Miracle-dz/UniFAR-FTUs huggingface dataset:
Clustering structure analysis in time-series data with density-based clusterability measure

Clustering is used to gain an intuition of the structures in the data. Most of the current clustering algorithms produce a clustering structure even on data that do not possess such structure. In these cases, the algorithms force a structure in the data instead of discovering one. To avoid false structures in the relations of data, a novel clusterability assessment method called density-based clusterability measure is proposed in this paper. It measures the prominence of clustering structure in the data to evaluate whether a cluster analysis could produce a meaningful insight to the relationships in the data. This is especially useful in time-series data since visualizing the structure in time-series data is hard. The performance of the clusterability measure is evaluated against several synthetic data sets and time-series data sets, which illustrate that the density-based clusterability measure can successfully indicate clustering structure of time-series data.
Hepatitis B virus is still the most common etiologic factor of liver cirrhosis: Results from a single center in Turkey

Objective: It is important to examine the epidemiology of liver cirrhosis (LC) because of it is a preventable disease. In this single-center study, we aimed to determine the epidemiological characteristics and etiology of LC in Central Anatolian region of Turkey. Methods: We reviewed data of patients with liver cirrhosis who presented to outpatient and inpatient clinics of our medical center between January 1, 2011 and September 31, 2014 Results: Overall, 135 patients were included to the study: 91 men (67.4%) and 44 women (33%) with a mean age of 63±14,3 years (range: 15–87years). The primary causes of cirrhosis were chronic hepatitis B (CHB) (n: 52, 38.5%) and cryptogenic cirrhosis (n: 33, 24.4%). CHB was the main etiology of cirrhosis in men (49.5%) and cryptogenic LC was predominant in women (40.9%). Patients with alcoholic cirrhosis were solely male. Percentage of patients with autoimmune hepatitis was significantly higher among women (70%). The percentage of patients with HBV was similar between patients aged 50 years (31.6% and 39.7%, respectively), but percentage of patients with hepatitis C virus was lower (5.3%) in patients aged 50 years (14.7%). There was no cirrhotic patients under 50 years of age due to a genetic disorder Conclusion: Despite national vaccination program, effective treatment regimens and intensive screening methods against hepatitis B virus, it remains to be the most common cause of LC in our country. Key words: Viral hepatitis, alcohol induced liver disease, autoimmune hepatitis, cryptogenic liver cirrhosis, non-alcoholic fatty liver disease
GraspCNN: Real-Time Grasp Detection Using a New Oriented Diameter Circle Representation

This paper proposes GraspCNN, an approach to grasp detection where a feasible robotic grasp is detected as an oriented diameter circle in RGB image, using a single convolutional neural network. By detecting robotic grasps as oriented diameter circles, grasp representation is thereby simplified. In addition to our novel grasp representation, a grasp pose localization algorithm is proposed to project an oriented diameter circle back to a 6D grasp pose in point cloud. GraspCNN predicts feasible grasping circles and grasp probabilities directly from RGB image. Experiments show that GraspCNN achieves a 96.5% accuracy on the Cornell Grasping Dataset, outperforming existing one-stage detectors for grasp detection. GraspCNN is fast and stable, which can process RGB image at 50 fps and meet the requirements of real-time applications. To detect objects and locate feasible grasps simultaneously, GraspCNN is executed in parallel with YOLO, which achieves outstanding performance on both object detection and grasp detection.
Performance Evaluation of Mobility-Aware Routing Protocol for Low power and Lossy Networks

Wireless Sensor Networks (WSNs) is a class of low power and Lossy networks (LLN) which are useful to monitor a physical/environmental phenomenon. The Routing Protocol for Low Power and lossy networks (RPL) is a standardized protocol specifically designed to route the data over LLN networks. In particular, to take into account the specific features of LLN the RPL protocol computes the best routes following one or several metrics (e.g., the residual energy of nodes or the link quality) based on the state of the nodes (a.k.a sensors) or links. Nevertheless, RPL was originally designed to handle static networks; when node(s) move(s), the performances of RPL drop down. This issue has motivated a great deal of interest in ensuring that sensors may route the sensing data even thought they move from place to place, establishing new links and moving away from the previously established links. In this paper, we present RPL and the solutions that permit to deal with mobile nodes. We introduce a simulation-based evaluation of these solutions, which is focused on the energy consumption and the packet losses. The comparison between RPL and MRPL shows that RPL consumes less energy due to many packet losses. In counterpart, RPL does not provide a continuous connectivity to mobile sensor contrary to MRPL that keeps connection.
Whom do I Choose to Diffuse Information onTwitter? An Agent-Based Model Approach

