Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance analysis of wireless ad-hoc network based on bidirectional full-duplex and saturated state

The transmission capacity (TC) of the wireless ad-hoc network is one of the key properties to ensure the quality of network service. The main goal of this study is to explore the mobility of the network to establish a node mobile model with a saturated state and determine the TC and delay for the network based on the bidirectional full-duplex (BFD) system. Considering the outage probability, packet arrival rate, and failure probability of packet attempting transmission, the node mobility models are established based on the node mobility and network self-organization. In analysis, this study determines the mobile state, static state, communication state, and outage stat by defining the mobile rule and range of nodes, then the steady-state probability of each state is obtained by calculating the state transition matrix. After optimising the existing saturated Markov model, the authors determine the network delay and TC based on the mobile nodes, contention window size, maximum number of given packet attempting retransmission, back-off time and signal-interference ratio (SIR) etc. The theoretical analysis and numerical simulation show that the TC of the bidirectional ad-hoc network can be significantly improved by using space resources in two directions, and the introduction of the BFD system is indeed to reduce the network delay.
Predicting the Outcome of Total Knee Arthroplasty Using the WOMAC Score: A Review of the Literature

Abstract It is estimated that up to a third of recipients of total knee arthroplasty (TKA) experience chronic pain postoperatively. However, there are no clear indications within the literature that predict which patients are at higher risk of being dissatisfied with their TKA. The Western Ontario and McMaster University Osteoarthritis Index (WOMAC) is one of the most commonly used, patient-reported outcome measures in patients with lower limb osteoarthritis. This review discusses the available evidence surrounding the predictability of the outcome of TKA using the WOMAC score as well as considering further patient factors that have been implicated in the level of improvement post TKA. It may be concluded from the available literature that a combination of knee scores and patient factors would be the most accurate way of predicting those patients most likely to have a good outcome from their TKA. There is some disparity within the literature about which patient factors and reported outcome measure scores lead to a positive postoperative outcome. Patient expectations following the procedure also need to be evaluated, as objective measures on a scoring system do not necessarily equate with the subjective patient experience.
Combining Full and Partial Haptic Guidance Improves Handwriting Skills Development

It has been shown in previous studies that haptic guidance improves the learning outcomes of handwriting motor skills. Full and partial haptic guidance are developed and evaluated in the literature. In this paper, we present two experimental studies to examine whether combining full and partial haptic guidance is more effective for improving handwriting skills than merely full or partial guidance methods. Experiment I, with 22 participants, compares the effectiveness of merely full and partial haptic guidance methods towards improving learning outcomes of Arabic handwriting. Even though haptic guidance in general is found to be effective and pleasant by all participants, experiment I concludes that there are no statistically significant differences in the learning outcomes between full and partial haptic guidance. Experiment II investigates whether a combination of full and partial haptic guidance could further improve the learning outcomes, compared to merely full or partial haptic guidance. The learning outcomes and quality of experience are measured to evaluate each group's performance. Results from experiment II demonstrate that the combination of full and partial haptic guidance results in statistically significant improvements in the quality of handwriting, compared to mere full or partial haptic guidance. In particular, starting with partial haptic guidance at early stage of learning and then using full guidance at intermediate/advanced learning stages seemed to be the most effective. This implies that partial haptic guidance is more effective to learn the gross shape of handwriting skills (at early stages of the learning process) whereas full haptic guidance is more effective to learn the fine details of the handwriting skills (at intermediate or advanced stage of learning). Therefore, partial-then-full haptic guidance seems to be the most effective to improve learning outcomes.
Automatic Detection of Aortic Valve Opening Using Seismocardiography in Healthy Individuals

Accurate detection of fiducial points in a seismocardiogram (SCG) is a challenging research problem for its clinical application. In this paper, an automated method for detecting aortic valve opening (AO) instants using the dorso-ventral component of the SCG signal is proposed. This method does not require electrocardiogram (ECG) as a reference signal. After preprocessing the SCG, multiscale wavelet decomposition is carried out to get signal components in different wavelet subbands. The subbands having possible AO peaks are selected by a newly proposed dominant-multiscale-kurtosis- and dominant-multiscale-central-frequency-based criterion. The signal is reconstructed using selected subbands, and it is emphasized using the weights derived from the proposed relative squared dominant multiscale kurtosis. The Shannon energy followed by autocorrelation coefficients is computed for systole envelope construction. Finally, AO peaks are detected by a Gaussian-derivative-filtering-based scheme. The robustness of the proposed method is tested using clean and noisy SCG signals from the combined measurement of ECG, breathing, and SCG database. Evaluation results show that the method can achieve an average sensitivity of 94%, a prediction rate of 90%, and a detection accuracy of 86% approximately over 4585 analyzed beats.
Fourier Domain Depth Migration for Plane-Wave Ultrasound Imaging

