Miracle-dz/UniFAR-FTUs huggingface dataset:
Decentralized Oracles via Peer-Prediction in the Presence of Lying Incentives Naman

We derive conditions under which a detail-free minimal peer prediction mechanism can be used to elicit truthful data from non-trusted rational agents when an aggregate statistic of the collected data affects the amount of their incentives to lie. Furthermore, we discuss the relative saving that can be achieved by the mechanism, compared to the rational outcome, if no such mechanism was implemented. Our work is motivated by distributed platforms, where decentralized data oracles collect information about real-world events, based on the aggregate information provided by often self-interested participants. We compare our theoretical observations with numerical simulations on two publicly available real datasets.
Application of Capillary Polypropylene Membranes for Microfiltration of Oily Wastewaters: Experiments and Modeling

Oily wastewaters are considered as one of the most dangerous types of environmental pollution. In the present study, the microfiltration (MF) process of model emulsions and real oily wastewaters was investigated. For this purpose, capillary polypropylene (PP) membranes were used. The experiments were conducted under transmembrane pressure (TMP) and feed flow rate (VF) equal to 0.05 MPa and 0.5 m/s, respectively. It was found that the used membranes ensured a high-quality permeate with turbidity equal to about 0.4 NTU and oil concentration of 7–15 mg/L. As expected, a significant decrease in the MF process performance was noted. However, it is shown that the initial decline of permeate flux could be slightly increased by increasing the feed temperature from 25 °C to 50 °C. Furthermore, Hermia’s models were used to interpret the fouling phenomenon occurring in studied experiments. It was determined that cake formation was the dominant fouling mechanism during filtration of both synthetic and real feeds. Through detailed studies, we present different efficient methods of membrane cleaning. Results, so far, are very encouraging and may have an important impact on increasing the use of polypropylene MF membranes in oily wastewater treatments.
DMSO_A_304904 1833..1840

1Department of Internal Medicine, “Victor Babes” University of Medicine and Pharmacy, Timisoara, Romania; 2Department of Functional Sciences, “Victor Babes” University of Medicine and Pharmacy, Timisoara, Romania; 3Department of Neuroscience, “Victor Babes” University of Medicine and Pharmacy, Timisoara, Romania Background: Diabetes mellitus (DM) is one of the most serious public health problems, involving increasing costs worldwide. The mental state of a person with DM is varied and ever-changing, such as stress, the pressure to always do everything by the book, sadness, anger, and even denial of the disease, all these are feelings patients with DM will experience throughout their life. Aim: The aim of our study was to assess the presence of mental and psychiatric disorders (anxiety, depressive states, paranoid ideation, phobia, obsessive-compulsive disorder) in a group of patients with DM after hospitalization in the Clinic for Diabetes, Nutrition, and Metabolic Diseases, for various exacerbations of the underlying condition, looking for possible correlations with other cardiovascular risk factors. Patients and Methods: Clinical and biological parameters, the presence of acute and chronic complications of the diabetic patients have been evaluated. To assess mental health, the symptom checklist (SCL)-90 questionnaire was conducted for all admitted patients. Results: We observed that the number of patients with obsessive-compulsive disorders was relatively high (23.3%), while depression was present in 17.1% of the patients. Also, 10.6% of patients had hostility and 15.6% had delusional ideation. The presence of psychiatric disorders was associated with a higher age (62 vs. 46.5 years; p <0.001), a longer diabetes duration (11 vs. 9 years; p <0.001), higher fasting glycemia (188 vs. 132 mg/dL; p <0.001) and postprandial glycemia (212 vs. 152 mg/dL; p <0.001), and triglycerides (125 vs. 110 mg/ dL; p <0.001). Patients with altered mental status have shown statistically significantly more altered clinical and biological parameters compared to those without these mental disorders. Conclusion: Patients with DM represent a psychologically vulnerable population, which is why they should undergo early and regular screening for both psychological and psychiatric conditions, especially at admission.
Spinodal decomposition and collapse of a polyelectrolyte gel