Social network sites such as Twitter have madeeasier the exchange of information about products amongconsumers that are users of these networks. Thereby, marketersare using word of mouth marketing campaigns to engage users ofthese social networks in spreading the word about their products.The choice of consumers who participate in these campaigns is keyin their success. However, it is not clear which type of consumersand its combination are the most effective in reaching this aim.Unfortunately, the difficulty in getting useful data due to its noiseor non-public availability do not allow researchers toappropriately apply traditional methodologies like statistics.Hence, this paper proposes an agent-based model to simulate theinformation diffusion through Twitter when some types ofconsumers participate in word of mouth marketing campaigns.This model was implemented in Java and its results show that,although hubs (users with many contacts) are better connectedthan opinion leaders, the latter ones reach higher informationdiffusion because of its influence; in addition, the use of both typesof consumers in the same campaign achieves as diffusion as usingjust opinion leaders. Apart from results, this paper gives to futureresearchers a guideline to develop ABMs that simulate differentbehaviors of Twitter’s users.
Transmission of Information in Non-Local Field Theories

Alessio Belenchia, Dionigi M. T. Benincasa, 3 Stefano Liberati, 3 and Eduardo Mart́ın-Mart́ınez 5, 6 Institute for Quantum Optics and Quantum Information (IQOQI), Boltzmanngasse 3 1090 Vienna, Austria. SISSA International School for Advanced Studies, Via Bonomea 265, 34136 Trieste, Italy. INFN, Sezione di Trieste, Trieste, Italy. Institute for Quantum Computing, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada Perimeter Institute for Theoretical Physics, 31 Caroline St N, Waterloo, Ontario, N2L 2Y5, Canada
The use of a probe stabilizer to reduce musculoskeletal overload of ultrasound operators in routine diagnostic echocardiographic imaging

Abstract Introduction: Echocardiography is essential in the evaluation of patients with cardiovascular disease. Repetitive movements, however, expose ultrasound operators to a high risk of musculoskeletal strain injuries. Aim: We investigated to what extent a probe stabilizer could reduce repetitive movements. Materials and methods: The study population consisted of 31 male patients referred for routine transthoracic echocardiography. A good apical acoustic window was prerequisite for inclusion. Standard apical views and measurements were first recorded without using the probe stabilizer. Afterwards, the same apical views and measurements were acquired with utilization of the probe stabilizer. During the entire procedure, shoulder abduction and muscle activity of right forearm flexor and extensor muscles were recorded. To this purpose, an EMG-sensor was attached to the right lower arm and a gyroscope to the right shoulder blade. Results: Extreme right arm abduction (>30˚) occurred in 58% of the time with use of the stabilizer and in 98% of the time without (p <0.01). Activity of right forearm extensor muscles was 42% with and 60% without stabilizer (p = 0.04). For the flexor muscles these percentages were 47% and 87%, respectively (p <0.01). Use of the stabilizer did not affect the time needed for image acquisition (308s versus 309s, respectively, p = 0.46). Conclusions: This study demonstrated that the use of a stabilizer during acquisition of apical views in routine transthoracic echocardiography reduces the total time of shoulder abduction and the use of the right forearm muscles, while acquisition time was not affected.
A Cartesian grid method using a direct discretization approach for simulations of heat transfer and fluid flow

