Miracle-dz/UniFAR-FTUs huggingface dataset:
Flow-Bounded Trajectory-Scaling Algorithm for Hydraulic Robotic Manipulators

On-line methods for trajectory scaling have focused on torque or acceleration bounded minimum time trajectories, while other system constraints have received little attention. For hydraulic systems, volumetric flow rate of the supply unit establishes a critical constraint, that has been neglected in control design. Consequently, commercial solutions for robotic control of hydraulic manipulators are typically limited to a compromise of a slower constant endpoint velocity, that can be achieved in any operating point without violating the constrained flow rate. However, with real-time analysis of the required volumetric flow rate, the desired trajectories can be executed much faster without violating the flow rate constraint or losing control accuracy. This study proposes an on-line method for trajectory scaling to perform predetermined trajectories in minimum time. Essentially, the method scales velocity along the trajectory to maintain achievable velocity at all times. The proposed method is capable of enforcing a global volumetric flow limit, whether it is constant or time-varying. The method is validated with simulations and experiments with a real hydraulic robotic manipulator. Experimental results show a very significant improvement in the trajectory tracking control, where the tracking error is reduced from 461 mm to 73 mm on a square trajectory.
SEMIOTIC ANALYSIS IN INSTAGRAM LOGO

This article investigated about semiotic that found in instagram logo. This article used Instagram, because many people nowadays used Instagram to express theirselves. The aim of this study is to find out the connotative and denotative meaning through the instagram. The researcher used John theory to reveal this study. In this research, the researcher applies the qualitative descriptive method. The method of collecting data is observation. The steps are choosing the logo that would be analyzed. The result is there are twelve data that has meaning from different logo on Instagram. They all have different functions based on the logo. The conclusion is Instagram as a social media app that allows users to share photos and videos, add captions, edit filters, engage with others, and explore the world by using this application.
Novel Materials in the Preparation of Edible Films and Coatings—A Review

The development of edible films and coatings has seen remarkable growth in recent decades and is expected to have an important impact on the quality of food products in the coming years. This growth is attributed to the increasing knowledge of edible films and edible coating technology, as well as advances in material science and processing technology. Packaging is used in order to reduce synthetic packaging and can play a role as an eco-friendly biodegradable package or a protective coating on the food surface. A large amount of bio-based polymers have been used in the production of edible films and coatings. Novel sources of edible materials, as well as the novel processing techniques, are a subject of great interest due to their promising potential as innovative food packaging systems. This paper presents the concept and potential for application of new film-forming materials and management of food wastes from the fruit and vegetable industry, which can encounter problems in appropriate disposal. It summarizes the extensive knowledge about the new film-forming materials such as plant residues, flours and gums to show their protective effectiveness and suitability in various types of foods.
Tracking Hurricane Dorian in GDELT and Twitter

GDELT is a machine coded database of events that uses both foreign and domestic news feeds and contains over a quarter of a billion worldwide event records categorized into three hundred categories. This paper compares the spatial footprint of GDELT event mentions with those of event related geotagged tweets for Hurricane Dorian in the South-Eastern United States. Besides examining event related GDELT and Twitter data abundance, the study relates areas of elevated GDELT news and tweeting activities to the locations of the hurricane track over a six-day period, and statistically analyzes distances between daily GDELT event mentions and tweets, and the hurricane center on different days. It assesses the potential role of the geographic coverage of the cone in hurricane prediction maps on the level of event related news and tweeting activities. The study also discusses pros and cons of both data sources for event tracking with regards to data abundance, spatial and temporal resolution, and thematic accuracy.
A multistate high gain antenna based on metasurface

A multi‐state high gain antenna based on metasurface is proposed. The antenna is composed of two stacking layers and a ground plane. The metasurface is constituted by two layers with the same size. And both of the two layers contain a copper patch array which is formed by 4 × 4 square copper cells uniformly distributed along x and y directions. The metasurface antenna is excited by the aperture coupled structure. The structure is consists of an anomaly microstrip line and a narrow slot etched in the ground plane. Genetic algorithm (GA) is adopted to optimize all the parameters and obtain the best performance of the metasurface antenna. By appropriately choosing the dimensions of the antenna, the proposed antenna can be achieved with the impedance bandwidth (RL≥10 dB) of about 340 MHz (7.8% at 4.36 GHz), 180 MHz (3.6% at 5.02 GHz), and 2800 MHz (41.1% at 6.81 GHz). The peak gain of the proposed antenna is 10.1dBi, 6.9 dBi, and 10.5dBi at 4.26 GHz, 5 GHz, and 7 GHz. In addition, the proposed metasurface antenna can work in multistate, which makes it an excellent candidate for practical applications.
Thermoeconomics, A Thermodynamic Approach to Economics, Third Edition, Chapter 1 Introduction

