Hiranya Sritart

Monolithic zirconia restorations are frequently affected by the unnoticed growth of subcritical cracks, a failure process that is not captured by traditional imaging methods like radiographs and ultrasounds in sophisticated dental architectures. To address this evaluative inadequacy, this research introduces a hierarchical deep learning framework for microcrack detection and spatial localization. We promote a hierarchical deep learning system that integrates Acoustic Emission (AE) detection alongside signal processing. Raw AE signals utilized during dynamic loading are enhanced via Kalman filtering and Continuous Wavelet Transform (CWT) to construct high-fidelity time–frequency scalograms. The diagnostic pipeline operates in two stages: first, a hybrid CNN–BiGRU network with temporal attention fulfills zirconia component-level classification; second, a ResNet-18 backbone integrated with Bidirectional LSTM and Multi-Head Attention precisely localizes defects across five anatomical crown regions. This hierarchical design effectively captures the non-stationary, transient nature of fracture-induced stress waves. The framework achieved an F1-score of 99.00% and an AUC of 0.994, significantly outperforming conventional convolutional networks. By enabling predictive maintenance through early, non-invasive damage localization, this study demonstrates a promising laboratory framework for AE-based crack detection in zirconia dental structures and prosthetics and toward enhanced clinical reliability in digital dentistry.

Background: In forensic investigation, sexual dimorphism plays a significant role in estimating the biological sex of unidentified human remains. In cases involving disasters or severely decomposed or fragmented bodies, teeth are highly valued for their resilience to environmental conditions. However, significant gaps remain in fully utilizing these characteristics for accurately estimating sex. Therefore, this study primarily aimed to address these gaps, investigating dental metric traits specific to the Thai population to provide population-specific norms for sex estimation. Results: This study analyzed 246 panoramic dental radiographs (123 males and 123 females), with measurements of crown width and root length obtained using ImageJ software under standardized conditions. Statistical analysis using independent t -tests revealed significant differences between sexes in several teeth. The mandibular right canine (FDI 43) showed the most statistically significant difference between sexes in both crown width and root length ( p < 0.001), with a percentage of sexual dimorphism in root length of 8.69%. This finding indicates that FDI 43 is a particularly reliable marker for sex estimation in this population. Conclusion: These findings underscore the importance of crown width and root length as reliable indicators for sex estimation. Given the population-specific nature of sexual dimorphism, this study provides essential baseline data for the Thai population. Its results contribute to improving the accuracy and reliability of forensic identification using dental radiographs.

Property crime is a pressing issue in maintaining social order and urban sustainability, particularly in regions marked by pronounced socioeconomic disparity. While the link between socioeconomic stress and crime is well established, regional variations in Thailand have not been fully examined. Therefore, the purpose of this research was to examine spatial patterns of property crime and identify the potential associations between property crime and socioeconomic environment across Thailand. Using nationally compiled property-crime data from official sources across all provinces of Thailand, we employed geographic information system (GIS) tools to conduct a spatial cluster analysis at the sub-national level across 76 provinces. Both global and local statistical techniques were applied to identify spatial associations between property-crime rates and neighborhood-level socioeconomic conditions. The results revealed that property-crime clusters are primarily concentrated in the south, while low-crime areas dominate parts of the north and northeast regions. To analyze the spatial dynamics of property crime, we used geospatial statistical models to investigate the influence of socioeconomic variables across provinces. We found that property-crime rates were significantly associated with monthly income, areas experiencing high levels of household debt, migrant populations, working-age populations, an uneducated labor force, and population density. Identifying associated factors and mapping geographic regions with significant spatial clusters is an effective approach for determining where issues concentrate and for deepening understanding of the underlying patterns and drivers of property crime. This study offers actionable insights for enhancing safety, resilience, and urban sustainability in Thailand’s diverse regional contexts by highlighting geographies of vulnerability.

