Miracle-dz/UniFAR-FTUs huggingface dataset:
MICROZONATION MODEL FOR LIQUEFACTION POTENTIAL OF MAKASSAR COASTAL AREA

The objectives of this research were to correlate liquefaction potential assessment using CPT and SPT data with NCEER method and using seismic vulnerability index of HVSR method, and then to develop microzonation of liquefaction vulnerability map on the coastal area of Makassar city. Brief descriptions of NCEER and HVSR methods were given. Data for HVSR method was obtained by using one set of short period seismograph of type TDS303 (with three components), and one set of accelerograph of type TDQ 303S with sampling frequency of 100Hz. Results showed that: 1)the calculations of SF with NCEER and of shear strain with HVSR methods, both resulted in more or less similar evaluation of vulnerability level against liquefaction, 2) available earthquake record in Makassar give much smaller acceleration compared to the results based on Indonesian SNI 1726-2012 code, and 3) using the values of PGA as suggested in SNI 1726-2012 for both methods, some area in Makassar indicated as vulnerable to liquefaction particularly near river and coast in southern part of the city. It was also concluded that since the extend of area indicated vulnerable is different from both methods due to fewer number of data in CPT and SPT compared to HVSR data, a focus research on indicated area was suggested.
Opinion mining and emotion recognition in an intelligent learning environment

In this paper, we present the development of an opinion‐mining module. The development of the module consisted of creating an emotion tagged dataset of opinions; implementing an opinion mining module that processes sentences about computer programming, predicting or recognizing their polarity (positive/negative) and their type of emotion (frustrated, bored, excited, engagement, and neutral); and integrating the previous module in an intelligent learning environment. We evaluated the corpus, the accuracy of text polarity, and emotion recognition. The results with respect to polarity are promising (88.26%), however, the results in the detection of emotions are still low (60.0%). The reasons which likely explain these outcomes include a relatively small (7,777 records) and unbalanced corpus.
ANTIBACTERIAL, ANTIFUNGAL AND ANTIOXIDANT ACTIVITIES OF TUNISIAN OLEA EUROPAEA SSP. OLEASTER FRUIT PULP AND ITS ESSENTIAL FATTY ACIDS

Objective : This study was conceived to evaluate the essential fatty acids, secondary metabolite, antimicrobial and antioxidant activities of Olea europaea ssp. oleaster fruits pulp methanolic extract. Methods: Analysis of the lipid content from unexploited Olea europaea ssp. oleaster pulp was carried out using gas chromatography. The antioxidant activity was evaluated by DPPH radical scavenging. The antimicrobial activity was also tested against seven pathogenic bacteria, two fungal species and one yeast strain using two methods. Results: The obtained results showed that the major components of fatty acids were oleic acid (77.4%) and elaidic acid (17.58%). Moreover, the tested extract was rich in phenol (84.04±0.01 mg GAE/g DW) than in flavonoids (60.41±0.02 mg RE/g DW). In addition, it showed puissant antioxidant (IC 50 = 28±0.01 µg/mL), antibacterial and antifungal activities. The inhibition zones diameters and the minimum inhibition concentration values for tested microorganisms were in the range of 13-18 mm and 3.125-25 mg/mL, respectively. Conclusion: This study shows that Olea europaea ssp. oleaster fruit pulp could be developed into ingredients for use in foods as the natural antioxidant and antimicrobial agent.
Lexical based automated teaching evaluation via students’ short reviews

Student evaluations of teaching (SET) have become a popular approach to assess faculties’ teaching. Question‐score‐based questionnaire is the most common SET measure adopted in universities. However, it fails to cover important facets of teaching process that not mentioned in the predefined questionnaire, which can be substantially obtained from students’ short reviews. In this paper, we propose two lexical‐based methods, specifically knowledge‐based and machine learning‐based, to automatically extract opinions from short reviews. Furthermore, the diversity of reviews’ themes and styles of same sentiment polarity reviews can be observed from the extracted opinion results. The experimental results show that the proposed methods are able to achieve accuracies of 78.13 and 84.78%, respectively in the task of student review sentiment classification. Further investigation on linguistic features shows that reviews with same sentiment polarity shares similar language patterns. Finally, we present an application scenario in real SET process by utilizing aforementioned methods and discoveries.
By Unanimous Decision? A Second Look At Consensus In The Film Industry

