Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical study of nanofluid flow in a square cavity with porous medium using a sinusoidal interface

Laminar natural-convective flow in a square composite vertically layered enclosure consisting of porous and nanofluid layers is studied numerically using the finite difference method. A sinusoidal corrugated interface is used between the porous and the nanofluid layers. The enclosure is filled with a Cu-water nanofluid at a constant solid volume fraction. The governing equation is solved using Darcy–Brinkman model in the non-uniform porous region. The selected parameters of this paper are the Darcy number, Rayleigh number, non-uniform porous layer thickness, number of undulations and the sinusoidal amplitude. The numerical results represented by the average Nusselt numbers, flow behavior and temperature distributions inside the cavity are discussed. The results show that the addition of 10% of Cu, used in this study, enhances the rate of heat transfer compared to the base fluid (water). The average Nusselt number increases by increasing the Rayleigh number as the intensity of the streamlines is much stronger. It is likewise found that by increasing the Darcy number of the porous media with a low value of the non-uniform porous layer thickness enhance the convective heat transfer in the cavity.
Wired Network Distributed Diagnosis and Sensors Fusion by Multi-carrier Time Domain Reflectometry and Graph Theory

This paper presents a new application of graph theory to the cancellation of location ambiguity of faults in branched networks. The main objective is to solve ambiguities due to multiple paths and to detect and locate accurately electrical faults. To do so, a distributed diagnosis where several reflectometers (sensors) are implemented at different point of the network with MCTDR (Multi-carrier Time Domain Reflectometry) method is used. The MCTDR signal has a good autocorrelation property that allows detecting and locating more accurately the soft-faults in the wired networks. In this paper, a new method for data fusion between the different reflectometers is presented. The fusion is performed by using MCTDR test signal combined with the graph theory to isolate the nodes surrounding the soft-fault first, and then identify the faulty branch. Based on the different graph theory algorithms and its connection matrix, graph theory seems to be a promising solution to facilitate soft-fault location in the complex wired networksaffected by several soft-faults.
Characterising fluid status, distribution and dynamics in haemodialysis patients to improve fluid management strategies

Both the removal of too much and the failure to remove enough fluid during haemodialysis are associated with morbidity and mortality. It has been suggested that bioimpedance measurements using the Body Composition Monitor (BCM) and relative blood volume (RBV) monitoring can improve fluid management. However, both lack a robust evidence base characterising measurements in the clinical setting. BCM measurements were made in healthy controls to characterise the effect of measuring outside standard conditions and were made alongside RBV measurements in haemodialysis cohorts with well-defined clinical characteristics that were perceived to influence BCM results. The results suggested that BCM protocols can be flexible regarding measurement paths and timing of measurement to ensure as many patients as possible can benefit from the technology. The observed tendency for patients with high body mass index (BMI) to finish dialysis fluid-depleted does not appear to be related to systematic bias in the BCM models, but to a greater tolerance of fluid removal. The tendency for elderly subjects to have a degree of excess fluid, as measured by BCM, does appear to be associated with changes in tissue hydration linked to sarcopenia. Standard measurements in patients with localised lower-limb oedema can lead to attempts to remove unrealistic fluid volumes and alternative paths can help optimise targets. Feasibility for a trial of interventions to promote fluid mobilisation from oedematous tissue was not demonstrated. Simultaneous use of RBV with BCM suggested that assumptions underlying current RBV shape analysis use for fluid management can be violated and that there is a need for further studies. BCM can be used across the haemodialysis population, but an understanding of its application in individuals with particular characteristics is essential. RBV provides complementary information to BCM but there is a need for re-evaluation of RBV interpretation, standardisation of measurement categorisation and development of intravascular volume measurements.
An Extended Variable Speed Limit Strategy for Intelligent Freeway Traffic Optimization*

