Miracle-dz/UniFAR-FTUs huggingface dataset:
Gait Trajectory Prediction on an Embedded Microcontroller Using Deep Learning

Achieving a normal gait trajectory for an amputee’s active prosthesis is challenging due to its kinematic complexity. Accordingly, lower limb gait trajectory kinematics and gait phase segmentation are essential parameters in controlling an active prosthesis. Recently, the most practiced algorithm in gait trajectory generation is the neural network. Deploying such a complex Artificial Neural Network (ANN) algorithm on an embedded system requires performing the calculations on an external computational device; however, this approach lacks mobility and reliability. In this paper, more simple and reliable ANNs are investigated to be deployed on a single low-cost Microcontroller (MC) and hence provide system mobility. Two neural network configurations were studied: Multi-Layered Perceptron (MLP) and Convolutional Neural Network (CNN); the models were trained on shank and foot IMU data. The data were collected from four subjects and tested on a fifth to predict the trajectory of 200 ms ahead. The prediction was made for two cases: with and without providing the current phase of the gait. Then, the models were deployed on a low-cost microcontroller (ESP32). It was found that with fewer data (excluding the current gait phase), CNN achieved a better correlation coefficient of 0.973 when compared to 0.945 for MLP; when including the current phase, both network configurations achieved better correlation coefficients of nearly 0.98. However, when comparing the execution time required for the prediction on the intended MC, MLP was much faster than CNN, with an execution time of 2.4 ms and 142 ms, respectively. In summary, it was found that when training data are scarce, CNN is more efficient within the acceptable execution time, while MLP achieves relative accuracy with low execution time with enough data.
Joint Communication and Action Learning in Multi-Target Tracking of UAV Swarms with Deep Reinforcement Learning

Communication is the cornerstone of UAV swarms to transmit information and achieve cooperation. However, artificially designed communication protocols usually rely on prior expert knowledge and lack flexibility and adaptability, which may limit the communication ability between UAVs and is not conducive to swarm cooperation. This paper adopts a new data-driven approach to study how reinforcement learning can be utilized to jointly learn the cooperative communication and action policies for UAV swarms. Firstly, the communication policy of a UAV is defined, so that the UAV can autonomously decide the content of the message sent out according to its real-time status. Secondly, neural networks are designed to approximate the communication and action policies of the UAV, and their policy gradient optimization procedures are deduced, respectively. Then, a reinforcement learning algorithm is proposed to jointly learn the communication and action policies of UAV swarms. Numerical simulation results verify that the policies learned by the proposed algorithm are superior to the existing benchmark algorithms in terms of multi-target tracking performance, scalability in different scenarios, and robustness under communication failures.
Natural Populations of Astrocaryum aculeatum Meyer in Amazonia: Genetic Diversity and Conservation

Astrocaryum aculeatum, a palm tree incipiently domesticated from upland ecosystems in the Brazilian Amazon, is especially adapted to anthropized areas. The pulp of the fruit, obtained by extractivism, is consumed fresh by the Amazonian population. The objective of the study is to evaluate the diversity and genetic structure of the natural populations of A. aculeatum, exploited by extractive farmers in Amazonas, Brazil, seeking to suggest conservation and management strategies for this species. A total of 218 plants were sampled in 15 populations in 14 municipalities in the state of Amazonas, evaluated by 12 microsatellite loci. A total of 101 alleles were observed. The means of the observed heterozygosities (HO = 0.6390) were higher than expected (HE = 0.557), with high levels of heterozygotes in the populations. The fixation index in the loci and populations was negative. The FST (0.07) and AMOVA showed moderate population structure. Bayesian analysis indicated the grouping k = 4 as the most adequate. There is a high genetic diversity in populations, with a moderate genetic structure due to possible historical events, which could be related to the process of subpopulation formation, possibly presenting three historical moments: before and after the beginning of deforestation and today. The conservation and management policies of this species must be carried out at a watershed level.
The influence of superstructures on bright galaxy environments: clustering properties