The collapse of a polyelectrolyte gel in a (monovalent) salt solution is analysed using a new model that includes interfacial gradient energy to account for phase separation in the gel, finite elasticity and multicomponent transport. We carry out a linear stability analysis to determine the stable and unstable spatially homogeneous equilibrium states and how they phase separate into localized regions that eventually coarsen to a new stable state. We then investigate the problem of a collapsing gel as a response to increasing the salt concentration in the bath. A phase space analysis reveals that the collapse is obtained by a front moving through the gel that eventually ends in a new stable equilibrium. For some parameter ranges, these two routes to gel shrinking occur together.
Spinal cord stimulation. Analysis of diagnostic indications

Introduction: Spinal cord stimulation (SCS) is a form of chronic pain treatment that has been shown to be effective in patients who have responded poorly to other therapies. The current indications for SCS devices are very varied. The aim of our study is to analyze the diagnostic indications of SCS therapy during the last 5 years in our hospital, to know the most frequent causes of pain for the indication of the implant of the device and what percentage of improvement patients present, as well as to study the relationship with gender and age. Material and methods: This is an observational, descriptive, retrospective study. The patients were identified from the surgical activity register of the Pain Unit of the General Hospital of Valencia. Results: The final number of patients included was 179. The Failed back surgery syndrome (FBSS) was the diagnostic indication in 112 patients (62.57 %). The mean percentage of improvement described by the patients after the SCS implant was 47.99 ± 27.3 %. There were no differences in the improvement with respect to age or gender. Discussion: In spite of the variability of diagnoses in which this therapy may be indicated, it is noteworthy that in more than half of the cases the indication is by FBSS.
Ethiopian Rue (Ruta chalapensis L.) Genotypes Morphological and Biological Performance at Different Locations of Southern Ethiopia

Rue is an aromatic is evergreen plant used for multipurpose that used for traditional medicine and in Ethiopia mainly used flavoring coffee addition to its medicinal benefit. There is limitation of knowledge, experience, modern scientific evidences and information on Ethiopian R. chalenpesnsis genotypes because the experiment was undergone the aim to minimize the aforementioned gaps. The experiment was held for one year in 2019 up to 2020 G.C. at three different locations using 10 (ten) selected promising superior genotypes from selected during characterization of accessions. Selected genotypes were tested using Randomized Complete Block Design following the procedures of Gomez and Gomez in three replications. Rue genotypes were planted in six rows of 3-meter length on the commencement of main rainy season using 60 cm between plant and row spacing. Data was collected from different traits such as; plant height, number of branches/plants, fresh leaf weight/branch, fresh leaf weight/plant, leaf to stem ratio, leaf yield/ha, percent essential oil content and essential oil yield/ha. All tested traits very significantly affected by locations and genotypes during this experiment. The phenology of the genotypes is highly affected by location different; rue genotypes give more fruits early at high land and more leaves at low lands. Maximum leaf yield and leaf essential oil yield/ha was obtained at Arba Minch and maximum fruit essential oil yield/ha at Wondo genet from genotypes 1and 5 respectively. Based on this experiment result high altitude agroecology is more suitable for production and low altitude is for leaf production.
Distinguish Melanoma from Benign Melanocytic and Non-Melanocytic Lesions

