Miracle-dz/UniFAR-FTUs huggingface dataset:
Maternal life and work stressors during pregnancy and asthma in offspring

Abstract Background Maternal stressors during pregnancy are potential risk factors for asthma in offspring. However, previous studies have been limited by the use of self-reported data focusing on stressors either in private life or at work. This study examined the association between maternal stressors both in private life and at work during pregnancy and asthma in offspring. Methods In the Danish National Birth Cohort, 75 156 live-born singletons born during 1996–2002 were identified. Maternal information on job title were available around weeks 12–16 of gestation. Data on maternal bereavement, life-threatening illness, suicide attempt and alcohol or drug abuse of a close relative and offspring childhood asthma (3–10 years of age) were obtained from Danish nationwide registers. Maternal psychosocial work stressors (job control, psychological job demands, emotional job demands, work-related violence and threats of work-related violence) were estimated by the use of job-exposure matrices. The association between maternal stress and childhood asthma was analysed in Cox models adjusted for maternal age, comorbidity and parity. Results Neither private-life nor work stressors were related to onset of asthma in offspring. Separate analyses by parental atopy or onset of asthma in offspring supported the main findings. Conclusions This study does not support an elevated risk of childhood asthma related to exposure to stress during pregnancy.
Toward Global Validation Standards for Health AI

Machine learning (ML) and artificial intelligence (AI) methods hold great potential for healthcare, for example, for purposes of diagnosis or prognosis that include a wide range of pattern recognition tasks. Ensuring that health ML/AI models are trustworthy will consequently become increasingly important soon. The ITU/WHO focus group on "AI for Health" is working on validation standards for health AI that can help to assess the quality of the powerful but complex technologies in a comparable and transparent manner. In particular, standardized benchmarking can serve as a valuable tool to determine the merits and limits of different health ML/AI models. In this article, ongoing work of the ITU/WHO initiative is introduced and set into perspective with related digital health and AI standardization efforts.
Facial Expression Recognition Based on Sparse Autoencoder and Shallow Convolutional Neural Network

Facial expression recognition is an important research issue in the pattern recognition and accurate recognition is a great challenge, especially in real scenes. In this paper, we propose a method of facial expression recognition based on sparse autoencoder and shallow convolution neural network, which can effectively solve the problems of insufficient feature extraction of shallow convolution neural network and limited sample datasets. The experimental data adopts the ICML2013 facial expression recognition contest’s dataset (FER-2013) and this data is more difficult to identify. The images are preprocessed before the experiment and the key parts are selected as the input of the model. After these features are extracted many times, they are finally classified by softmax classifier. The experimental results indicate that the model performs well on the FER2013 dataset and the accuracy has been greatly improved compared with other methods.
Binary Similarity Analysis for Vulnerability Detection

Binary similarity has been widely used in function recognition and vulnerability detection. How to define a proper similarity is the key element in implementing a fast detection method. We proposed a scalable method to detect binary vulnerabilities based on similarity. Procedures lifted from binaries are divided into several comparable strands by data dependency, and those strands are transformed into a normalized form by our tool named VulneraBin, so that similarity can be determined between two procedures through a hash value comparison. The low computational complexity allows semantically equivalent code to be identified in binaries compiled from million lines of source code in a fast and accurate way.
Recurrent spontaneous coronary artery dissection in a middle-aged male athlete patient: a case report.

Background Spontaneous coronary artery dissection (SCAD) is a rare condition, mainly affecting young women. Cases in male patients are rare, especially with recurrence. Case summary A 59-year-old male non-elite athlete presented as an ST-elevation myocardial infarction following a 5-km run. Urgent coronary angiogram was normal, but cardiac magnetic resonance showed a myocardial infarction. Four years later, he experienced similar chest pain with no ST-elevation on electrocardiogram and a mild troponin rise. Urgent coronary angiogram was initially thought normal but subsequent close inspection confirmed a Type 2b SCAD. Cardiac magnetic resonance showed a small additional myocardial infarction contained within an area of acute myocardial oedema. Discussion Spontaneous coronary artery dissection is more common in young women compared to men and recurrent dissection has been rarely reported in the literature. Cohort studies have shown the rate of recurrent dissection to be 13-16%, but most of the patients in these cohorts are female. Poor data exists on the best treatment of SCAD in men, but given the presence of intramural thrombus, dual antiplatelet therapy was discontinued on the presumption that it may exacerbate an intramural bleeding process.
Towards Aggregation Based I/O Optimization for Scaling Bioinformatics Applications

