User Identification Using Gait Patterns on UbiFloorII

This paper presents a system of identifying individuals by their gait patterns. We take into account various distinguishable features that can be extracted from a user’s gait and then divide them into two classes: walking pattern and stepping pattern. The conditions we assume are that our target environments are domestic areas, the number of users is smaller than 10, and all users ambulate with bare feet considering the everyday lifestyle of the Korean home. Under these conditions, we have developed a system that identifies individuals’ gait patterns using our biometric sensor, UbiFloorII. We have created UbiFloorII to collect walking samples and created software modules to extract the user’s gait pattern. To identify the users based on the gait patterns extracted from walking samples over UbiFloorII, we have deployed multilayer perceptron network, a feedforward artificial neural network model. The results show that both walking pattern and stepping pattern extracted from users’ gait over the UbiFloorII are distinguishable enough to identify the users and that fusing two classifiers at the matching score level improves the recognition accuracy. Therefore, our proposed system may provide unobtrusive and automatic user identification methods in ubiquitous computing environments, particularly in domestic areas

Charge-spin correlation in van der Waals antiferromagenet NiPS3

Strong charge-spin coupling is found in a layered transition-metal trichalcogenide NiPS3, a van derWaals antiferromagnet, from our study of the electronic structure using several experimental and theoretical tools: spectroscopic ellipsometry, x-ray absorption and photoemission spectroscopy, and density-functional calculations. NiPS3 displays an anomalous shift in the optical spectral weight at the magnetic ordering temperature, reflecting a strong coupling between the electronic and magnetic structures. X-ray absorption, photoemission and optical spectra support a self-doped ground state in NiPS3. Our work demonstrates that layered transition-metal trichalcogenide magnets are a useful candidate for the study of correlated-electron physics in two-dimensional magnetic material.Comment: 6 pages, 3 figur

OCT for non-destructive examination of the internal biological structures of mosquito specimen

The Study of mosquitoes and their behavioral analysis are of crucial importance to control the alarmingly increasing mosquito-borne diseases. Conventional imaging techniques use either dissection, exogenous contrast agents. Non-destructive imaging techniques, like x-ray and microcomputed tomography uses ionizing radiations. Hence, a non-destructive and real-time imaging technique which can obtain high resolution images to study the anatomical features of mosquito specimen can greatly aid researchers for mosquito studies. In this study, the three-dimensional imaging capabilities of optical coherence tomography (OCT) for structural analysis of Anopheles sinensis mosquitoes has been demonstrated. The anatomical features of An. sinensis head, thorax, and abdomen regions along with internal morphological structures like foregut, midgut, and hindgut were studied using OCT imaging. Two-dimensional (2D) and three-dimensional (3D) OCT images along with histology images were helpful for the anatomical analysis of the mosquito specimens. From the concurred results and by exhibiting this as an initial study, the applicability of OCT in future entomological researches related to mosquitoes and changes in its anatomical structure is demonstrated

MiR-9 Controls Chemotactic Activity of Cord Blood CD34⁺ Cells by Repressing CXCR4 Expression

Improved approaches for promoting umbilical cord blood (CB) hematopoietic stem cell (HSC) homing are clinically important to enhance engraftment of CB-HSCs. Clinical transplantation of CB-HSCs is used to treat a wide range of disorders. However, an improved understanding of HSC chemotaxis is needed for facilitation of the engraftment process. We found that ectopic overexpression of miR-9 and antisense-miR-9 respectively down- and up-regulated C-X-C chemokine receptor type 4 (CXCR4) expression in CB-CD34＋ cells as well as in 293T and TF-1 cell lines. Since CXCR4 is a specific receptor for the stromal cell derived factor-1 (SDF-1) chemotactic factor, we investigated whether sense miR-9 and antisense miR-9 influenced CXCR4-mediated chemotactic mobility of primary CB CD34＋ cells and TF-1 cells. Ectopic overexpression of sense miR-9 and antisense miR-9 respectively down- and up-regulated SDF-1-mediated chemotactic cell mobility. To our knowledge, this study is the first to report that miR-9 may play a role in regulating CXCR4 expression and SDF-1-mediated chemotactic activity of CB CD34＋ cells

