Miracle-dz/UniFAR-FTUs huggingface dataset:
special medical editorial Why should we study the coronary microcirculation

THE QUESTION we are posing to the readers is why should we study the microcirculation of the heart. First, let us deﬁne who are “we. ” In this issue of the American Journal of Physiology: Heart and Circulatory Physiology, a collection of papers are included as part of a Special Call on “Emerging Concepts in the Control of the Coronary Microcirculation. ” It is obvious why the coronary microcirculation should be studied from a basic perspective; even the most seminal reactions, autoregulation and metabolic dilation, are incom-pletely understood. Our editorial will not highlight basic problems but rather discuss the clinical importance of the coronary microcirculation. Our opinion is that the “we” who should study the coronary microcirculation are both basic scientists and clinicians, so that a much clearer understanding of the role of coronary microvessels in the etiology of ischemic heart disease may be elucidated.
Multi-Exponential Relaxation Times Maps Reconstruction and Unsupervised Classification in Magnitude Magnetic Resonance Imaging

In clinical and biological applications of T2 relaxometry, a multi-exponential decay model proved to be representative of the relaxation signal inside each voxel of the MRI images. However, estimating and exploiting the model parameters for magnitude data is a large-scale ill- posed inverse problem. This paper presents a parameter estimation method that combines a spatial regularization with a Maximum-Likelihood criterion based on the Rician distribution of the noise. In order to properly carry out the estimation on the image level, a Majorization-Minimization approach is implemented alongside an adapted non-linear least-squares algorithm. We propose a method for exploiting the reconstructed maps by clustering the parameters using a K-means classification algorithm applied to the extracted relaxation time and amplitude maps. The method is illustrated on real MRI data of food sample analysis.
Switch-Based Hybrid Precoding in mmWave Massive MIMO Systems

In mmWave communications, large-scale arrays can be very advantageous. In such arrays, switch-based hybrid precoding (beamforming) is very promising in terms of energy efficiency and reduced complexity, as opposed to phase-shifter structures for beamforming. However, switch-based structures are binary, which means that the design of an optimum beamformer, at large-scale in the analog domain, is a difficult task. We address this problem and propose a new method for the design of a switch-based hybrid precoder for massive MIMO communications in mmWave bands. We first cast the relevant maximization of mutual information as a binary, rank-constrained quadratic maximization, and solve it iteratively for each column of the analog precoder. The solution is then effectively approximated via a set of relaxations and sequential convex programming (SCP). Finally, we show the feasibility, and effectiveness of our method via numerical results.
Developing an Integration Architecture to Manage Heterogeneous Data by Private Healthcare Practitioners: A Case of Namibia

Patients consult different private healthcare practitioners (PHP) due to the uniqueness of their illnesses and need for different specialisation. However, patient’s health records are stored across autonomous systems of these different PHPs, leading to the establishment of heterogeneous health records. Consequently this creates a challenge of information sharing among PHPs. This study argues that these challenges are attributed to the non-integrated healthcare systems among PHPs and thus causing delay in treatment of patients. The study employs multiple case approach, using the semi-structured interview technique to investigate how health records are stored and shared amongst PHPs. The data collected was interpretively analysed to gain better understanding of the manipulation of patient’s health record. According to the study findings, majority of PHP do not invest in IS and information sharing amongst PHPs is conducted manually. Sharing of medical records manually is challenging and is threatened by issues such as data theft and sabotage. In this study, an integrated architecture to guide the management of healthcare heterogeneous data among healthcare practitioners is developed.
Should-blockers and diuretics remain as first line therapy for hypertension ? Thiazide-Type Diuretics and-Adrenergic Blockers as First-Line Drug Treatments for Hypertension

Systemic arterial hypertension affects 72 million US adults and an additional hundreds of millions of persons worldwide.1,2 Most of these are candidates for pharmacological treatment to reduce risk of cardiovascular disease (CVD) events, based primarily on a very large body of epidemiological and intervention research in humans. Because of this high prevalence and the cardiovascular consequences of untreated or inadequately treated hypertension, the selection of drugs for initial and continuing, long-term treatment has large public health and economic implications. Fortunately, such decisions and the expert recommendations that seek to guide them can call on evidence from 4 decades of randomized multicenter clinical trials evaluating effects of treatment on clinical CVD. We summarize that evidence in this article, in approximate chronological order, and we comment on the related treatment guidelines. We close with some of the major clinical questions yet to be resolved.
Context-Aware Neural Voice Activity Detection Using Auxiliary Networks for Phoneme Recognition, Speech Enhancement and Acoustic Scene Classification