Plane-wave (PW) ultrasound imaging allows for ultrafast image acquisition rates, thus enabling new biomedical applications, such as ultrasound-based blood flow and tissue motion characterization. We propose two novel Fourier domain techniques for PW ultrasound image reconstruction that can be used as an alternative to conventional delay-and-sum beamforming. In particular, we show how to modify two classic algorithms used for geophysical data processing (namely, Stolt’s and slant-stack depth migration under zero-offset constant-velocity assumptions), so that their new versions can be used for PW ultrasound data processing. To demonstrate the merits and limitations of our approach, we provide qualitative and quantitative comparisons with other Fourier domain methods reported in the ultrasound literature. Our evaluation results are based on the image resolution, contrast, and similarity metrics obtained for several public-domain experimental benchmark data sets.
Analyses of Gait Parameters of Younger and Older Adults During (Non-)Isometric Virtual Walking

Understanding real walking in virtual environments (VEs) is important for immersive experiences, allowing users to move through VEs in the most natural way. Previous studies have shown that basic implementations of real walking in virtual spaces, in which head-tracked movements are mapped isometrically to a VE, are not estimated as entirely natural. Instead, users estimate a virtual walking velocity as more natural when it is slightly increased compared to the user’s physical locomotion. However, these findings have been reported in most cases only for young persons, e.g., students, whereas older adults are clearly underrepresented in such studies. Recently, virtual reality (VR) has received significant public and media attention. Therefore, it appears reasonable to assume that people at different ages will have access to VR, and might use this technology more and more in application scenarios such as rehabilitation or training. To better understand how people at different ages walk and perceive locomotion in VR, we have performed a study to investigate the effects of (non-)isometric mappings between physical movements and virtual motions in the VE on the walking biomechanics across generations, i.e., younger and older adults. Three primary domains (pace, base of support and phase) of spatio-temporal parameters were identified to evaluate gait performance. The results show that the older adults walked very similar in the real and VE in the pace and phasic domains, which differs from results found in younger adults. In contrast, the results indicate differences in terms of base of support domain parameters for both groups while walking within a VE and the real world. For non-isometric mappings, we found in both younger and older adults an increased divergence of gait parameters in all domains correlating with the up- or down-scaled velocity of visual self-motion feedback. The results provide important insights into the design of future VR applications for older adults in domains ranging from medicine and psychology to rehabilitation.
Toward Support-Free 3D Printing: A Skeletal Approach for Partitioning Models

Minimizing support structures is crucial in reducing 3D printing material and time. Partition-based methods are efficient means in realizing this objective. Although some algorithms exist for support-free fabrication of solid models, no algorithm ever considers the problem of support-free fabrication for shell models (i.e., hollowed meshes). In this paper, we present a skeleton-based algorithm for partitioning a 3D surface model into the least number of parts for 3D printing without using any support structure. To achieve support-free fabrication while minimizing the effect of the seams and cracks that are inevitably induced by the partition, which affect the aesthetics and strength of the final assembled surface, we put forward an optimization system with the minimization of the number of partitions and the total length of the cuts, under the constraints of support-free printing angle. Our approach is particularly tailored for shell models, and it can be applicable to solid models as well. We first rigorously show that the optimization problem is NP-hard and then propose a stochastic method to find an optimal solution to the objectives. We propose a polynomial-time algorithm for a special case when the skeleton graph satisfies the requirement that the number of partitioned parts and the degree of each node are bounded by a small constant. We evaluate our partition method on a number of 3D models and validate our method by 3D printing experiments.
Fast Texture Mapping Adjustment via Local/Global Optimization

This paper deals with the texture mapping of a triangular mesh model given a set of calibrated images. Different from the traditional approach of applying projective texture mapping with model parameterizations, we develop an image-space texture optimization scheme that aims to reduce visible seams or misalignment at texture or depth boundaries. Our novel scheme starts with an efficient local (and parallel) texture adjustment scheme at these boundaries, followed by a global correction step to rectify potential texture distortions caused by the local movement. Our phased optimization scheme achieves 50$\sim$∼100 times speed up on GPU (or 6× on CPU) compared to previous state-of-the-art methods. Experiments on a variety of models showed that we achieve this significant speed-up without sacrificing texture quality. Our approach significantly improves resilience to modeling and calibration errors, thereby allowing fast and fully automatic creation of textured models using commodity depth sensors by untrained users.
Sequence-Based Prediction of Putative Transcription Factor Binding Sites in DNA Sequences of Any Length