Cross-domain interoperability using federated interoperable semantic IoT/Cloud testbeds and applications: The FIESTA-IoT approach

This work is funded by the European Commission under the EU-H2020 Project Grant ”Federated Interoperable Semantic IoT/cloudTestbeds andApplications (FIESTA)” with the Grant Agreement No. CNECT-ICT-643943

Pretransplant malnutrition, inflammation, and atherosclerosis affect cardiovascular outcomes after kidney transplantation

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.Background Malnutrition, inflammation, and atherosclerosis (MIA) syndrome is associated with a high mortality rate in patients with end-stage renal disease. However, the clinical relevance of MIA syndrome in kidney transplantation (KT) recipients remains unknown. Methods We enrolled 1348 adult KT recipients. Recipients were assessed based on serum albumin, cholesterol, or body mass index for the malnutrition factor and C-reactive protein level for the inflammation factor. Any history of cardiovascular (CV), cerebrovascular, or peripheral vascular disease satisfied the atherosclerosis factor. Each MIA factors were assessed by univariate analysis and we calculated an overall risk score by summing up scores for each independent variable. The enrolled patients were divided into 4 groups depending on the MIA score (0, 2–4, 6, 8–10). Results The patients with higher MIA score showed worse outcome of fatal/non-fatal acute coronary syndrome (ACS) (p < 0.001) and composite outcomes of ACS and all-cause mortality (p < 0.001) than with the lower MIA score. In multivariate analysis, ACS showed significantly higher incidence in the MIA score 8-10 group than in the MIA score 0 group (Hazard ratio 6.12 95 % Confidence interval 1.84–20.32 p = 0.003). Conclusions The presence of MIA factors before KT is an independent predictor of post-transplant CV outcomes

Human Movement Detection and Identification Using Pyroelectric Infrared Sensors

Pyroelectric infrared (PIR) sensors are widely used as a presence trigger, but the analog output of PIR sensors depends on several other aspects, including the distance of the body from the PIR sensor, the direction and speed of movement, the body shape and gait. In this paper, we present an empirical study of human movement detection and identification using a set of PIR sensors. We have developed a data collection module having two pairs of PIR sensors orthogonally aligned and modified Fresnel lenses. We have placed three PIR-based modules in a hallway for monitoring people; one module on the ceiling; two modules on opposite walls facing each other. We have collected a data set from eight subjects when walking in three different conditions: two directions (back and forth), three distance intervals (close to one wall sensor, in the middle, close to the other wall sensor) and three speed levels (slow, moderate, fast). We have used two types of feature sets: a raw data set and a reduced feature set composed of amplitude and time to peaks; and passage duration extracted from each PIR sensor. We have performed classification analysis with well-known machine learning algorithms, including instance-based learning and support vector machine. Our findings show that with the raw data set captured from a single PIR sensor of each of the three modules, we could achieve more than 92% accuracy in classifying the direction and speed of movement, the distance interval and identifying subjects. We could also achieve more than 94% accuracy in classifying the direction, speed and distance and identifying subjects using the reduced feature set extracted from two pairs of PIR sensors of each of the three modules

Building Environment Analysis Based on Temperature and Humidity for Smart Energy Systems

In this paper, we propose a new HVAC (heating, ventilation, and air conditioning) control strategy as part of the smart energy system that can balance occupant comfort against building energy consumption using ubiquitous sensing and machine learning technology. We have developed ZigBee-based wireless sensor nodes and collected realistic temperature and humidity data during one month from a laboratory environment. With the collected data, we have established a building environment model using machine learning algorithms, which can be used to assess occupant comfort level. We expect the proposed HVAC control strategy will be able to provide occupants with a consistently comfortable working or home environment