Miracle-dz/UniFAR-FTUs huggingface dataset:
Angiogenesis in skeletal and cardiac muscle.

The role that mechanical factors deriving from blood flow play in the growth of blood vessels was most probably mentioned for the first time by John Hunter in his “Treatise on the Blood Flow, Inflammation and Gunshot Wounds,” published in 1794. Hunter was an anatomist who kept a deer herd in Richmond Park. He described enlargement of the carotid arteries in deer, coincident with the development of new antlers, which is known to involve intensive vascular growth. Almost a century later, Thoma (1) demonstrated that ontogenetic growth of vessels is caused by a combination of hemodynamic forces — increased blood flow, blood pressure, and wall tension — and mechanical stretch of vessels owing to the growth of the surrounding tissue. The importance of flow for capillary development was later shown in tadpole tails by Clark (2), who exposed to observation under the microscope the same site over many days, and in wound healing in the rabbit ear chamber (3). Today, the roles in angiogenesis of mechanical factors such as shear stress, circumferential stress, stretch, or physical forces owing to modification of the extracellular matrix are widely studied in tissue culture.
University of Birmingham Understanding the effect of gamification on learners with different personalities

: Gamiﬁcation has been shown to enhance the motivation of learners in online courses. However, learners respond differently to gamiﬁcation depending on their personalities. For this reason, it has been suggested to build a learner model that would enable a system to match gamiﬁcation elements to learners’ personalities. To do this, we need to understand the relationship between gamiﬁcation and personalities. Thus, two versions of a learning website have been built: one with gamiﬁcation elements and the other without these elements. We measured learners’ motivation, knowledge gain, and satisfaction in both versions. The results conﬁrm the beneﬁt of gamiﬁcation overall in enhancing learners’ motivation. However, the knowledge gain of learners was worse in the gamiﬁed version. The results vary between personalities. This ﬁnding may be explained by the optional nature of the chat and the learners’ tendency to take the initiative. Further study of more gamiﬁcation elements and compulsory chat might be considered.
User Adaptation to cybersickness in Virtual Reality: a Qualitative Study

Virtual reality has been gaining attention due to its capacity to immerse users into virtual environments. However, it has been reported that many users experience physical discomfort caused by cybersickness. Despite the increasing scholarly and practical attention paid to virtual reality, the influence of cybersickness on user experience is unclear, and research findings are inconsistent. Some researchers argue that cybersickness negatively influences user experience by significantly decreasing enjoyment and perceived presence, yet some studies reveal insignificant associations between cybersickness and user experience. The conflict between such results remains unsolved. Therefore, this study seeks to provide more a nuanced understanding of how users experience cybersickness. Toward this end, this research employs a qualitative approach using the interview method and derived factors associated with cybersickness triggers, symptoms and user adaptation. Based on the findings of this research, we propose a time-varying cybersickness model that accounts for the interplay between cybersickness triggers, symptoms, and user adaptation.
Uncovering the Privacy Paradox: the Influence of Distraction on Data Disclosure Decisions

The discrepancy between individuals’ intention to disclose data and their actual disclosure behaviour is called the privacy paradox. Although a wide range of research has investigated the privacy paradox, it remains insufficiently understood due to mental processes’ role in decision-making being mostly neglected. This research-in-progress provides a theoretical concept that examines the cognitive processes underlying data disclosure decisions to provide a better understanding of the privacy paradox. We apply the Elaboration Likelihood Model (ELM), which suggests that the mental shortcuts that individuals take when making their actual data disclosure decision, which differs from their selfreported data disclosure intention, cause the privacy paradox. We propose a two-step, mixed method approach comprising a survey and an online experiment to empirically explore the intended and actual data disclosure. The study takes theoretical and methodological issues in prior literature into account and enhances our understanding of individuals’ paradoxical data disclosure behaviour from a psychological point of view.
Static Analysis for Worst-Case Battery Utilization

Lithium-ion batteries demonstrate two kinds of nonlinear behavior: rate capacity effects and recovery effects. These effects are dual to each other and show the dependency between the power consumption profile and battery utilization, i.e., the fraction of the battery charge that can be withdrawn.We propose a static analysis for computing the lower bound of battery utilization based on the Kinetic Battery Model (KiBaM), an analytical model capturing nonlinear battery behavior. Our method does not depend on the battery charge level and can be composed with other flow analyses and model checking techniques for improved accuracy. We propose a modification to the worklist algorithm for totally ordered semilattices with computable fixedpoints of transfer functions, which is necessary for computing our analysis. We prove the termination and correctness of our algorithm and introduce a nonforgetful extension to it for speeding up convergence. We implement the battery-utilization in the software verification tool CPAchecker by encoding the analysis together with our algorithm in a relaxed instance of a configurable program analysis (CPA). Our experiments show that the convergence speed up is sometimes achievable, but does not necessarily lead to a performance improvement.
3D Point Clouds in PostgreSQL/PostGIS for Applications in GIS and Geodesy