This third edition of the book stems from work by the author published in Energy Economics, the International Journal of Exergy and follow-up working papers. Topics covered include the gas laws, the distribution of income, first and second laws of thermodynamics, economic processes, elasticity, entropy and utility, production processes, reaction kinetics, empirical monetary analysis of UK and USA economies, interest rates, bonds, yield curves, yield spread, unemployment, entropy maximisation and the cycle, empirical analysis of world energy resources and climate change as factors affecting economic output, and lastly a discussion of sustainability
Reverse Engineering Website Navigation Using an Information Architecture Approach (Case Study: Kanal Pengetahuan Universitas Gadjah Mada)

In its development, the website of Kanal Pengetahuan UGM is built through a practical approach, so there are many shortcomings in defining the need and strategy information for the needs of the service business. As for the current problem, i.e., poor navigation related to the definition of primary content and links in the navigation menu. This is causing the granularity of information that makes users difficult to understand and find the information they need. This research aims to do reverse engineering of website navigation using the information architecture method to investigate the way how users to find and interact with information content from the website. The main contribution of this research is the analysis of the result of card sorting has directed the research to gain insight into how the users in categorizing information on the website. As a result, in the current system, there are only 3 categories that have not yet represented all the information provided by the website, so that research conducted with 102 participants produced a new navigation proposal with 4 new categories that fit the user’s mental model.
A Phenomenological Study of an Effective English Language Classroom from the Iranian EFL Learners’ Perspectives at the Tertiary Level

Since little is known about how EFL learners experience and understand effective English language classroom, this study was an attempt to find out about the Iranian EFL learners’ experiences of an effective English language classroom at the tertiary level. The design of the current research drew on a phenomenological study in which the lived experiences of EFL learners concerning the effective English language classroom were explored. The primary data collection method was in-depth interviewing with seven Ph.D. candidates (two males and five females) who had been studying English for years and were selected through purposive sampling. The data were coded and explicitated using Colaizzi’s seven-step coding strategy. The three major resulting themes were a) Teacher characteristics, b) Classroom interaction, and c) Class performance. In addition, key findings from the study suggest that two elements of personal experiences and personal beliefs constitute effective English language classroom from the Iranian EFL learners’ perspectives. Recommendations are provided for EFL researchers as the current study reveals.
When Status Broke the ‘Color Line’. Capitalism, Labour and Conflicts on Mexican Migration in Arizona (1960s–1970s)

This paper investigates the conflicts between the conservative elite, workers’ organizations, and Mexican migrants in Arizona in the 1960s and 1970s when major confrontations occurred around the so-called ‘undocumented problem’. Through a labour history approach and the use of primary sources, this study returns the voice of seasonal workers, governors and Arizona’s Republican electoral body, union organizers and citizens, workers and anti-union associations. This article investigates, a) the role played by conservative political groups and southwestern capitalists in spreading anti-union and anti-migrant sentiments; b) the relation between status and contract in agricultural work; c) the recruitment and working conditions imposed on migrant workers; d) the political confrontation and conflicts that emerged between unions and migrant workers’ organizations in Arizona’s agricultural labour regime. In conclusion, although transnational organizing efforts led to success in labour confrontations, the exclusionary political practices against undocumented workers–like the wet line – resulted in the fragmentation of the ‘color line’ that ultimately exacerbated the frictions between farmworker unions and migrant workers. Resumen Este articulo investiga los conflictos entre la elite conservadora, las organizaciones de trabajadores y los migrantes mexicanos en Arizona en las decadas de 1960 y 1970, cuando se produjeron importantes enfrentamientos en torno al llamado ‘problema de indocumentados’. Mediante un enfoque de historia laboral y el uso de fuentes primarias, el articulo da voz a los trabajadores temporales, gobernadores y al cuerpo electoral republicano de Arizona, organizadores sindicales y ciudadanos, trabajadores y asociaciones antisindicales. El estudio investiga el papel desempenado por los grupos politicos conservadores y los capitalistas del sudoeste en la difusion de los sentimientos anti-sindicales y migratorios; la relacion entre estatus y contrato en el trabajo agricola; las condiciones de reclutamiento y trabajo impuestas a los trabajadores migrantes; la confrontacion politica y los conflictos surgieron entre los sindicatos y las organizaciones de trabajadores migrantes en el regimen laboral agricola de Arizona. En conclusion, si bien los esfuerzos de organizacion transnacionales condujeron al exito en las confrontaciones laborales, las practicas politicas excluyentes contra los trabajadores indocumentados–como la wet line–resultaron en la fragmentacion de la ‘linea de color’ que finalmente exacerbo las fricciones entre los sindicatos de trabajadores agricolas y los trabajadores migrantes. Palabras clave: Mexico; Arizona; migracion; trabajo; sindicato; conservador
On the detectability of density change in steam-assisted gravity drainage reservoirs using muon tomography