Shockwaves induced by the converging of different traffic flows have extensive impacts on freeway traffic, causing capacity drops and congestion. Although other studies have introduced many traffic management schemes in the view of road construction and theoretical analytic method to dominate shockwaves, there are many limitations on those existing methods. In order to explore more efficient and flexible speed control strategies to relieve congestion and queues caused by shockwaves on freeway, the extended variable speed limit strategy is proposed in this paper. Firstly, we explore insights into the traffic evolution based on the macroscopic traffic model METANET and traffic fundamental diagram. By taking full advantages of intelligent vehicles and inter-vehicle systems on control and communication, the optimal speed limit scheme which is continuous in time and space then is investigated using genetic algorithm. Specially, the optimized scheme aims at improving the traffic evolution process not only on increasing discharge rates and alleviating capacity drops of bottlenecks, but also on restoring the free-flow traffic state and decreasing total travel time delay in traffic. Finally, compared with the uncontrolled case and a referenced analytic method, the extended variable speed limit strategy has demonstrated its potential benefits on relieving/solving congestion and queues caused by shockwaves in the freeway traffic.
Flying Robots with Wind Awareness: Estimation of the Wind Velocity and Direction through the Data Log and a Closed-Form Energy Model

Flying robots must predict their remaining flight time at each time a decision should be taken. Estimating aircraft states is a challenging problem, especially if the aircraft is a light and small Unmanned Aerial Vehicle (UAV), under the effect of strong winds. In those cases, the energy consumption can vary in such a way that it would directly compromise the mission. This paper proposes a method for estimating the wind velocity and direction based on how much energy the aircraft needs to fly a predetermined path. The energy is calculated using a closed-form energy model based on the dynamic of the movement, the principles of superposition, and energy conservation. The model considers each parcel of energy consumed separately in the equation, even the drag force that is normally despised in other models. This model should be extended for other types of multi-rotors because it is a function of quadcopter parameters. The model has been validated against another model published recently in the literature. Here the model is applied in a reverse form. The consumed energy estimated by a Kalman Filter is applied as an input to the model such that the velocity of the quadcopter relative to the mass of air is calculated. That data and information supplied only by the flight computer allow determining the wind parameters. Despite the noisy characteristics of wind, it works properly, and the results demonstrate the feasibility of the proposed approach.
Electromagnetic Analysis and Performance Comparison of Fully 3D-printed Antennas

In this work, the possibility of directly prototyping antennas by exploiting additive manufacturing 3D-printing technology is investigated. In particular, the availability of printable filaments with interesting conductive properties allows for printing of even the antenna conductive elements. Three samples of a 2.45 GHz microstrip patch antenna have been 3D-printed by using different approaches and materials, and their performance evaluated and compared. In particular, the same dielectric substrate printed in polylactic acid (PLA) has been adopted in all cases, whilst copper tape and two different conductive filaments have been used to realize the conductive parts of the three antenna samples, respectively. Even if an expected radiation efficiency reduction has been observed for the conductive filament case, the comparative analysis clearly demonstrates that 3D-printing technology can be exploited to design working fully-printed antennas, including the conductive parts.
The Comparison of Different Visual Features for Visual Odometry

For robots to work effectively, the availability of a map with detailed information surrounding the workspace is an important requirement for indoor and outdoor tasks. This is usually achieved with using visual odometry techniques with feature-based methods. In this paper, we compare the performance of three different feature extraction methods: Scale Invariant Feature Transform (SIFT), Speed Up Robust Features (SURF) and Oriented FAST Rotated BRIEF (ORB). This paper presents experimental results on standard evaluation datasets and all experiments use measurement of the number of image correspondences as well as the ratio of good matched for the evaluation purpose. The results of experiments demonstrate that the performances of three methods in processing time, matching capability and accuracy. SIFT presents its stability in most scenarios although it is very slow. SURF is faster than SIFT and outperform SIFT on some scenarios. ORB is the most efficient feature and shows strong performance.
Facial Emotion Recognition in Videos using HOG and LBP

