Electronic structure and magnetic properties of transition metal kagome metal-organic frameworks.

Kagome metal-organic frameworks (MOFs) are considered a new class of materials that can host two-dimensional (2D) magnetism and correlated electron phenomena such as superconductivity and quantum anomalous Hall effect. Despite its potential for spintronics applications and others, the systematic understanding between the electronic structure and magnetic properties of kagome MOFs is still missing. This work determines the crystal structure, magnetic ground states, and anisotropy of a series of transition metal atoms and ligands from first-principles calculations. We reveal that the coexistence of covalent and ionic bonding characters of 3d orbitals is a distinctive feature of the 2D kagome MOFs. Furthermore, we demonstrate that the occupancies of active bands near the Fermi level are responsible for different superexchange mechanisms: the partially filled bands with empty for V and Co MOFs lead to antiferromagnetic ordering, and the partially filled bands with full for Mn, Fe, and Co MOFs lead to ferromagnetic ordering between transition metal ions. It is pointed out that the bands are formed through dpπ-hybridization between the transition metal dyz, dzx and ligand pz orbitals in the square planar coordination of metal atoms. This mechanism provides valuable insights into understanding magnetism in 2D kagome MOFs.

Pre-existing and machine learning-based models for cardiovascular risk prediction.

Predicting the risk of cardiovascular disease is the key to primary prevention. Machine learning has attracted attention in analyzing increasingly large, complex healthcare data. We assessed discrimination and calibration of pre-existing cardiovascular risk prediction models and developed machine learning-based prediction algorithms. This study included 222,998 Korean adults aged 40-79 years, naïve to lipid-lowering therapy, had no history of cardiovascular disease. Pre-existing models showed moderate to good discrimination in predicting future cardiovascular events (C-statistics 0.70-0.80). Pooled cohort equation (PCE) specifically showed C-statistics of 0.738. Among other machine learning models such as logistic regression, treebag, random forest, and adaboost, the neural network model showed the greatest C-statistic (0.751), which was significantly higher than that for PCE. It also showed improved agreement between the predicted risk and observed outcomes (Hosmer-Lemeshow χ2 = 86.1, P < 0.001) than PCE for whites did (Hosmer-Lemeshow χ2 = 171.1, P < 0.001). Similar improvements were observed for Framingham risk score, systematic coronary risk evaluation, and QRISK3. This study demonstrated that machine learning-based algorithms could improve performance in cardiovascular risk prediction over contemporary cardiovascular risk models in statin-naïve healthy Korean adults without cardiovascular disease. The model can be easily adopted for risk assessment and clinical decision making.

Conditions for CNT - Coated Textile Sensors Applied to Wearable Platforms to Monitor Limb Joint Motion.

Despite recent research on joint motion measurement to monitor human body movement, current measurement techniques and tools have significant limitations, including requiring large space for measurement and causing discomfort in test subjects wearing motion sensors. Our study aims, first, to develop carbon nanotube (CNT)-based textile joint motion sensors. Second, ours study aims to identify the most suitable CNT-based sensor structure and attachment method for use on a wearable platform during general exercise speeds. Lastly, we used these sensors on the human body, using sleeves and legs to find the most stable location, and we used the CNT-based sensor condition to monitor joint motions. We utilized our CNT-based sensor, which has proper elasticity as well as conductivity, and applied it to the elbow and knee joints. Based on the strain gauge principle, we monitored the variance of electric resistance that occurred when the CNT-based sensor was stretched due to limb motion. Our study tested 48 types of sensors. These sensors were applied to the CNT using different base knit textiles as well as different attachment methods, layers, sensor lengths, and sensor widths. The four most successful sensor types, which showed superior efficacy over the others in joint motion measurement, were selected for further study. These four sensors were then used to measure the elbow and knee joint motions of human subjects by placing them on different locations on sleeves and legs. The CNT knit textile sensors best suited to measuring joint motions are those with a double-layered CNT knit and 5 cm long × 0.5 cm or 1 cm wide sensors attached to a polyester¬-based knit using a welding method. The best position for the sensor to more stably monitor joint motions was the "below hinge position" from the elbow or knee hinge joint. Our study suggests an alternative strategy for joint-motion measurement that could contribute to the development of more comfortable and human-friendly methods of human limb motion measurement.