In the automobile industry, computational fluid dynamics (CFD) has been extensively used at various engineering stages including fundamental studies and product design processes in the related areas to heat transfer and fluid flow problems. Although body conforming structured and unstructured grids are commonly used in those simulations, mesh generation especially for complex geometries is always a crucial issue for shortening the duration of a total analysis cycle. The Cartesian grid method, in which underlying grids do not need to coincide with body geometries, is expected to be a feasible approach for that issue. The immersed boundary (IB) method using the direct forcing approach [1, 2, 3] is one of the Cartesian grid methods widely used in incompressible flow simulations. In this method, velocities in the cells adjacent to the immersed boundaries are calculated by interpolations using the velocities at the boundaries and surrounding fluid cells, which may cause a reduced accuracy especially under low grid resolutions. Therefore, in this study, a new numerical scheme of a Cartesian grid method using a direct discretization approach is proposed for simulations of heat transfer and fluid flow, and the effectiveness of the proposed method is validated in some fundamental problems.
Laparoscopic management of two unusual ectopic pregnancies.

Introduction Ectopic pregnancy occurs when the developing blastocyst implants at a site other than the endometrial lining, most commonly the fallopian tube. However, approximately 5% of pregnancies implant in unusual locations, such as the cervix, the ovary, previous caesarean scars, the interstitial portion of the fallopian tube and the abdomen. Early diagnosis can prevent sequelae associated with this potentially lifethreatening condition. This paper reports laparoscopic management of two unusual ectopic pregnancies. Case report A 32-year-old patient sought treatment for vaginal bleeding without pain at a gestation of 5 weeks and 2 days. A pelvic ultrasound scan revealed a 3.7 x 4.3 cm right adnexal mass with a gestational sac that contained a secondary yolk sac and a 9 mm non-viable foetal pole. The βHCG level was 15,818 mU/mL. Laparoscopy was performed, and a mobile uterus with a 4 cm bulge in the right horn of the uterus was detected. The cornual area with pregnancy was resected using bipolar forceps and scissors. The distal portion of the right tube was cauterised and removed. In the second case, a 33-year-old woman was admitted to our emergency room with complaints of abdominal pain and slight vaginal bleeding. The β-HCG level was 7,001 mU/mL. A transvaginal sonogram revealed a right-sided solid mass with a 4 mm central cystic area and a sac in the left tubal region containing a 2.3 mm foetal pole with a positive heart rate. During the laparoscopy, both fallopian tubes were distended by unruptured tubal pregnancies. Bilateral salpingostomy was performed. Pathological examination confirmed the diagnosis of bilateral ampullary tubal pregnancy. Conclusion Unusual ectopic gestations should be considered in the differential diagnosis of obstetric emergencies. The basic management approaches for ectopic pregnancy are expectant management, medical treatment and surgical therapy, including laparotomy and laparoscopy. Management should be decided on a case-by-case basis.
Thompson Sampling for m-top Exploration

The multi-armed bandit game concerns a bandit with K stochastic arms (e.g., a slot machine with K levers). When an arm ak is pulled, a reward rk is drawn from that arm’s reward distribution Rk. For each arm ak, we have the expected reward μk = E [rk]. Our aim is to solve the m-top exploration problem (m < K), where the objective is to identify the m best arms, with respect to the expected reward μk of the arms. Formally, we have μ1 ≥ . ≥ μm ≥ μm+1 ≥ . ≥ μK , and the objective is to identify the set {μ1, . , μm}. Most commonly, the m-top exploration problem is studied in a fixed confidence or fixed budget setting. On the one hand, fixed confidence algorithms attempt to recommend the m best arms with probability 1− δ using a minimal number of arm pulls, where δ is a failure probability that needs to be chosen up front. On the other hand, the goal for fixed budget algorithms is to recommend the top m arms, within a given budget of arm pulls. Recently, a third setting was introduced, where the top m arms are to be recommended after every time step [2]. This setting, referred to as anytime explore-m, is more challenging than the fixed confidence and fixed budget setting, but offers a more realistic framework. An example of an m-top exploration problem presented in [2] is a crowdsourcing task, i.e., the New Yorker cartoon caption contest. In this application, the aim is to collect ratings for the captions submitted for each week’s cartoon, and to identify the top-m captions at a requested time. In a crowd sourcing application, the sampling budget corresponds to the number of ratings that are obtained. Therefore, as this budget is unknown a priori, the fixed-budget setting cannot be used. Moreover, the fixed-confidence setting is not applicable either, as this setting requires that an unlimited stream of samples is available until a certain confidence threshold has been reached. The crowd sourcing application is thus a natural fit for the anytime explore-m problem. Apart from this example, we believe that there is a great potential for the anytime m-top exploration bandit to support decision makers with complex societal
FORM OF APPREHENSION AND THE CONTENT–APPREHENSION MODEL IN HUSSERL’S LOGICAL INVESTIGATIONS