Bioinformatics software often integrates multiple off-the-shelf programs into a single compute pipeline. Each stand-alone program generates output, that is frequently saved into a plain-text file, which is then processed as input by the program that is responsible for the next stage of the computation. Modern advances in genome sequencing and protein structure resolution methods have yielded large amounts of data, that when processed by bioinformatics compute pipelines, results in vast numbers of file reads and writes. Since computation capabilities of large-scale distributed systems grow much faster than their I/O bandwidth, processing such big data using these compute pipelines will not scale as the amount of data grows. For this work, we motivate and demonstrate a dynamic interception-based I/O analysis tool to assess the file read and write characteristics of a protein mutation generation pipeline. We discuss how our analysis tool can be further extended to apply compression and in-site analysis and has the potential to scale I/O-intensive bioinformatics applications on high performance computing (HPC) systems.
Delay-Aware SFC Provisioning in Hybrid Fog-Cloud Computing Architectures

Network function virtualization (NFV) technology enables service providers to implement software-based network processing functionalities on standard computing servers (nodes). As such, this approach mandates the need for mapping virtual network functions (VNFs) in service function chains (SFCs) on these nodes for incoming service requests. Now traditional VNF mapping schemes use cloud nodes for its abundant available resources, at the detriment of prolonged network delays. Alternative schemes use fog nodes that return reduced delays, at the detriment of limited resources. Hence in this work, a novel SFC provisioning scheme is proposed for a hybrid fog-cloud architecture of various resources. The architecture is composed of a single fog and single cloud layer, in order to accommodate both delay-sensitive and delay-tolerant requirements for large number of incoming requests, respectively. This scheme yields a tradeoff between standalone cloud and fog solutions when implemented on the proposed hybrid architecture, in terms of the number of satisfied requests, network delay, resources consumption, energy consumption, and realization cost at large traffic volumes. The proposed scheme achieves 15–40% higher traffic capacity than fog solutions, 21–43% reduced delay, 45–52% less energy consumption levels and 28–30% less cost as compared to cloud solutions.
Research on Application of Optimal Algorithm Based on Simulated Annealing in Intelligent Decision Model

Rough set theory has long been applied to the formulation and induction of rules. Rough set theory approximates a rule through three regions (positive, boundary, and negative region), uses the rule boundary as the limiting condition, and uses the rough set to build a model for decision-making. In this case, the decision cost needs to be considered. Based on this, we will propose an optimized simulated annealing algorithm (OSAA), which is an optimized algorithm for minimizing cost attributes. The algorithm proposes a heuristic method combined with a coarse-grained parallel algorithm to solve the DTRS optimization problem. We combined the optimized simulated annealing algorithm and the adaptive learning method to design an experiment. The experiment compares the running time and decision-making cost, and experiment shows the proposed new method has higher efficiency and lower cost.
The Isolation Concept in the 5G Network Slicing

The fifth generation (5G) of cellular networks shall host a number of tenants and provide services tailored to meet a wide range of requirements in terms of performance, dependability and security. Network slicing will be a key enabler, by assigning dedicated resources and functionalities to meet such requirements, where the isolation between slices, i.e., that a slice may operate without interference from other slices, becomes a core issue. The objective of this paper is to give a thorough insight into the isolation concept, discuss the challenges involved in providing it, and outline the means available to provide various levels of isolation. Fundamental concepts that can be used in further work to build an isolation solution tailored to specific needs. This paper defines important concepts such as the Provider Management, the Tenant Management, and the Means of Isolation in the context of the Isolation Dimensions. The conclusion of the study is that dealing with isolation between slices needs extensions in state of the art on the mentioned concepts, and in how to tailor the isolation to meet the needs in a cost-efficiency manner.
Cultivating Personal Capabilities Based on Problem-Based Learning: A Practice in Web Development

In this paper, two teaching methods are proposed as complementary of Problem-Based Learning methodology by practicing in Web application development pedagogy, these methods are "Problem Decomposition Method" for improving problem-solving skills and "One Problem with Multiple Solutions Method" for enhancing critical thinking abilities, both are explained with a case study. The outcomes show that students make great progress and proficiently solve a new web development problem by using these two methods, especially in coding aspects.
GRACE: A Graph-Based Cluster Ensemble Approach for Single-Cell RNA-Seq Data Clustering

