Metformin use in elderly population with diabetes reduced the risk of dementia in a dose-dependent manner, based on the Korean NHIS-HEALS cohort.

AIMS: The aim of this study is to investigate the association between metformin usage and dementia in an elderly Korean population. METHODS: Participants were divided into five groups: metformin non-users with diabetes mellitus (DM), metformin users with DM (low-, mid-, and high-users), and non-diabetic Individuals. Dementia was defined with primary diagnostic dementia codes according to the 10th edition of the International Classification of Diseases. To compare the incidence rate of dementia among the five groups, Kaplan-Meier estimates and log-rank test were employed. Also, to control the confounding factors, Cox proportional hazards regression models were fitted in a sequential adjustment. RESULTS: The median follow-up was 12.4 years. The overall incidence rate of dementia was 11.3% (8.4% in men and 13.9% in women). Compared with metformin non-users, hazard ratios (95% confidence intervals) of low-, mid-, and high-users and non-diabetic individuals for dementia were 0.97 (0.73-1.28), 0.77 (0.58-1.01), 0.48 (0.35-0.67), and 0.98 (0.84-1.15), respectively, in men, respectively, and 0.90 (0.65-0.98), 0.61 (0.50-0.76), 0.46 (0.36-0.58), and 0.92 (0.81-1.04), respectively, in women, after full adjustment of confounding variables. CONCLUSIONS: Metformin use in an elderly population with DM reduced dementia risk in a dose-response manner.

Sensitivity-Based Fault Detection and Isolation Algorithm for Road Vehicle Chassis Sensors.

Vehicle control systems such as ESC (electronic stability control), MDPS (motor-driven power steering), and ECS (electronically controlled suspension) improve vehicle stability, driver comfort, and safety. Vehicle control systems such as ACC (adaptive cruise control), LKA (lane-keeping assistance), and AEB (autonomous emergency braking) have also been actively studied in recent years as functions that assist drivers to a higher level. These DASs (driver assistance systems) are implemented using vehicle sensors that observe vehicle status and send signals to the ECU (electronic control unit). Therefore, the failure of each system sensor affects the function of the system, which not only causes discomfort to the driver but also increases the risk of accidents. In this paper, we propose a new method to detect and isolate faults in a vehicle control system. The proposed method calculates the constraints and residuals of 12 systems by applying the model-based fault diagnosis method to the sensor of the chassis system. To solve the inaccuracy in detecting and isolating sensor failure, we applied residual sensitivity to a threshold that determines whether faults occur. Moreover, we applied a sensitivity analysis to the parameters semi-correlation table to derive a fault isolation table. To validate the FDI (fault detection and isolation) algorithm developed in this study, fault signals were injected and verified in the HILS (hardware-in-the-loop simulation) environment using an RCP (rapid control prototyping) device.

Integrated Fault Diagnosis Algorithm for Motor Sensors of In-Wheel Independent Drive Electric Vehicles.

An integrated fault-diagnosis algorithm for a motor sensor of in-wheel independent drive electric vehicles is presented. This paper proposes a method that integrates the high- and low-level fault diagnoses to improve the robustness and performance of the system. For the high-level fault diagnosis of vehicle dynamics, a planar two-track non-linear model is first selected, and the longitudinal and lateral forces are calculated. To ensure redundancy of the system, correlation between the sensor and residual in the vehicle dynamics is analyzed to detect and separate the fault of the drive motor system of each wheel. To diagnose the motor system for low-level faults, the state equation of an interior permanent magnet synchronous motor is developed, and a parity equation is used to diagnose the fault of the electric current and position sensors. The validity of the high-level fault-diagnosis algorithm is verified using Carsim and Matlab/Simulink co-simulation. The low-level fault diagnosis is verified through Matlab/Simulink simulation and experiments. Finally, according to the residuals of the high- and low-level fault diagnoses, fault-detection flags are defined. On the basis of this information, an integrated fault-diagnosis strategy is proposed.