This paper proposes a novel fully neural network based voice activity detection (VAD) method that estimates whether each speech segment is speech or non-speech even in very low signal-to-noise ratio (SNR) environments. Our innovation is to improve context-awareness of speech variability by introducing multiple auxiliary networks into the neural VAD framework. While previous studies reported that phonetic-aware auxiliary features extracted from a phoneme recognition network can improve VAD performance, none examined other effective auxiliary features for enhancing noise robustness. Thus, this paper present a neural VAD that uses auxiliary features extracted from not only the phoneme recognition network but also a speech enhancement network and an acoustic scene classification network. The last two networks are expected to improve context-awareness even in extremely low SNR environments since they can extract de-noised speech awareness and noisy environment awareness. In addition, we expect that combining these multiple auxiliary features yield synergistic improvements in VAD performance. Experiments verify the superiority of the proposed method in very low SNR environments.
Sensitivity Comparison of Open-Circuit Airgap Flux Between Surface-Mounted Permanent Magnet and Spoke-Type Permanent Magnet Machines Considering Manufacturing Tolerances

The study compares the sensitivities of open-circuit airgap flux (OCAF) between a surface-mounted permanent magnet (SPM) machine and a spoke-type PM machine based on variations in airgap length including additional airgaps between permanent magnets and rotor core and between segmented stator cores to achieve high quality electric machines. Analytical equations deduced from magnetic equivalent circuits (MECs) are used to directly compare natural-born characteristics of the OCAF of the two machines. First, the MEC of each machine is modeled by considering two additional airgaps between the PMs and rotor core and between the segmented stator cores. Second, the OCAF equation of each machine is derived from the MEC to analyze the effects of the design variables on the OCAF. Subsequently, the partial derivative equation of the OCAF equation with respect to the airgap length is obtained for sensitivity analysis. A comparison of the equations of the two machines indicates that the spoke-type PM machine exhibits inherently higher sensitivity and average value of the OCAF when compared to that of the SPM machine. Finally, the results are validated via a two-dimensional finite element method (FEM) by considering the variations in airgap lengths.
5G Service Based Core Network Design

5G has put forward higher demand for networking and simply evolving current system cannot meet the demand. Along with the basic Service-Based Architecture(SBA) standardized by 3GPP, 5G core network architecture is undergoing an evolution. In this paper, we design and evaluate Not-Only Stack(NoStack), a mobile network controller which features Message Bus(MB) and Global Network View(GNV), to address the potential enhancement to SBA. NoStack MB decouples business logic from inter-service communication. It improves service availability and agility. NoStack GNV decouples storage from computing to enable the sharing of service context between different services or service instances at the cost of acceptable increase on latency. It improves reliability and flexibility, as well as reduces signalling cost. The performance of NoStack is evaluated with initial attach procedure on OpenairInterface(OAI). The impact of NoStack on current system procedure is analysed as well. Finally, the outlook of NoStack in beyond 5G stage is shown.
Association of metabolic syndrome with atherothrombotic blood phenotypes in Asian Indian families with premature coronary artery disease

Introduction: Metabolic syndrome (MS) and its co-morbidities including coronary artery disease (CAD) are highly prevalent among Asian Indians and share common risk factors. The aim of this study was to look for an association of atherothrombotic biomarkers with MS among Asian Indian families with premature CAD. Material and methods: MS subjects were identified employing standard and adapted definitions. Lipids were measured on an auto-analyzer while the non- lipid markers were assayed by the ELISA method. Standard and modified MS definitions were applied to 2316 individuals from 531 families. Results: The ATP III criteria identified a significantly larger proportion of people with MS (N=933, P<0.0001) as compared to WHO (N=708) while maximum gain was seen in the modified MS criteria group (MSmod, N=1333), which included a lowered cut-off for waist circumference (WC) and body mass index (BMI) (P=0.0056). Atherothrombotic blood phenotypes, namely TC, TG, Ox-LDL, ApoB100, fibrinogen, FVII.c, PAI-1, IL-6, hsCRP, P-selectin, sICAM and leptin, were significantly elevated, while HDL-c and adiponectin were lower in the MSmod group (P<0.01). All the above phenotypes correlated significantly with WC and BMI but not with waist-hip ratio. Conlusions: In conclusion, an association of the atherothrombotic blood phenotypes with MS indicates the propensity of people with MS to develop CAD, thereby emphasizing the need for early and precise diagnosis of MS among Asian Indians.
Enabling Massive Real-Time Applications in IEEE 802.11be Networks

Next generation Wi-Fi networks are expected to support real-time applications that impose strict requirements on the packet transmission delay and packet loss ratio. Such applications form an essential target for the future Wi-Fi standard, namely IEEE 802.11be, the development process of which started in 2019. A promising way to provide efficient real-time communications in 802.11be networks requires some modification of the uplink OFDMA feature originally introduced in the IEEE 802.11ax amendment to the Wi-Fi standard. This feature allows the access point to reserve channel resources for upcoming urgent transmissions. The paper explains why uplink OFDMA random access of 802.11ax does not perfectly fit the requirements of real-time applications and proposes an easy-to-implement modification of the channel access rules for future 802.11be networks. With extensive simulation, it is shown that this modification together with a new resource allocation algorithm outperforms the existing ways to support real-time applications, especially for a heavy load and a high number of users. In particular, they provide extremely low delays for real-time traffic, while the throughput for non-real-time traffic is reduced insignificantly.
InterOpT: A new testing platform based on oneM2M standards for IoT systems