Chern insulator with a nearly flat band in the metal-organic-framework-based Kagome lattice.

Based on first-principles density-functional theory (DFT) calculations, we report that the transition-metal bis-dithiolene, M3C12S12 (M = Mn and Fe), complexes can be a two-dimensional (2D) ferromagnetic insulator with nontrivial Chern number. Among various synthetic pathways leading to metal bis-dithiolenes, the simplest choice of ligand, Benzene-hexathiol, connecting metal cations to form a Kagome lattice is studied following the experimental report of time-reversal symmetric isostructural compound Ni3C12S12. We show sulfur and carbon-based ligands play the key role in making the complexes topologically nontrivial. An unusual topological quantum phase transition induced by the on-site Coulomb interaction brings a nearly flat band with a nonzero Chern number as the highest occupied band. With this analysis we explain the electronic structure of the class M3C12S12 and predict the existence of nearly flat band with nonzero Chern number and it can be a fractional Chern insulator candidate with carrier doping.

Potentials of Smart dynamometer use for clinical and self-management of rehabilitation in breast cancer survivors: a feasibility study.

The aim of this study was to examine the feasibility of the Smart dynamometer as a rehabilitation exercise device in a daily care by comparing with the existing medical devices. We used and analyzed clinical and measurement data of breast cancer survivors who have used Smart dynamometer during their rehabilitation after breast cancer surgery. The Smart dynamometer was compared with the two existing devices of Takei dynamometer and surface electromyography (sEMG) that were used in routine care, respectively. Three key components of the rehabilitation exercise devices were analyzed to validate the feasibility of the Smart dynamometer: grip strength, reaction time, and grip endurance time. Pearson's correlation analysis was performed to compare the statistical significance between the devices. The data of 12 and 15 female breast cancer patients were analyzed for comparing the Smart dynamometer with Takei dynamometer and sEMG, respectively. There was a very weak correlation between the maximum values from the Takei and the Smart dynamometers in the affected and non-affected arms of breast cancer patients (r = 0.5321, 0.4733). Comparisons of 3 features between the Smart dynamometer and sEMG showed that there were strong positive correlations for both reaction time and endurance time in the affected and non-affected arms (r > 0.9). The feasibility of the Smart dynamometer for the possible use in a daily rehabilitation exercise was partially verified. Moreover, since the Smart dynamometer was highly correlated with time-related variables, it was important and significant to measure both grip strength and time-related information.

Survey on the demand for adoption of Internet of Things (IoT)-based services in hospitals: Investigation of nurses' perception in a tertiary university hospital.

In hospitals, while the opportunities and challenges of Internet of Things (IoT) applications are continuously increasing, research on what IoT services are actually in demand in hospitals has not been conducted. In this study, a survey of working hospital nurses was conducted to confirm the demand for IoT services. A total of 1086 (90.2%) participants responded. Five out of seven points for all service questions were obtained, which indicates a high demand for all services. The highest demand was shown for a vital sign device interface system. A comparison between ward and non-ward nurses showed that individuals working in wards had a high demand for patient care related IoT services, and individuals working in non-ward departments demonstrated a high demand for IoT services to improve work efficiency. Overall, the results provide a framework for future directions of services that can improve the efficiency of medical staff and health outcomes of patients.

Laboratory Environment Monitoring: Implementation Experience and Field Study in a Tertiary General Hospital.

OBJECTIVES: To successfully introduce an Internet of Things (IoT) system in the hospital environment, this study aimed to identify issues that should be considered while implementing an IoT based on a user demand survey and practical experiences in implementing IoT environment monitoring systems. METHODS: In a field test, two types of IoT monitoring systems (on-premises and cloud) were used in Department of Laboratory Medicine and tested for approximately 10 months from June 16, 2016 to April 30, 2017. Information was collected regarding the issues that arose during the implementation process. RESULTS: A total of five issues were identified: sensing and measuring, transmission method, power supply, sensor module shape, and accessibility. CONCLUSIONS: It is expected that, with sufficient consideration of the various issues derived from this study, IoT monitoring systems can be applied to other areas, such as device interconnection, remote patient monitoring, and equipment/environmental monitoring.

Enhancing User Experience Through User Study: Design of an mHealth Tool for Self-Management and Care Engagement of Cardiovascular Disease Patients.