An act’s form of apprehension (Auffassungsform) determines whether it is a perception, an imagination, or a signitive act. It must be distinguished from the act’s quality, which determines whether the act is, for instance, assertoric, merely entertaining, wishing, or doubting. The notion of form of apprehension is explained by recourse to the so-called content–apprehension model (Inhalt-Auffassung Schema); it is characteristic of the Logical Investigations that in it all objectifying acts are analyzed in terms of that model. The distinction between intuitive and signitive acts is made, and the notion of saturation (Fülle) is described, by recourse to the notion of form of apprehension.
Design And Characterisation Of Novel Sorafenib-Loaded Carbon Nanotubes With Distinct Tumour-Suppressive Activity In Hepatocellular Carcinoma

Purpose Over the past 30 years, no consistent survival benefits have been recorded for anticancer agents of advanced hepatocellular carcinoma (HCC), except for the multikinase inhibitor sorafenib (Nexavar®), which clinically achieves only ~3 months overall survival benefit. This modest benefit is attributed to limited aqueous solubility, slow dissolution rate and, consequently, limited absorption from the gastrointestinal tract. Thus, novel formulation modalities are in demand to improve the bioavailability of the drug to attack HCC in a more efficient manner. In the current study, we aimed to design a novel sorafenib-loaded carbon nanotubes (CNTs) formula that is able to improve the therapeutic efficacy of carried cargo against HCC and subsequently investigate the antitumour activity of this formula. Materials and methods Sorafenib was loaded on functionalized CNTs through physical adsorption, and an alginate-based method was subsequently applied to microcapsulate the drug-loaded CNTs (CNTs-SFN). The therapeutic efficacy of the new formula was estimated and compared to that of conventional sorafenib, both in vitro (against HepG2 cells) and in vivo (in a DENA-induced HCC rat model). Results The in vitro MTT anti-proliferative assay revealed that the drug-loaded CNTs formula was at least two-fold more cytotoxic towards HepG2 cells than was sorafenib itself. Moreover, the in vivo animal experiments proved that our innovative formula was superior to conventional sorafenib at all assessed end points. Circulating AFP-L3% was significantly decreased in the CNTs-SFN-MCs-treated group (14.0%) in comparison to that of the DENA (40.3%) and sorafenib (38.8%) groups. This superiority was further confirmed by Western blot analysis and immunofluorescence assessment of some HCC-relevant biomarkers. Conclusion Our results firmly suggest the distinctive cancer-suppressive nature of CNTs-SFN-MCs, both against HepG2 cells in vitro and in a DENA-induced HCC rat model in vivo, with a preferential superiority over conventional sorafenib.
Self-tuning Fuzzy Inference based on Spline-membership Function

Recently, fuzzy systems are used in many fields and places. In order to apply the fuzzy system to wider fields, it is necessary to study the tuning methods of the fuzzy system. Some self-tuning methods were proposed so far. However these conventional self-tuning methods do not have sufficient capability of generalization. In this paper, we propose new self-tuning fuzzy inference. The fuzzy inference consist of membership functions that are expressed by spline junction. Descent method is applied to tune the membership functions and consequent parts. The effectiveness of the proposed methods is shown by some numerical examples. Key Word: Self-tuning fuzzy inference, Spline-membership function, descent method
The Possible Role of S100b Protein as A Prognostic Biomarker for Brain Injury in Acute Carbon Monoxide Poisoning