This article reports analyses of the verdicts of various film organizations that annually present awards to motion pictures and investigates whether they award/nominate the same movies in a given year. This research disputes previous findings that reported a high level of agreement between those juries, by the means of reliability analysis and the Cronbach's alpha composite. Arguments were raised for why these earlier findings were flawed and why the use of Cronbach's alpha is problematic. Different aspects of consensus are discussed, after which a new measure (β) is introduced. This is followed by a detailed comparison between particular juries with regard to the percentage share of their decisions that award the most successful (chosen by multiple other juries as well) and the least successful (uniquely awarded) films. This measure shows how often a singular jury decides in line with the others and how much does it stray from the consensus. The article also broadens the theoretical discussions about the reasons for (not) expecting a consensus to arise between various expert juries. It argues that by adopting a cultural economic perspective we become aware of various reasons, most importantly competition between the award events and the juries tend towards a lower level of consensus.
Meta-analysis to Identify and Evaluate Factors Associated with Regulatory Approval of Orphan Drugs (OD) to Develop an Algorithm for Predicting Regulatory Approval (Success) and to Develop a Standardized Tool to Improve Orphan Drug Portfolio Decision-making

Meta-analysis to identify and evaluate factors associated with regulatory approval of Orphan Drugs (OD) to develop an algorithm for predicting regulatory approval (success) and to develop a standardized tool to improve orphan drug portfolio decision-making. Background and Purpose of the Study: Developed an algorithm (AODI) for predicting probability of regulatory success (PRS) for new orphan drugs after phase II testing has been conducted with the objective of providing a tool to improve drug portfolio decision-making. Methods: Examined 132 studies from recent publications (2005 onwards). Data on safety, efficacy, operational, market, and company characteristics were obtained from public sources. Meta-analysis and metaregressions were used to provide an unbiased approach to assess overall predictability and to identify the most important individual predictors. Results: Found that a simple three-factor model (disease prevalence, clinical trial duration and clinical trial participation) had high specificity for predicting regulatory approval (success). Conclusion: smaller clinical trial participation, shorter clinical trials duration and lower rare disease prevalence were found to be highly associated with the Probability of Regulatory Success (PRS) of orphan drugs.
Reduction of peak sar in human head for handset applications with resistive sheets (R-cards)

In this paper, reduction of peak speciflc absorption rate (SAR) for handsets with monopole type antenna through R-cards is investigated. While the numerical analysis was performed using flnite integration in time domain (FIT), real measurement has been made to validate the simulation results. Both the simulation and measurement results revealed that a minimum SAR Reduction Factor (SRF) of 60% was achieved. The good agreement between the simulation and measurement results has evidenced the efiectiveness of the proposed approach for SAR reduction in human head for handset applications.
A mixture model to detect edges in sparse co-expression graphs with an application for comparing breast cancer subtypes

We develop a method to recover a gene network’s structure from co-expression data, measured in terms of normalized Pearson’s correlation coefficients between gene pairs. We treat these co-expression measurements as weights in the complete graph in which nodes correspond to genes. To decide which edges exist in the gene network, we fit a three-component mixture model such that the observed weights of ‘null edges’ follow a normal distribution with mean 0, and the non-null edges follow a mixture of two lognormal distributions, one for positively- and one for negatively-correlated pairs. We show that this so-called L2 N mixture model outperforms other methods in terms of power to detect edges, and it allows to control the false discovery rate. Importantly, our method makes no assumptions about the true network structure. We demonstrate our method, which is implemented in an R package called edgefinder, using a large dataset consisting of expression values of 12,750 genes obtained from 1,616 women. We infer the gene network structure by cancer subtype, and find insightful subtype characteristics. For example, we find thirteen pathways which are enriched in each of the cancer groups but not in the Normal group, with two of the pathways associated with autoimmune diseases and two other with graft rejection. We also find specific characteristics of different breast cancer subtypes. For example, the Luminal A network includes a single, highly connected cluster of genes, which is enriched in the human diseases category, and in the Her2 subtype network we find a distinct, and highly interconnected cluster which is uniquely enriched in drug metabolism pathways.
Dynamics of Ride Sharing Competition