Muon tomography is applied to realistic density models of a steam-assisted gravity drainage (SAGD) reservoir at 1.25 and 5 years after initial reservoir production. Forward models of muon count and opacity based on the density models are computed, as well as inverse models of the synthetic muon observations for various simulated detector arrays. The results demonstrate that both phases of reservoir development, namely the rising phase and the spreading phase, can be resolved by muon detectors placed 30 m below the bitumen reservoir at 230 m total vertical depth. The total mass change in the reservoir was recovered from the inversion model and differs from the true mass change by 20%–29%. The spatial distribution of density change shows very good agreement in the horizontal direction, while the vertical is less well constrained in this modeled sensor array configuration. The inverse models provide improved insights into reservoir depletion patterns and indicate muon tomography to be an applicable tool for continuous reservoir monitoring. The numerical modeling approach developed herein is able to model a wide range of SAGD reservoir geometries and detector arrays toward planning of optimized monitoring solutions.
n-Gage

The study of student engagement has attracted growing interests to address problems such as low academic performance, disaffection, and high dropout rates. Existing approaches to measuring student engagement typically rely on survey-based instruments. While effective, those approaches are time-consuming and labour-intensive. Meanwhile, both the response rate and quality of the survey are usually poor. As an alternative, in this paper, we investigate whether we can infer and predict engagement at multiple dimensions, just using sensors. We hypothesize that multidimensional student engagement level can be translated into physiological responses and activity changes during the class, and also be affected by the environmental changes. Therefore, we aim to explore the following questions: Can we measure the multiple dimensions of high school student's learning engagement including emotional, behavioural and cognitive engagement with sensing data in the wild? Can we derive the activity, physiological, and environmental factors contributing to the different dimensions of student learning engagement? If yes, which sensors are the most useful in differentiating each dimension of the engagement? Then, we conduct an in-situ study in a high school from 23 students and 6 teachers in 144 classes over 11 courses for 4 weeks. We present the n-Gage, a student engagement sensing system using a combination of sensors from wearables and environments to automatically detect student in-class multidimensional learning engagement. Extensive experiment results show that n-Gage can accurately predict multidimensional student engagement in real-world scenarios with an average mean absolute error (MAE) of 0.788 and root mean square error (RMSE) of 0.975 using all the sensors. We also show a set of interesting findings of how different factors (e.g., combinations of sensors, school subjects, CO2 level) affect each dimension of the student learning engagement.
Media Attention vs. Sentiment as Drivers of Conditional Volatility Predictions: An Application to Brexit

Using data on international, on-line media coverage and tone of the Brexit referendum, we test whether it is media coverage or tone to provide the largest forecasting performance improvements in the prediction of the conditional variance of weekly FTSE 100 stock returns. We find that versions of standard symmetric and asymmetric Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models augmented to include media coverage and especially media tone scores outperform traditional GARCH models both in- and out-of-sample.
Développements méthodologiques en spectrométrie de masse et en mobilité ionique pour l'étude d'assemblages supramoléculaires en biologie

Ce travail de these a ete focalise sur le developpement d’approches MS et IM-MS supramoleculaires pour la caracterisation fine des interactions proteine/ligand et pour l’analyse de melanges proteiques complexes. La maitrise des instruments de MS supramoleculaire ainsi que les optimisations instrumentales et methodologiques realisees ont permis d’etendre le potentiel des approches MS et IM-MS pour la caracterisation d’assemblages moleculaires particulierement complexes. Nous avons ainsi pu suivre la cinetique de formation de complexes proteine/ligand ainsi que les changements conformationnels qui y sont associes, montrant l’interet du couplage IM-MS en recherche pharmaceutique. De plus, ce travail a porte sur l’etude de complexes de tres hauts poids moleculaires et l’evaluation de l’IM-MS pour obtenir des informations structurales sur ces complexes. Nous avons ainsi permis de repousser certaines limites de la MS et de placer cette technique au cœur des etudes de biologie structurale.
Fine-grained Image Classification of Red Tide Algae Based on Feature Pyramid Networks and Computer Aided Technique

Based on the demand of fine-grained image classification and recognition technology of red tide algae, the Faster R-CNN algorithm in the deep learning algorithm is studied. At the same time, according to the characteristics of subtle inter-class difference and intra-class difference between red tide algae, FPN algorithm was studied in the detection algorithm by computer aided technology. On this basis, TensorFlow is used to carry out experimental simulation, and the results of classification and recognition of fine-grained image of tidal algae based on different scale feature information are compared. The experimental results show that the Faster R-CNN algorithm performs generally in the classification and recognition of fine-grained image of tidal algae, but the detection results are significantly improved by combining the FPN algorithm.
The Process of Structure Sense of Group Prerequisite Material: A Case in Indonesian Context