A transcription factor (TF) is a protein that regulates gene expression by binding to specific DNA sequences. Despite recent advances in experimental techniques for identifying transcription factor binding sites (TFBS) in DNA sequences, a large number of TFBS are to be unveiled in many species. Several computational methods developed for predicting TFBS in DNA are tissue- or species-specific methods, and therefore cannot be used without prior knowledge of tissue or species. Some computational methods are applicable to identifying TFBS in short DNA sequences only. In this paper, we propose a new learning method for predicting TFBS in DNA of any length using the composition, transition, and distribution of nucleotides and amino acids in DNA and TF sequences. In independent testing of the method on datasets that were not used in training the method, the accuracy and MCC were as high as 81.84 percent and 0.634, respectively. The proposed method can be a useful aid for selecting potential TFBS in a large amount of DNA sequences before conducting biochemical experiments to empirically determine TFBS. The program and data sets are available at http://bclab.inha. ac.kr/TFbinding.
Parental stress and resilience in CHD: a new frontier for health disparities research

Abstract Parental stress is a universal experience for parents who have children diagnosed with CHD and has been studied within the context of the child’s illness, but not through a broader health disparity lens. This paper provides a thorough synthesis of the current literature on parental stress addressing disparities in parents of children with CHD. Several theories and models from within this literature are described and a new comprehensive framework, the Parental Stress and Resilience in CHD Model, is presented. Future research and clinical implications are discussed.
Continuous Online Auditing: A Program of Research

The progressive computerization of business processes and widespread availability of computer networking make it possible to dramatically increase the frequency of periodic audits by redesigning the auditing architecture around Continuous Online Auditing (COA). Continuous auditing is viewed here as a type of auditing that produces audit results simultaneously with, or a short period of time after, the occurrence of relevant events. It is arguable that continuous auditing can be implemented only as an online system, i.e., a system that is permanently connected through computer networking to both auditees and auditors. This article proposes a research agenda for the emerging field of COA. First, the history, institutional background, feasibility of and some experiences in COA are briefly reviewed. Thereafter, a number of research issues relating to the architecture of COA, factors affecting the use of COA, and the major consequences of COA are presented. Finally, a selected number of research issues are hig.
Visual Kinship Recognition of Families in the Wild

We present the largest database for visual kinship recognition, Families In the Wild (FIW), with over 13,000 family photos of 1,000 family trees with 4-to-38 members. It took only a small team to build FIW with efficient labeling tools and work-flow. To extend FIW, we further improved upon this process with a novel semi-automatic labeling scheme that used annotated faces and unlabeled text metadata to discover labels, which were then used, along with existing FIW data, for the proposed clustering algorithm that generated label proposals for all newly added data–both processes are shared and compared in depth, showing great savings in time and human input required. Essentially, the clustering algorithm proposed is semi-supervised and uses labeled data to produce more accurate clusters. We statistically compare FIW to related datasets, which unarguably shows enormous gains in overall size and amount of information encapsulated in the labels. We benchmark two tasks, kinship verification and family classification, at scales incomparably larger than ever before. Pre-trained CNN models fine-tuned on FIW outscores other conventional methods and achieved state-of-the art on the renowned KinWild datasets. We also measure human performance on kinship recognition and compare to a fine-tuned CNN.
Altered branched chain amino acid metabolism: toward a unifying cardiometabolic hypothesis

Purpose of review Atherosclerotic cardiovascular disease (CVD) and type II diabetes (T2D) share common etiologic pathways that may long precede the development of clinically evident disease. Early identification of risk markers could support efforts to individualize risk prediction and improve the efficacy of primary prevention, as well as uncover novel therapeutic targets. Recent findings Altered metabolism of branched-chain amino acids (BCAAs), and their subsequent accumulation in circulation, may precede the development of insulin resistance and clinically manifest cardiometabolic diseases. BCAAs – the essential amino acids leucine, isoleucine and valine – likely promote insulin resistance through activation of mammalian target of rapamycin complex 1. Epidemiologic studies demonstrate robust associations between BCAAs and incident T2D, and Mendelian randomization supports a potentially causal relationship. More recently, there is emerging evidence that BCAAs are also associated with incident atherosclerotic CVD, possibly mediated by the development of T2D. Summary In this article, we review the biochemistry of BCAAs, their potential contribution to cardiometabolic risk, the available evidence from molecular epidemiologic studies to date, and, finally, consider future research and clinical directions. Overall, BCAAs represent a promising emerging target for risk stratification and possible intervention, to support efforts to mitigate the burden of cardiometabolic disease in the population.
Human Pathway-Based Disease Network

