Bus accidents are a serious issue, with high rates of injury and fatality in Thailand. However, no studies have been conducted on the factors affecting bus accident severity in Thailand. A cross-sectional study was conducted by the Department of Highways, Thailand over the 2010–2019 period. A multinomial logit model was used to evaluate the factors associated with bus accident severity. This model divided accidents into three categories: non-injury, injury, and fatality. The risk factors consisted of three major categories: the bus driver, characteristics of the crash, and environmental characteristics. The results showed that characteristics of the bus driver, the crash, and the environment where the crash occurred all increased the probability of bus accidents causing injury. These three main factors included driving on sloped roads (relative risk ratio [RRR] 3.03, 95% confidence level [CI] 1.73 to 5.30), drowsy driving (RRR 2.60, 95% CI 1.71 to 3.96), and driving in the wrong direction (RRR 2.37, 95% CI 1.77 to 3.19). Moreover, the factors that increased the probability of the accidents causing fatality were drowsy driving (RRR 3.40, 95% CI 2.07 to 5.57) and drivers not obeying or following traffic rules (RRR 3.02, 95% CI 1.95 to 4.67), especially in the northern part of Thailand (RRR 3.01, 95% CI 1.98 to 4.62). The results can provide a valuable resource to help road authorities in development targeting road safety programs at sloped roads in the northern part of Thailand. Stakeholders should increase road safety efforts and implement campaigns, such as raising public awareness of the risks of not obeying or following traffic rules and drowsy driving which could possibly reduce the risk of both injury and fatality.

In the field of medical engineering, Triply Periodic Minimal Surfaces (TPMS) structures have been studied widely owing to their physical attributes similar to those of human bones. Computational Fluid Dynamics (CFD) is often used to reveal the interaction between structural architectures and flow fields. Nevertheless, a comprehensive study on the effect of manufacturing defects and non-Newtonian behavior on the fluid responses in TPMS scaffolds is still lacking. Therefore, the present study fabricated Gyroid TPMS with four relative densities from 0.1 to 0.4. Non-destructive techniques were used to examine surface roughness and geometric deviation. We found that the manufacturing defects had a minor effect on fluid responses. The pressure drop comparison between defect-containing and defect-free models could be differed up to 7%. The same comparison for the average shear stress showed a difference up to 23%, in which greater deviation between both models was observed at higher relative density. On the contrary, the viscosity model played a significant role in flow prediction. By comparing the Newtonian model with Carreau-Yasuda non-Newtonian model, the resulting pressure drop and average wall shear stress from non-Newtonian viscosity could be higher than those of the Newtonian model by more than a factor of two. In addition, we matched the fluid-induced shear stress from both viscosity models with desirable ranges of shear stresses for tissue growth obtained from the literature. Up to 70% from the Newtonian model fell within the desirable range while the matching stress reduced to lower than 8% for the non-Newtonian results. Furthermore, by correlating geometric features with physical outputs, the geometric deviation was seen associated with surface curvature while the local shear stress revealed a strong correlation with inclination angle. Overall, the present work emphasized the importance of the viscosity model for CFD analysis of the scaffolds, especially when resulting fluid-induced wall shear stress is of interest. In addition, the geometric correlation has introduced the alternative consideration of structural architectures from local perspectives, which could assist the further comparison and optimization among different porous scaffolds in the future.

