Miracle-dz/UniFAR-FTUs huggingface dataset:
A Combined Mixed Finite Element and Discontinuous Galerkin Method for Compressible Miscible Displacement Problem

We consider a combined method consisting of the mixed finite element method for flow and the discontinu ous Galerkin method for transport for the coupled system of compressible miscible displacement problem . Acut-off oper ator Misused in the discontinuous Galerkin for mular as the reference [5 ]. We canobtain the optimal priori erroresti mate for both concentration and pressure .
A Search for Radio-Quiet Gamma-Ray Pulsars in EGRET Data Using a Time-Differencing Technique

Most of the sources in the Galactic plane detected by the EGRET instrument on board the Compton Gamma Ray Observatory remain unidentified. Since the energy spectra of some of them strongly suggest that the gamma-ray emission originates from pulsars, it is interesting to look for pulsations in the data. However, detecting pulsations in gamma-ray emission by a blind search, that is, without prior knowledge of the pulsar spin parameters, is exceedingly difficult. Previous gamma-ray pulsar blind searches have relied on the execution of very long FFTs, requiring the use of supercomputers. We present a pulsar blind search, based on our time-differencing technique, that can be carried out more economically, in this case, on a standard PC. We present results from the application of this method to a periodicity search on all 271 sources from the third EGRET catalog. No statistically significant new detection was found, for a flux sensitivity of about 10−6 photons cm−2 s−1.
Evaluating the effectiveness of treatment of corneal ulcers via computer-based automatic image analysis

Corneal Ulcers are a common eye disease that requires prompt treatment. Recently a number of treatment approaches have been introduced that have been proven to be very effective. Unfortunately, the monitoring process of the treatment procedure remains manual and hence time consuming and prone to human errors. In this research we propose an automatic image analysis based approach to measure the size of an ulcer and its subsequent further investigation to determine the effectiveness of any treatment process followed. In Ophthalmology an ulcer area is detected for further inspection via luminous excitation of a dye. Usually in the imaging systems utilised for this purpose (i.e. a slit lamp with an appropriate dye) the ulcer area is excited to be luminous green in colour as compared to rest of the cornea which appears blue/brown. In the proposed approach we analyse the image in the HVS colour space. Initially a pre-processing stage that carries out a local histogram equalisation is used to bring back detail in any over or under exposed areas. Secondly we deal with the removal of potential reflections from the affected areas by making use of image registration of two candidate corneal images based on the detected corneal areas. Thirdly the exact corneal boundary is detected by initially registering an ellipse to the candidate corneal boundary detected via edge detection and subsequently allowing the user to modify the boundary to overlap with the boundary of the ulcer being observed. Although this step makes the approach semi automatic, it removes the impact of breakages of the corneal boundary due to occlusion, noise, image quality degradations. The ratio between the ulcer area confined within the corneal area to the corneal area is used as a measure of comparison. We demonstrate the use of the proposed tool in the analysis of the effectiveness of a treatment procedure adopted for corneal ulcers in patients by comparing the variation of corneal size over time.
Gross myofascial release of trunk with leg pull technique on low back pain with radiculopathy — A randomised controlled trial

Background: Lower Back Pain (LBP) with radiculopathy is a potentially more serious form of mechanical low back pain. A paucity of literature exists about the effect of the gross myofascial release (MFR) technique on the management of LBP. Objective: The study aimed to evaluate the effect of gross MFR when given as an adjunct to conventional physical therapy in subjects with low back pain with radiculopathy. Methods: Forty subjects (n=40) clinically diagnosed with LBP with radiculopathy were enrolled and randomly allocated to either the control group (n=20) or the experimental group (n=20). Both study groups received 5 sessions of intervention. The control group received conventional physical therapy while the experimental group received gross MFR of the trunk and lower limb along with conventional physical therapy. The outcome measures included were pressure pain threshold for the lower back and lower extremity, lumbar flexion and extension range of motion (ROM), percentage disability, and patient satisfaction towards the treatment which were measured pre-intervention (day 1) and post-treatment (day 5). The interaction between group and time was analysed using two-way mixed ANOVA. Results: The results suggested that the experimental group was statistically significant over the control group in terms of pressure pain threshold in the lower back (p<0.001) and lower limb (p=0.003), disability (p<0.001), and patient satisfaction (p=0.034) and lumbar flexion (p=0.002) except lumbar extension ROM (p=0.973). Conclusion: When given as an adjuvant to conventional physical therapy, gross myofascial release proved to provide a significant and faster short-term improvement over conventional treatment alone in subjects diagnosed with low back pain with radiculopathy.
Lattice Boltzmann in micro- and nano-flow simulations