This paper studies the dynamics of ride-sharing competition. Ride-sharing is modelled as a spatial two-sided market with heterogeneous passengers and drivers, both located on a Salop (1979) circle. The model is simulated to study four aspects of ride-sharing competition:(i) price distribution and dynamics, (ii) strategic pricing, (iii) fixed pricing vs. surge pricing, and (iv) information-sharing. Dynamic platform competition in a spatial setting can generate distinct and persistent bands of fluctuating prices. Space and stochastic luck can mitigate winner-take-all effects in price competition. Platforms adopting fixed pricing can compete with platform with surge pricing provided the former are not set too high. However, space and stochastic luck can also render the outcomes of such competition uncertain. Information sharing eliminates price fluctuations by pooling information on demand. The complexity of ride-sharing implies that the impact of policy interventions cannot be known in advance in some cases.
Heuristic Evaluation of a Gamified Application for Education in Patients with Diabetes

Diabetes is a chronic degenerative disease with increasing number of patients due to unhealthy lifestyles and the lack of information regarding the disease and its correct treatment. In this research work, we propose the design and development of an application to educate patients with diabetes, about their disease, using interactive gamified activities. This paper presents the results obtained from a heuristic evaluation of usability to the application, done by experts, where favorable results were presented in some heuristics such as "Recognition rather than recall". The app presented problems in the heuristic "Help and documentation" that should be improved to ensure a good usability and therefore a friendly experience for users.
Willingness to communicate: A critical overview

Willingness to communicate (WTC) has recently become an important concept across disciplines of Teaching Language (TL) and communication. It has been proposed that pedagogic goals should be to increase learners’ WTC so as to facilitate language learning. Some students seek, while others avoid, communication. Many language teachers have encountered students high in linguistic competence who are unwilling to communicate whereas other students, with only minimal linguistic knowledge, seem to communicate whenever possible. English teachers are highly suggested that they pave the way for the students to move beyond their linguistic or communicative competence as the primary goal of language instruction.
Understanding Hybrid Additive Manufacturing of Functional Devices

The fabrication of electronic components in three-dimensional space is critical for building multifunctional devices. Traditional manufacturing approaches are limited to building planar device configurations. In order to address this limitation, our group has developed a novel hybrid additive manufacturing process that can deposit multi-material patterns on conformal substrates. This direct-write approach involves the combination of microextrusion, pico jet and laser systems. A multi-axis robot was employed to deposit slurries and colloids of conductive materials on different substrates. Fine deposition of the slurries was achieved by tuning the process parameters such as deposition line speed, extrusion pressure, curing temperature, Z-height, laser beam spot, material type and laser intensity. Precise deposition of the medium viscous colloidal liquids was achieved by pico jet unit. Sintering of the micro/nano particles was achieved using a fiber optic laser system and micro wave furnace. The conductance of the deposited electrical trace varied based on the material type and trace cross-section area. This research lays the foundation to build a three-dimensional hierarchy of electronic components. By embedding components at various depths and orientations complex electronic circuitry with versatile material compositions and design flexibility can be fabricated in situ.
Theatre Audience on Stage: Three Experiments Analysing the Effects of Survey Design Features on Survey Response in Audience Research