Homicide's impact transcends the individual, shattering social trust, damaging community cohesion, and casting a shadow of fear over entire nations, demanding urgent efforts to address root causes and prevent further loss. Although Thailand's homicide rates are comparatively lower than those of certain neighboring Southeast Asian countries, this issue continues to pose a significant threat to public safety within the nation. To gain deeper insights into their spatial distribution and potential clustering, this research employed Geographic Information Systems (GIS) to conduct a spatial analysis of homicide report cases across Thailand from 2019 to 2021. Utilizing national data sourced from police reports, the study investigated the existence of spatial autocorrelation, indicating whether homicide occurrences are statistically dependent on their geographic location. This analysis shed light on whether homicides are randomly distributed or exhibit clustering tendencies, suggesting potential underlying spatial factors at play. Furthermore, the research employed hotspot mapping techniques to identify statistically significant clusters of homicide cases. These hotspots revealed areas with an elevated concentration of homicides, providing crucial information for targeted prevention strategies. By pinpointing high-risk areas, law enforcement agencies can allocate resources more effectively, focusing on factors specific to each hotspot area. This research leverages GIS to unlock new insights into Thailand's homicide patterns, leading to a more comprehensive understanding. The identification of spatial autocorrelation and hotspots provided valuable insights for policymakers and law enforcement agencies to develop targeted prevention strategies and enhance public safety across the country.

The medical laboratory plays a crucial role within a hospital setting and is responsible for the examination and analysis of patient specimens to accurately diagnose various ailments. The burden on medical laboratory personnel has significantly increased, particularly in the context of the ongoing global COVID-19 pandemic. Worldwide, the implementation of comprehensive and extended COVID-19 screening programs has placed a significant strain on healthcare professionals. This burden has led to exhaustion among medical employees, limiting their ability to effectively track laboratory resources, such as medical equipment and consumables. Therefore, this study proposed an artificial intelligence (AI)-based solution that contributes to a more efficient and less labor-intensive workflow for medical workers in laboratory settings. With the ultimate goal to reduce the burden on healthcare providers by streamlining the process of monitoring and managing these resources, the objective of this study is to design and develop an AI-based system for consumables tracking in medical laboratories. In this work, the effectiveness of two object detection models, namely, YOLOv5x6 and YOLOv8l, for the administration of consumables in medical laboratories was evaluated and analyzed. A total of 570 photographs were used to create the dataset, capturing the objects in a variety of settings. The findings indicate that both detection models demonstrate a notable capability to achieve a high mean average precision. This underscores the effectiveness of computer vision in the context of consumable goods detection scenarios and provides a reference for the application of real-time detection models in tracking systems within medical laboratories.

In the medical laboratory, equipment and material management is well real-ized as one of the high-priority tasks in handling of information about the periodic quality inspection and maintenance of test equipment and other in-strument monitoring in the laboratory. Lack of managing and neglecting the equipment can result into malfunction that ends up costing more time and resources. This paper proposed a designed web-based information system in order to address the issues of managing the expensive medical equipment (ME) and various consumable medical materials (CMM) in the laboratories. In regard to data administration improvement of medical laboratories' day-to-day operations, the objective of this study is to design to complete the func-tionality and evaluate the information system based on the user specifica-tion. More than 200 initiate materials list and 50 medical equipment records were collected and transferred into the system. Three independent laborato-ries utilized the developed ME/CMM information system to evaluate its per-formance compared to the traditional system. The t-test statistical analysis was used to assess feedback surveys. The finding shows that the system pro-vides users convenience and effectiveness in handling ME and CMM; thus, our system significantly reduces the workload of staffs and inefficient costs.

Globally, rapid economic growth has contributed to an overall increase in the incidence of childhood obesity. Although the prevalence of obesity has been well recognized, the disparities related to a region’s socioeconomic environment in terms of the incidence of obesity are still less understood. Therefore, the purpose of this study was to examine the spatial pattern of childhood obesity and identify the potential associations between childhood obesity and socioeconomic environment in the northeastern region of Thailand, Isaan. Using nationally collected obesity data from children aged 0–5 years in 2019, we employed a geographic information system (GIS) to perform obesity cluster analysis at the smaller regional level, investigating a total of 322 districts in study area. Global and local statistical approaches were applied to calculate spatial associations between the socioeconomic status of neighborhoods and childhood obesity. The study revealed that 12.42% of the total area showed significant clusters at the district level, with high values observed in the western and northeastern areas. The results of the spatial statistical model revealed that childhood obesity was significantly positively associated with areas exhibiting high levels of socioeconomic environment factors. Identifying the associated factors and highlighting geographic regions with significant spatial clusters is a powerful approach towards understanding the role of location and expanding the knowledge on the factors contributing to childhood obesity. Our findings, as a first step, offer valuable references that could support policy-makers and local authorities in enhancing policy development with the aim of reducing childhood obesity and improving public health.