Melanoma is a very aggressive type of cancer. Consequently, the detection of melanoma at early stages is crucial to prevent it from metastasizing, increasing survival rate. However, distinguishing between melanoma and other types of lesions, especially non-melanocytic ones, remains a difficult task. Dermoscopy is a procedure used by physicians for skin cancer diagnosis, that has shown to be more accurate than nakedeye analysis, since it can magnify the size of the inspected lesions by up to 100x. Several medical procedures are used to differentiate lesions, mostly for melanoma diagnosis, such as ABCD rule or Menzies scoring method. Several Computer-Aided Diagnosis systems have been developed in the interest of helping physicians better detect melanomas. Nonetheless, most of CAD systems aren’t able to distinguish between melanocytic and nonmelanocytic lesions, which is still an open problem and poorly addressed in literature. This thesis proposes a deep neural network to automatically discriminate melanoma from melanocytic (benign nevi) and non-melanocytic (seborrheic keratosis) lesions. To achieve this goal several regularization techniques were investigated in order to improve the performance of the developed model. The best evaluated configuration achieved a Balanced Accuracy (BACC) of 59, 9% for the validation dataset and 53, 1% for the test dataset. Furthermore, the best two models at correctly detecting melanomas, reached a same Sensitivity (SE) of 56, 7% in validation dataset and SE = 32, 5% and SE = 49, 6% for test dataset. The dataset used in this thesis was collected from the 2017 ISIC Challenge database.
Multi-omics of MLL-rearranged B-cell precursor acute lymphoblastic leukemia reveals broad glycoproteome remodelling

B-cell precursor acute lymphoblastic leukemia (pre-B ALL) with mixed-lineage leukemia gene rearrangement (MLL-r) is a poor-prognosis subtype for which additional therapeutic targets are needed. To identify changes that could distinguish leukemic from normal precursor B-cells, we performed integrated multi-omics on primary patient samples. MLL-r cells feature an extensive remodelling of their glycoprotein glycocalyx, with increased Core 2 type O-glycans and complex N-glycans as well as significant changes in sialylation and fucosylation levels. Notably, a glycosaminoglycan remodelling from chondroitin sulfate to heparan sulfate was also observed. A survival screen to determine if glycan remodelling enzymes are redundant identified MGAT1 and NGLY1, components of the N-glycosylation/degradation pathway, as essential. OGT and OGA, unique enzymes that regulate intracellular O-GlcNAcylation, were also indispensable. Transcriptomics and proteomics further identified Fes and GALNT7-mediated glycosylation as possible therapeutic targets. Finally, next to well-known MLL-r pre-B ALL glycoprotein markers, our integrated multi-omics workflow identified previously unidentified diagnostic/therapeutic protein candidates.
Detecting Objects from No-Object Regions: A Context-Based Data Augmentation for Object Detection

Data augmentation is an important technique to improve the performance of deep learning models in many vision tasks such as objectdetection.Recently,someworksproposedthecopy-pastemethod,whichaugmentstrainingdatasetbycopyingforeground objectsandpastingthemonbackgroundimages.Bydesigningalearning-basedcontextmodeltopredictrealisticplacementregions,theseapproacheshavebeenprovedtobemoreeffectivethantraditionaldataaugmentationmethods.However,the performanceoftheexistingcontextmodelwaslimitedbythreeproblems:(1)Thedefinitionsofpositiveandnegativesamples generatetoomuchlabelnoise. (2)Theexampleswithmaskedregionslosealotofcontextinformation. (3)Thesizes(i.e.,scaleand aspectratios)ofpredictedregionsaresampledfromapriorshapedistribution,whichleadstoacoarseestimation.Inthiswork,wefirstexploretheplacementrulesthatgeneraterealismandeffectivetrainingexamplesfordetectors.Andthen,wepropose atrainablecontextmodelinordertofindproperplacementregionsbyclassifyingandrefiningdensepriordefaultboxes.Wealsodesignacorrespondingreasonablegenerationfortrainingexamplesbyannotatinggroundtruthonfreespaceaccording totheplacementrules.TheexperimentalresultsonPASCALVOCshowthatourapproachoutperformsthestate-of-the-art-relatedwork. © 2021 The Authors . Published by Atlantis Press B.V. This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
Personality network neuroscience: Promises and challenges on the way toward a unifying framework of individual variability