Constructing disease-disease similarity network is important in elucidating the associations between the origin and molecular mechanism of diseases, and in researching disease function and medical research. In this paper, we use a high-quality protein interaction network and a collection of pathway databases to construct a Human Pathway-based Disease Network (HPDN) to explore the relationship between diseases and their intrinsic interactions. We find that the similarity of two diseases has a strong correlation with the number of their shared functional pathways and the interaction between their related gene sets. Comparing HPDN with disease networks based on genes and symptoms respectively, we find the three networks have high overlap rates. Additionally, HPDN can predict new disease-disease correlations, which are supported by Comparative Toxicogenomics Database (CTD) benchmark and large-scale biomedical literature database. The comprehensive, high-quality relations between diseases based on pathways can further be applied to study important matters in systems medicine, for instance, drug repurposing. Based on a dense subgraph in our network, we find two drugs, prednisone and folic acid, may have new indications, which will provide potential directions for the treatments of complex diseases.
Iterative Learning Control for a Flapping Wing Micro Aerial Vehicle Under Distributed Disturbances

This paper addresses a flexible micro aerial vehicle (MAV) under spatiotemporally varying disturbances, which is composed of a rigid body and two flexible wings. Based on Hamilton’s principle, a distributed parameter system coupling in bending and twisting, is modeled. Two iterative learning control (ILC) schemes are designed to suppress the vibrations in bending and twisting, reject the distributed disturbances and regulate the displacement of the rigid body to track a prescribed constant trajectory. At the basis of composite energy function, the boundedness and the learning convergence are proved for the closed-loop MAV system. Simulation results are provided to illustrate the effectiveness of the proposed ILC laws.
Oscillatory inertial focusing in infinite microchannels

Significance Inertial microfluidics is a widely used technology which enables label-free manipulation of particles in microchannels. However, this technology has been limited to bioparticles larger than RBCs, due to the strong correlation between the inertial lift forces and the particle size. This paper presents a method to extend the capabilities of inertial microfluidics to smaller bioparticles, of which a plethora of clinically relevant types exist in the human body. Therefore, this method can be integrated with microfluidic devices for inertial manipulation of bioparticles that have defied all prior attempts, enabling a variety of applications in clinical diagnosis including cytometry of micron-scale bioparticles, isolation and characterization of pathogens and extracellular microvesicles, or phenotyping of cancer or stem cells at physiological shear stresses. Inertial microfluidics (i.e., migration and focusing of particles in finite Reynolds number microchannel flows) is a passive, precise, and high-throughput method for microparticle manipulation and sorting. Therefore, it has been utilized in numerous biomedical applications including phenotypic cell screening, blood fractionation, and rare-cell isolation. Nonetheless, the applications of this technology have been limited to larger bioparticles such as blood cells, circulating tumor cells, and stem cells, because smaller particles require drastically longer channels for inertial focusing, which increases the pressure requirement and the footprint of the device to the extent that the system becomes unfeasible. Inertial manipulation of smaller bioparticles such as fungi, bacteria, viruses, and other pathogens or blood components such as platelets and exosomes is of significant interest. Here, we show that using oscillatory microfluidics, inertial focusing in practically “infinite channels” can be achieved, allowing for focusing of micron-scale (i.e. hundreds of nanometers) particles. This method enables manipulation of particles at extremely low particle Reynolds number (Rep < 0.005) flows that are otherwise unattainable by steady-flow inertial microfluidics (which has been limited to Rep > ∼10−1). Using this technique, we demonstrated that synthetic particles as small as 500 nm and a submicron bacterium, Staphylococcus aureus, can be inertially focused. Furthermore, we characterized the physics of inertial microfluidics in this newly enabled particle size and Rep range using a Peclet-like dimensionless number (α). We experimentally observed that α >> 1 is required to overcome diffusion and be able to inertially manipulate particles.