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In the current global context of climate change, the frequency and intensity of disasters, including landslides, are on the rise. During the initial three quarters of 2023, a notable incidence of fatalities resulting from landslides was observed across several nations, with specific instances including India reporting 26 casualties, South Korea documenting 43 fatalities, and Burma recording 31 lives lost, among others. Moreover, there has been significant damage to critical infrastructure, encompassing various forms of infrastructure, such as transportation networks and buildings. To address this challenge, researchers have turned to Geographic Information Systems (GIS) for studying and analyzing landslide risks. However, they face a limitation: the analysis results mainly consist of static maps, lacking a real-time digital platform for efficient data management and dynamic display of landslide information. This limitation hampers effective monitoring and surveillance efforts. To overcome this limitation, the present study introduces an innovative approach that integrates GIS with the development of a digital platform, effectively addressing the existing challenges. This integrated solution not only enables accurate landslide risk analysis with an impressive accuracy rate of 83.50% but also provides users with the ability to access information about specific landslide sites within a defined radius, regardless of their geographical location. Unlike previous studies, this research demonstrates the potential to address past methodological limitations by utilizing a real-time data platform. Accuracy varies based on geographic context, and landslide occurrences are influenced by diverse factors in different terrains. The digital platform elucidated in this study holds applicability for utilization by disaster prevention and relief organizations, as well as government agencies. [ABSTRACT FROM AUTHOR]

In the age of digital disruption, universities and educational institutions all over the world provide many bachelor’s, postgraduate, and non-degree curricula. Information is mostly presented in different forms, i.e., public relations (PR) on websites, mobile applications, or social media; including field PR at different sites, e.g., department stores and schools. However, the limitations and the problems are that the presented information fails to attract or motivate the perception, interest, and participation of the target groups. Also, traditional PR requires resources in terms of persons, time, and many expenditures. Thus, this research aimed to present the development of a game to present information for PR of bachelor’s curriculums using Unity3D, which could reduce the limitation of presentation or PR in traditional forms. It could also save the PR budget and could be done anywhere and anytime. [ABSTRACT FROM AUTHOR]

The data of impacts and damage caused by floods is necessary for manipulation to assist and relieve those impacts in each area. The main issue for data acquisition was acquisition methods that affect the durations, accuracy, and completeness of data obtained. Most data are currently obtained by field survey for data on impacts in each area. However, this method contains limitations, i.e., taking a long time, high cost, and no real-time data visualization. Thus, this research presented the study to develop an application for inspecting areas under impact and damage caused by floods using deep learning classification for flood classification and land use type classification in the affected areas using digital images, remote sensing data, and crowdsource data notified by users through the accuracy assessment application of classification. It was found that deep learning classification for flood classification had 97.50% accuracy, with Kappa = 0.95. Land use type classification had 93.72% accuracy, with Kappa = 0.91. Flood damage assessment process in this research was different from other previous research that used geospatial data for flood damage inspection. In previous research, there was no platform to provide users with information about the impact and damage caused by floods in each area. Also, the data cannot be visualized in real-time. In contrast, this research brought damage data notified by users for processing with flood data in each area by satellite image processing and land use types of classification. The proposed application can calculate damage in each area and visualize real-time results in maps and graphs on the dashboard via the application. Besides, the presented method can be used to verify and visualize data of areas under impact and damage caused by floods in different areas. [ABSTRACT FROM AUTHOR]

Phuket is an outstanding province in promoting its economy to domestic and international tourism in Thailand. An investigation of relevant factors for understanding the accident process is one approach to reduce traffic accidents and thereby support tourism industry. This study aimed 1) to examine the characteristics of traffic accidents, physical and surface conditions, and driving behavior in Phuket province; 2) to investigate for an in-depth understanding the factors related to road accidents, including human and vehicle factors, and environmental conditions; and 3) to construct and verify a model concordant with the empirical data. The research instruments were a structural questionnaire to drivers and a checklist assessment of the road surface conditions. A stratified random sampling technique was used for selecting the drivers. The data were statistically analyzed using exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and second-order confirmatory factor analysis (SCFA). The majority of drivers were males (56.75%), aged between 21 and 40 years (57.00 %), married (62.25%), and working as company employees (73.25%). The study revealed that nearly half (47.15%) of the road traffic accidents in Phuket province involved motorcycles, surpassing other types of vehicles. Traffic accidents were more likely to occur during the daytime (38.11%), followed by night-time at 37.03%. Guided by the EFA and CFA, the three categories of factors, namely human, environmental and vehicle factors, were confirmed as appropriate in fitted models. The results of SCFA revealed that almost all traffic accidents were caused by human factors, followed by environmental, and vehicle factors, in this rank order. The fitted model was concordant with the empirical data (X2/df = 1.847, GFI = 0.972, AGFI = 0.951, CFI = 0.945, NFI = 0.890, and RMSEA = 0.046). Moreover, substandard road surfaces contributed to traffic accidents as an enabling factor. The responsible agency, therefore, should assist in improving the physical road conditions. Safety consciousness must be set as the default behavior for drivers to avoid accidents. Road accident reduction in Phuket province will increase the confidence among tourists for choosing Thailand as their tourist destination. [ABSTRACT FROM AUTHOR]