Besides the common approach of an exclusively file based management of 3D point clouds, meanwhile it is possible to store and process this special type of massive geodata within spatial database systems. Users benefit from the general advantages of database solutions and especially from the potentials of a combined analysis of original 3D point clouds, 2D rasters, 3D voxel stacks and 2D and 3D vector data in order to gain valuable geoinformation. This paper describes the integration of 3D point clouds into an open source PostgreSQL/PostGIS database using the Pointcloud extension and functions of the Point Data Abstraction Library (PDAL). The focus is on performing three-dimensional spatial queries and the evaluation of different tiling methods for the organization of 3D point clouds into table rows, regarding memory space, performance of spatial queries and effects on interactions between point clouds and other GIS features within the database. A new approach for an optimized point cloud tiling, considering the individual geometric characteristic of a 3D point cloud, is presented. The results show that an individually selected storage structure for a point cloud is crucial for low memory consumption and high-performance 3D queries in PostGIS applications, taking account of its three-dimensional spatial extent and point density.
How do Large Stakes Influence Bitcoin Performance? Evidence from the MT.Gox Liquidation Case

Bitcoin as the first and still most important decentralized cryptocurrency has gained wide popularity due to the steep rise of its price during the second half of 2017. Because of its digital nature, Bitcoin cannot be valuated exclusively with fundamental approaches, which is why factors such as investor sentiment have become a common alternative to capture its performance. In this work, we studied whether and how the sale of Bitcoins from the insolvency assets of Mt.Gox, which represent about 1.1% of the current global total, relates to Bitcoin price movements. We used social media sentiment analysis of Twitter data to examine how investors are influenced in their decision to buy or sell Bitcoin when confronted with the trade actions of Nobuaki Kobayashi, the trustee in charge of the Mt.Gox case. We built a vector error correction model to analyse the long-run relationship between cointegrated variables. Our analysis confirms the positive association of Bitcoin performance with positive Twitter sentiment and tweet volume and the negative association with negative sentiment. We further found empirical evidence that Mt.Gox selloff events have a lasting negative impact on the Bitcoin price and that we can measure this effect by Twitter sentiment and tweet volume.
Damage Feature Extraction and Parameter Characterization of Large Generator Stator Insulation Based on Lamb Waves Detection Method

Insulation degradation assessment plays a vital role in the life management of power equipment. Due to the merits of Lamb wave to Structural Health Monitoring (SHM) of composite, the Lamb wave-based damage detection method shows a potential solution for the evaluation of the insulation condition of stator windings in high-voltage rotating machines. The main objective of this paper is to study damage characteristic parameters of large generator stator insulation based on Lamb wave detection method. The fundamental asymmetric mode A0 wave packet of Lamb wave was used to detect the stator insulation damage. Firstly, the damage characteristic parameters are extracted from time domain, frequency domain and fractal dimension respectively. Hilbert transform (HT), power spectral density (PSD) analysis, fast Fourier transform (FFT) and wavelet fractal dimension (WFD) methods were used to extract features of Lamb wave signals received in stator insulation damage detection and the change rule of damage characteristics were explored. Then, the correlation between characteristic parameters and damage size were concluded based on the partial least square (PLS) multivariate statistical analysis approach. Finally, detection of four typical insulation damages, i.e. void, delamination, puncture, and crack are carried out by FEM simulations and experiments for validation. These damage feature extraction methods and characteristic parameters of stator insulation can provide a valuable reference information for the condition assessment of large generator stator insulation furtherly.
Double dissociation of memory capacities after bilateral occipital-lobe or medial temporal-lobe lesions.

Memory for recently encountered information can be reflected in conscious recall and recognition of that material, or in facilitated reprocessing of that material, an effect known as repetition priming. Repetition priming may be perceptual (form-based) or conceptual (meaning-based). A patient with bilateral occipital-lobe lesions (L.H.) and a patient with bilateral medial-temporal lobe lesions (H.M.) showed a double dissociation between visuoperceptual priming (impaired in L.H. and intact in H.M.) and visual recognition memory (intact in L.H. and impaired in H.M.). L.H. showed intact conceptual priming for visually presented words; his pattern of impaired visuoperceptual priming and intact conceptual priming is the reverse dissociation to that observed in prior studies of patients with Alzheimer's disease, in whom occipital cortices are relatively spared. These double dissociations suggest that a memory system localized to the occipital lobe mediates visuoperceptual priming effects, and that this system is independent of neural circuits mediating conceptual priming effects, and independent of the limbic-diencephalic system supporting conscious recognition of recently encountered information.
Adoption of Machine Learning Techniques to Perform Secondary Studies: A Systematic Mapping Study for the Computer Science Field