In this article a two-stage method for the on site collection of data on a theatre audience is introduced and a number of techniques, acknowledged to reduce unit nonresponse in household surveys, are applied. More particularly, three experimental designs are used to test the impact of a number of survey design features on response rates and sample composition. The various survey design features included are the way in which the sample units are contacted on site, the use of incentives and the specific sequence of the questions in the questionnaire. Only type of c ontact proved to have a significant impact on the response rate. A selection bias with regard to age and topic salience was found. Suggestions for further research are presented.
Accelerating Synchronization Using Moving Compute to Data Model at 1,000-core Multicore Scale

Thread synchronization using shared memory hardware cache coherence paradigm is prevalent in multicore processors. However, as the number of cores increase on a chip, cache line ping-pong prevents performance scaling for algorithms that deploy fine-grain synchronization. This article proposes an in-hardware moving computation to data model (MC) that pins shared data at dedicated cores. The critical code sections are serialized and executed at these cores in a spatial setting to enable data locality optimizations. In-hardware messages enable non-blocking and blocking communication between cores, without involving the cache coherence protocol. The in-hardware MC model is implemented on Tilera Tile-Gx72 multicore platform to evaluate 8- to 64-core count scale. A simulated RISC-V multicore environment is built to further evaluate the performance scaling advantages of the MC model at 1,024-cores scale. The evaluation using graph and machine-learning benchmarks illustrates that atomic instructions based synchronization scales up to 512 cores, and the MC model at the same core count outperforms by 27% in completion time and 39% in dynamic energy consumption.
The antioxidant capacity of subjects with Gilbert syndrome

Unterschiedliche Studien konnten die antioxidative Wirkung von Bilirubin und dessen Potential fur die Risikominderung von oxidativen Stress bedingten Erkrankungen belegen. Daher wird angenommen, dass Personen mit erhohten Plasmabilirubinwerten (= Gilbert Syndrome, GS) im Vergleich zu gesunden Personen eine geringere Empfanglichkeit gegenuber oxidativer Schadigung und einhergehenden Krankheiten besitzen. Die aktuelle Fall-Kontroll-Studie, welche vom Osterreichischen Wissenschaftsfonds (FWF, Projektnummer P21162) gefordert wurde, hatte das Ziel den antioxidativen Status von Personen mit GS, einer leichten Hyperbilirubinamie im direkten Vergleich zu gesunden Kontroll-Personen zu untersuchen. Dafur wurden 104 Personen rekrutiert und 76 von ihnen aufgrund ihrer unkonjugierten Serum-Bilirubinkonzentrationen in eine GS- und eine alters- und geschlechtsangepasste gesunde Kontroll-Gruppe unterteilt. Um die antioxidative Kapazitat zu messen, wurden die Methoden ORAC und FRAP angewandt und um den oxidativen Stress zu beurteilen, wurde der Biomarker MDA untersucht. Die Ergebnisse waren unterschiedlich, was moglicherweise auf die verschiedenen Mechanismen der Methoden und deren unterschiedliche Affinitat zu Bilirubin zuruckzufuhren ist. Wahrend ORAC und MDA keine Unterschiede zwischen der GS- und Kontroll-Gruppe aufwiesen, waren die FRAP Werte bei den GS Personen signifikant erhoht und zeigten eine positive Korrelation mit unkonjungiertem Bilirubin (UCB). Dies weist auf eine gesteigerte antioxidative Kapazitat bei Personen mit GS hin, die auf eine erhohte UCB Konzentration zuruckzufuhren ist. Die vorliegenden Ergebnisse stutzen daher die Hypothese, dass Menschen mit erhohten UCB Werten einen besseren Schutz gegenuber oxidativem Stress und dessen Folgeerkrankungen besitzen. Eine signifikant positive Korrelation zwischen ORAC und Harnsaure in der Kontroll-Gruppe zeigt das Potential von Harnsaure im Plasma, was sich in der Gruppe der GS zu Gunsten von erhohtem UCB verschiebt.
In vivo radio imaging studies on designed swelling gastro retentive drug delivery system

