Development of a Wearable-Sensor-Based Fall Detection System

Fall detection is a major challenge in the public healthcare domain, especially for the elderly as the decline of their physical fitness, and timely and reliable surveillance is necessary to mitigate the negative effects of falls. This paper develops a novel fall detection system based on a wearable device. The system monitors the movements of human body, recognizes a fall from normal daily activities by an effective quaternion algorithm, and automatically sends request for help to the caregivers with the patient’s location

Extended Ellipsoidal Outer-Bounding Set-Membership Estimation for Nonlinear Discrete-Time Systems with Unknown-but-Bounded Disturbances

This paper develops an extended ellipsoidal outer-bounding set-membership estimation (EEOB-SME) algorithm with high accuracy and efficiency for nonlinear discrete-time systems under unknown-but-bounded (UBB) disturbances. The EEOB-SME linearizes the first-order terms about the current state estimations and bounds the linearization errors by ellipsoids using interval analysis for nonlinear equations of process and measurement equations, respectively. It has been demonstrated that the EEOB-SME algorithm is stable and the estimation errors of the EEOB-SME are bounded when the nonlinear system is observable. The EEOB-SME decreases the computation load and the feasible sets of EEOB-SME contain more true states. The efficiency of the EEOB-SME algorithm has been shown by a numerical simulation under UBB disturbances

Efficient Algorithms for Cache-Throughput Analysis in Cellular-D2D 5G Networks

In this paper, we propose a two-tiered segment-based Device-to-Device (S-D2D) caching approach to decrease the start up and playback delay experienced by Video-on-Demand (VoD) users in a cellular network. In the S-D2D caching approach cache space of each mobile device is divided into two cache-blocks. The first cache-block reserve for caching and delivering the beginning portion of the most popular video les and the second cache-block caches the latter portion of the requested video les ‘fully or partially’ depending on the users’ video watching behaviour and popularity of videos. In this approach before caching, video is divided and grouped in a sequence of fixed-sized fragments called segments. To control the admission to both cache-blocks and improve the system throughput, we further propose and evaluate three cache admission control algorithms. We also propose a video segment access protocol to elaborate on how to cache and share the video segments in a segmentation based D2D caching architecture. We formulate an optimisation problem and the optimal cache probability and beginning-segment size that maximise the cache-throughput probability of beginning-segments. To solve the non-convex cache-throughout maximisation problem, we derive an iterative algorithm, where the optimal solution is derived in each step. We used extensive simulations to evaluate the performance of our proposed S-D2D caching system

Efficient algorithms for cache-throughput analysis in cellular-D2D 5G networks

In this paper, we propose a two-Tiered segment-based Device-To-Device (S-D2D) caching approach to decrease the startup and playback delay experienced by Video-on-Demand (VoD) users in a cellular network. In the S-D2D caching approach cache space of each mobile device is divided into two cache-blocks. The first cache-block reserve for caching and delivering the beginning portion of the most popular video files and the second cacheblock caches the latter portion of the requested video files fully or partially depending on the users video watching behaviour and popularity of videos. In this approach before caching, video is divided and grouped in a sequence of fixed-sized fragments called segments. To control the admission to both cacheblocks and improve the system throughput, we further propose and evaluate three cache admission control algorithms. We also propose a video segment access protocol to elaborate on how to cache and share the video segments in a segmentation based D2D caching architecture.We formulate an optimisation problem and find the optimal cache probability and beginning-segment size that maximise the cache-Throughput probability of beginning-segments. To solve the non-convex cache-Throughout maximisation problem, we derive an iterative algorithm, where the optimal solution is derived in each step.We used extensive simulations to evaluate the performance of our proposed S-D2D caching system

An optimized algorithm for optimal power flow based on deep learning

With the increasing requirements for power system transient stability assessment, the research on power system transient stability assessment theory and methods requires not only qualitative conclusions about system transient stability but also quantitative analysis of stability and even development trends. Judging from the research and development process of this direction at home and abroad in recent years, it is mainly based on the construction of quantitative index models to evaluate its transient stability and development trend. Regarding the construction theories and methods of quantitative index models, a lot of results have been achieved so far. The research ideas mainly focus on two categories: uncertainty analysis methods and deterministic analysis methods. Transient stability analysis is one of the important factors that need to be considered. Therefore, this paper proposed an optimized algorithm based on deep learning for preventive control of the transient stability in power systems. The proposed algorithm accurately fits the generator’s power and transient stability index through a deep belief network (DBN) by unsupervised pre-training and fine-tuning. The non-linear differential–algebraic equation and complex transient stability are determined using the deep neural network. The proposed algorithm minimizes the control cost under the constraints of the contingency by realizing the data-driven acquisition of the optimal preventive control. It also provides an efficient solution to stability and reliability rules with similar safety into the corresponding control model. Simulation results show that the proposed algorithm effectively improved the accuracy and reduces the complexity as compared with existing algorithms.National Research Foundation of Korea [2019R1C1C1007277]

