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The building sector is the largest energy consumer accounting for 40% of global energy usage. An energy forecast model supports decision-makers to manage electric utility management. Identifying optimal values of hyperparameters of prediction models is challenging. Therefore, this study develops a novel time-series Wolf-Inspired Optimized Support Vector Regression (WIO-SVR) model to predict 48-step-ahead energy consumption in buildings. The proposed model integrates the support vector regression (SVR) and the grey wolf optimizer (GWO) in which the SVR model serves as a prediction engine while the GWO is used to optimize the hyperparameters of the SVR model. The 30-min energy data from various buildings in Vietnam were adopted to validate model performance. Buildings include one commercial building, one hospital building, three authority buildings, three university buildings, and four office buildings. The dataset is divided into the learning data and the test data. The performance of the WIO-SVR was superior to baseline models including the SVR, random forests (RF), M5P, and decision tree learner (REPTree). The WIO-SVR model obtained the highest value of correlation coefficient (R) with 0.90. The average root-mean-square error (RMSE) of the WIO-SVR was 2.02 kWh which was more accurate than those of the SVR model with 10.95 kWh, the RF model with 16.27 kWh, the M5P model with 17.73 kWh, and the REPTree model with 26.44 kWh. The proposed model improved 442.0–1207.9% of the predictive accuracy in RMSE. The reliable WIO-SVR model provides building managers with useful references in efficient energy management.

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Introduction: In Singapore, tissue donation is covered under the Medical (Therapy, Education and Research) Act. The objective of this study is to review the demographic and psychosocial factors, which may cause hesitation/unwillingness amongst healthcare professionals towards tissue donation. Materials and Methods: A survey comprising 18-items was conducted at the Singapore General Hospital and National Heart Centre Singapore. A total of 521 individuals participated in the survey. Descriptive statistics were performed for the demographic profiles of participants, the factors leading to the support of tissue donation, reasons for hesitation/reluctance to donate tissue and motivating factors to discuss tissue donation with next-of-kin. Pearson’s chi-square and Fisher’s exact tests were employed to assess possible association between various factors and support towards tissue donation. Analyses were performed using Statistical Package for Social Sciences V.21.0 software. Results: A total of 64.9% of participants had heard about skin donation; 48.9% had heard about heart valve donation; 4.5% were tissue pledgers. The primary reason for pro-donation was the altruism of “improving someone’s quality of life”. However, a majority stated they “can decide this in the later part of life” as their main reason for hesitation; 82.3% were willing to discuss their tissue donation wish with next-of-kin, while 53.1% were likely to make the decision of donation on behalf of their deceased next-of-kin. Conclusion: Results highlighted important psychosocial and professional factors that influence the hesitation/reluctance towards donation. Hence, there is a need to re-strategise educational efforts in accordance with the target audiences and address specific misconceptions and concerns. Key words: Heart valve banking, Tissue donation, Skin allografts, Skin banking