Context: Secondary studies such as systematic literature reviews (SLR) have been used to collect and synthesize empirical evidence from relevant studies in several areas of knowledge, including Computer Science. However, secondary studies are time-consuming and require a significant effort from researchers. Goal: This paper aims to identify contributions derived from the adoption of machine learning (ML) techniques in Computer Science SLRs. Method: We performed a systematic mapping study querying well-known repositories and first found 399 studies as a result of applying the search string in each of the selected search engines. Following the research protocol, we analyzed titles and abstracts and applied inclusion, exclusion and quality criteria to finally obtain a set of 17 studies to be further analyzed. Results: The selected papers provided evidence of relevant contributions of the machine learning usage in performing secondary studies. We found that ML techniques have not been applied yet to all the stages of a SLR. Typically, the preferred stage to apply ML in an SLR is the study selection phase (typically the initial phase). For assessing the effectiveness of ML support while performing SLRs, researchers have provided a comparison either across different ML techniques tested or between manual and ML-supported SLRs. Conclusion: There is significant evidence that the use of machine learning applied to SLR activities (especially the study selection activity) in Computer Science is feasible and promising, and the findings can be potentially extended to other research fields. Also, there is a lack of studies exploring ML techniques for other stages than study selection.
Toward an overview of ship collision avoidance maneuvers approaches in compliance with COLREG convention

For decades, the calculus and optimization of ship’s navigation without collision risks presents a major issue for the maritime domain. The continuous increasing of maritime traffic rends the mitigation of this risk a challenge for the scientific researches’ community. The safety researchers are called to cope with the complexity of safe ship routing optimization. Several solutions are proposed to enhance maritime safety. The topic is covered using different approaches. These approaches consist of studying the conflict problem as an optimal control problem with state constraints, calculating risks of collisions in ocean navigation by stochastic methods and also, optimization of marine navigation using evolutionary and genetic algorithms. This paper is focused on a comparative study of these approaches. In order to highlight the advantages of each approach as its limitations.
Recursive Pedagogy: Automatic Question Generation using Real-time Learning Analytics

In this paper we introduce the notion of Recursive Pedagogy, which is a computer-supported approach to teaching and learning in which students solve problems assigned by an instructor using a Problem Solving Learning Environment, and their attempts to solve that problem are then used by the system to create new kinds of problems to help them build high level cognitive skills. The Recursive Pedagogy approach generalizes an approach we introduced earlier: the Solve-Then-Debug (STD) pedagogy to teach coding. In STD, students write a program given its description and, when their code passes all of the unit tests, they debug a sequence of incorrect programs submitted by their peers, starting with those with the most common errors. In this paper, we discuss two new Recursive Pedagogies that we have implemented in the Spinoza Python Tutor: Solve-Then-Critique-Correct-Solutions shows students correct programs from their peers and asks them to critique the code relative to a rubric; Solve-Then-Analyze-Unit-Tests shows students results of a set of unit tests and asks them to describe the probable error based on the unit test results. After students submit their Recursive Pedagogy analysis of a peer’s attempt, they are shown other students’ analyses of that attempt and are asked to select the best one. Recursive Pedagogies are designed to build skills in problem-solving, debugging, testing, and composing high quality solutions. Moreover, they can be used in a flipped class and they keep all students engaged in either problem solving or one of the other skills.
Optimized Social Explanation for Educational Platforms

Recommender Systems have became extremely appealing for all technology enhanced learning researches aimed to design, develop and test technical innovations which support and enhance learning and teaching practices of both individuals and organizations. In this scenario a new emerging paradigm of explainable Recommander Systems leverages social friend information to provide (social) explanations in order to supply users with his/her friends’ public interests as explained recommendation. In this paper we introduce our educational platform called “WhoTeach”, an innovative and original system to integrate knowledge discovery, social networks analysis, and educational services. In particular, we report here our work in progress for providing “WhoTeach” environment with optimized Social Explainable Recommandations oriented to design new teachers’ programmes and courses.
A Clinical Decision Support System based on an Unobtrusive Mobile App

Clinical decision support systems typically rely on medical records and information collected in the doctor’s office. We propose a clinical decision support system that uses data collected from patients continuously and in an unobtrusive manner. The system uses data collected from a mobile app installed on the patient’s device (such as a mobile phone, smart-watch etc). The app collects data without user interference and combines it with conventional medical records. Our system uses machine learning methods to extract meaningful insights from the data. The output from the learning process is then presented to the doctor in a clear and meaningful fashion on a web based platform. This system can be used to assist effective treatment selection, enable early diagnosis, trigger alarms in case of an emergency and provide a tool for disease monitoring. We describe our clinical decision support system and directions for future work.
Surface Modification of Fumed Silica by Dry Silanization for PP-based Dielectric Nanocomposites