We analyse the dependence of clustering properties of galaxies as a function of their large-scale environment. In order to characterize the environment on large scales, we use the catalogue of future virialized superstructures (FVS) by Luparello et al. and separate samples of luminous galaxies according to whether or not they belong to FVS. In order to avoid biases in the selection of galaxies, we have constructed different subsamples so that the distributions of luminosities and masses are comparable outside and within FVS. As expected, at large scales, there is a strong difference between the clustering of galaxies inside and outside FVS. However, this behaviour changes at scales r ≤ 1 h−1 Mpc, where the correlations have similar amplitudes. The amplitude of the two-halo term of the correlation function for objects inside FVS does not depend on their mass, but rather on that of the FVS. This is confirmed by comparing this amplitude with that expected from extended Press–Schechter fits. In order to compare these observational results with current models for structure formation, we have performed a similar analysis using a semi-analytic implementation in a Λcold dark matter (ΛCDM) cosmological model. We find that the cross-correlation functions from the mock catalogue depend on the large-scale structures in a similar way to the observations. From our analysis, we conclude that the clustering of galaxies within the typical virialized regions of groups mainly depends on the halo mass, irrespective of the large-scale environment.
Equilibrium states in negative curvature

With their origin in thermodynamics and symbolic dynamics, Gibbs measures are crucial tools to study the ergodic theory of the geodesic flow on negatively curved manifolds. We develop a framework (through Patterson-Sullivan densities) allowing us to get rid of compactness assumptions on the manifold, and prove many existence, uniqueness and finiteness results of Gibbs measures. We give many applications, to the Variational Principle, the counting and equidistribution of orbit points and periods, the unique ergodicity of the strong unstable foliation and the classification of Gibbs densities on some Riemannian covers.
Anti-Inflammatory, Antioxidative, and Nitric Oxide-Scavenging Activities of a Quercetin Nanosuspension with Polyethylene Glycol in LPS-Induced RAW 264.7 Macrophages

Quercetin (Qu) is a dietary antioxidant and a member of flavonoids in the plant polyphenol family. Qu has a high ability to scavenge reactive oxygen species (ROS) and reactive nitrogen species (RNS) molecules; hence, exhibiting beneficial effects in preventing obesity, diabetes, cancer, cardiovascular diseases, and inflammation. However, quercetin has low bioavailability due to poor water solubility, low absorption, and rapid excretion from the body. To address these issues, the usage of Qu nanosuspensions can improve physical stability, solubility, and pharmacokinetics. Therefore, we developed a Qu and polyethylene glycol nanosuspension (Qu-PEG NS) and confirmed its interaction by Fourier transform infrared analysis. Qu-PEG NS did not show cytotoxicity to HaCaT and RAW 264.7 cells. Furthermore, Qu-PEG NS effectively reduced the nitrogen oxide (NO) production in lipopolysaccharide (LPS)-induced inflammatory RAW 264.7 cells. Additionally, Qu-PEG NS effectively lowered the levels of COX-2, NF-κB p65, and IL-1β in the LPS-induced inflammatory RAW 264.7 cells. Specifically, Qu-PEG NS exhibited anti-inflammatory properties by scavenging the ROS and RNS and mediated the inhibition of NF-κB signaling pathways. In addition, Qu-PEG NS had a high antioxidant effect and antibacterial activity against Escherichia coli and Bacillus cereus. Therefore, the developed novel nanosuspension showed comparable antioxidant, anti-inflammatory, and antibacterial functions and may also improve solubility and physical stability compared to raw quercetin.
Multiple Subformulae Cooperative Control for Multiagent Systems Under Conflicting Signal Temporal Logic Tasks

