Cortisol is a biomarker for stress monitoring; however, the biomedical and clinical relevance is still controversial due to the complexity of cortisol secretion mechanisms and their circadian cycles as well as environmental factors that affect physiological cortisol level, which include individual mood and dietary intake. To further investigate this multifaceted relationship, a human pilot study examined cortisol concentration in sweat and saliva samples collected from 48 college-aged participants during aerobic exercise sessions along with mental distress and nutrition surveys. Enzyme-linked immunosorbent assays determined highly significant differences between apocrine-dominant sweat (AP), saliva before exercise (SBE), and saliva after exercise (SAE) cortisol concentration (AP-SBE: p = 0.0017, AP-SAE: p = 0.0102). A significantly greater AP cortisol concentration was detected in males compared to females (p = 0.0559), and significant SAE cortisol concentration differences were also recorded between recreational athletes and non-athletes (p = 0.044). However, Kessler 10 Psychological Distress Scale (K10) scores, an examination administered to deduce overall wellness, provided no significant differences between males and females or athletes and non-athletes in distress levels, which statistically signifies a direct relationship to cortisol was not present. For further analysis, dietary intake from all participants was considered to investigate whether a multiplexed association was prevalent between nutrition, mood, and cortisol release. Significant positive correlations between AP cortisol, SAE cortisol, K10 scores, and fat intake among female participants and athletes were discovered. The various machine learning algorithms utilized the extensive connections between dietary intake, overall well-being, sex factors, athletic activity, and cortisol concentrations in various biofluids to predict K10 scores. Indeed, the understanding of physiochemical stress response and the associations between studied factors can advance algorithm developments for cortisol biosensing systems to mitigate stress-based illnesses and improve an individual’s quality of life.

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This paper aims to propose a novel placing method, i.e., place-between-paste-andpad (PB), for mini-scale passive components to enhance electronic assembly lines' yield. PB means a component is designed to be placed at the midpoint between the pastes and pads on the length direction while it aligns with the pads' center on the width direction. An experiment that involves 12 printed circuit boards (PCB) is designed and conducted to get comparative results. Four PCBs are employed for place-on-pad (PP), place-on-paste (PPS), and PB separately. On each board, 375 resistors R0402M (0.40 mm × 0.20 mm) are assembled horizontally.To study the components' misalignment under various solder paste offset conditions in different placement methods, a stencil with 25 solder paste offset settings is utilized. Based on this experiment's results, PB has superior performance to the other two methods to minimize components' misalignment. Regarding the number of acceptable components when post-reflow offsets are within 25% of components' dimensions, PB and PP have equivalent performances, and they both outperform PPS. Furthermore, PB is a lowcost placing strategy because PB needs not the real-time communication between the solder paste inspection machine and the pickand-place machine. With the miniaturization trend in electronic products, the postreflow components' misalignment is more frequently observed than before. The placement method proposed in this study is expected to offer a low-cost exploration in the component pick-and-place procedure to enhance the surface mount technology (SMT) assembly quality.Keywords SMT assembly • mini-scale passive components • pick-and-place • place-on-pad • place-on-paste beneficial for large components in decreasing 56 the misalignment [3]-[5], it did not perform so 57 well for mini-scale components.

Motivation: As passive components’ size gets smaller, quality rejects due to overhang and misalignment after the reflow appear more frequently. This situation is partly because the pass-fail criterion is set based on the offset concerning the component dimensions. Therefore, understanding the self-alignment characteristics of electronic components becomes very critical for surface-mount assembly yield. This research investigates the dissimilarity of self-alignment in the length and width directions. Approach: To avoid the argument of sample to sample variations, data are collected from 81 printed circuit boards (PCB) and 182,250 assembled components. Within a PCB, 25 different solder paste printing offset locations and 81 component placement offset settings are implemented. Component-placement positions before and after the reflow are monitored. The results are compared to identify different component sizes’ self-alignment characteristics in the length and width directions. Key findings: The misalignment of smaller passive components, e.g., R0402M(0.40 mm × 0.20 mm), is worse than the larger component under the identical solder paste printing and component placement conditions. Furthermore, the self-alignment characteristic in the length direction of these passive components, e.g., R0402M, to R1005M (1.00 mm × 0.50 mm) is superior to that of width direction. The observations are not consistent with the results found in earlier research that reported on larger components, e.g., C0402M(0.40 mm × 0.20 mm), to C3216M(3.20 mm × 1.50 mm).

Purpose This paper aims to provide the proper preset temperatures of the convection reflow oven when reflowing a printed circuit board (PCB) assembly with varied sizes of components simultaneously. Design/methodology/approach In this study, computational fluid dynamics modeling is used to simulate the reflow soldering process. The training data provided to the machine learning (ML) model is generated from a programmed system based on the physics model. Support vector regression and an artificial neural network are used to validate the accuracy of ML models. Findings Integrated physical and ML models synergistically can accurately predict reflow profiles of solder joints and alleviate the expense of repeated trials. Using this system, the reflow oven temperature settings to achieve the desired reflow profile can be obtained at substantially reduced computation cost. Practical implications The prediction of the reflow profile subjected to varied temperature settings of the reflow oven is beneficial to process engineers when reflowing bulky components. The study of reflowing a new PCB assembly can be started at the early stage of board design with no need for a physical profiling board prototype. Originality/value This study provides a smart solution to determine the optimal preset temperatures of the reflow oven, which is usually relied on experience. The hybrid physics–ML model providing accurate prediction with the significantly reduced expense is used in this application for the first time.

Purpose This paper aims to study the component pick-and-place (P&P) defect patterns for different root causes based on automated optical inspection data and develop a root cause identification model using machine learning. Design/methodology/approach This study conducts experiments to simulate the P&P machine errors including nozzle size and nozzle pick-up position. The component placement qualities with different errors are inspected. This study uses various machine learning methods to develop a root cause identification model based on the inspection result. Findings The experimental results revealed that the wrong nozzle size could increase the mean and the standard deviation of component placement offset and the probability of component drop during the transfer process. Moreover, nozzle pick-up position can affect the rotated component placement offset. These root causes of defects can be traced back using machine learning methods. Practical implications This study provides operators in surface mount technology assembly lines to understand the P&P machine error symptoms. The developed model can trace back the root causes of defects automatically in real line production. Originality/value The findings are expected to lead the regular preventive maintenance to data-driven predictive and reactive maintenance.

This paper aims to propose a novel placing method, i.e., place-between-paste-and-pad (PB), for mini-scale passive components to enhance electronic assembly lines' yield. PB means a component is designed to be placed at the midpoint between the pastes and pads on the length direction while it aligns with the pads' center on the width direction. An experiment that involves 12 printed circuit boards (PCB) is designed and conducted to get comparative results. Four PCBs are employed for place-on-pad (PP), place-on-paste (PPS), and PB separately. On each board, 375 resistors R0402M (0.40 mm X 0.20 mm) are assembled horizontally. To study the components' misalignment under various solder paste offset conditions in different placement methods, a stencil with 25 solder paste offset settings is utilized. Based on this experiment's results, PB has superior performance to the other two methods to minimize components' misalignment. Regarding the number of acceptable components when post-reflow offsets are within 25% of components' dimensions, PB and PP have equivalent performances, and they both outperform PPS. Furthermore, PB is a low-cost placing strategy because PB needs not the real-time communication between the solder paste inspection machine and the pick-and-place machine. With the miniaturization trend in electronic products, the post-reflow components' misalignment is more frequently observed than before. The placement method proposed in this study is expected to offer a low-cost exploration in the component pick-and-place procedure to enhance the surface mount technology (SMT) assembly quality.

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