Humans spend approximately 90% of the daytime in buildings, and greenhouse gases (GHGs) emitted by buildings account for approximately 20% of total GHG emissions. As the energy consumed during building operation from a building life-cycle perspective amounts to approximately 70–90% of the total energy, it is essential to accurately predict the energy consumption of buildings for their efficient operation. This study aims to optimize a model for predicting the thermal energy consumption of buildings by (i) first extracting major variables through feature selection and deriving significant variables in addition to the collected data and (ii) predicting the thermal energy consumption using a machine learning model. Feature selection using random forest was performed, and 11 out of 17 available data were selected. The accuracy of the prediction model was significantly improved when the hour of day variable was added. The prediction model was constructed using an artificial neural network (ANN), and the improvement in the prediction accuracy was analyzed by comparing different cases of variable combinations. The ANN prediction accuracy was improved by 15% using the feature selection process compared to when all data were used as input data, and 25% coefficient of variation of the root mean square error (CVRMSE) accuracy was achieved.

Objectives Several experimental studies have reported antiobesity and lipid-improving effects of Citrus unshiu. However, clinical studies on its effects are lacking. This study was designed to evaluate the impact of Citrus unshiu peel pellet (CUPP) on obesity and lipid profile. Methods For 118 patients with body mass index (BMI) > 23 who took Citrus unshiu peel pellet (CUPP) for 4 weeks in a Public Health Center, laboratory and biometric readings before and after CUPP administration were analyzed. Results Mean age of these subjects was 53.8±10.6 years (range: 18-75 years). There were 88 (74.6%) females in the study sample (n = 118). A significant (p < 0.01) decrease in BMI from 27.47±2.24 to 27.27±2.22 was observed in all subjects after CUPP treatment and 65.3% (N = 77) of them lost 1.03±0.83 kg of weight after 4 weeks of treatment. Total cholesterol level was significantly (p < 0.01) decreased from 204.0±37.4 mg/dL to 193.5±36.5 mg/dL. Significant (p < 0.05) decreases in levels of low-density lipoprotein, cholesterol, and triglyceride were also observed. Conclusions These results suggest that CUPP in practice could help weight control and improve total cholesterol level. Findings of this study provide clinical foundation for future large-scale trials to establish clinical benefits of CUPP.

As density of a wireless LAN grows, per-user throughput degrades severely, deteriorating user experience. To improve service quality, it is important to increase system spectral efficiency. Controlling carrier-sense threshold is one of the key techniques to achieve the goal, because frequently transmissions are unnecessarily blocked by carrier sensing, even though these transmissions can take place without causing packet losses. Using high carrier-sense threshold and allowing nodes to transmit aggressively may increase the system throughput, but this approach can lead to unfair channel share and cause starvation for the edge nodes. In this paper, we propose a medium access control protocol where transmitters include the carrier-sense threshold required to protect its packet in the preamble. Nodes receiving the preamble only transmit concurrently, when they are confident that their own transmission as well as the on-going transmission will both be successfully received at the respective receivers. The simulation results show that this dual-threshold approach can achieve higher system throughput compared to using a single carrier-sense threshold, without penalizing edge nodes.

Incorporating Internet of Things (IoT) technology into the operation of buildings is expected to generate immense synergy, thereby saving energy and improving occupant comfort by overcoming the limitations of the existing system. Preventing operations in the absence of occupants can save energy, and the occupants’ preferred operating temperature should be used as the control set-point rather than the nominal temperature. In this study, IoT technology and image sensors are used to rapidly detect indoor environment changes, and a method is proposed to utilize air quality and thermal comfort as the control set-points. A real-time ventilation control algorithm is proposed based on the CO2 concentration calculated according to the number of occupants. To check the thermal comfort level, the real-time operating temperature estimated from the surface temperature data of the infrared array sensor is reflected in the comfort zone defined by the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE). The deficiencies in indoor environment conditions caused by the temporal and spatial lag of sensors in the old system are minimized using IoT technology, which also facilitates wireless communications. The image sensors can be used for multiple purposes based on various interpretations of the image information obtained.

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