As the ESG principles are rapidly becoming critical in the decision-making process of investment undertaking, companies must deal with ESG reporting either to fulfil obligatory requirements according to EU legislation or, to stay competitive and in line with the investing trends. In the market, several ESG rating methodologies exist consisting of numerous criteria and indicators that companies must consider in their reporting. Nevertheless, neither all ESG rating frameworks consider the same criteria, nor do all criteria have the same materiality weighting when estimating the final ESG rating. Thus, the establishment of a standardized and normalized ESG criteria materiality framework is considered crucial. This will enable the fine-tuning and calibration of the ESG evaluation, with materiality values that reflect accordingly the significance of the most important criteria. In addition, this methodology will enhance the comparability of the results of the different companies’ evaluations while creating a harmonized framework. The present manuscript introduces an integrated methodology for the estimation of the ESG materiality factors putting emphasis on the most frequent criteria of the main economic sectors. The methodology analyses data from several sources, including academic papers and methodologies, companies’ reports and globally established rating frameworks. The proposed approach results in the estimation of the materiality values for each criterion of a specific ESG rating scorecard, as well as introduces an overview of the materiality issues for each economic sector.
The purpose of this study is to develop a metaheuristic design for primary parameters and architectures of two models of artificial neural network (ANN) in predicting a cargo aircraft's exergo-emissions (exergy destruction ratio, r(ex,dest) , and waste exergy ratio, r(wex) ) at different flight stages. Hybrid genetic algorithm (GA)-ANN models have been accomplished utilizing real databases of r(ex,dest) and r(wex) at various powers. Implementing a metaheuristics-based optimization on multilayer perceptron (MLP)-ANNs has produced the most favourable initial weights, step-size, biases, and training algorithm's back-propagation (BP) momentum rate in addition to optimum number of neurons in the hidden layer(s). In accordance with an error assessment, a close fit linking real data and r(wex) (linear correlation ratio, R, value of 0.999851) as well as r(ex,dest) (R value of 0.999985) predicted values is found. In the r(ex, dest) estimation model, the accuracy among single-hidden-layer networks has been confirmed to be higher; whereas, highly accurate testing outcomes have been obtained in two-hidden-layer networks as far as modeling of r(wex) is concerned. ANN models' optimization by GAs has increased the accuracy of the resulting models (R value of 0.999987 and 0.999869 for r(ex,dest) and r(wex) , in that order ascertaining a drop-off in the testing stage errors).
Purpose To compare physical, psychological, and physiological adaptations between rotating and morning shift health workers using objective and subjective approaches. Methods Forty nurses [ n = 20 morning shift (MS) group; n = 20 rotating shift (RS) group] were evaluated for anthropometry, body composition, and handgrip strength. Quality of life, depression, fatigue, daytime sleepiness, and sleep quality were assessed with SF-36, Zung Self-Rating Depression Scale (SDS), Fatigue Severity Scale (FSS), Epworth Sleepiness Scale (ESS), and Pittsburgh Sleep Quality Index (PSQI), respectively. Physical activity was assessed by the International Physical Activity Questionnaire (IPAQ) and triaxial accelerometers. Sleep-related data were monitored with sleep actigraphy. Salivary melatonin levels were analyzed before/after sleep, and blood lipid profiles were measured the following morning. Results The RS group had higher mean BMI and total and abdominal fat and scored lower in the SF-36 ( p < 0.01). All nurses showed reduced physical activity levels, which, in the RS group, were negatively correlated with FSS ( p = 0.033) and SDS scores ( p = 0.025). Poor sleep was revealed in 53% of nurses. The RS group had worse sleep quality by PSQI than the MS group ( p = 0.045). PSQI scores were inversely related to SF-36 scores and positively correlated with FSS, BMI, waist circumference, and body fat ( p < 0.05). Conclusion RS nurses showed increased body mass and total and abdominal fat along with decreased quality of life and sleep quality compared to MS counterparts. A strong relationship was found between physical, psychological, and physiological domains. Further studies should consider workplace interventions to prevent obesity, promote physical activity, and manage poor sleeping patterns in nurses.
BACKGROUND The use of rapeseed protein for human nutrition is primarily limited by its strong bitterness, which is why the key bitter compound, kaempferol 3-O-(2"'-O-sinapoyl-beta-sophoroside), is enzymatically degraded. RESULTS Mass spectrometry analyses of an extract from an untreated rapeseed protein isolate gave three signals for m/z 815 [M-H]. The predominant compound among the three compounds was confirmed as kaempferol-3-O-(2"'-O-sinapoyl-beta-sophoroside). Enzymatic hydrolysis of this key bitter compound was achieved using a sinapyl ester cleaving side activity of a ferulic acid esterase (FAE) from the basidiomycete Schizophyllum commune (ScoFAE). Recombinant ferulic acid esterases from Streptomyces werraensis (SwFAE) and from Pleurotus eryngii (PeFAE) possessed better cleavage activity towards methyl sinapate but did not hydrolyze the sinapyl ester linkage of the bitter kaempferol sophoroside. CONCLUSION Kaempferol-3-O-(2"'-O-sinapoyl-beta-sophoroside) was successfully degraded by enzymatic treatment with ScoFAE, which may provide a means to move the status of rapeseed protein from feed additive to food ingredient. (c) 2021 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
