Miracle-dz/UniFAR-FTUs huggingface dataset:
Modélisation et identification du comportement mécanique des matériaux composites 2D SiC-SiC

La finalite de l'etude est de modeliser et d'identifier le comportement mecanique, a temperature ambiante, de deux materiaux ceramique-ceramique, a structure tissee, obtenus par c. V. I. Differents modeles de comportement mecanique sont proposes, les plus simples sont isotropes et le plus fin est un meso-modele. Tous ces modeles ont pour base la theorie generale de l'endommagement developpee par p. Ladeveze. Ils ont ete construits a partir de l'exploitation d'essais mecaniques realises avec une methodologie particuliere qui comprend notamment des suivis visuels et acoustiques in situ sur des essais de traction hors-axes. Ces essais ont permis de mettre en evidence une isotropie de comportement avant endommagement et d'identifier trois phases successives d'endommagement. Les domaines de validite des differents modeles ont ete precises a partir d'essais de pression interne et de torsion sur des eprouvettes tubulaires. La gamme de sollicitations choisies pour ces essais sur tubes a aussi permis de valider l'essai iosipescu pour l'obtention du module de cisaillement et de mettre en evidence que l'endommagement de cisaillement est fortement lie aux endommagements dans les directions des fibres
A Transformer-Based Prior Legal Case Retrieval Method

In this work, BERTurk-Legal, a transformer-based language model, is introduced to retrieve prior legal cases. BERTurk-Legal is pre-trained on a dataset from the Turkish legal domain. This dataset does not contain any labels related to the prior court case retrieval task. Masked language modeling is used to train BERTurk-Legal in a self-supervised manner. With zero-shot classification, BERTurk-Legal provides state-of-the-art results on the dataset consisting of legal cases of the Court of Cassation of Turkey. The results of the experiments show the necessity of developing language models specific to the Turkish law domain.
Comparative Analysis of VGG and ResNet for the Prediction of Rice Leaf Disease

Deep learning (DL) algorithms have developed efficient image recognition technologies in recent years. Convolutional Neural Networks (CNN) have produced outstanding results in this domain. Moreover, this research study highlights the merits and demerits of the different rice leaf disease techniques such as Visual Geometry Group (VGG) and Residual Networks (RESNET) models in the existing related works. This comparative study will make it easier for researchers working on the identification and categorization of plant leaf diseases to use DL techniques.
PPMM-DA: Privacy-Preserving Multidimensional and Multisubset Data Aggregation With Differential Privacy for Fog-Based Smart Grids

The smart grid (SG) is a new type of grid that integrates traditional power grid with the Internet of Things (IoT) to make the entire grid system more compatible, controllable, and self-healing. However, the flourishing of SG still faces some challenges in term of privacy-preserving data aggregation. Previous multidimensional data aggregation schemes need heavy computation operations, cannot support multisubset data aggregation, and resist neither collusion attack among the gateway (GW) and control center (CC) nor differential attack. To solve these issues, we propose a privacy-preserving data aggregation scheme for fog-based SGs to achieve multidimensional and multisubset data aggregation. The parallel composability of differential privacy is used to reasonably allocate the privacy budget, which can provide higher data utility in multidimensional data aggregation. In addition, each user’s multidimensional power consumption data will be structured as a composite data by utilizing the Chinese Remainder Theorem (CRT), which will further reduce the computational overhead. Security analysis shows that our scheme can resist differential attack, eavesdropping attack, collusion attack, and active attack. Evaluation of the performance also demonstrates that our scheme is more efficient in terms of computational overhead and communication overhead.
A review of surgical soft robot

This article demonstrates the background information about the surgical soft robot at the beginning, showing clearly the developing history of surgical soft robots. Then the article discusses and explains the material, actuation, sensing and manipulation of the surgical soft robot in depth. What’s more, the specific applications of minimally invasive surgery are demonstrated, as well. At last, there are some challenges and problems reflecting that we still have a long road to go in designing better and better surgical soft robots.
CUSTOMER SATISFACTION TOWARDS FARM AT SAN BENITO BASED ONLINE REVIEWS FROM SELECTED TRAVEL WEBSITE