Rapid development of single cell RNA sequencing (scRNA-seq) technology has accelerated the exploration in biomedical researches. One of the focal interests in scRNA-seq data analysis is to classify cells into different types, which significantly assists in studying inter-cellular heterogeneity, such as cell types, cell states, and cell lineages, at the resolution of single cells. Although a number of tailored approaches have been developed for scRNA-seq data, their performance varies with different datasets and their clustering accuracy need to be improved. In this paper, we propose a novel ensemble clustering framework for scRNA-seq data called GRACE (GRAph-based Cluster Ensemble approach). First, we construct a highly reliable graph network for single cells by combining the clustering outcomes from five leading scRNA-seq data clustering methods. Then, we remeasure the relationships between cells by exploring the topology structure of network using random walk distance. Finally, we build a hierarchical cell-tree and obtain the clustering labels by cutting the tree structure into an appropriate number of sub-trees. Experimental results on twelve benchmark datasets show that GRACE has the higher clustering accuracy and is more robust among a variety of datasets than the state-of-the-art individual approaches. In addition, the graph structure of the network which is built upon the ensemble clusters is more reliable than the networks which are constructed according to the conventional similarity metrics.
Liquid-Metal Nodal Sheet for Reconfigurable Devices and Circuits

Gallium-based liquid metals have attracted attention as a conductive adaptable material that can be shaped to obtain reconfigurability. We demonstrate a two-dimensional array of liquid-metal conductive nodes, in which reconfigurability is achieved by merging and splitting adjacent nodes to reversibly shape a two-dimensional structure. The nodes are reconfigured using three different actuation schemes: 1) pressure-point actuation, where the liquid metal is actuated by pressing on the flexible superstrate; 2) surface-tension self-splitting, where the geometric shape of the node itself exerts a separating force on merged nodes; and 3) electrocapillary actuation where the liquid metal is actuated by a DC electrical signal. This is the first demonstration of a reconfigurable liquid-metal pixel array using liquid-liquid interconnects for prototyping reconfigurable devices. As a proof of concept, the proposed actuation techniques are used to reconfigure a liquid-metal nodal patch antenna, in which the liquid-metal nodes are reversibly connected and disconnected to change the operating frequency and polarization.
Impact of Psychological Distress and Sleep Quality on Balance Confidence, Muscle Strength, and Functional Balance in Community-Dwelling Middle-Aged and Older People

The objective was to evaluate the associations of psychological distress and sleep quality with balance confidence, muscle strength, and functional balance among community-dwelling middle-aged and older people. An analytical cross-sectional study was conducted (n = 304). Balance confidence (Activities-specific Balance Confidence scale, ABC), muscle strength (hand grip dynamometer), and functional balance (Timed Up-and-Go test) were assessed. Psychological distress and sleep quality were evaluated by the Hospital Anxiety and Depression Scale and the Pittsburgh Sleep Quality Index, respectively. Age, sex, physical activity level, nutritional status, and fatigue were included as possible confounders. Multivariate linear and logistic regressions were performed. Higher values of anxiety (OR = 1.10), fatigue (OR = 1.04), and older age (OR = 1.08) were associated with an increased risk of falling (ABC < 67%). Greater muscle strength was associated with male sex and improved nutritional status (adjusted R2 = 0.39). On the other hand, being older and using sleeping medication were linked to poorer functional balance (adjusted R2 = 0.115). In conclusion, greater anxiety levels and the use of sleep medication were linked to a high risk of falling and poorer functional balance, respectively. No associations were found between muscle strength and sleep quality, anxiety, or depression.
Field Reconstruction Method for Linear Tubular Permanent Magnet Motor

This paper considers a linear tubular permanent magnet motor (LTPMM) for an active suspension system. The LTPMM has an end effect due to its structure. This can be an important factor for analysis and design of the LTPMM because it distorts the air-gap magnetic flux distribution. The field reconstruction method (FRM) was developed for an effective evaluation of the magnetic field in the electric machine. It can reduce the computation time using the basis-function which reconstructs the air-gap magnetic flux distribution with a static finite element analysis. In this paper, we adopted the FRM to evaluate the LTPMM. However, the FRM has been applied only to the rotating machines and does not take into account the distortion of the magnetic flux distribution in the LTPMM. To deal with this problem, we proposed an enhanced FRM with new basis-function. The proposed method is verified by comparing between experiment result, conventional and enhanced FRM.
Two Approaches to Machine Learning Classification of Time Series Based on Recurrence Plots

The article considers the task of classifying fractal time series based on the construction of their recurrence plots. Short realizations of EEG signals were used as input data. Two classification machine learning methods were considered: in the first case, quantitative fractal and recurrent characteristics of the time series were classification features, in the second case, image recognition of recurrence plots was carried out. The results showed fairly high classification quality for both methods, and relative advantage of the image classification method.
Low Temperature SiN Waveguides Optimization for Photonic Platform