This article studies the temporal logic problem for multiagent systems, where each agent is subject to signal temporal logic (STL) tasks with multiple subtasks. In the distributed framework, due to the existence of coupling subtasks, the satisfaction of the conjunction of all subtasks may be conflicting. A two-step distributed model predictive control (DMPC) strategy is proposed to maximize the amount of the satisfied subtasks and minimize the violation degree of those failed subtasks. In step 1, a novel robustness metric of STL is proposed to measure whether each subtask is satisfied or not, and is directly incorporated into the DMPC optimization problem to determine the satisfiability of each subtask. Based on the planning results of step 1, a DMPC optimization problem with a short planning horizon is designed in step 2 to minimize the violation degree of the unsatisfiable subtasks while ensuring the satisfaction of those satisfiable ones. All agents solve their two-step DMPC problems sequentially to realize the satisfaction verification of the coupled subtasks at each time instant. Further, the soundness and feasibility of the two-step DMPC algorithm are formally guaranteed. Simulations and experiments illustrate the effectiveness of the proposed algorithm.
Genomic Analysis and Characterization of Pseudotabrizicola formosa sp. nov., a Novel Aerobic Anoxygenic Phototrophic Bacterium, Isolated from Sayram Lake Water

Aerobic anoxygenic photosynthetic bacteria (AAPB) are a kind of heterotrophic prokaryote that can use bacteriochlorophyll (BChl) for photosynthesis without oxygen production and they are widely distributed in aquatic environments, including oceans, lakes, and rivers. A novel aerobic anoxygenic photosynthetic bacterium strain XJSPT was isolated during a study of water microbial diversity in Sayram Lake, Xinjiang Province, China. Strain XJSPT was found to grow optimally at 33 °C, pH 7.5 with 1.0% (w/v) NaCl, and to produce bacteriochlorophyll a and carotenoids. Phylogenetic analysis based on 16S rRNA gene sequence and concatenated alignment sequences of 120 ubiquitous single-copy proteins both supported that strain XJSPT belonged to the genus Pseudotabrizicola. Both average nucleotide identity (ANI) and DNA–DNA hybridization (DDH) values were below the species delineation threshold. The primary polar lipids were phosphatidylcholine, phosphatidylglycerol, phosphatidylethanolamine, one unknown lipid, and one unidentified phospholipid. Based on the results of polyphasic analyses performed in this study, strain XJSPT represents a new member of the genus Pseudotabrizicola, for which the name Pseudotabrizicola formosa sp. nov. is proposed. The type strain is XJSPT (= KCTC 52636T = MCCC 1H00184T = SDUM 107003T). Comparative genomic analysis showed that four species of the genus Pseudotabrizicola shared 2570 core genes and possessed a complete anoxygenic photosystem II.
Transport coefficients of dissipative particle dynamics with finite time step

The viscosity and self-diffusion constant of a mesoscale hydrodynamic method, dissipative particle dynamics (DPD), are investigated. The viscosity of DPD with finite time step, including the Lowe-Anderson thermostat, is derived analytically for the ideal-gas equation of state and phenomenologically for systems with soft repulsive potentials. The results agree well with numerical data. A velocity-scaling version of the profile-unbiased thermostat is shown to be useful to obtain faster diffusion than for the DPD thermostat.
Data-Driven Analysis of Mass-Action Kinetics

The physical interconnection of spatial distributed biochemical systems has some advantages when dealing with large-scale problems that require separated agents to be con- trolled locally but with an overall objective. The analysis and control of such systems becomes a difficult task, because of the nonlinear nature of the dynamics of the individual subsystems, and the added complexity due to the interconnection. Therefore, analysis tools from a data-driven perspective are employed in contrast with the analytical classical way that becomes intractable once the subsystems grow in size or complexity.
A PARAMETER SPACE EXPLORATION OF GALAXY CLUSTER MERGERS. I. GAS MIXING AND THE GENERATION OF CLUSTER ENTROPY

