An efficient building should be able to control its internal temperature in a manner that considers both the building’s energy efficiency and the comfort level of its occupants. Thermostats help to control the temperature within a building by providing real-time data on the temperature inside that space to determine whether it is within the acceptable range of that building’s control system, and proper thermostat placement helps to better control a building’s temperature. More thermostats can provide better control of a building, as well as a better understanding of the building’s temperature distribution. In order to determine the minimum number of thermostats required to accurately measure and control the internal temperature distribution of a building, it is necessary to find the locations that show similar environmental conditions. In this paper, we analyzed high resolution temperature measurements from a commercial building using wireless sensors to assess the performance and health of the building’s HVAC zoning and controls system. Then we conducted two cluster analyses to evaluate the efficiency of the existing zoning structure and to find the optimal number of clusters. K-means and time series clustering were used to identify the temperature clusters per building floor. Based on statistical assessments, we observed that time series clustering showed better results than k-means clustering.

Background: The COVID-19 pandemic is a substantial public health crisis that negatively affects human health and well-being. As a result of being infected with the coronavirus, patients can experience long-term health effects called long COVID syndrome. Multiple symptoms characterize this syndrome, and it is crucial to identify these symptoms as they may negatively impact patients’ day-to-day lives. Breathlessness, fatigue, and brain fog are the 3 most common continuing and debilitating symptoms that patients with long COVID have reported, often months after the onset of COVID-19. Objective: This study aimed to understand the patterns and behavior of long COVID symptoms reported by patients on the Twitter social media platform, which is vital to improving our understanding of long COVID. Methods: Long COVID–related Twitter data were collected from May 1, 2020, to December 31, 2021. We used association rule mining techniques to identify frequent symptoms and establish relationships between symptoms among patients with long COVID in Twitter social media discussions. The highest confidence level–based detection was used to determine the most significant rules with 10% minimum confidence and 0.01% minimum support with a positive lift. Results: Among the 30,327 tweets included in our study, the most frequent symptoms were brain fog (n=7812, 25.8%), fatigue (n=5284, 17.4%), breathing/lung issues (n=4750, 15.7%), heart issues (n=2900, 9.6%), flu symptoms (n=2824, 9.3%), depression (n=2256, 7.4%) and general pains (n=1786, 5.9%). Loss of smell and taste, cold, cough, chest pain, fever, headache, and arm pain emerged in 1.6% (n=474) to 5.3% (n=1616) of patients with long COVID. Furthermore, the highest confidence level–based detection successfully demonstrates the potential of association analysis and the Apriori algorithm to establish patterns to explore 57 meaningful relationship rules among long COVID symptoms. The strongest relationship revealed that patients with lung/breathing problems and loss of taste are likely to have a loss of smell with 77% confidence. Conclusions: There are very active social media discussions that could support the growing understanding of COVID-19 and its long-term impact. These discussions enable a potential field of research to analyze the behavior of long COVID syndrome. Exploratory data analysis using natural language processing methods revealed the symptoms and medical conditions related to long COVID discussions on the Twitter social media platform. Using Apriori algorithm–based association rules, we determined interesting and meaningful relationships between symptoms.

Background: Children with neurodevelopmental disorders, such as cerebral palsy (CP), often experience motor impairments in manual dexterity, which hinder daily tasks and social interactions. Traditional rehabilitation methods require repetitive task practice, which can be difficult for children to sustain due to low engagement. Game-based rehabilitation devices and robots offer a promising alternative by combining therapy with digital play, improving motivation and compliance. However, many systems fail to incorporate actual object manipulation, which is essential for motor learning through sensory feedback. To address this limitation, a low-cost, easy-to-use robotic manipulandum device (RMD) was developed. The RMD enables real-time object manipulation during gameplay while providing assistive force, allowing the practice of a wide range of manual dexterity skills beyond gross reaching. This system offers an engaging and effective rehabilitation approach to enhance hand function in children with CP. Objective: This study aimed to provide evidence for the feasibility and therapeutic value of the RMD game–based exercise program for children with CP. Methods: In total, 34 children with CP, aged 4 to 10 years, were randomly assigned to the experimental group (XG) or the control group (CG). The XG received a computer game–based exercise program using the RMD, focusing on object manipulation tasks, while the CG received task-specific training similar to constraint-induced movement therapy. Both groups received their respective therapy programs 3 times per week for 8 weeks. Semistructured interviews with parents and children, along with qualitative analysis, were conducted to evaluate their experiences with the exercise program. The following outcome measures were used: (1) the Peabody Developmental Motor Scale-2 (PDMS-2) grasping and visual-motor integration subtests and (2) the computer game–based upper extremity (CUE) assessment of manual dexterity. Results: No dropouts occurred during the 8-week program. Both groups showed significant improvements in the PDMS-2 subtests (P<.001) and the CUE assessment of manual dexterity, including success rates (tennis ball: P=.001; cone: P<.001; medicine ball: P=.001; and peanut ball: P<.001) and movement errors (tennis ball: P=.01; cone: P<.001; medicine ball: P=.04; and peanut ball: P<.001). The XG outperformed the CG, showing greater improvements in PDMS-2 grasping (P=.002) and visual-motor integration (P=.01). In the CUE assessment, the XG demonstrated higher success rates (medicine ball: P=.001 and peanut ball: P=.02) and fewer movement errors (cone: P<.001). Parents reported an increase in the children’s independence in daily tasks. Conclusions: This study demonstrates the feasibility, acceptability, and positive outcomes of the RMD game–based exercise program for improving hand function in children with CP. The findings support further research and development of computer game–assisted rehabilitation technologies. Trial Registration: Clinical Trials Registry - India CTRI/2021/07/034903; https://ctri.nic.in/Clinicaltrials/pmaindet2.php?EncHid=NTc4ODU

This article develops a Bayesian approach for meta-analysis using the Dirichlet process. The key aspect of the Dirichlet process in meta-analysis is the ability to assess evidence of statistical heterogeneity or variation in the underlying effects across study while relaxing the distributional assumptions. We assume that the study effects are generated from a Dirichlet process. Under a Dirichlet process model, the study effects parameters have support on a discrete space and enable borrowing of information across studies while facilitating clustering among studies. We illustrate the proposed method by applying it to a dataset on the Program for International Student Assessment on 30 countries. Results from the data analysis, simulation studies, and the log pseudo-marginal likelihood model selection procedure indicate that the Dirichlet process model performs better than conventional alternative methods.