Service quality has been identified as a critical problem in the tourism industry. Despite several studies on service quality, the function of service quality and customer satisfaction in the tourism business, as well as their relationship, remains unsolved. The main focus of this paper is to assess the level of Customer Satisfaction in The Farm at San Benito Based on Online Reviews from Selected Travel Websites. This study also addresses the well-known SERVQUAL model and describes additional service quality and customer satisfaction models used in the tourism sector. The model focuses on the five variables namely, tangible, reliability, responsiveness, assurance, and empathy focusing on their core products as a Holistic Medical Wellness Resort. Based on the data gathered from five hundred reviews from the four travel websites, the study concludes that the respondents are overall satisfied with the Wellness Resort Services.
Circulating miRNA Expression Is Inversely Correlated with Tumor Tissue or Sentinel Lymph Nodes in Estrogen Receptor-Positive Early Breast Cancer Patients

The deregulation of microRNAs (miRNAs) is associated with the various steps of the metastatic process. In addition, circulating miRNAs are remarkably stable in peripheral blood, making them ideal noninvasive biomarkers for disease diagnosis. Here, we performed a proof-of-principle study to determine whether tumor-tissue-derived miRNAs are traceable to plasma in ER-positive early breast cancer patients. We performed RNA-sequencing on 30 patients for whom plasma, sentinel lymph nodes (SLNs) and tumor tissue were available. We carried out differential expression, gene ontology and enrichment analyses. Our results show that circulating miRNAs are inversely expressed compared with tumor tissue or SLNs obtained from the same patients. Our differential expression analysis shows the overall downregulation of circulating miRNAs. However, the expression of miR-643a-3p and miR-223 was up-regulated in patients with positive SLNs. Furthermore, gene ontology analysis showed the significant enrichment of biological processes associated with the regulation of epithelial cell proliferation and transcriptional regulation commonly involved in the promotion of metastases. Our results suggest the potential role of several circulating miRNAs as surrogate markers of lymph node metastases in early breast cancer patients. Further preclinical and clinical studies are required to understand the biological significance of the most significant miRNAs and to validate our results in a larger cohort of patients.
Nemo: First Glimpse of a New Rule Engine

This system demonstration presents Nemo, a new logic programming engine with a focus on reliability and performance. Nemo is built for data-centric analytic computations, modelled in a fully declarative Datalog dialect. Its scalability for these tasks matches or exceeds that of leading Datalog systems. We demonstrate uses in reasoning with knowledge graphs and ontologies with 10^5 to 10^8 input facts, all on a laptop. Nemo is written in Rust and available as a free and open source tool.
A nonlinear Bayesian filtering approach to estimating adaptive market effciency

Abstract The adaptive market hypothesis (AMH) supplies a convincing motivation for why market efficiency should not be regarded as a stable property in time. This paper explores a Bayesian methodology for estimating weak-form market efficiency under the AMH using a test of evolving efficiency (TEE). More precisely, a generalized TEE (GTEE) approach is proposed in which the conditional first moment of a time series is assumed to be a nonlinear function of its conditional second moment, i.e., a nonlinear feedback term is present in the conditional mean equation. We then discuss a maximum likelihood estimation procedure for the resulting nonlinear model using the state-space approach and extended Kalman filtering. This methodology is used to estimate time-varying, weak-form market efficiency in four, specifically chosen, markets over a time-period that includes the global financial crisis of 2007/2008.
Simultaneous Super Resolution and Moving Object Detection from Low Resolution Surveillance Videos