We present a high-resolution set of adiabatic binary galaxy cluster merger simulations using FLASH. These are the highest resolution simulations to date of such mergers using an adaptive mesh refinement grid-based code with Eulerian hydrodynamics. In this first paper in a series, we investigate the effects of merging on the entropy of the hot intracluster gas, specifically with regard to the ability of merging to heat and disrupt cluster “cool cores. ” We find, in line with recent works, that the effect of fluid instabilities that are well resolved in grid-based codes is to significantly mix the gases of the two clusters and to significantly increase the entropy of the gas of the final merger remnant. This result is characteristic of mergers over a range of initial mass ratio and impact parameter. In line with this, we find that the kinetic energy associated with random motions is higher in our merger remnants which have high-entropy floors, indicating that the motions have efficiently mixed the gas and heated the cluster core with gas of initially high entropy. We examine the implications of this result for the maintenance of high-entropy floors in the centers of galaxy clusters and the derivation of the properties of dark matter from the thermal properties of the X-ray-emitting gas.
Factors Influencing Self-Management Behaviors among Hemodialysis Patients

Aim: To investigate the factors affecting hemodialysis patients’ self-management ability at a dialysis center in Taiwan. Background: Taiwan has the highest incidence and prevalence of end-stage kidney disease (ESKD) in the world. Over 90% of patients with ESKD receiving hemodialysis (HD) and self-management behaviors are critical among these patients. Failure to adhere to self-managed care increases the cost of medical care and the risk of morbidity and mortality. Methods: In this cross-sectional study, a total of 150 HD patients were observed for their self-management behaviors and the factors influencing these behaviors including education level, comorbid conditions, biochemical analysis, depression, and social support, etc., were analyzed. Results: Self-management behaviors in HD patients were significantly impaired in the presence of diabetes mellitus, hypertension, anemia, hypoalbuminemia, and depression. The major predictor of patients’ self-management was depression, explaining 14.8% of the total variance. Further addition of social support, hypertension, and diabetes mellitus into the regression model increased the total explained variance to 28.6%. Of the various domains of self-management, the partnership domain received the highest score, whereas emotional processing received the lowest score. Conclusions: This study found the important factors influencing self-management behaviors; through this acknowledgement and early correction of these factors, we hope to improve HD patients’ individual life quality and further decrease their morbidity and mortality.
Tax Compliance Reporting: Antecedent and Moderating Effect

To make it easier for taxpayers to report their taxes, the Balikpapan city government provides facilities in the form of an online tax reporting application called E-SPTPD. To reduce fraud in tax reporting, the government has also installed transaction recording tools in restaurants in Balikpapan City. The purpose of this study was to obtain empirical evidence of a relationship between the use of E-SPTPD on tax reporting compliance with the antecedent factors of information technology readiness and transaction recording tools as moderators. This type of research is quantitative research using primary data and for the sample using quota sampling method with a total of 57 respondents. Data analysis using SEM-PLS assisted by SmartPLS Version 3. The results of this study obtained empirical evidence that: (1) readiness of information technology has a positive and significant effect on the use of E-SPTPD: (2) the use of E-SPTPD E-SPTPD has a positive and significant effect on tax reporting compliance: (3) transaction recording tools cannot moderate the relationship between the use of E-SPTPD and tax reporting compliance. This study also produces new findings, namely there is an antecedent effect of information technology readiness which has a considerable influence before using E-SPTPD.
Did massive black holes in globular clusters initially satisfy galactic scaling relations

The masses of supermassive black holes (SMBHs, MBH = 10 6 ‐10 11 M⊙) in the centres of galaxies are related to the host stellar spheroid mass and ve locity dispersion. A key question is how these relations originate, and over which range of black hole masses they hold. It has been speculated that intermediate-mass black holes (IMBHs, MBH = 10 2 ‐10 5 M⊙) could play a fundamental role in the growth of SMBHs. A handful of IMBHs has recently been detected in Galactic globular clusters (GCs), but their masses are inconsistent with the galactic scaling relations of SMBHs. In this Letter, we derive the initial properties of the GCs using a standard analytical evolutionary model, of which the free parameters are fixed by independent constraints. We find that the observed IMBH masses initially followed the galactic SMBH scaling relations, and subsequently moved off these relati ons due to the dynamical evolution of their host GCs. This work is concluded with a brief discussion of the uncertainties and the implications of our results for the possible universality o f massive black hole growth.
Electrical impedance tomography-based pressure-sensing using conductive membrane