Integration of SiN on Si photonics platform becomes attractive for 3D integration of different waveguide levels in an optical routing circuit, and can be used for the realization of athermal devices needing specifics group index or group velocity dispersion. This paper is focused on the determination of the optical properties of different SiN deposited by PVD (Plasma Vapor Deposition) and PECVD (Plasma Enhanced Plasma Vapor Deposition) equipment with temperatures lower than 400°C to keep the compatibility with CMOS process. A set of designs including optical routing basic building blocks such as bends, MMI splitters and asymmetric Mach-Zehnder interferometer has been designed to determine propagation losses and propagation constants with high accuracy, for a large spectral range, around $1.31\ \mu \mathrm{m}$ and $1.55\ \mu \mathrm{m}$ [1]. Devices were manufactured on the STMicroelectronics DAPHNE (Datacom Advanced Photonics Nanoscale Environment) 300 mm Photonic R&D platform [2]. Comparison of experimental data with theoretical models will be made. Especially, we discuss the development of Finite Difference Full Vectorial mode solvers [3]–[4] coupled with the Mode Matching method. This method, used to simulate the propagation of light, also allows to evaluate side wall roughness contributions, Rayleigh scattering and absorption due to 2nd harmonic vibrations of Si-OH and N-H bonds.
Maritime Search and Rescue Based on Group Mobile Computing for Unmanned Aerial Vehicles and Unmanned Surface Vehicles

Accidents often occur at sea, so effective maritime search and rescue is essential. In the current process of sea search and rescue, the operation efficiency of large search and rescue equipment is low and it cannot provide stable communication link. In this article, unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) are used to form a cognitive mobile computing network for co-operative search and rescue, and reinforcement learning (RL) is used to plan search path and improve communication throughput. Based on the scene of marine search and rescue, the grid method is used to model the search and rescue area. Meanwhile, an intragroup communication architecture based on UAVs and USVs is designed to assist intragroup communication by recognizing the link channel state between UAVs. Search and rescue path planning is carried out through the strategy iteration of Markov decision process (MDP). Furthermore, distributed RL is used to recognize the channel state and perform mobile computing, so as to optimize the data throughput in the communication group. The simulation results show that we have successfully completed the path planning task. Compared with conventional methods, RL based on different reward functions has better throughput performance under the same number of UAVs auxiliary communications.
Prototype and Simulation Model of a Robotic Hand

This elaboration describes the whole process of robotic hand design and modelling in order to improve some of the key performance aspects of tendon driven mechanisms. The hand model is simulated in Simscape Multibody Environment to accurately predict the performance of various designs. Some of developed mechanical solutions lead to reducing the wear, improving torque figures and enhancing rigidity.
Enhancing Path Quality of Real-Time Path Planning Algorithms for Mobile Robots: A Sequential Linear Paths Approach

The traditional trade-off between execution speed and path quality has forced real-time robotic path planning algorithms to sacrifice path quality in order to execute in real-time. Producing a path planning algorithm that targets enhancing both, the path quality and swiftness is a challenging problem. However, this article proposes a novel path planning strategy that aims to break this traditional trade-off, by targeting both, increasing the swiftness, and enhancing the path quality represented by the path length and smoothness. The proposed strategy is based on the observation that most path planning algorithms waste the processing efforts in less critical areas of the map. Therefore, the proposed path planning strategy tends to focus on critical areas such as the areas around obstacles and areas around the goal point, and exhausts the processing power on these critical areas. This is done by neglecting all static obstacles that do not lie between the robot and the destination. For obstacles that intersect with the linear line from the robot to the destination, a basis traditional path planning algorithm such as A*, D* or the Probabilistic RoadMap (PRM) technique is only implemented around the obstacles in order to find a feasible path around each selected obstacle. This procedure would minimize the computational efforts compared to applying the basis algorithm on the whole map. Finally, the path quality is enhanced by finding any linear shortcuts between any two points in the path and fix these shortcuts as the final path from the starting point to the goal point. The proposed path planning strategy was tested on a P3-DX Pioneer mobile robot using a kinematic controller. The experimental results have proven that the path planning strategy was able to show a superior advantage over other path planning techniques in both aspects, computational time (reached up to 97.05% improvement) and path quality (reached up to 16.21% improvement for path length and 98.50% for smoothness).
Detection of Android Malicious Family Based on Manifest Information

The current number of Android malicious applications is growing rapidly, which brings great troubles and losses to mobile phone users. Therefore, we propose a lightweight and efficient method for Android malicious family detection. The method uses DREBIN's Android malicious application dataset to build a detection model that using the application manifest information and machine learning algorithms. Through the training and testing of 10 types of Android malicious family samples, it is found that the detection model has the characteristics of low complexity and high classification accuracy, which can detect the Android malicious family very well and effectively protect the security of user mobile phones.