BACKGROUND: As patient communication, engagement, personal health data tracking, and up-to-date information became more efficient through mobile health (mHealth), cardiovascular diseases (CVD) and other diseases that require behavioral improvements in daily life are now capable of being managed and prevented more effectively. However, to increase patient engagement through mHealth, it is important for the initial design to consider functionality and usability factors and accurately assess user demands during the developmental process so that the app can be used continuously. OBJECTIVE: The purpose of the study was to provide insightful information for developing mHealth service for patients with CVD based on user research to help enhance communication between patients and doctors. METHODS: To drive the mobile functions and services needed to manage diseases in CVD patients, user research was conducted on patients and doctors at a tertiary general university hospital located in the Seoul metropolitan area of South Korea. Interviews and a survey were performed on patients (35 participants) and a focus group interview was conducted with doctors (5 participants). A mock-up mobile app was developed based on the user survey results, and a usability test was then conducted (8 participants) to identify factors that should be considered to improve usability. RESULTS: The majority of patients showed a positive response in terms of their interest or intent to use an app for managing CVD. Functional features, such as communication with doctors, self-risk assessment, exercise, tailored education, blood pressure management, and health status recording had a score of 4.0 or higher on a 5-point Likert scale, showing that these functions were perceived to be useful to patients. The results of the mock-up usability test showed that inputting and visualizing blood pressure and other health conditions was required to be easier. The doctors requested a function that offered a comprehensive view of the patient's daily health status by linking the mHealth app data with the hospital's electronic health record system. CONCLUSIONS: Insights derived from a user study for developing an mHealth tool for CVD management, such as self-assessment and a communication channel between patients and doctors, may be helpful to improve patient engagement in care.

The Effect of Electrode Designs Based on the Anatomical Heart Location for the Non-Contact Heart Activity Measurement.

This research is an extension of a previous research [1] on the different effects of sensor location that is relatively suitable for heart rate sensing. This research aimed to elucidate the causes of wide variations in heart rate measurements from the same sensor position among subjects, as observed in previous research [1], and to enhance designs of the inductive textile electrode to overcome these variations. To achieve this, this study comprised two parts: In part 1, X-ray examinations were performed to determine the cause of the wide variations noted in the findings from previous research [1], and we found that at the same sensor position, the heart activity signal differed with slight differences in the positions of the heart of each subject owing to individual differences in the anatomical heart location. In part 2, three types of dual-loop-type textile electrodes were devised to overcome variations in heart location that were confirmed in part 1 of the study. The variations with three types of sensor designs were compared with that with a single-round type of electrode design, by using computer simulation and by performing a t-test on the data obtained from the experiments. We found that the oval-oval shaped, dual-loop-type textile electrode was more suitable than the single round type for determining morphological characteristics as well as for measuring appropriate heart activity signals. Based on these results, the oval-oval, dual-loop-type was a better inductive textile electrode that more effectively overcomes individual differences in heart location during heart activity sensing based on the magnetic-induced conductivity principle.

Measurement of high-resolution mechanical contraction of cardiac muscle by induced eddy current.

There are many types of devices which help to manage a personal health conditions such as heartbeat chest belt, pedometer and smart watch. And the most common device has the relationship with heart rate or ECG data. However, users have to attach some electrode or fasten the belt on the bare skin to measure bio-signal information. Therefore, most of people want more convenient and short-ready-time and no-need to attach electrode. In this paper, we proposed the high-resolution measuring system of mechanical activity of cardiac muscle and thereby measure heartbeat. The principle of the proposed measuring method is that the alternating current generate alternating magnetic field around coil. This primary magnetic field induces eddy current which makes magnetic field against primary coil in the nearby objects. To measure high-resolution changes of the induced secondary magnetic fields, we used digital Phase-locked loop(PLL) circuit which provides more high-resolution traces of frequency changes than the previous studies based on digital frequency counter method. As a result of our preliminary experiment, peak-peak intervals of the proposed method showed high correlation with R-R intervals of clinical ECG signals(r=0.9249). Also, from signal traces of the proposed method, we might make a conjecture that the contraction of atrium or ventricle is reflected by changing conductivity of cardiac muscle which is beating ceaselessly.