The current study was carried out on sixty two carbon monoxide (CO) poisoned patients admitted to Poison Control Center Ain Shams University (PCCA), from January 2008 to June 2009. Patients were divided into four groups: Group I: twenty patients (mild CO poisoning). Group II: fifteen patients (moderate CO poisoning). Group III: twenty seven patients (severe CO poisoning). Group IV (control): fifteen healthy non-smokers. Serum S100B protein, blood CO level, random blood sugar (RBS) and arterial blood PH were assessed in all groups. Highly significant increase in serum S100B protein was found in all studied patients in comparison with the control. Patients who developped neurological sequelae had significantly high S100B protein. Blood CO level was significantly high in all tested groups. There was also highly significant increase in RBS in group III only. In addition, there was significant increase in the RBS in patients who died and in those with neurological sequelae. It can be concluded that protein S100B may be used as a useful biomarker of brain damage in acute CO poisoning and is helpful in assessment of its severity and prediction of neurological sequelae and mortality.
Price Level vs. Nominal Income Targeting and the Cost Channel of Monetary Policy Transmission

Most of the recent literature on monetary policy that compares alternative targeting regimes focuses on the traditional interest rate channel. However, several researchers have emphasized on an alternative channel, the cost channel, as a powerful collaborator in the transmission of short run effects of monetary policy. In this paper I incorporate both the traditional and the cost channel of monetary policy in a baseline ‘new Keynesian model and study two targeting regimes --pricelevel targeting and nominal income targeting. In light of empirical considerations, I also consider alternative specifications for the aggregate demand side of the economy --the IS-type relationship and for the aggregate supply side of the economy --a Phillips curve type. The main result of the paper is that the cost channel matters; with the cost channel operating the volatility of real output increases under both price-level and nominal income targeting. Moreover, nominal income targeting performs better than price level targeting in bringing down the volatility of real output in almost all the specifications of the macro models used in the analysis. JEL Classification: E30, E32, E52
The Relationship between Big-Five Personality Traits and Academic Achievement of Bachelor Students: A Review

Academic achievements are assessed through multiple criteria such as exams, quizzes, assignments, and personality is considered as one of the factors that contribute to attainment among students. Besides, majority of the society agreed that student’s academic achievement or performance have significant impact towards the students’ future. Most studies conducted under this topic are among different countries. Meanwhile in Malaysia, there are limited study on the relationship of Big-Five personality traits and students’ academic achievement. Therefore, the purpose of the study is to examine the relationship between Big-Five personality traits and academic achievement of degree students in UiTM Samarahan 2. Big-Five are consists of conscientiousness, extraversion, agreeableness, neuroticism and openness to experience. Not all five traits are considered as influential factors because various studies show different results between conscientiousness, agreeableness, openness to experience, extraversion and neuroticism towards academic achievement. Conscientiousness has been found by various educational researchers to be the most consistently associated or positively related with all academic achievement criteria which also lead to higher and better results. Hence, the objectives of this paper are to investigate the most dominant traits and also identify the association between Big-Five personality traits with degree students’ academic achievement in UiTM Samarahan 2. The researchers also review whether there is any gender differences in personality traits that influence academic achievement.
Representation Learning for Bipartite Graph with Graph Convolutional Networks

Graph embedding can be fed into machines learning methods to address various task such as classification, ranking, link prediction, clustering, and visualization. By far most research efforts have focused on homogeneous networks like social networks where vertices are of the same type, or heterogeneous networks like knowledge graphs where vertices (and/or edges) are of different types. There has been relatively little research dedicated to bipartite graphs. Arguably, generic graph embedding methods like node2vec and LINE can also be applied to learn graph embeddings for bipartite graph by ignoring the vertex type information. However, real-world bipartite graphs also concern the relationship between two types of entities, which usually exhibit different properties and patterns from other types of graph structured data. Recently, Graph Neural Networks(GNNs) model has shown its power in addressing graph structured data. In this work, we aim to learn graph representation for bipartite graph with GNN. We evaluate our GNN model on large-scale social network dataset with classification task.