Nowadays, with the developing technologies, IoT based solutions are increasing day by day. In the scope of this study, a test platform has been developed to test the IoT solutions developed by different companies according to oneM2M standards. The main objective of the study is to provide a national standardization for the control of IoT solutions. The developed InterOpT (InterOpability Tool) testing platform software tool is the automation system for controlling interoperability and compliance with standards on IoT network components. Based on the OneM2M IoT standard recognized by ETSI, this system performs the validity tests defined by the RFC and analyzes and automates the results. Provides interoperability and system audit reports on IoT structures. Improved testing platform, web and cloud-based service can offer. It can do traffic and stress tests especially for the system and it can also do log collection analysis automatically. The platform can support different embedded IoT protocols such as HTTP, CoAP, MQTT. As a result, with this project, we have developed a successful test automation platform that can perform interoperability and compliance tests by adhering to IoT protocols and OneM2M standards.
Predicting Prevalence of Respiratory Disease with Multi-task Gaussian Process: A Case Study in East China

Chronic respiratory disease has become the third leading cause of non-communicable disease death in China. Predicting its prevalence could provide guidance for medical resource allocation given the unbalanced socioeconomic development cross areas. In this study, we investigated the prevalence of respiratory disease in Funan, a representative poverty-stricken county in East China, and developed a multi-task Gaussian process model to predict respiratory disease encounters in Funan. The mean squared error of our model improved from 0.019 to 0.018 compare to using single-task Gaussian process.
A 0.5 V, 1 nA Switched Capacitor PTAT Current Reference Circuit

This paper presents a low voltage, low power Proportional to Absolute Temperature (PTAT) current reference circuit. Switched capacitor is used instead of a large resistor, which in turn reduces the area of the circuit and makes the circuit less process sensitive. The proposed circuit has been designed using UMC 65 nm CMOS technology and simulated in Cadence Virtuoso. It generates a reference current (Iref) of 1 nA at 0.5 V supply voltage (Vdd) at room temperature (27°C) and follows PTAT characteristics in the temperature range of -10°C to 80°C. The total power consumption of the circuit is as low as 1.5 nW. The supply voltage sensitivity of Iref is 2.7 %/V, which shows that the proposed circuit is less sensitive to supply voltage variations. Index Terms—Low voltage, Low power, PTAT current, Switched capacitor
Transmitarray Element with Three Phase States for Reconfigurable Transmitarrays

In this paper, a three-phase state element for reconfigurable transmitarrays is presented. The element is formed by C- patches and C-slots. The three phase states are obtained by controlling four p-i-n diodes. The element is optimized using full-wave electromagnetic simulation and then validated by using an ad-hoc waveguide simulator. A transmitarray consisting of 12 × 12 elements has been also simulated to validate the steering capabilities. Experimental results indicate good characteristics of the transmitarray element and excellent phase change capabilities.
Optimal Planning of Charging Piles Considering Temporal-spatial Characteristics of Charging Load

In order to adapt the rapid development of electric vehicles (EVs) in the future and reduce negative impacts of charging load on distribution networks, the reasonable charging facilities configuration of EVs is particularly important. This paper proposes an optimal planning method of charging piles. Firstly, a forecasting model of charging load is established based on the concept of trip chain and Monte Carlo Simulation Method (MCSM). Charging load profiles in different locations is then calculated. An optimal planning model is established to optimize the configuration of charging piles. Simulation results show that the proposed method can decrease both peak-valley difference and voltage deviation after the access of EV. This study can accurately forecast charging load demand in residential area, workplace and shopping center, and provide guidance for future planning of charging piles.
A Data Augmentation Approach for Sampling Gaussian Models in High Dimension

Recently, methods based on Data Augmentation (DA) strategies have shown their efficiency for dealing with high dimensional Gaussian sampling within Gibbs samplers compared to iterative-based sampling (e.g., Perturbation-optimization). However, they are limited by the feasibility of the direct sampling of the auxiliary variable. This paper reviews DA sampling algorithms for Gaussian sampling and proposes a DA method which is especially useful when direct sampling of the auxiliary variable is not straightforward from a computational viewpoint. Experiments in two vibration analysis applications show the good performance of the proposed algorithm.
ROLE OF ELECTROPHORETICALLY RESOLVED SERUM PROTEIN FRACTIONS FOR THE DIAGNOSIS OF RHEUMATIC HEART DISEASE

