Applying deep learning to defect detection in printed circuit boards via a newest model of you-only-look-once.

In this paper, a new model known as YOLO-v5 is initiated to detect defects in PCB. In the past many models and different approaches have been implemented in the quality inspection for detection of defect in PCBs. This algorithm is specifically selected due to its efficiency, accuracy and speed. It is well known that the traditional YOLO models (YOLO, YOLO-v2, YOLO-v3, YOLO-v4 and Tiny-YOLO-v2) are the state-of-the-art in artificial intelligence industry. In electronics industry, the PCB is the core and the most basic component of any electronic product. PCB is almost used in each and every electronic product that we use in our daily life not only for commercial purposes, but also used in sensitive applications such defense and space exploration. These PCB should be inspected and quality checked to detect any kind of defects during the manufacturing process. Most of the electronic industries are focused on the quality of their product, a small error during manufacture or quality inspection of the electronic products such as PCB leads to a catastrophic end. Therefore, there is a huge revolution going on in the manufacturing industry where the object detection method like YOLO-v5 is a game changer for many industries such as electronic industries.

Numerical assessment of directional energy performance for 3D printed midsole structures.

Energy can be represented in the form of deformation obtained by the applied force. Energy transfer is defined in physics as the energy is moved from one place to another. To make the energy transfer functional, energy should be moved into the right direction. If it is possible to make a better use of the energy in the right direction, the energy efficiency of the structure can be enhanced. This idea leads to the concept of directional energy transfer (DET), which refers to transferring energy from one direction to a specific direction. With the recent development of additive manufacturing and topology optimization, complex structures can be applied to various applications to enhance performances, like a wheel and shoe midsole. While many works are related to structural strength, there is limited research in optimization for energy performance. In this study, a theoretical approach is proposed to measure the directional energy performance of a structure, which can be used to measure the net energy in an intended direction. The purpose is to understand the energy behavior of a structure and to measure if a structure is able to increase energy in the desired direction.

Mean Daily Dosage of Aspirin and the Risk of Incident Alzheimer's Dementia in Patients with Type 2 Diabetes Mellitus: A Nationwide Retrospective Cohort Study in Taiwan.

Background. Type 2 diabetes mellitus patients are known to have higher risk of developing dementia while aspirin use has been shown to prevent incident dementia. This study was conducted to evaluate the potential benefits of aspirin use on dementia in patients with type 2 diabetes mellitus and identify the appropriate dosage of aspirin that provides the most benefit. Method. A Taiwan nationwide, population-based retrospective 8-year study was employed to analyze the association between the use of aspirin and incidence of dementia including Alzheimer's disease and non-Alzheimer's dementia using multivariate Cox-proportional hazards regression model and adjusting for several potential confounders. Results. Regular aspirin use in mean daily dosage of within 40 mg was associated with a decreased risk of developing incident Alzheimer's dementia in patients with type 2 diabetes mellitus (adjusted HR of 0.51 with 95% CI of 0.27-0.97, p value 0.041). Conclusion. A mean daily dosage of aspirin use within 40 mg might decrease the risk of developing Alzheimer's disease in patients with type 2 diabetes mellitus.

Effects of statins on incident dementia in patients with type 2 DM: a population-based retrospective cohort study in Taiwan.

BACKGROUND: Patients with Type 2 diabetes (T2DM) are prone to develop dementia. Results from a recent study indicated that statin users had lower chance of developing incident dementia. However there is little information on the potential benefits of statin use on dementia in patients with T2DM cohort. METHOD: A population-based retrospective study using a nationwide cohort of National Health Insurance Research Database in Taiwan was performed. T2DM cohort with regular use of statins was followed up to 8 years. Multivariate cox-proportional hazards regression model was used to estimate the association between statin use and incidence of dementia including Alzheimer's disease and non-Alzheimer dementia after adjusting for several potential confounders. RESULTS: Among 28,321 patients diagnosed with T2DM age above 50 and without history of dementia before 2000/1/1, 15,770 patients who had never used statin and 2,400 patients who regularly used statin drugs were enrolled. After adjusting for age group, gender, CCI (Charlson-Deyo comorbidity index) group, stroke types and anti-diabetic drugs, regular statin use was associated with a decreased risk of developing incident Alzheimer's disease dementia (adjusted HR: 0.48, 95% CI 0.30 - 0.76, p<0.001), but not in non-Alzheimer dementia (adjusted HR: 1.07, 95% CI 0.54-2.12 p = 0.844) in patients with T2DM. Further analysis showed significant protective effects of the use of atorvastatin and simvastatin. CONCLUSION: Regular use of statins might decrease the risk of developing Alzheimer's disease in patients with T2DM while no benefit was observed in non-Alzheimer dementia. Among statins, both atorvastatin and simvastatin showed significant benefits.