One of the fundamental difficulties in microand nano-flow simulations is that the validity’s of the continuum assumption and the hydro-dynamic equations start to become questionable in this flow regime. The lower-level kinetic/molecular alternatives are often either prohibitively expensive for practical purposes or poorly justified from a fundamental perspective. The lattice Boltzmann (LB) method, which originated from a simplistic Boolean kinetic model, is recently shown to converge asymptotically to the continuum Boltzmann-BGK equation and therefore offers a theoretically sound and computationally effective approach for microand nano-flow simulations. In addition, its kinetic nature allows certain microscopic physics to be modeled at the macroscopic level, leading to a highly efficient model for multiphase flows with phase transitions. With the inherent computational advantages of a lattice model, e.g., the algorithm simplicity and parallelizability, the ease of handling complex geometry and so on, the LB method has found many applications in various areas of Computational Fluid Dynamics (CFD) and matured to the extend of commercial applications. In this talk, I shall give an introduction to the LB method with the emphasis given to the theoretical justifications for its applications in microand nano-flow simulations. Some recent examples will also be reported.
Robustness Properties of k Means and Trimmed k Means

Abstract The generalized k means method is based on the minimization of the discrepancy between a random variable (or a sample of this random variable) and a set with k points measured through a penalty function Φ. As in the M estimators setting (k = 1), a penalty function, Φ, with unbounded derivative, Ψ, naturally leads to nonrobust generalized k means. However, surprisingly the lack of robustness extends also to the case of bounded Ψ; that is, generalized k means do not inherit the robustness properties of the M estimator from which they came. Attempting to robustify the generalized k means method, the generalized trimmed k means method arises from combining k means idea with a so-called impartial trimming procedure. In this article study generalized k means and generalized trimmed k means performance from the viewpoint of Hampel's robustness criteria; that is, we investigate the influence function, breakdown point, and qualitative robustness, confirming the superiority provided by the trimming. We inc.
Integration of Agricultural Wireless Sensor Networks to Web-of-Things Through an Edge-Computing-Enriched WSNs/WoT Gateway

To connect agricultural wireless sensor networks (WSNs) to web services and applications, the agricultural WSNs/WoT gateway is empowered with local data management and network maintenance functions for downstream WSNs in addition to traditional upstream data collection. This work demonstrates a low-cost, highly scalable, rapidly deployable web of things (WoT) gateway with edge computing capabilities. First, an agricultural WSNs/WoT topology is architected, connecting a ZigBee WSNs to the Web for remote monitoring the local environmental and agronomical information, and simultaneously for managing the solar-powered WSNs for a prolonged lifespan according to the instant data scrawled from the cloud. Second, a WSNs/WoT gateway is designed with the hardware platform Raspberry Pi 3, which serves multiple needs for bidirectional information exchange and local WSNs management. Finally, experimentation demonstrates the proposed hardware platform and architecture can perform edge computing, and efficiently realize the up and down transmission and distribution of data stream.
Duty Cycle of Doppler Ground‐based Asteroseismic Observations