In the present study the blood samples of patients diagnosed for rheumatic heart disease (RHD) were obtained from the Punjab Institute of Cardiology, Lahore. Blood samples of the normal subjects of comparable age group with an absent history of cardiac ailment were also collected for the control comparison. The sera of all categories were separated and used for the study of the protein profile with sodium dodecyle sulfate polyacrylamide gel electrophoresis (SDS-PAGE) in first dimension. Gene Genius Bio-imaging Gel Documentation System was used for the quantification of various protein fractions. This provides the data of molecular weight and percent raw volume for each of the fraction. The protein fractions that showed significant variations were separated by using the technique of electroblotting and electroelution and run on isoelectric focusing (IEF) in second dimension to determine the isoelectric points. In rheumatic heart disease group apolipoprotein B, alpha-2-macroglobulin, ceruloplasmin and immunoglobulin heavy chain were found to be significantly elevated compared to control subjects. A fraction of 23.5 kDa had expressed weakly in 70 % of the patients of rheumatic heart disease but not expressed in control subjects. These results show that level of apolipoprotein B, alpha-2- macroglobulin, ceruloplasmin, immunoglobulin heavy chain and 17 kDa proteins and expression of 23.5 kDa protein fractions are strong predictor of RHD and can be used for its diagnosis.
Machine Learning and Optimization for Resource-Constrained Platforms

Artificial intelligence (AI) and machine learning (ML) have been growing at an incredible rate in recent years and they show no sign of stopping. Manufacturing, educational systems, transportation architecture, and genetic research are industries where artificial intelligence algorithms have been developed and found practical applications in which they can increase task efficiency and reduce cost through process optimization, pattern recognition, and automation. At NASA, one of the goals of the cognitive communications project has been to find applications for such algorithms to next-generation communication systems. The goal of this effort is to identify areas and approaches to intelligent system design and implementation which could allow NASA to support a larger space-and ground-based network while simultaneously reducing the operational costs involved with maintaining such a system This paper will evaluate the state of various approaches by searching for algorithms which are feasible to deploy directly onto future space systems with improved processing requirements. We begin by describing a set of heuristics through which algorithms may be compared, emphasizing memory and computational requirements, and heuristic bounds. We then evaluate general-purpose processing platforms onto which such algorithms may be deployed. We also evaluate how such systems may be packaged so as to offer a deterministic set of performance and decision metrics, to make the devices easier for system designers to include in present and future systems. We conclude the paper with a discussion of our findings, as well as where and how this study might continue in the future.
VFH* Based Local Path Planning for Mobile Robot

Vector Field Histogram(VFH) and its variants are effective obstacles avoidance method for mobile robots. Under the more consideration of realistic, we proposed a new local path planning method for mobile robot in this paper. The proposed method takes global path point generated by the A* algorithm as temporary target point and adopts the basic concepts of the VFH* algorithm, the difference is mainly lies in the representation of vector magnitude of the obstacles, the determination of the threshold, the selection of the candidate direction, and the definition of the node cost function in search tree. Besides, according to the optimal obstacle avoidance direction obtained in the above steps, we also give a speed control method to control the movement of the omnidirectional mobile robot to the target point. Simulation and experiment results shows that the proposed method is suited for mobile robot moving in realistic situation.
Noise exposure among dental assisting students from dental instruments during work-integrated learning: a review

The purpose of the review is to provide the interlocution on the effects of noise on the hearing levels of dental assisting students from dental instruments during work-integrated learning. Primarily, this review provides knowledge on the health and safety within dental practices and laboratories where dental assisting students undergoing practical sessions and/or work-integrated learning and subsequently initiate and develop understanding on the effects on hearing levels. And to identify those dental instruments that have noise level which may potentiate the hearing levels of dental students. Exposure to high levels of noise has been a well-known cause of noise-induced hearing loss (NIHL). Several studies have been completed to determine the effects of noise in dental schools for dental students. Moreover, research has implicated old drills and dental instruments as common causes of noise and this noise can be extremely high, up to 100dB occasionally, and exceeding recommended maximum levels. Many of studies have examined that noise found in a dental practices exceeds exposure limits guidelines. Exposure limits from different agencies has been described. Periodic audiometric evaluation for all dental students should be made mandatory as to identify hearing loss at an early stage and to prevent a permanent hearing loss. Also health education stressing on use of noise protectors, proper maintenance of devices should be included in the curriculum of all dental students. In South Africa, the information and literatures about the exposure of noise on hearing levels of dental assisting students during their practical sessions is still lacking and therefore, this review aims to provide the informed discussion on the exposure levels of noise which may potentiate on the hearing levels of dental assisting student during work integrated learning. At the end, this review will provide recommendations which will assist in limiting noise exposure within dental practices and laboratories where practical sessions are performed.