Video surveillance has become a significant method to preserve public security specifically in the forensic field where high resolution videos are vital. However, the security video footage captured is often found to be low in clarity and deteriorated with noise, blurs and uneven illumination which can cause false detections and wrong interpretations. In light of this relevant fact, this work proposes a novel technique that does video super resolution with simultaneous detection of the moving objects using a tensor based convex optimization problem. The ideas of multilinear algebra are utilized in the super resolution framework to implement re-weighted tensor nuclear norm minimization for separating the highly correlated scene background. The spatio-temporal continuity featured by the sparse foreground components is grabbed by the Tensor Total Variation (TTV) regularization to achieve precise object detection along with l1 regularization. The brilliance of the proposed approach is in the joint formulation and implementation of video super resolution and simultaneous moving object detection from the available deteriorated videos. This is solved using the Augmented Lagrangian Method (ALM) with alternating direction scheme and the results validate the superior performance of the proposed method against the compared approaches.
Overweight or Obesity among Chinese Han People with Schizophrenia: Demographic, Clinical and Cognitive Correlations

People with schizophrenia are more likely to be afflicted by obesity or overweight compared to the general population. This study aimed to explore the incidence of overweight and obesity, clinical features and cognitive performance of Chinese Han patients with chronic schizophrenia who had overweight or obesity. We obtained data from 985 schizophrenia inpatients about overweight and obesity through body mass index (BMI). All patients were evaluated with the positive and negative syndrome scale, the Mini-mental State Examination (MMSE) and the repeated battery for evaluation of the neuropsychological status (RBANS) scale. We collected demographic and clinical data using self-reported questionnaires. We divided patients into normal weight (BMI < 24 kg/m2), overweight (24 ≤ BMI < 28 kg/m2) and obese (≥28 kg/m2) groups according to the Working Group on Obesity in China (WGOC) criteria. We compared the clinical data between the three groups and then conducted binary logistic regression and linear regression to assess variables that were significantly associated with overweight and obesity and higher BMI. Of the sample, 324 (32.9%) and 191 (19.4%) patients had overweight and obesity, respectively. Patients who had overweight and obesity were younger, had less education, had higher waist and hip circumferences, higher rates of diabetes and a higher sumPANSP score (compared with patients in the normal group, p < 0.05). There were more female patients with obesity (compared with patients in the normal and overweight groups, p < 0.05). Logistic regression analysis indicated that overweight and obesity were associated with sumPANSP (OR = 1.03, 95%CI = 1–1.061, p = 0.049) and diabetes (OR = 1.891, 95%CI = 1.255–2.849, p = 0.002). Further linear regression showed that age (B = −0.004, t = −2.83, p = 0.005), educational level (B = −0.037, t = −2.261, p = 0.024), diabetes (B = 0.133, t = 2.721, p = 0.007) and sumPANSP (B = 0.008, t = 2.552, p = 0.011) were risk factors for higher BMI. We did not find cognitive performance differences between patients with or without overweight and obesity. Overweight and obesity were associated with some demographic and clinical factors in patients with persistent schizophrenia.
Peptides From the Variable Domain of Immunoglobulin G as Biomarkers in Chronic Inflammatory Demyelinating Polyradiculoneuropathy