Emotions are found using verbal and non-verbal cues by analyzing voices and facial expressions. Monitoring emotional patterns of human is gaining importance in predicting the mood of a person. Facial emotion recognition is the process of detecting and recognizing different types of emotions in humans using facial expressions. The various steps include detection of the face and its landmarks, feature extraction of facial landmarks, and emotional state classification. The Haar cascading approach is used to detect different facial components such as eyes, mouth, and nose in an image. Facial features are analyzed using Histogram of Gradients (HOG) and Local Binary Pattern (LBP). The resultant feature vector is formed from the feature points. The three emotional states namely happy, sad and angry are classified using neural network classifier. The new feature points of test data are compared against trained data and their corresponding label values are displayed as the output for emotion recognition with the accuracy of 87% and 64% is being achieved using HOG and LBP techniques.
Revision Rotator Cuff Repair: Tips and Tricks to Get it Repaired and to Get it to Heal

Revision rotator cuff repair is a complex and increasingly common problem. Failure of initial tendon healing is caused by a variety of factors, most prominently biological, but technical factors of the primary surgery and post-operative trauma also contribute. Identifying a failed rotator cuff surgery is based on a combination of imaging and physical exam. However, the decision to proceed with a revision rotator cuff repair is more complex and based on predicted success of the revision procedure as well as an extensive discussion with the patient that revision cuff surgery is more complex procedure with higher failure rates than the initial cuff repair. Despite the complexity of a revision surgery, patient outcomes are significantly improved if cuff healing can be obtained. In this paper we discuss our methods for identifying patients who are good candidates for revision rotator cuff surgery as well as discussing in detail surgical “tips and tricks” for getting the cuff back down to the footprint and getting it to heal in a revision setting.
RAS and G-CSF Receptor Mutations Are Mutually Exclusive in Leukemogenesis in Severe Congenital Neutropenia.

Activating RAS mutations are common in myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) and were found in some patients with severe congenital neutropenia (CN) after transformation to MDS/AML (Kalra et al. Blood1995;86:4579). CN is a rare genetic disorder characterized by severe neutropenia leading to recurrent life-threatening bacterial infections due to the failure of myeloid cells to mature from promyelocytes and myelocytes to neutrophils. The risk for malignant transformation in CN is currently estimated at 14%. Based on earlier investigations one proposed model of the genetic events associated with progression of CN to MDS/leukemia starts with the acquirement of a somatic G-CSF Receptor gene ( G-CSFR ) mutation. In this model, RAS mutations and chromosomal aberrations such as monosomy 7 or trisomies represent late events in the transformation process. We have screened bone marrow specimens from 44 CN patients for mutations in the G-CSFR and in the KRAS2 and NRAS genes. Remarkably, we not only identified G-CSFR mutations in 10 out of 14 CN patients with MDS/AML, but also in 21 out of 30 patients without transformation. Only 1 out of 44 patients with CN harbored an activating RAS mutation ( NRAS , c.37G>C; G13R). In this patient, who developed secondary chronic myelomonocytic leukemia at the age of 18, the NRAS mutation was not detected in a bone marrow sample obtained at age 2. Results of cytogenetic investigations were available for 10 out of the 14 CN/MDS/leukemia patients. Chromosomal abnormalities were detected in all 10 patients, and included monosomy 7 (n=3), trisomies (n=3), deletions (n=2) and translocations (n=5). In addition to the cohort of 44 patients mentioned above, we analyzed 3 of the 5 CN patients with secondary myeloid leukemia in whom a NRAS mutation, c.35G>A (G12D), was described previously (Kalra et al. Blood1995;86:4579). None of these patients carried a G-CSFR mutation. Combining our data with data available from the literature the frequency of RAS mutations in CN patients is now 6/55 (11%). Accordingly, RAS mutations are less common during leukemic progression in CN than suggested by earlier studies, which may reflect changes in demographics of the CN population and/or effects of early treatment with G-CSF. The sequential occurrence of G-CSFR and RAS mutations in the leukemogenesis is very rare. Instead, our genetic data suggest RAS and the G-CSFR mutations are largely mutually exclusive in CN and may deregulate common signaling pathways.
Intra-Host Evolution of Norovirus GII.4 in a Chronic Infected Patient With Hematopoietic Stem Cell Transplantation