We report the observations of the clear‐sky fraction at the Concordia station during winter 2006 and derive from it the duty cycle for the astronomical observations. The duty cycle and observation duration at Dome C allow for efficient asteroseismic observations. This performance is analyzed and compared to network observations. For network observations, simulations were run using the helioseismic Global Oscillation Network Group as a reference. Observations with one site in Antarctica provide performance similar to or better than that with a six‐site network, since the duty cycle limited by meteorology is as high as 92%. On bright targets, a 100 day long time series with a duty cycle of about 87% can be observed, which is not possible for a network observation.
Strip Surface Defect Detection Algorithm Based on YOLOv5

In order to improve the detection accuracy of the surface defect detection of industrial hot rolled strip steel, the advanced technology of deep learning is applied to the surface defect detection of strip steel. In this paper, we propose a framework for strip surface defect detection based on a convolutional neural network (CNN). In particular, we propose a novel multi-scale feature fusion module (ATPF) for integrating multi-scale features and adaptively assigning weights to each feature. This module can extract semantic information at different scales more fully. At the same time, based on this module, we build a deep learning network, CG-Net, that is suitable for strip surface defect detection. The test results showed that it achieved an average accuracy of 75.9 percent (mAP50) in 6.5 giga floating-point operation (GFLOPs) and 105 frames per second (FPS). The detection accuracy improved by 6.3% over the baseline YOLOv5s. Compared with YOLOv5s, the reference quantity and calculation amount were reduced by 67% and 59.5%, respectively. At the same time, we also verify that our model exhibits good generalization performance on the NEU-CLS dataset.
Gly-Pro-Val-Gly-Pro-Ser Peptide Fish Collagen Improves Skin Moisture and Wrinkles with Ameliorated the Oxidative Stress and Pro-inflammatory Factors in Skin Photoaging Mimic Models

This study aimed to investigate whether low molecular fish collagen peptide (FC) from Oreochromis niloticus had protective effects on skin of photoaging mimic models. We observed that FC supplementation improved antioxidant enzymes activities and regulated the pro-inflammatory cytokines [e.g., tumor necrosis factor-α, interleukin (IL)-1β, and IL-6] by reducing the protein expressions of pro-inflammatory factors IκBα, p65, and cyclooxygenase-2 in ultraviolet-B (UV-B) irradiated in vitro and in vivo. Furthermore, FC increased hyaluronic acid, sphingomyelin, and skin hydration by reg-ulating the mRNA expression of hyaluronic acid synthases 1∼3, serine palmitoyltransferase 1, delta 4-desaturase, sphingolipid 1, and protein expressions of ceramide synthase 4, matrix metalloproteinase (MMP)-1, -2, and -9. In UV-B irradiated in vitro and in vivo, FC down-regulated the protein expression of the c-Jun N-terminal kinase, c-Fos, c-Jun, and MMP pathways and up-reg-ulated that of the transforming growth factor-β receptor I, collagen type I, procollagen type I, and small mothers against decapentaplegic homolog pathways. Our results suggest that FC can be effective against UV-B induced skin photoaging by improving skin dryness and wrinkle formation through antioxidant and anti-inflammatory properties.
A CLOUD-BASED INVANSION DETECTION SYSTEM THAT USES SECURE HASHING METHODS

—All industries have the same fundamental problem of insufficient defence, but today's adversaries are making significant efforts to penetrate these defences in order to engage in illegal insider trading. They identify a wide variety of information theft conduits. Private details is more vulnerable to intrusion in the modern world. While there are numerous defences against various forms of assault, hackers are always developing novel methods of breaching existing defences. As a result, in this article, we attempted to create a novel approach that would be very resistant to such an assault. The suggested scheme includes the implementation of a hash map-based system for detecting intrusions. In this system, the item is hashed and then stored as a shared key. The safe transfer of data is a key issue nowadays. Users in the data-sharing system may encrypt their files using their own personal keys before uploading them. If a user leaks the key information, it becomes tough for the cloud provider maintain stay secure, making this attribute extremely crucial to any large-scale data sharing system. In this work, we provide a secure and efficient implementation of the technique, as well as a proof of its security. Many obstacles stand in the way of data owners when they want to make their data available through server or cloud storage. The issues may be addressed in a variety of ways. For the safe management of a shared key belonging to the data's owner, several methods are required. In this article, we'll discuss the concept of a trusted authority as a means of verifying the identities of cloud data users. The trusted authority will utilize the SHA algorithm to produce the key, and then distribute it to the user and the owner. To determine the hash value, the certified authority system utilizes the MD-5 method after receiving an AES-encrypted file from the data owner. It maintains a database with keys that may be accessed during dynamic operations and utilized to identify any cheaters inside the network (CSP or Owner). The CSP module stores files sent from the trusted authority in the cloud. It is shown that the generated key sets have many desired qualities that protect reliable communication from being snooped on by other the network's nodes.
Low Molecular Weight Hydrogel for Wound Healing