Novel nanocomposites for dielectric applications based on a polypropylene (PP) blend filled with nanosilica are developed in the frame of the European ‘GRIDABLE’ project. A systematic study of the influence of surface modification of the nanosilica on the dielectric properties of the PP/silica blend was performed. The main goal of this investigation was to modify the chemical composition of the silica surface, which is expected to improve the charge trapping properties of the nanocomposites. For the modification of the silica surface, a “green” approach was utilized: a dry silanization method, which is performed without the need of a solvent. Eight different silanes were investigated in this study, which are categorized into three different groups: I)Aliphatic silanes with a different number of alkoxy groups II)Hydrocarbon silanes containing delocalized electron clouds III)Polar silanes containing hetero elements (nitrogen, sulfur or oxygen) The results of the thermogravimetric analysis (TGA) show higher weight loss of the modified silicas in comparison to the unmodified one. This indicates that the dry process is an effective method to perform silica surface modification using alkoxysilanes. The charge trapping properties were studied by Thermally Stimulated Depolarization Current (TSDC) measurements. The obtained TSDC results show that the trap density peak is not significantly shifted in temperature when the silica is modified with functional groups elementally similar to the polymer matrix. However, their incorporation influences the traps density and suppress the deeper traps occurring near the range of the melting temperature of PP. When the silica surface is modified with a precursor containing a hetero element, it has an effect on both, trap level depth as well as density. Depending on the type of the hetero element (sulfur, nitrogen, oxygen), the trap depth shifted to higher temperatures, and the trap density decreased to significantly lower levels. Nitrogen appears to have the strongest effect on the charge trap properties. All these first stage of results show that incorporation of modified nanosilica into a PP matrix seems to be a promising approach to tailor its electric properties. Further development of these composites would lead to benefits for high-voltage cable and capacitors applications.
Review of Intense Terahertz Radiation from Relativistic Laser-Produced Plasmas

Recent years have witnessed a rapid development of sources for intense terahertz (THz) pulses and an increasing number of their applications. The advent of laser pulses with intensities in excess of $10^{18}$ W/cm2 offers an immense opportunity for generating extremely energetic THz pulses from laser-produced plasmas. This paper will review recent progress in the study of THz radiation generated from relativistically intense laser–plasma interactions, mainly focusing on the underlying THz generation models under different laser and plasma parameters, albeit still not yet fully explored. Various THz generation scenarios are discussed associated with laser-excited waves in plasmas, the transport and emission of laser-accelerated electrons, and structured targets.
Millimeter-Wave Circularly Polarized Array Antenna Using Substrate-Integrated Gap Waveguide Sequentially Rotating Phase Feed

A substrate-integrated gap waveguide (SIGW) sequentially rotating phase (SRP)-fed 2 × 2 circularly polarized (CP) patch antenna array is presented for the wideband millimeter-wave application. The antenna element is composed of a truncated square patch, SIGW feedline with three layers of printed circuit board (PCB), and another layer of PCB with an air hole for supporting radiation patch. Each radiation patch is driven by an underneath coupling slot etched on SIGW. A wide axial-ratio (AR) bandwidth is achieved by deploying a 2 × 2 SIGW SRP feed network. Moreover, simple calculation expressions of the characteristic impedance and guide wavelength of SIGW are given for the design of SRP feed network. The fabricated prototype shows an impedance bandwidth of 25.6% covering 22.2–28.8 GHz, 3 dB AR bandwidth of 19% from 22.8 to 27.6 GHz, and peak gain of 11.53 dBic.
A Broadband Dual-Polarized Antenna With Low Profile Using Nonuniform Metasurface

A novel dual-polarized antenna with very low profile using nonuniform metasurface is presented in this letter. The proposed antenna is composed of two dipoles placed crosswise, of which the radiators are formed by four square metal plates. The antenna is designed to work at the lower frequency band (0.69–0.96 GHz) of base station. By adding the metasurface atop the dipole antennas, the overall height of the antenna together with metasurface can be reduced to 0.1 λ0 (wavelength of the center frequency), which is much lower compared to the normal height of a dipole antenna (0.25 λ0). Simulated and measured results show that the height reduction is achieved without degrading the performance of the antenna, making the proposed antenna a competitive candidate for base-station application.
Dynamic model of power MOSFET for SPICE circuit simulation

In this paper, dynamic SPICE model of the power MOSFET is presented. This model is physics-based using empirical parameters extracting from the static and dynamic datasheet or experimental characteristics. In addition, the proposed model uses only one "effective" voltage source to describe the reduction of the drain current at high gate voltages. The interelectrode capacitances are modeled using voltage dependent equations to capture their electrical behavior with gate and drain voltage variations. The proposed model is simple requiring reduced number of parameters without any problem of convergence. It will be shown that this model allows fast and accurate simulation results. Finally, comparison of simulated, measured and datasheet data will be given for validation of the model.