[Purpose] The purpose of this study was to investigate the effects of a virtual reality (VR) program on cognitive function and balance in the elderly with mild cognitive impairment (MCI) attending G welfare center in Gurye. [Subjects and Methods] Twenty-four patients with MCI were studied. The patients were exposed to the VR program for 30 min per experiment, which was conducted 20 times for four weeks. [Results] The cognitive function and balancing ability of the experimental group, when compared to the control group, showed a statistically significant increase in Visual Span Test (VST), Word Color Test (WCT), and Limit of Stability (LOS), which are the sub-categories of CNT 4.0, after the exposure to the program. In all test categories, the experimental group exhibited statistically significant differences compared to the control group. [Conclusion] Thus, the VR program is an effective intervention for the elderly with MCI.

Recently, artificial intelligence (AI) technologies have been employed to predict construction and demolition (C&D) waste generation. However, most studies have used machine learning models with continuous data input variables, applying algorithms, such as artificial neural networks, adaptive neuro-fuzzy inference systems, support vector machines, linear regression analysis, decision trees, and genetic algorithms. Therefore, machine learning algorithms may not perform as well when applied to categorical data. This article uses machine learning algorithms to predict C&D waste generation from a dataset, as a way to improve the accuracy of waste management in C&D facilities. These datasets include categorical (e.g., region, building structure, building use, wall material, and roofing material), and continuous data (particularly, gloss floor area), and a random forest (RF) algorithm was used. Results indicate that RF is an adequate machine learning algorithm for a small dataset consisting of categorical data, and even with a small dataset, an adequate prediction model can be developed. Despite the small dataset, the predictive performance according to the demolition waste (DW) type was R (Pearson’s correlation coefficient) = 0.691–0.871, R2 (coefficient of determination) = 0.554–0.800, showing stable prediction performance. High prediction performance was observed using three (for mortar), five (for other DW types), or six (for concrete) input variables. This study is significant because the proposed RF model can predict DW generation using a small amount of data. Additionally, it demonstrates the possibility of applying AI to multi-purpose DW management.

[Purpose] The purpose of the present study was to conduct Computer-Assisted Cognitive Rehabilitation (COMCOG) to examine the effects of COMCOG on Alzheimer’s dementia patients’ memories. [Subjects] Thirty-five patients diagnosed with Alzheimer’s dementia received COMCOG for 30 minutes per day, five days per week for four weeks. [Methods] Before and after the COMCOG intervention, subjects’ cognitive functions were evaluated using the Cognitive Assessment Reference Diagnosis System (CARDS) and Mini-Mental State Examination-Korea (MMSE-K) test. [Results] According to the results of the evaluation, among the CARDS scores of the subjects who received COMCOG, the scores of the delayed 10-word list, delayed 10-object list, recognition 10-object, and recent memory significantly increased while the scores of recognition 10-word significantly decreased after intervention compared to before intervention. In addition, among the MMSE-K items, the orientation, registration, and recall showed significant increases. [Conclusion] Based on these results, delay in the progress of memory deterioration can be expected when COMCOG is conducted for Alzheimer’s dementia patients who show declines in cognitive functions.

광양보건대학교 작업치료과, 2 김해대학교 임상병리과This study examined the effects of a dual-task virtual reality program on the cognitive function and EEG for patients with mild cognitive impairment. A dual-task virtual reality program was performed in the experimental groups while conventional occupational therapy was carried out in the control group for 30 minutes per session, which was done five days per week for 6 weeks. The results were as follows. First, the memory of the cognitive function and balance was improved significantly in the experimental group with the dual-task virtual reality program compared to the control group with the traditional occupational therapy. Second, EEG was also increased significantly in the experimental group compared to the control group. The results of this study suggest that the dual-task virtual reality program was an effective treatment method for the elderly with mild cognitive impairment and would be a cornerstone of basic data that will be helpful to those suffering from a range of diseases.

This study examined a method to reduce energy consumption in office buildings. Correspondingly, an optimal control method was proposed for heating, ventilation, and air conditioning (HVAC) systems via two control algorithms that considered the indoor thermal environment. The control algorithms were developed by considering temperature and humidity as the factors of the indoor thermal environment that influence the control of HVAC systems and the predicted mean vote comfort ranges. Furthermore, an experiment was performed using office equipment that incorporated the two control algorithms for HVAC systems, and the correlation between changes in the thermal environment within the office and the occupant’s comfort levels was estimated via an actual survey. The results demonstrated that the proposed control method for HVAC systems, which considered the comfort ranges of temperature and humidity and the thermal adaptation capability, can efficiently maintain the occupant’s comfort with lower energy usage compared with conventional HVAC systems. Thus, the use of the control method contributes to the reduction of total energy consumption in buildings with HVAC systems.