This paper presents a mathematical framework for a flexible pressure-sensor model using electrical impedance tomography (EIT). When pressure is applied to a conductive membrane patch with clamped boundary, the pressure-induced surface deformation results in a change in the conductivity distribution. This change can be detected in the current-voltage data ({\it i.e.,} EIT data) measured on the boundary of the membrane patch. Hence, the corresponding inverse problem is to reconstruct the pressure distribution from the data. The 2D apparent conductivity (in terms of EIT data) corresponding to the surface deformation is anisotropic. Thus, we consider a constrained inverse problem by restricting the coefficient tensor to the range of the map from pressure to 2D-apparent conductivity. This paper provides theoretical grounds for the mathematical model of the inverse problem. We develop a reconstruction algorithm based on a careful sensitivity analysis. We demonstrate the performance of the reconstruction algorithm through numerical simulations to validate its feasibility for future experimental studies.
The CMBR ISW and HI 21 cm cross-correlation angular power spectrum

The late-time growth of large scale structures is imprinted in the CMBR anisotropy through the Integrated Sachs Wolfe (ISW) effect. This is perceived to be a very important observational probe of dark energy. Future observations of redshifted 21-cm radiation from the cosmological neutral hydrogen (HI) distribution hold the potential of probing the large scale structure over a large redshift range. We have investigated the possibility of detecting the ISW through cross-correlations between the CMBR anisotropies and redshifted 21-cm observations. Assuming that the HI traces the dark matter, we find that the ISW-HI cross-correlation angular power spectrum at an angular multipole l is proportional to the dark matter power spectrum evaluated at the comoving wave number l/r, where r is the comoving distance to the redshift from which the HI signal originated. The amplitude of the cross-correlation signal depends on parameters related to the HI distribution and the growth of cosmological perturbations. However, the cross-correlation is extremely weak as compared to the CMBR anisotropies and the predicted HI signal. Even in an ideal situation, the cross-correlation signal is smaller than the cosmic variance and a statistically significant detection is not very likely.
Separability Probability Formulas and Their Proofs for Generalized Two-Qubit X-Matrices Endowed with Hilbert-Schmidt and Induced Measures

Two-qubit X-matrices have been the subject of considerable recent attention, as they lend themselves more readily to analytical investigations than two-qubit density matrices of arbitrary nature. Here, we maximally exploit this relative ease of analysis to formally derive an exhaustive collection of results pertaining to the separability probabilities of generalized two-qubit X-matrices endowed with Hilbert-Schmidt and, more broadly, induced measures. Further, the analytical results obtained exhibit interesting parallels to corresponding earlier (but, contrastingly, not yet fully rigorous) results for general 2-qubit states--deduced on the basis of determinantal moment formulas. Geometric interpretations can be given to arbitrary positive values of the random-matrix Dyson-index-like parameter $\alpha$ employed.
Detecting A Child’s Stimming Behaviours for Autism Spectrum Disorder Diagnosis using Rgbpose-Slowfast Network

Autism Spectrum Disoder (ASD) is a neurodevelopmental disorder characterized by (a) persistent deficits in social communication and interaction, and (b) presence of restrictive, repetitive patterns of behaviours, interests or activities. The stereotyped repetitive behaviours are also referred to as stimming behaviours. We propose a deep learning based approach to automatically predict a child’s stimming behaviours from videos recorded in unconstrained conditions. The child’s region in the video is tracked and its skeletal joints are derived using the pose estimator. The heatmap representation of skeletal joints and the raw video signals are used as inputs to the two pathways of the RGBPose-SlowFast deep network to model stimming behaviours. The proposed model is evaluated using the publicly available Self-Stimulatory Behaviour Dataset (SSBD) of stimming behaviours. The generalization ability of the model is validated using the Autism dataset containing child’s motor actions. Our experiments demonstrate state-of-the-art results on both datasets.