Background and Objectives Chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) is a clinically heterogeneous immune-mediated disease. Diagnostic biomarkers for CIDP are currently lacking. Peptides derived from the variable domain of circulating immunoglobulin G (IgG) have earlier been shown to be shared among patients with the same immunologic disease. Because humoral immune factors are hypothesized to be involved in the pathogenesis of CIDP, we evaluated IgG variable domain–derived peptides as diagnostic biomarkers in CIDP (primary objective) and whether IgG-derived peptides could cluster objective clinical entities in CIDP (secondary objective). Methods IgG-derived peptides were determined in prospectively collected sera of patients with CIDP and neurologic controls by means of mass spectrometry. Peptides of interest were selected through statistical analysis in a discovery cohort followed by sequence determination and confirmation. Diagnostic performance was evaluated for individual selected peptides and for a multipeptide model incorporating selected peptides, followed by performance reassessment in a validation cohort. Clustering of patients with CIDP based on IgG-derived peptides was evaluated through unsupervised sparse principal component analysis followed by k-means clustering. Results Sixteen peptides originating from the IgG variable domain were selected as candidate biomarkers in a discovery cohort of 44 patients with CIDP and 29 neurologic controls. For all 16 peptides, univariate logistic regressions and ROC curve analysis demonstrated increasing peptide abundances to associate with increased odds for CIDP (area under the curves [AUCs] ranging from 64.6% to 79.6%). When including age and sex in the logistic regression models, this remained the case for 13/16 peptides. A model composed of 5/16 selected peptides showed strong discriminating performance between patients with CIDP and controls (AUC 91.5%; 95% CI 84.6%–98.4%; p < 0.001). In the validation cohort containing 45 patients and 43 controls, 2/16 peptides demonstrated increasing abundances to associate with increased odds for CIDP, while the five-peptide model demonstrated an AUC of 61.2% (95% CI 49.3%–73.2%; p = 0.064). Peptide-based patient clusters did not associate with clinical features. Discussion IgG variable domain–derived peptides showed a valid source for diagnostic biomarkers in CIDP, albeit with challenges toward replication. Our proof-of-concept findings warrant further study of IgG-derived peptides as biomarkers in more homogeneous cohorts of patients with CIDP and controls. Classification of Evidence This study provides Class III evidence that the pattern of serum IgG-derived peptide clusters may help differentiate between patients with CIDP and those with other peripheral neuropathies.
Strength and Limitations of Publicly Available Anti-Malware Tools Against Obfuscated Malware

Many industries, including the automobile industry, have seen inventive development in various application areas as a result of the Internet revolution. Autonomous vehicles aren't science fiction anymore; they are a reality. The transformation of existing Vehicular Networks (VANETs) into the Internet of Vehicles (IoV) resulted in numerous advantages, including im-proved traffic management and a safer driving experience. In the past, there have been reports of VANET cyberattacks involving vehicles that are not entirely autonomous. These attacks, however, are equally viable in a VANET with fully autonomous vehicles. Currently, available anti-virus tools are highly sophisticated, but malware writers are commonly one step ahead of the soft-ware, and new obfuscated viruses that current anti-virus software cannot recognize are constantly released. As a result, before adopting currently available anti-malware tools as a malware detection system in the Vehicular networks, it is necessary to evaluate their effectiveness against advanced malware. This research work investigates and presents the effectiveness of 72 publicly available anti-virus engines against 300 live malicious android applications and 70 benign applications. The experiment is conducted on Ubuntu 20.04, Intel i7 machine with 16 GB of memory to scan the malicious samples as well as benign samples from their respective local repositories through 72 anti-virus engines. A script is developed in python 3.5, which uses VirusTotal API to upload the malicious as well as benign samples to VirsuTotal and fetch the scanning report to our local log file. The results presented in the study show that majority of the malicious samples could not be detected. Out of 72 AV scanners considered in the study, the detection accuracy of only 5 scanners is in the range of 10% to the highest detection accuracy of 30.67 %, whereas the detection accuracy of the rest of the 67 AV scanners is below 6 %. Based on the literature review and experimental results derived in the study, the authors conclude that most of the known anti-virus tools can detect all known malwares but are in-efficient against highly dynamic, evolving, obfuscated mal wares and zero-day attacks.
Open Access Journal of Sports Medicine Dovepress Ankle Sprain: Pathophysiology, Predisposing Factors, and Management Strategies

With the high percentage (up to 75%) of initial lateral ankle sprains (LAS) leading to repetitive sprains and chronic symptoms, it is imperative to better understand how best to treat and rehabilitate LAS events. The purpose of this paper is to review LAS pathophysiology, predisposing factors, and the current evidence regarding therapeutic modalities and exercises used in the treatment of LAS. Functional rehabilitation, early mobilization with support, is the current standard of care for LAS. However, the high percentage of reinjury occurrence and development of chronic symptoms (up to 75%) after a LAS, suggests the current standard of care may not be effective. Recent evidence has shown the need for more stringent immobilization to facilitate ligament healing and restoration of joint stability and function after a LAS. Additionally, the importance of adding adjunctive therapies, specifically joint mobilizations and balance training have been shown to improve function and decrease the incidence of reinjury after a LAS. Modifying current rehabilitation protocols to include protecting the ankle joint with stringent immobilization, and including joint mobilizations and balance training may be the first step to decreasing the incidence of short and long term ankle joint dysfunction.
High-Throughput Screening of the Repurposing Hub Library to Identify Drugs with Novel Inhibitory Activity against Candida albicans and Candida auris Biofilms