Octadecylazanediyl dipropionic acid (C18ADPA) is a zwitterionic amphiphile with a dendritic headgroup. C18ADPA self-assembles to lamellar networks, which encompasses water and forms a low-molecular-weight hydrogel (LMWG). In this study, we use the C18ADPA hydrogel as a drug carrier for the in vivo delivery of a copper salt for wound healing in a mouse model. A structural transition was observed based on cryo-scanning electron microscope (cryo-SEM) images after drug loading. The C18ADPA hydrogel, which had a layered structure, transformed into a self-assembled fibrillar network (SAFiN). The mechanical strength of the LMWG has always been an important issue in its applications. However, due to the structural transition, both the storage and loss moduli increased. In vivo tests showed that wound closure was faster after applying the hydrogel formulation compared with the Vaseline formulation. For the first time, we have also provided histological evidence of these effects on skin tissue. The hydrogel formulation exhibited clear advantages in regenerating tissue structure over traditional delivery formulations.
Anti-Pseudomonas aeruginosa Vaccines and Therapies: An Assessment of Clinical Trials

Pseudomonas aeruginosa is a Gram-negative opportunistic pathogen that causes high morbidity and mortality in cystic fibrosis (CF) and immunocompromised patients, including patients with ventilator-associated pneumonia (VAP), severely burned patients, and patients with surgical wounds. Due to the intrinsic and extrinsic antibiotic resistance mechanisms, the ability to produce several cell-associated and extracellular virulence factors, and the capacity to adapt to several environmental conditions, eradicating P. aeruginosa within infected patients is difficult. Pseudomonas aeruginosa is one of the six multi-drug-resistant pathogens (ESKAPE) considered by the World Health Organization (WHO) as an entire group for which the development of novel antibiotics is urgently needed. In the United States (US) and within the last several years, P. aeruginosa caused 27% of deaths and approximately USD 767 million annually in health-care costs. Several P. aeruginosa therapies, including new antimicrobial agents, derivatives of existing antibiotics, novel antimicrobial agents such as bacteriophages and their chelators, potential vaccines targeting specific virulence factors, and immunotherapies have been developed. Within the last 2–3 decades, the efficacy of these different treatments was tested in clinical and preclinical trials. Despite these trials, no P. aeruginosa treatment is currently approved or available. In this review, we examined several of these clinicals, specifically those designed to combat P. aeruginosa infections in CF patients, patients with P. aeruginosa VAP, and P. aeruginosa–infected burn patients.
Fuzzy pattern recognition by fuzzy integrals and fuzzy rules

We give an overview of the application of fuzzy rules and fuzzy integrals in classification, presenting the general methodology and illustrating it by giving real applications. Fuzzy rules have been most of the time devoted to fuzzy control, using the so-called “Mamdani rules”. Here we present a broader view of the topic in the framework of possibility theory. It is seen that uncertainty rules are the best-suited ones for modeling human knowledge in pattern recognition. On the other hand, fuzzy integrals, by assigning weights to groups of attributes, permit to define non linear classifiers, with powerful performances.
A Comparative Study Between Bayesian Network and Hybrid Statistical Predictive Models for Proactive Power System Network Resilience Enhancement Operational Planning

