Cross-Regional Data Initiative for the Assessment and Development of Treatment for Neurological and Mental Disorders

Purpose: To describe and categorize detailed components of databases in the Neurological and Mental Health Global Epidemiology Network (NeuroGEN). Methods: An online 132-item questionnaire was sent to key researchers and data custodians of NeuroGEN in North America, Europe, Asia and Oceania. From the responses, we assessed data characteristics including population coverage, data follow-up, clinical information, validity of diagnoses, medication use and data latency. We also evaluated the possibility of conversion into a common data model (CDM) to implement a federated network approach. Moreover, we used radar charts to visualize the data capacity assessments, based on different perspectives. Results: The results indicated that the 15 databases covered approximately 320 million individuals, included in 7 nationwide claims databases from Australia, Finland, South Korea, Taiwan and the US, 6 population-based electronic health record databases from Hong Kong, Scotland, Taiwan, the Netherlands and the UK, and 2 biomedical databases from Taiwan and the UK. Conclusion: The 15 databases showed good potential for a federated network approach using a common data model. Our study provided publicly accessible information on these databases for those seeking to employ real-world data to facilitate current assessment and future development of treatments for neurological and mental disorders.</p

Cross-Regional Data Initiative for the Assessment and Development of Treatment for Neurological and Mental Disorders

Purpose: To describe and categorize detailed components of databases in the Neurological and Mental Health Global Epidemiology Network (NeuroGEN). Methods: An online 132-item questionnaire was sent to key researchers and data custodians of NeuroGEN in North America, Europe, Asia and Oceania. From the responses, we assessed data characteristics including population coverage, data follow-up, clinical information, validity of diagnoses, medication use and data latency. We also evaluated the possibility of conversion into a common data model (CDM) to implement a federated network approach. Moreover, we used radar charts to visualize the data capacity assessments, based on different perspectives. Results: The results indicated that the 15 databases covered approximately 320 million individuals, included in 7 nationwide claims databases from Australia, Finland, South Korea, Taiwan and the US, 6 population-based electronic health record databases from Hong Kong, Scotland, Taiwan, the Netherlands and the UK, and 2 biomedical databases from Taiwan and the UK. Conclusion: The 15 databases showed good potential for a federated network approach using a common data model. Our study provided publicly accessible information on these databases for those seeking to employ real-world data to facilitate current assessment and future development of treatments for neurological and mental disorders.</p

Cross-Regional Data Initiative for the Assessment and Development of Treatment for Neurological and Mental Disorders

Purpose: To describe and categorize detailed components of databases in the Neurological and Mental Health Global Epidemiology Network (NeuroGEN). Methods: An online 132-item questionnaire was sent to key researchers and data custodians of NeuroGEN in North America, Europe, Asia and Oceania. From the responses, we assessed data characteristics including population coverage, data follow-up, clinical information, validity of diagnoses, medication use and data latency. We also evaluated the possibility of conversion into a common data model (CDM) to implement a federated network approach. Moreover, we used radar charts to visualize the data capacity assessments, based on different perspectives. Results: The results indicated that the 15 databases covered approximately 320 million individuals, included in 7 nationwide claims databases from Australia, Finland, South Korea, Taiwan and the US, 6 population-based electronic health record databases from Hong Kong, Scotland, Taiwan, the Netherlands and the UK, and 2 biomedical databases from Taiwan and the UK. Conclusion: The 15 databases showed good potential for a federated network approach using a common data model. Our study provided publicly accessible information on these databases for those seeking to employ real-world data to facilitate current assessment and future development of treatments for neurological and mental disorders.</p

Predicting the Remaining Useful Life of Landing Gear with Prognostics and Health Management (PHM)

Landing gear is an essential part of an aircraft. However, the components of landing gear are susceptible to degradation over the life of their operation, which can result in the shimmy effect occurring during take-off and landing. In order to reduce unplanned flight disruptions and increase the availability of aircraft, the predictive maintenance (PdM) technique is investigated in this study. This paper presents a case study on the implementation of a health assessment and prediction workflow for remaining useful life (RUL) based on the prognostics and health management (PHM) framework of currently in-service aircraft, which could significantly benefit fleet operators and aircraft maintenance. Machine learning is utilized to develop a health indicator (HI) for landing gear using a data-driven approach, whereas a time-series analysis (TSA) is used to predict its degradation. The degradation models are evaluated using large volumes of real sensor data from in-service aircraft. Finally, the challenges of implementing a built-in PHM system for next-generation aircraft are outlined

The Optimization of a Model for Predicting the Remaining Useful Life and Fault Diagnosis of Landing Gear