Candidiasis is one of the most frequent nosocomial infections affecting an increasing number of at-risk patients. Candida albicans remains the most frequent causative agent of candidiasis, but, in the last decade, C. auris has emerged as a formidable multi-drug-resistant pathogen. Both species are fully capable of forming biofilms, which contribute to resistance, increasing the urgency for new effective antifungal therapies. Repurposing existing drugs could significantly accelerate the development of novel therapies against candidiasis. Here, we have screened the Repurposing Hub library from the Broad Institute, containing over 6000 compounds, in search for inhibitors of C. albicans and C. auris biofilm formation. The primary screen identified 57 initial hits against C. albicans and 33 against C. auris. Confirmatory concentration-dependent assays were used to validate the activity of the initial hits and, at the same time, establish their anti-biofilm potency. Based on these results, ebselen, temsirolimus, and compound BAY 11-7082 emerged as the leading repositionable compounds. Subsequent experiments established their spectrum of antifungal activity against yeasts and filamentous fungi. In addition, their in vivo activity was examined in the murine models of hematogenously disseminated C. albicans and C. auris infections. Although promising, further in vitro and in vivo studies are needed to confirm their potential use for the therapy of candidiasis and possibly other fungal infections.
Tropical Warm Pool International Cloud Experiment (TWP-ICE): Cloud and Rain Characteristics in the Australian Monsoon

The impact of oceanic convection on its environment and the relationship between the characteristics of the convection and the resulting cirrus characteristics is still not understood. An intense airborne measurement campaign combined with an extensive network of ground-based observations is being planned for the region near Darwin, Northern Australia, during January-February, 2006, to address these questions. The Tropical Warm Pool International Cloud Experiment (TWP-ICE) will be the first field program in the tropics that attempts to describe the evolution of tropical convection, including the large scale heat, moisture, and momentum budgets, while at the same time obtaining detailed observations of cloud properties and the impact of the clouds on the environment. The emphasis will be on cirrus for the cloud properties component of the experiment. Cirrus clouds are ubiquitous in the tropics and have a large impact on their environment but the properties of these clouds are poorly understood. A crucial product from this experiment will be a dataset suitable to provide the forcing and testing required by cloud-resolving models and parameterizations in global climate models. This dataset will provide the necessary link between cloud properties and the models that are attempting to simulate them.
High School Students’ Perceptions of the Role of Social Support in Cultivating Their Interests in and Aspirations to STEM Degrees and Careers—A Middle Eastern Case Study

This case study intends to comprehend students’ perceptions of social support in cultivating their interests and aspirations for science, mathematics, engineering, and technology (STEM) degrees and careers. Survey-based quantitative research was employed, incorporating data from 1426 high school (grade 11th–12th) students in Qatar. The survey instrument encompassed four dimensions, i.e., (1) participants’ demographics, (2) STEM interests, (3) STEM supports/barriers and (4) STEM career aspirations to understand students’ perceptions. Spearman’s Rho correlation test demonstrated a positive correlation between students’ perceived social support (from family, teachers, and society) and their STEM interests (p < 0.01). Findings from the Mann-Whitney U test illustrated that females perceived enhanced social support (from teachers and society) in Qatar (p < 0.05). Even though teachers and society have been the stimulus to developing students’ STEM interests, there is still room to implement a policy for the consequential influence in constructing students’ STEM career aspirations. Thus, we believe these findings would urge policymakers to design tools that enable teachers and society to nurture, cultivate and sustain interest in STEM among the youth to meet Qatar’s National Vision 2030.