasonic assisted extraction technique was employed. A second-order polynomial model satisfactorily fitted to the experimental findings concerning antioxidant activity (R2 = 0.968, P P
As synthetic antioxidants that are widely used in foods are known to cause detrimental health effects, studies on natural additives as potential antioxidants are becoming increasingly important. In this work, the total phenolic content (TPC) and antioxidant activity of Ficus carica Linn latex from 18 cultivars were investigated. The TPC of latex was calculated using the Folin-Ciocalteu assay. 1,1-Diphenyl-2-picrylhydrazyl (DPPH), 2,2'-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) and ferric ion reducing antioxidant power (FRAP) were used for antioxidant activity assessment. The bioactive compounds from F. carica latex were extracted via maceration and ultrasound-assisted extraction (UAE) with 75% ethanol as solvent. Under the same extraction conditions, the latex of cultivar 'White Genoa' showed the highest antioxidant activity of 65.91% ± 1.73% and 61.07% ± 1.65% in DPPH, 98.96% ± 1.06% and 83.04% ± 2.16% in ABTS, and 27.08 ± 0.34 and 24.94 ± 0.84 mg TE/g latex in FRAP assay via maceration and UAE, respectively. The TPC of 'White Genoa' was 315.26 ± 6.14 and 298.52 ± 9.20 µg GAE/mL via the two extraction methods, respectively. The overall results of this work showed that F. carica latex is a potential natural source of antioxidants. This finding is useful for further advancements in the fields of food supplements, food additives and drug synthesis in the future.
Fake news and disinformation (FNaD) are increasingly being circulated through various online and social networking platforms, causing widespread disruptions and influencing decision-making perceptions. Despite the growing importance of detecting fake news in politics, relatively limited research efforts have been made to develop artificial intelligence (AI) and machine learning (ML) oriented FNaD detection models suited to minimize supply chain disruptions (SCDs). Using a combination of AI and ML, and case studies based on data collected from Indonesia, Malaysia, and Pakistan, we developed a FNaD detection model aimed at preventing SCDs. This model based on multiple data sources has shown evidence of its effectiveness in managerial decision-making. Our study further contributes to the supply chain and AI-ML literature, provides practical insights, and points to future research directions.