With the development of next-generation airplanes, the complexity of equipment has increased rapidly, and traditional maintenance solutions have become cost-intensive and time-consuming. Therefore, the main objective of this study is to adopt predictive maintenance techniques in daily maintenance in order to reduce manpower, time, and the cost of maintenance, as well as increase aircraft availability. The landing gear system is an important component of an aircraft. Wear and tear on the parts of the landing gear may result in oscillations during take-off and landing rolling and even affect the safety of the fuselage in severe cases. This study acquires vibration signals from the flight data recorder and uses prognostic and health management technology to evaluate the health indicators (HI) of the landing gear. The HI is used to monitor the health status and predict the remaining useful life (RUL). The RUL prediction model is optimized through hyperparameter optimization and using the random search algorithm. Using the RUL prediction model, the health status of the landing gear can be monitored, and adaptive maintenance can be carried out. After the optimization of the RUL prediction model, the root-mean-square errors of the three RUL prediction models, that is, the autoregressive model, Gaussian process regression, and the autoregressive integrated moving average, decreased by 45.69%, 55.18%, and 1.34%, respectively. In addition, the XGBoost algorithm is applied to simultaneously output multiple fault types. This model provides a more realistic representation of the actual conditions under which an aircraft might exhibit multiple faults. With an optimal fault diagnosis model, when an anomaly is detected in the landing gear, the faulty part can be quickly diagnosed, thus enabling faster and more adaptive maintenance. The optimized multi-fault diagnosis model proposed in this study achieves average accuracy, a precision rate, a recall rate, and an F1 score of more than 96.8% for twenty types of faults

Failures of Cu-Cu Joints under Temperature Cycling Tests

In this study, the failure mechanisms of Cu-Cu joints under thermal cycling were investigated. Two structures of dielectrics (PBO/underfill/PBO and SiO2) were employed to seal the joints. Stress gradients induced in the joints with the different dielectrics were simulated using a finite element method (FEM) and correlated with experimental observations. We found that interfacial voids were forced to move in the direction from high stress regions to low stress ones. The locations of migrated voids varied with the dielectric structures. Under thermal cycling, such voids were likely to move forward to the regions with a small stress change. They relocated and merged with their neighboring voids to lower the interfacial energy

Effect of Tin Grain Orientation on Electromigration-Induced Dissolution of Ni Metallization in SnAg Solder Joints

In this study, symmetrical solder joints (Cu/Ni/SnAg2.3/Ni/Cu) were fabricated. They were electromigration (EM)-stressed at high (8 &times; 104 A/cm2) or low (1.6 &times; 104 A/cm2) current densities. Failures in the solder joints with different grain orientations under EM stressing were then characterized. Results show that Ni under-bump-metallurgy (UBM) was quickly dissolved into the solder joints possessing low angles between Sn c-axis and electron direction and massive NiCuSn intermetallic compounds formed in the Sn matrix. The diffusion rate of Ni increased with decreasing orientation grain angle. A theoretical model was also established to analyze the consumption rate of Ni UBM. Good agreement between the modeling and experimental results was obtained. Additionally, we found that voids were more likely to form in the solder joints under high EM stressing

Entry of Scotophilus Bat Coronavirus-512 and Severe Acute Respiratory Syndrome Coronavirus in Human and Multiple Animal Cells

Bats are natural reservoirs of severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome CoV (MERS-CoV). Scotophilus bat CoV-512 demonstrates potential for cross-species transmission because its viral RNA and specific antibodies have been detected in three bat species of Taiwan. Understanding the cell tropism of Scotophilus bat CoV-512 is the first step for studying the mechanism of cross-species transmission. In this study, a lentivirus-based pseudovirus was produced using the spike (S) protein of Scotophilus bat CoV-512 or SARS-CoV as a surface protein to test the interaction between coronaviral S protein and its cell receptor on 11 different cells. Susceptible cells expressed red fluorescence protein (RFP) after the entry of RFP-bound green fluorescence protein (GFP)-fused S protein of Scotophilus bat CoV-512 (RFP-Sco-S-eGFP) or RFP-SARS-S pseudovirus, and firefly luciferase (FLuc) activity expressed by cells infected with FLuc-Sco-S-eGFP or FLuc-SARS-S pseudovirus was quantified. Scotophilus bat CoV-512 pseudovirus had significantly higher entry efficiencies in Madin Darby dog kidney epithelial cells (MDCK), black flying fox brain cells (Pabr), and rat small intestine epithelial cells (IEC-6). SARS-CoV pseudovirus had significantly higher entry efficiencies in human embryonic kidney epithelial cells (HEK-293T), pig kidney epithelial cells (PK15), and MDCK cells. These findings demonstrated that Scotophilus bat CoV-512 had a broad host range for cross-species transmission like SARS-CoV