Human noroviruses (NVs) are the leading cause of acute gastroenteritis outbreaks worldwide. The majority of outbreaks are caused by genogroup II.4 (GII.4), with new variants emerging every 2 to 4 years. Immunocompromised patients are hypothesized to be important reservoirs where new NV variants emerge. Here, we examined intra-host NV variants and assessed immune-driven NV evolution in chronically infected immunocompromised hosts. Three NV GII.4-positive samples were collected from the same patient in different clinical phases following allogeneic hematopoietic stem cell transplantation, and had viral RNA concentrations of 2.46 × 106, 1.47 × 106, and 2.26 × 106 genome copies/mL. The non-synonymous (dN) and synonymous (dS) substitution ratio of the sequences in the partial P domain were >1, indicating strong positive selection in the patient. Both the number and the frequency of the single nucleotide variants increased over time in the patient. Also, the majority of capsid amino acid changes were located at blocking epitopes and histo-blood group antigen (HBGA)-binding sites, and 11 positive selection sites were found in the capsid region, of which 8 sites were presented in blocking epitopes or HBGA-binding sites. Homodimeric P-domain capsid models also suggested a structural change in the epitopes and HBGA-binding sites. The results suggested that novel variants of NV GII.4 with HBGA and antigenic site changes were produced in the immunocompromised patient. Further functional and epidemiological studies are needed to determine whether the new variants are a risk to public health.
The Value of Herbarium Collections to the Discovery of Novel Treatments for Alzheimer’s Disease, a Case Made With the Genus Eriodictyon

Plants, in particular those with a history in traditional medicine, hold enormous potential as sources of new therapies for dementias such as Alzheimer’s disease (AD). The largest collections of plants can be found in herbaria all over the world, but the value of these collections to AD drug discovery has been significantly neglected. As a proof of principle, we investigated the neuroprotective activity of herbarium specimens of Eriodictyon (yerba santa), a genus with a long history of usage by the indigenous tribes in California to treat respiratory and age-related complications. Dichloromethane extracts were prepared from leaves of 14 Eriodictyon taxa preserved in the SD Herbarium located at the San Diego Natural History Museum. The extracts were tested for neuroprotection in nerve cells against oxytosis and ferroptosis and for anti-inflammatory activity in brain microglial cells exposed to bacterial lipopolysaccharide. In parallel, the levels of the flavanones sterubin, eriodictyol and homoeriodictyol were measured by mass spectrometry. Several Eriodictyon species presented strong neuroprotective and anti-inflammatory activities. The protective properties of the extracts correlated with the amount of sterubin, but not with eriodictyol or homoeriodictyol, indicating that sterubin is the major active compound in these species. The occurrence of eriodictyol and homoeriodictyol may be predictive of the phylogenetic relationship between members in the genus Eriodictyon. The data offer insight into the traditional use of yerba santa across indigenous tribes in California, while demonstrating the value of herbarium collections for the discovery of novel therapeutic compounds for the treatment of neurodegenerative diseases.
An Optimized Image Fusion Method for Fume Removal in Automated Welding Robots Field

Welding is a commonplace process in many industrial sectors. Once the welding environment can be harmful to human’s health, researchers are presenting solutions to automate this process. Many of these solutions use robots guided by computer vision (using cameras). Fume produced by the arcwelding process usually adheres to the camera lens and affects the robots’ perception. Once fume adheres to the lenses or protective glass, none of them can be changed until the whole welding process is completed. The impossibility of changing protective glass makes restoring the acquired image even more important. In this paper, we propose an optimized method to minimize the interference of the fume present in the acquisition system. The solution presented is based on a fusion of different image processing methods. The results show the method is able to improve the groove detection allowing better welding.
Emerging Trends of ML-based Intelligent Services for Industrial Internet of Things (IIoT)