The objective of the study was to correlate the results of the in vitro release of a previously reported swelling gastro retentive drug delivery system with the in vivo radio imaging in rabbits. Tablets containing metformin hydrochloride evaluated for in vitro drug release profile showed drug release up to 14 h. Tablets containing barium sulfate was administered to rabbits after overnight fast. Radio imaging study showed that swelling matrix tablets remains in the upper small intestine of rabbit for the period of 10 h and it correlates with ex vivo retention time of 10 h.
Arbitrage opportunities between NYSE and XETRA?: A comparison of simulation and high frequency data

This paper investigates both the no-arbitrage condition and the efficiency of financial markets by comparing two stock markets: the New York Stock Exchange (NYSE) and the German Exchange Electronic Trading System (XETRA). We analyze German stocks that are traded simultaneously at both exchanges using high frequency data for XETRA, the NYSE, and the foreign exchange rates. Converting Euro-prices into Dollar-prices reveals possibilities to explore the efficiency as well as arbitrage opportunities of these two stock markets using the phenomenon of stock price clustering. We see the result of differing extents of clustering on both exchanges, thus violating the no-arbitrage condition. We propose a trading strategy that exploits these differences. Furthermore, we compare our empirical findings with the results we obtain from simulating financial markets and conclude that simulations based on the no-arbitrage condition are not consistent with our empirical observations.
Negative-Temperature Onsager Vortex Clusters in a Quantum Fluid

Turbulence in classical fluids is a ubiquitous non-equilibrium phenomenon, yet a complete theoretical description for turbulent flow remains a challenging problem. A useful simplification for ideal two-dimensional (2D) fluids is to describe the turbulent flow with long-range-interacting point vortices, each possessing quantised circulation. In 1949, Onsager applied statistical mechanics to determine the equilibria of this model. He showed that at sufficiently high energies, like-circulation vortices preferentially aggregate into large-scale clusters, and are characterised by a negative absolute temperature. Onsager's theory has been highly influential, providing understanding of diverse quasi-2D systems such as turbulent soap films, guiding-centre plasmas, and self-gravitating systems. It also predicts the striking tendency of 2D turbulence to spontaneously form large-scale, long-lived vortices -- Jupiter's Great Red Spot is a well-known example. However, Onsager's theory doesn't quantitatively apply to classical fluids where vorticity is continuous, and experimental systems demonstrating Onsager's point-vortex statistical mechanics have remained elusive. Here we realise high energy, negative-temperature vortex clusters in a uniform superfluid Bose-Einstein condensate. Our results confirm Onsager's prediction of negative temperature clustered phases of quantum vortices, and demonstrate the utility of point-vortex statistical mechanics in 2D quantum fluids. This work opens future directions for the study of turbulent dynamics and we anticipate exploring the entire phase diagram of 2D quantum vortices, including the formation of clusters from 2D quantum turbulence.
Challenging Environments for Traffic Sign Detection: Reliability Assessment under Inclement Conditions

State-of-the-art algorithms successfully localize and recognize traffic signs over existing datasets, which are limited in terms of challenging condition type and severity. Therefore, it is not possible to estimate the performance of traffic sign detection algorithms under overlooked challenging conditions. Another shortcoming of existing datasets is the limited utilization of temporal information and the unavailability of consecutive frames and annotations. To overcome these shortcomings, we generated the CURE-TSD video dataset and hosted the first IEEE Video and Image Processing (VIP) Cup within the IEEE Signal Processing Society. In this paper, we provide a detailed description of the CURE-TSD dataset, analyze the characteristics of the top performing algorithms, and provide a performance benchmark. Moreover, we investigate the robustness of the benchmarked algorithms with respect to sign size, challenge type and severity. Benchmarked algorithms are based on state-of-the-art and custom convolutional neural networks that achieved a precision of 0.55 and a recall of 0.32, F0.5 score of 0.48 and F2 score of 0.35. Experimental results show that benchmarked algorithms are highly sensitive to tested challenging conditions, which result in an average performance drop of 0.17 in terms of precision and a performance drop of 0.28 in recall under severe conditions. The dataset is publicly available at this https URL.