This study was to support the understanding of the set structure, binary operations, and their properties as a prerequisite of group theory material categorized as 9 structure senses. This study aimed at investigating the process of students’ structure sense in recognizing the structure of mathematical properties or objects as a prerequisite of group theory material. A task-based case study by exploring 9 categories of structure senses through three integrated process frameworks in the questionnaire was employed in this study. It involved 26 students who had obtained a prerequisite of group theory material and would take abstract algebra course. The choice of subjects was determined based on the results of the questionnaire, in which it identifies the type of structure sense processes. There were 6 out of 26 subjects were chosen. The 6 subjects consisted of 2 subjects from the first path process, 2 subjects from the second path process, and 2 subjects from the third path process. Then, the 6 subjects were interviewed. The choice of 2 subjects for each path process was because it used a fixed comparison theory. Then, the data were validated by using triangulation methods by comparing the students’ work on assignments and questionnaires as well as audio recordings of interviews. The results show the tendency of the process of structure sense was more dominated by students from the second type of path process, in which the subjects still depend on the well-known structure of the properties or mathematical objects in the form of sample questions. The subjects were unable to understand definitions in order to construct structures of properties or mathematical objects.
Salinomycin Treatment Specifically Inhibits Cell Proliferation of Cancer Stem Cells Revealed by Longitudinal Single Cell Tracking in Combination with Fluorescence Microscopy

A cell line derived from a tumor is a heterogeneous mixture of phenotypically different cells. Such cancer cell lines are used extensively in the search for new anticancer drugs and for investigating their mechanisms of action. Most studies today are population-based, implying that small subpopulations of cells, reacting differently to the potential drug go undetected. This is a problem specifically related to the most aggressive single cancer cells in a tumor as they appear to be insensitive to the drugs used today. These cells are not detected in population-based studies when developing new anticancer drugs. Thus, to get a deeper understanding of how all individual cancer cells react to chemotherapeutic drugs, longitudinal tracking of individual cells is needed. Here we have used digital holography for long time imaging and longitudinal tracking of individual JIMT-1 breast cancer cells. To gain further knowledge about the tracked cells, we combined digital holography with fluorescence microscopy. We grouped the JIMT-1 cells into different subpopulations based on expression of CD24 and E-cadherin and analyzed cell proliferation and cell migration for 72 h. We investigated how the cancer stem cell (CSC) targeting drug salinomycin affected the different subpopulations. By uniquely combining digital holography with fluorescence microscopy we show that salinomycin specifically targeted the CD24− subpopulation, i.e., the CSCs, by inhibiting cell proliferation, which was evident already after 24 h of drug treatment. We further found that after salinomycin treatment, the surviving cells were more epithelial-like due to the selection of the CD24+ cells.
Influence of Design Aesthetics and Brand Name On Generation Y Students’ Intention to Use Wearable Activity-Tracking Devices

where the captured data were analysed using descriptive statistics, correlation analysis, reliability and validity measures and structural equation modelling. The findings indicate that design aesthetics and brand name significantly influence Generation Y students’ intention to use wearable activity-tracking devices. A device’s design aesthetics as well as brand name are significant in determining Generation Y students’ intention to use such tracking devices. As such, retailers and marketers should focus on enhancing design aesthetics and brand awareness of wearable activity-tracking devices when targeting the lucrative Generation Y student market segment.
Tool-mediated HCI modelling instruction: evidence from three studies

ABSTRACT Effective teaching of concepts related to human computer interaction (HCI) requires introduction of the core paradigms as well as the design and evaluation methodologies to the learners. In this paper, we investigate the learning effectiveness of a tool-mediated learning activity to support instruction of established human performance models, namely the Keystroke Level Model (KLM) and Fitts’ law, and their application in web form design practice. The tool, named Keystroke Level Model Form Analyser (KLM-FA), supports learning through exploration by providing step-by-step tracing of the KLM modelling for any web form filling task. KLM-FA can be used in modelling exercises of either online or offline web forms according to different interaction strategies or users’ characteristics. Three pretest-posttest studies involving 52 students in total enrolled in computer science curricula with different education delivery methods (campus-based, blended learning, distance learning) and levels (undergraduate, postgraduate) provide evidence for the effectiveness of the proposed learning activity. In all three studies, it was found that the KLM-FA activity had a significant positive effect on students’ learning gain. In addition, students rated their perceived educational experience and KLM-FA usability as rather high. Content analysis of students’ comments also found that KLM-FA is an educationally valuable and usable tool.