Abstract We propose that the application of network theory to established psychological personality conceptions has great potential to advance a biologically plausible model of human personality. Stable behavioral tendencies are conceived as personality “traits. ” Such traits demonstrate considerable variability between individuals, and extreme expressions represent risk factors for psychological disorders. Although the psychometric assessment of personality has more than hundred years tradition, it is not yet clear whether traits indeed represent “biophysical entities” with specific and dissociable neural substrates. For instance, it is an open question whether there exists a correspondence between the multilayer structure of psychometrically derived personality factors and the organizational properties of traitlike brain systems. After a short introduction into fundamental personality conceptions, this article will point out how network neuroscience can enhance our understanding about human personality. We will examine the importance of intrinsic (task-independent) brain connectivity networks and show means to link brain features to stable behavioral tendencies. Questions and challenges arising from each discipline itself and their combination are discussed and potential solutions are developed. We close by outlining future trends and by discussing how further developments of network neuroscience can be applied to personality research.
Pedestrian and Mobile Robot Detection with 2D LIDAR

The first problem to be overcome in robotics is the positioning of the robot and surrounding objects. Detection and positioning of moving objects around the robot are an important point to prevent accidents. Deep learning and 3D LIDAR technology are often used, especially in pedestrian detection. Although these studies have high performance, they are not widely used yet due to their high cost. In this paper, a robot and human sensing system is proposed for use in lower cost 2D LIDARs. The system detects robot and human beam patterns by scanning the 2D LIDAR beam with the sliding window. Thanks to the sliding window technique, it marks whether there is a robot or a human in the part it scans. A new end-to-end deep neural network architecture is proposed in this study for pedestrian and mobile robot recognition based on 2D LIDAR data collected in a simulation environment. It has been observed that the system perceives robot and human models in a static environment with 91.6% accuracy.
Angiogenic Properties of NK Cells in Cancer and Other Angiogenesis-Dependent Diseases

The pathogenesis of many serious diseases, including cancer, is closely related to disturbances in the angiogenesis process. Angiogenesis is essential for the progression of tumor growth and metastasis. The tumor microenvironment (TME) has immunosuppressive properties, which contribute to tumor expansion and angiogenesis. Similarly, the uterine microenvironment (UME) exerts a tolerogenic (immunosuppressive) and proangiogenic effect on its cells, promoting implantation and development of the embryo and placenta. In the TME and UME natural killer (NK) cells, which otherwise are capable of killing target cells autonomously, enter a state of reduced cytotoxicity or anergy. Both TME and UME are rich with factors (e.g., TGF-β, glycodelin, hypoxia), which support a conversion of NK cells to the low/non-cytotoxic, proangiogenic CD56brightCD16low phenotype. It is plausible that the phenomenon of acquiring proangiogenic and low cytotoxic features by NK cells is not only limited to cancer but is a common feature of different angiogenesis-dependent diseases (ADDs). In this review, we will discuss the role of NK cells in angiogenesis disturbances associated with cancer and other selected ADDs. Expanding the knowledge of the mechanisms responsible for angiogenesis and its disorders contributes to a better understanding of ADDs and may have therapeutic implications.
Balancing Quality and Efficiency in Private Clustering with Affinity Propagation

: In many machine learning applications, training data consists of sensitive information from multiple sources. Privacy-preserving machine learning using secure computation enables multiple parties to compute on their joint data without disclosing their inputs to each other. In this work, we focus on clustering, an unsupervised machine learning technique that partitions data into groups. Previous works on privacy-preserving clustering often leak information and focus on the k-means algorithm, which provides only limited clustering quality and ﬂexibility. Additionally, the number of clusters k must be known in advance. We analyze several prominent clustering algorithms’ capabilities and their compatibility with secure computation techniques to create an efﬁcient, fully privacy-preserving clustering implementation superior to k-means. We ﬁnd afﬁnity propagation to be the most promising candidate and securely implement it using various multi-party computation techniques. Privacy-preserving afﬁnity propagation does not require any input parameters and consists of operations that are relatively efﬁcient with secure computation. We consider passive security as well as active security with an honest and dishonest majority. We offer the ﬁrst comparison of privacy-preserving clustering between these scenarios, enabling an understanding of the exact trade-offs between them. Based on the clustering quality and the computational and communication costs, privacy-preserving afﬁnity propagation offers a good trade-off between quality and efﬁciency for practical privacy-preserving clustering.
A Lane Extraction Algorithm Based on Fuzzy Set

