The use of microreactors in the continuous fluidic system has been rapidly expanded over the past three decades. Developments in materials science and engineering have accelerated the advancement of the microreactor technology, enabling it to play a critical role in chemical, biological, and energy applications. The emerging paradigm of digital additive manufacturing broadens the range of the material flexibility, innovative structural design, and new functionality of the conventional microreactor system. The control of spatial arrangements with functional printable materials determines the mass transport and energy transfer within architected microreactors, which are significant for many emerging applications, including use in catalytic, biological, battery, or photochemical reactors. However, challenges such as lack of design based on multiphysics modeling and material validation are currently preventing the broader applications and impacts of functional microreactors conjugated with digital manufacturing beyond the laboratory scale. This review covers a state-of-the-art of research in the development of some of the most advanced digital manufactured functional microreactors. We then the outline major challenges in the field and provide our perspectives on future research and development directions.

ImportanceEarly detection of attention-deficit/hyperactivity disorder (ADHD) and sleep problems is paramount for children’s mental health. Interview-based diagnostic approaches have drawbacks, necessitating the development of an evaluation method that uses digital phenotypes in daily life.ObjectiveTo evaluate the predictive performance of machine learning (ML) models by setting the data obtained from personal digital devices comprising training features (ie, wearable data) and diagnostic results of ADHD and sleep problems by the Kiddie Schedule for Affective Disorders and Schizophrenia Present and Lifetime Version for Diagnostic and Statistical Manual of Mental Disorders, 5th edition (K-SADS) as a prediction class from the Adolescent Brain Cognitive Development (ABCD) study.Design, Setting, and ParticipantsIn this diagnostic study, wearable data and K-SADS data were collected at 21 sites in the US in the ABCD study (release 3.0, November 2, 2020, analyzed October 11, 2021). Screening data from 6571 patients and 21 days of wearable data from 5725 patients collected at the 2-year follow-up were used, and circadian rhythm–based features were generated for each participant. A total of 12 348 wearable data for ADHD and 39 160 for sleep problems were merged for developing ML models.Main Outcomes and MeasuresThe average performance of the ML models was measured using an area under the receiver operating characteristics curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In addition, the Shapley Additive Explanations value was used to calculate the importance of features.ResultsThe final population consisted of 79 children with ADHD problems (mean [SD] age, 144.5 [8.1] months; 55 [69.6%] males) vs 1011 controls and 68 with sleep problems (mean [SD] age, 143.5 [7.5] months; 38 [55.9%] males) vs 3346 controls. The ML models showed reasonable predictive performance for ADHD (AUC, 0.798; sensitivity, 0.756; specificity, 0.716; PPV, 0.159; and NPV, 0.976) and sleep problems (AUC, 0.737; sensitivity, 0.743; specificity, 0.632; PPV, 0.036; and NPV, 0.992).Conclusions and RelevanceIn this diagnostic study, an ML method for early detection or screening using digital phenotypes in children’s daily lives was developed. The results support facilitating early detection in children; however, additional follow-up studies can improve its performance.

Introduction: Perioperative stroke is one of the most devastating events after surgery. To prevent perioperative stroke and stratify the patients at risk, several prediction models or scores based on the preoperative factors were suggested. However, there had been never reported a prediction model using intraoperative physiologic parameters. Aim: We aimed to develop a prediction model for perioperative stroke by analyzing pre- and intraoperative factors using machine learning techniques. Methods: This retrospective cohort study included patients who underwent non-cardiac surgery between 2016 and 2019 at Seoul National University Hospital. Perioperative stroke was defined as a newly developed ischemic lesion at diffusion weighted imaging within 30 days after surgery. Preoperative factors were age, risk factors and laboratory data. Intraoperative variables were blood pressure, heart rate, saturation monitoring value, total amount of fluid and urine volume during surgery. We developed a random forest based prediction model composed of pre- and intraoperative factors and compared with a model consisting of only preoperative features. We validated the model in an external cohort of patients at another hospital between 2020 and 2021. Results: A total of 15752 patients were included in the development cohort, and 109 patients had perioperative stroke. In external validation cohorts, stroke occurred in 11 of 449 patients. The area under the receiver operating characteristics curves (AUC) for integrated models using pre- and intraoperative parameters was significantly higher than that of model using only preoperative factors, as shown in figure(0.822, 95% confidence interval, 0.760-0.883, p <0.001). The AUC of integrated model in external validation cohorts showed the trend in line with that of development cohorts. Conclusions: We demonstrated that using both pre- and intraoperative features may improve the accuracy of prediction for perioperative stroke.

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Arc plasma flow between electrodes has been investigated in several studies. However, in the industrial field, arc plasma flow between electrodes is hindered by interfering materials such as filler metal in arc welding, substrates in chemical vapor deposition, and powders in sintering. Therefore, in this study, high temperature arc plasma flow analysis via three obstruction structure shapes was performed to understand the inter-electrode interference phenomena. COMSOL Multiphysics was used for the analysis; COMSOL interface such as electric field, magnetic field, heat transfer, and fluid flow (laminar flow) was applied and Multiphysics such as plasma heat source and temperature coupling were considered. The temperature and velocity of the arc plasma were determined and the energy transfer between the electrodes was analyzed. We confirmed that the concave shape has a lower average heat flux than the other shapes, with the arc pressure evenly distributed in the anode. It is concluded that the concave shape can reduce the flow of the plasma from the anode and obtain even distribution of the arc plasma in the radial direction.

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