Due to an ongoing epidemic, the number of hospitalized bedridden patients has increased. It is imperative to closely monitor the hospital room and maintain and clean it regularly to avoid harm to the patients. The accommodations may not be within the standards for a bed -bound patient’s room. The patient has a risk of contracting a respiratory disease, or having an asthmatic attack, if exposed to high levels of PM1, PM2 .5, or PM10 dust that cannot be seen with bare eyes, so the risk factor is not easy to notice. The goal of this study was to develop a dust monitoring system for hospital bedrooms using IoT, so that the caregivers can monitor air quality in the room. By applying the Internet of Things (IoTs) technology to communicate between sensors and mobile phones, the internet serves as the medium for communication. The demonstration system in the room was equipped with 5 sensor cluster, each measuring: temperature, humidity and dust sensors for PM1, PM2 .5, and PM10. Decision trees were trained to predict the outcome of cases after collecting data. The final decision tree model reached an overall classification accuracy of 92.8%. The system could alert for housekeeping or turn on or off an automatic dust remover based on the amount of dust in the room. It also supports cleaning and dust removal to ensure that the bed patient’s room is appropriate and reduces the risk of respiratory diseases caused by dust. [ABSTRACT FROM AUTHOR]

Hospitals tend to have high density of occupancy. Poor indoor environmental quality in hospital buildings can exacerbate the health problems of patients and also harm visitors and staff. This study investigated the environmental characteristics and ventilation affecting the concentration of microorganisms in multiple-bed hospital wards. The measurements were accomplished by using a biosampler and an open plate method at four wards, different positions of electric fans, and different times. Data were analyzed by t-test and MANOVA. The results revealed that the concentrations of airborne bacteria were higher than the concentrations of fungi. There were significant differences in the concentrations of bacteria and fungi between sampling times and between hospital wards (p < 0.05), while no difference was observed by positions of electric fans. Correlations between the concentrations and other environmental parameters indicate that temperature, number of occupants, and humidity were physical factors associated with the concentrations of microorganisms. In addition, mostly, Gram-positive bacteria were observed. This indicates the conditions in buildings in the tropical climate, and regular housekeeping of all room areas is needed to prevent the growth of airborne contaminants and the related risks to patients, visitors, and staff. [ABSTRACT FROM AUTHOR]

Sick building syndrome (SBS) is commonly found among office staff. There have been few studies on SBS among hotel staff. The main objective of this research was to establish a risk factor model related to the occurrence of SBS among hotel staff. A questionnaire on SBS was used to survey 246 hotel staff based on purposive sampling. Significant factors were detected using chi- square and likelihood ratio tests while binary logistic regression was used to determine the significant difference among personal information, workplace characteristics, and environmental and psychosocial factors in relation to the occurrence of SBS. The outcome of the evaluation indicates the prevalence of SBS as about 7%. The statistical factors related to SBS were female staff (OR = 20.52, 95%CI = 2.91-144.92), smoking habits (OR = 10.67, 95%CI = 1.94-58.61), and presence of animals (OR = 8.09, 95%CI = 2.52-26.01). This study indicates that female smokers surrounded by animals had an elevated risk of SBS. This study could be useful for guiding hotel staff to avert health risk behavior. [ABSTRACT FROM AUTHOR]

Background: Cerebrovascular diseases or stroke tend to cause high mortality in Thailand. An essential responsibility of a hospital is the development of medical care to support the safety of patients. For this purpose, a smartphone application was developed for the risk assessment and emergency system for stroke treatment in a hospital in Thailand., Methods: The proposed application involved the risk assessment related to the occurrence of stroke evaluated by the health status and face image using analytical geometry and face detection technology. The social network Application Programming Interface (API), LINE Notify API, and Global Positioning System (GPS) were used to inform the Stroke team in the Suratthani hospital about emergency cases, followed their requirement in 2020., Results: From the testing, the facial angulation classification, calculated using a support vector machine (SVM), had 92.38% accuracy. The system also provided an emergency call and text messaging that includes patient's current location and personal information to the stroke team directly, which gave an opportunity for the patient to receive treatment quickly., Conclusion: The emergency system can help quickly perform the risk assessment of stroke. Our proposed system provides automated management., Competing Interests: Conflicts of interest The authors declare that there are no conflicts of interest., (Copyright © 2022 Pruitikanaee et al. Published by Tehran University of Medical Sciences.)