Lanes are difficult to be extracted completely. A lane extraction algorithm is proposed according to vehicle driving rules. Vehicles are moving constantly, so the foreground area and background area cannot be defined effectively in the image. Therefore, based on the theory of fuzzy set, multidimensional degree is used to judge the membership degree of target and foreground in order to extract the moving area accurately. Then, the logistic regression model is established to determine the moving vehicles. Finally, based on the vehicle track, the lane extraction is realized by regional growth. The results show that the proposed algorithm can extract the road effectively.
Recent Advances in the Electrocatalytic Application of Transition Metal Nitrides Nanocrystalline

The energy crisis and environmental problems are becoming more and more severe due to the long-term consumption of fossil fuels. Therefore, energy conversion devices with high energy density and environmental friendliness (such as fuel cells, metal-air batteries, etc.) have been ex-pected to be reliable alternatives to traditional fossil energy. However, due to the inevitable use of precious metals as the electrode catalysts for these devices, the popularization of these alternatives is seriously hindered. Transition metal nitrides (TMNs) exhibit similar surface and adsorption properties to noble metals since the atomic distance between metal atoms increases and the d-band center of metal atoms downshift after the nitrogen atoms enter the metal lattice. TMNs have become one of the best electrode materials to replace noble metal electrocatalysts in energy storage and conversion devices. In this review, the latest development in the electrocatalytic ap-plication of TMNs nanocrystalline is covered. Firstly, we briefly discuss the structure and activity origin of TMNs and introduce the common physical and chemical methods for the preparation of TMNs. Subsequently, we illustrate the applications of unary TMNs and multivariate TMNs in oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). Finally, we summarize the challenges and problems of TMNs encountered at the present stage, and expect its future de-velopment.
Prevalence and factors favoring overweight and obesity in Algerian child and adolescent; case of the population of Constantine / Algeria

Background: Obesity today affects almost the entire planet, including many emerging countries such as Algeria. According to the World Health Organization / WHO; 39% of adults worldwide are overweight and 13% are obese in 2019, so the number of obesity cases has almost tripled since 1975. Our main goal through this paper is to measure the prevalence of overweight and obesity in children between 5-11 years and in adolescents between 12-17 years among the population of the province of Constantine / Algeria. Methods: This is a descriptive, transversal survey which focused on a representative sample of children and adolescents during the month of July 2018. The study population consisted of 1022 children and adolescents aged 5 to 17 years, who frequent a municipal swimming pool located in the province of Constantine / Algeria. These children and adolescents were selected by chance for a few days over the four weeks of July 2018. Information concerning the socio-demographic characteristics, eating habits and sedentary behaviour for each individual was specified by means of a questionnaire. Results: Among the results of this study: overweight affects 14% of children between 5-11 years old, while with moderate obesity, it affects 4% of children in this age category; for adolescents aged 12-17 years, 17% of them are overweight, and 7% have moderate obesity; several factors contribute to gaining extra pounds (gender and age, some hereditary factors, eating habits and physical inactivity). Conclusion: Identifying the risk factors for overweight and obesity, in children and adolescents under 18 years old, would help identify risky habits and behaviours in order to propose appropriate prevention measures. These measures must be included in a health education strategy and a health promotion strategy.
Research on User Operation of High-density Entity Interactive Target Quantitative Simulation Interface

The paper determines the general cognitive model of people in visual information search, and summarizes the basic design criteria that conform to the user’s cognitive model. Under the premise of the consistency of user needs, we disassemble and analyse each step of the user interaction process through the tool visualization simulation model, find out the user’s inner cognitive law under the guidance of different interfaces, and discuss the interface layout the impact of the difference on the user’s search time. The study found that through the numerical comparison of the experiment combined with the simulation analysis of Cog Tool and the target interaction experiment, the intrinsic cognitive law of the user in the interaction process was extracted. Therefore, we can conclude that the design guidelines for interface layout proposed in the paper can help art designers improve the efficiency of information cognition.