Enhancing the operational resilience of the distribution system network (DSN) proactively in a hurricane-prone region requires a pre-hurricane event DSN optimization model, built on accurate hurricane-induced DSN line damage prediction scenarios. In the past, the resilience evaluation methods such as statistical sequential and non-sequential Monte Carlo simulation (MCS) contingency-based technique, and Machine learning-based Bayesian Networks (BN) technique, have been proposed to strengthen the operational resilience of the DSN proactively against forecasted oncoming hurricane events. However, a comparative study is largely unexplored to evaluate which of these two methods is best for proactive operational planning decision-making against forecasted oncoming hurricane events. In this paper, the Bayesian network (BN) and hybrid statistical DSN’s Fragility-curve (FC)-Monte Carlo simulation (MCS)-Scenario reduction (SCENRED) predictive algorithms were developed. The DSN line fault prediction scenarios simulated leveraging the predicted oncoming hurricane Ewiniar data were utilized to perform pre-hurricane DSN optimization to proactively decrease the DSN expected load loss. The pre-event system optimization problems were formulated in a mixed integer linear programming (MILP) approach and solved using a CPLEX solver in the general algebraic modelling system (GAMS) on a redesigned 48-bus DSN. The simulated initial expected load loss of 39% of 35 MW was decreased to 35.34%, and then to 30.71% with the use of hybrid statistical DSN’s FC-MCS-SCENRED, and the BN-DSN predictive models. These results were validated using the Electrical transient analyzer program (ETAP). This study confirmed that the BN-DSN predictive model is a better operational planning tool compared to hybrid statistical DSN’s line FC-MCS-SCENRED predictive model.
Diagnosis of Acute Q Fever in a Patient by Using Metagenomic Next-Generation Sequencing: A Case Report

Background Q fever is a zoonotic disease caused by Coxiella burnetii infection, with domestic ruminants as the main source of infection and tick bites as one of the transmission vectors. The clinical manifestations of Q fever are varied and atypical. For the reason that C. burnetii is a strictly intracellular pathogen, it is difficult to be diagnosed by traditional culture methods. Additionally, serological and molecular diagnostic methods to assist in the diagnosis of Q fever are not routinely performed in most clinical laboratories. Therefore, early and rapid diagnosis of Q fever is a challenge. Case Presentation In the present study, a 34-year-old male patient presented with an acute onset and symptoms such as high fever, lethargy, pulmonary infection, and liver damage. In addition, he had a history of tick bites. Despite conducting relevant laboratory and radiological examinations, the etiology remained unknown. Subsequently, we detected the sequence reads of C. burnetii in a venous blood sample using metagenomic next-generation sequencing (mNGS), and the symptoms of patients were significantly improved after timely treatment with the special drug tetracycline. To our knowledge, this is the first report of Q fever associated with C. burnetii detected directly from venous blood sample in Wuhan, China. Conclusion Metagenomic next-generation sequencing is a new diagnostic technology that provides rapid and accurate detection of unexplained infections, including Q fever. Its application plays a crucial role in clinical diagnosis for identifying elusive pathogens.
Molecular Modeling of Viral Type I Fusion Proteins: Inhibitors of Influenza Virus Hemagglutinin and the Spike Protein of Coronavirus

The fusion of viral and cell membranes is one of the basic processes in the life cycles of viruses. A number of enveloped viruses confer fusion of the viral envelope and the cell membrane using surface viral fusion proteins. Their conformational rearrangements lead to the unification of lipid bilayers of cell membranes and viral envelopes and the formation of fusion pores through which the viral genome enters the cytoplasm of the cell. A deep understanding of all the stages of conformational transitions preceding the fusion of viral and cell membranes is necessary for the development of specific inhibitors of viral reproduction. This review systematizes knowledge about the results of molecular modeling aimed at finding and explaining the mechanisms of antiviral activity of entry inhibitors. The first section of this review describes types of viral fusion proteins and is followed by a comparison of the structural features of class I fusion proteins, namely influenza virus hemagglutinin and the S-protein of the human coronavirus.