Intelligent information technology is a notable feature in the context of industry 4.0. A key factor in obtaining intelligent industrial Internet of things (IIoT) services is to integrate machine learning (ML) into IIoT. With the increasing scale of deployed terminals, IIoT becomes heterogeneous, diverse, and dynamically changeable. Traditional optimization methods are difficult to deal with the emerging network problems. This paper first proposes a ML-based IIoT architecture for intelligent IIoT services and expounds two ML methods for IIoT analysis, namely, deep learning (DL) and reinforcement learning (RL). Secondly, advanced applications and development trends of ML in industrial field are summarized. Opportunities and challenges of ML for IIoT analysis are discussed finally. The purpose of this paper is to point out the role of artificial intelligence (AI) technology in IIoT from the macroscopic view.
The Effects of Temperature on Animal Gut Microbiomes

Temperature is a prominent abiotic environmental variable that drives the adaptive trajectories of animal lineages and structures the composition of animal communities. Global temperature regimes are expected to undergo rapid shifts in the next century, yet for many animal taxa we lack an understanding of the consequences of these predicted shifts for animal populations. In this review, we synthesize recent evidence that temperature variation shapes the composition and function of animal gut microbiomes, key regulators of host physiology, with potential consequences for host population responses to climate change. Several recent studies spanning a range of animal taxa, including Chordata, Arthropoda, and Mollusca, have reported repeatable associations between temperature and the community composition and function of the gut microbiome. In several cases, the same microbiome responses to temperature have been observed across distantly related animal taxa, suggesting the existence of conserved mechanisms underlying temperature-induced microbiome plasticity. Extreme temperatures can disrupt the stability of alpha-diversity within the gut microbiomes individual hosts and generate beta-diversity among microbiomes within host populations. Microbiome states resulting from extreme temperatures have been associated, and in some cases causally linked, with both beneficial and deleterious effects on host phenotypes. We propose routes by which temperature-induced changes in the gut microbiome may impact host fitness, including effects on colonization resistance in the gut, on host energy and nutrient assimilation, and on host life history traits. Cumulatively, available data indicate that disruption of the gut microbiome may be a mechanism by which changing temperatures will impact animal fitness in wild-living populations.
Histone Lysine Demethylases KDM5B and KDM5C Modulate Genome Activation and Stability in Porcine Embryos

The lysine demethylases KDM5B and KDM5C are highly, but transiently, expressed in porcine embryos around the genome activation stage. Attenuation of KDM5B and KDM5C mRNA hampered embryo development to the blastocyst stage in fertilized, parthenogenetically activated and nuclear transfer embryos. While KDM5B attenuation increased H3K4me2-3 levels on D3 embryos and H3K4me1-2-3 on D5 embryos, KDM5C attenuation increased H3K9me1 on D3 embryos, and H3K9me1 and H3K4me1 on D5 embryos. The relative mRNA abundance of EIF1AX and EIF2A on D3 embryos, and the proportion of D4 embryos presenting a fluorescent signal for uridine incorporation were severely reduced in both KDM5B- and KDM5C-attenuated compared to control embryos, which indicate a delay in the initiation of the embryo transcriptional activity. Moreover, KDM5B and KDM5C attenuation affected DNA damage response and increased DNA double-strand breaks (DSBs), and decreased development of UV-irradiated embryos. Findings from this study revealed that both KDM5B and KDM5C are important regulators of early development in porcine embryos as their attenuation altered H3K4 and H3K9 methylation patterns, perturbed embryo genome activation, and decreased DNA damage repair capacity.