Mechanical Properties of Atomically Thin Tungsten Dichalcogenides::WS2, WSe2, and WTe2

Two-dimensional (2D) tungsten disulfide (WS$_2$), tungsten diselenide (WSe$_2$), and tungsten ditelluride (WTe$_2$) draw increasing attention due to their attractive properties deriving from the heavy tungsten and chalcogenide atoms, but their mechanical properties are still mostly unknown. Here, we determine the intrinsic and air-aged mechanical properties of mono-, bi-, and trilayer (1-3L) WS$_2$, WSe$_2$ and WTe$_2$ using a complementary suite of experiments and theoretical calculations. High-quality 1L WS$_2$ has the highest Young's modulus (302.4+-24.1 GPa) and strength (47.0+-8.6 GPa) of the entire family, overpassing those of 1L WSe$_2$ (258.6+-38.3 and 38.0+-6.0 GPa, respectively) and WTe$_2$ (149.1+-9.4 and 6.4+-3.3 GPa, respectively). However, the elasticity and strength of WS$_2$ decrease most dramatically with increased thickness among the three materials. We interpret the phenomenon by the different tendencies for interlayer sliding in equilibrium state and under in-plane strain and out-of-plane compression conditions in the indentation process, revealed by finite element method (FEM) and density functional theory (DFT) calculations including van der Waals (vdW) interactions. We also demonstrate that the mechanical properties of the high-quality 1-3L WS$_2$ and WSe$_2$ are largely stable in the air for up to 20 weeks. Intriguingly, the 1-3L WSe$_2$ shows increased modulus and strength values with aging in the air. This is ascribed to oxygen doping, which reinforces the structure. The present study will facilitate the design and use of 2D tungsten dichalcogenides in applications, such as strain engineering and flexible field-effect transistors (FETs)

Relationship between self-care compliance, trust, and satisfaction among hypertensive patients in China

IntroductionHypertension is a growing public health concern worldwide. It is a leading risk factor for all-cause mortality and may lead to complications such as cardiovascular disease, stroke, and kidney failure. Poor compliance of hypertensive patients is one of the major barriers to controlling high blood pressure. Compliance is not ideal among Chinese patients, and increasing patient self-care compliance with hypertension is necessary.MethodsThis article analyzes the status of self-care compliance, trust, and satisfaction among Chinese hypertensive patients using cross-sectional data from Zhejiang Province. We use a multi-group structural equation model (MGSEM) to compare the interrelationships across genders.ResultsThe study's findings show that the average trust, satisfaction, and compliance scores are 3.92 ± 0.55, 3.98 ± 0.61, and 3.33 ± 0.41, respectively. Female patients exhibit higher average total scores for trust and compliance than male patients. The SEM results indicate that trust has a direct positive association with compliance [β = 0.242, 95% CI: (0.068, 0.402)] and satisfaction [β = 0.260, 95% CI: (0.145, 0.367)], while their satisfaction is not directly associated with compliance. The results of MGSEM show that trust has an indirect effect on compliance in the male group through satisfaction [β = 0.051, P < 0.05, 95% CI: (0.012, 0.116)]. In the female group, trust has a direct effect on satisfaction [β = 0.235, P < 0.05, 95% CI: (0.041, 0.406)] and compliance [β = 0.319, P < 0.01, 95% CI: (0.086, 0.574)].DiscussionThis study reveals the mechanisms of self-care compliance, trust, and satisfaction among Chinese hypertensive patients. Its findings may serve as a reference for guiding primary healthcare providers to improve hypertension patients' compliance and implement gender-targeted health interventions

Using Interpolation to Estimate System Uncertainty in Gene Expression Experiments

The widespread use of high-throughput experimental assays designed to measure the entire complement of a cell's genes or gene products has led to vast stores of data that are extremely plentiful in terms of the number of items they can measure in a single sample, yet often sparse in the number of samples per experiment due to their high cost. This often leads to datasets where the number of treatment levels or time points sampled is limited, or where there are very small numbers of technical and/or biological replicates. Here we introduce a novel algorithm to quantify the uncertainty in the unmeasured intervals between biological measurements taken across a set of quantitative treatments. The algorithm provides a probabilistic distribution of possible gene expression values within unmeasured intervals, based on a plausible biological constraint. We show how quantification of this uncertainty can be used to guide researchers in further data collection by identifying which samples would likely add the most information to the system under study. Although the context for developing the algorithm was gene expression measurements taken over a time series, the approach can be readily applied to any set of quantitative systems biology measurements taken following quantitative (i.e. non-categorical) treatments. In principle, the method could also be applied to combinations of treatments, in which case it could greatly simplify the task of exploring the large combinatorial space of future possible measurements