Background: Depression has a growing trend in the population worldwide. In this cross-sectional study, we investigated the prevalence and associated factors of depression among individuals residing in rural (Ban Luang district, Nan Province) and urban (Lak Si, Bangkok) areas of Thailand. Understanding the differences in depression between these two settings can provide insights for specific targeted interventions and mental health policies. Methods: The multistage stratified random sampling was applied to select the study participants. We recruited participants from rural and urban communities in Thailand using a structured survey questionnaire through either face-to-face interviews or in paper or electronic form. We collected data on depression using the Patient Health Questionnaire-9 (PHQ-9) tool and sociodemographic characteristics and conducted descriptive statistics and logistic regression analysis. Results: Of 867 survey participants, 420 were from rural areas (Nan) and 447 were from urban areas (Bangkok). Participants’ mean age was 55.9±9.5 years in rural areas and 56.0±12.0 years in urban areas. Most participants in urban areas were women, married, and had lower education levels (71.1%, 50.3%, 58.8%, respectively). The overall prevalence of depression across both settings was 18.6%. We found a higher prevalence of depression in the urban (31.8%) than the rural (4.5%) setting. In multiple logistic regression analysis, urban residence was significantly associated with higher rates of depression compared with rural residence (adjusted odds ratio [AOR] 9.43, 95% confidence interval [CI] 5.08–17.52). Nuclear family and using social media were associated with lower levels of depression in urban areas (AOR 0.50 and 0.43, 95% CI 0.27–0.93 and 0.22–0.84, respectively). Higher education level was significantly associated with higher levels of depression in rural areas (AOR 3.84, 95% CI 1.19–12.42). Conclusion: This study emphasized the difference in depression and related factors between rural and urban areas of Thailand, highlighting a greater prevalence in urban areas. To help prevent depression, it is important to address specific challenges in each setting, such as those faced by highly educated individuals living in rural areas with high depression rates, exploring social media use patterns in urban populations, and understanding dynamics of the nuclear family. Our findings can inform the development of public health policies aimed at effectively mitigating the burden of depression and improving overall mental well-being in specific settings.

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Increasing the availability of alternative protein from insects is important for solving food shortages. Not only are insects a rich source of protein, but using insect as ingredients could reduce food waste. Insects are thus a potentially valuable ingredient for food industries and even sustainable food. The three-dimensional production of food for future food has gained attention owing to its potential to reduce autonomous food production and produce sustainable food. This study investigated the printability and rheological properties of a high-protein food system derived from mealworms and guar gum used to improve printability. The stability and rheological properties were analyzed for various printing parameters. The results indicate that the yield stress of the mealworm paste dramatically increased (39 to 1096 Pa) with even a small guar gum concentration resulting in an increase (0 to 1.75%). Increasing the guar gum concentration thus resulted in a mealworm paste that had a more significant value of hardness and cohesiveness but reduced adhesiveness (p < 0.05). In conclusion, the addition of guar gum increased viscosity, and caused the paste to exhibit a shear thinning behavior and ability to support itself and was thus more stable. In summary, introducing guar gum resulted in a mealworm paste with rheological properties more suitable for printing in terms of printability and stability.

Background: Volumetric modulated arc therapy (VMAT) planning is a time-consuming process of radiation therapy. With a deep learning approach, 3D dose distribution can be predicted without the need for an actual dose calculation. This approach can accelerate the process by guiding and confirming the achievable dose distribution in order to reduce the replanning iterations while maintaining the plan quality. Methods: In this study, three dose distribution predictive models of VMAT for prostate cancer were developed, evaluated, and compared. Each model was designed with a different input data structure to train and test the model: (1) patient CT alone (PCT alone), (2) patient CT and generalized organ structure (PCTGOS), and (3) patient CT and specific organ structure (PCTSOS). The generative adversarial network (GAN) model was used as a core learning algorithm. The models were trained slice-by-slice using 46 VMAT plans for prostate cancer, and then used to predict and evaluate the dose distribution from 8 independent plans. Results: VMAT dose distribution was generated with a mean prediction time of approximately 3.5 s per patient, whereas the PCTSOS model was excluded due to a mean prediction time of approximately 17.5 s per patient. The highest average 3D gamma passing rate was 80.51 ± 5.94, while the lowest overall percentage difference of dose-volume histogram (DVH) parameters was 6.01 ± 5.44% for the prescription dose from the PCTGOS model. However, the PCTSOS model was the most reliable for the evaluation of multiple parameters. Conclusions: This dose prediction model could accelerate the iterative optimization process for the planning of VMAT treatment by guiding the planner with the desired dose distribution.