Miracle-dz/UniFAR-FTUs huggingface dataset:
Event-triggered Backstepping Control Scheme for Networked Mobile Robots

Remote control of mobile robotic systems over a communication network is indeed a challenging task. With the benefit of event-triggered strategy, a dynamic tracking controller based on backstepping approach is proposed for a remotely-controlled mobile robot considering the limitation of network bandwidth. The designed controller aims to reduce the required transmissions of control update through the network while ensuring the system performance and stability of closed-loop system. For this purpose, a well-designed Lyapunov-based triggering condition is derived. Simulation results illustrate the efficacy of the proposed event-triggered scheme compared to a traditional time-triggered implementation in terms of network usage.
Optimizing Task Execution for Mobile Edge Computing

Computation-intensive applications can be enabled by mobile edge computing (MEC) in 5G networks because MEC carries cloud computing almost near to smart devices. In this paper, we study a multi-user MEC system, where several smart devices (SDs) can fulfill computation offloading over wireless channels to a MEC server. we study the minimization of a total sum cost which is energy consumption and time delay for all the smart devices (where smart devices can choose one out of three scenarios to execute the task, i.e., full local computing scenario, full offloading execution scenario, and partial offloading execution scenario) as our objective function optimization. We mutually optimize task partition, offloading decision and computation resource sharing to reduce the total cost of the MEC system. We used an extensive search method and Lagrange method to solve these problems. Statistical results prove the effectiveness of our proposed scheme.
Research on Cooperative Game Theory based on complexity of network structure

Based on the prisoner's dilemma(PD) game, we study the effect of network structure on the evolution of cooperation. Among various statistical properties of network, we focus on the network structure entropy, which can assess how complex the structure of complex networks is. In this paper, we analyze and discuss the Prisoner's Dilemma Game Model based on Dynamic topology (PDG-DT) and Static topology (PDG-ST) under small world network and scale-free network respectively. There are no significant differences in experimental results on the BA and NW networks. For the PDG-ST and PDG-DT, there are appropriate network structure entropy values, which makes the density of the cooperator peak. Simulation results show that the network structure entropy plays an universal role in the cooperation of these networks and can effectively describe the influence of network structure on cooperation.
dCACH: Content Aware Clustered and Hierarchical Distributed Deduplication

In deduplication, index-lookup disk bottleneck is a major obstacle which limits the throughput of backup processes. One way to minimize the effect of this issue and boost speed is to use very high course-grained chunks for deduplication at a cost of low storage saving and limited scalability. Another way is to distribute the deduplication process among multiple nodes but this approach introduces storage node island effect and also incurs high communication cost. In this paper, we explore dCACH, a content-aware clustered and hierarchical deduplication system, which implements a hybrid of inline course grained and offline fine-grained distributed deduplication where routing decisions are made for a set of files instead of single files. It utilizes bloom filters for detecting similarity between a data stream and previous data streams and performs stateful routing which solves the storage node island problem. Moreover, it exploits the negligibly small amount of content shared among chunks from different file types to create groups of files and deduplicate each group in their own fingerprint index space. It implements hierarchical deduplication to reduce the size of fingerprint indexes at the global level, where only files and big sized segments are deduplicated. Locality is created and exploited first using the big sized segments deduplicated at the global level and second by routing a set of consecutive files together to one storage node. Furthermore, the use of bloom filter for similarity detection between streams has low communication and computation cost while it enables to achieve duplicate elimination performance comparable to single node deduplication. dCACH is evaluated using a prototype deployed on a server environment distributed over four separate machines. It is shown to have 10× the speed of Extreme_Binn with a minimal communication overhead, while its duplicate elimination effectiveness is on a par with a single node deduplication system.
The Use of Embedded Interaction Mechanisms for Low-Level Analysis Tasks

The use of information visualization is a strategy to reduce information overload and cognitive efforts. Interaction mechanisms aid the exploration of data when it is not practical to display all data points in one visual display. This study reports the results of a pilot study. The purpose of the study is to determine what interactive mechanisms are used and how they support a task or set of tasks.
To evaluate the impact of patient education on self-reported adherence, and management behavior of children with asthma

The failure to stick to a recommended remedial program for the treatment of an incessant illness might be mindful to some extent for proceeding with the infection movement. Adherence the key territories of enthusiasm for Asthma. The focal point of examination were to recognize issues detailed by families to influence their Adherence to asthma care. To evaluate the effect of patient education to Self-reported Adherence, Management, and Barriers. Children introducing during an intense assault of asthma were enrolled in this investigation. The restorative record of the experience were preoccupied and contrasted and data that were acquired at first visit and after 3 months. There are 986 youngsters 4 to 15 years old living in city evaluation tracts in the examination. The parental report of drugs endorsed, and the data on the disconnected report concurred 95.15% of the ideal opportunity β-Agonists, 86.24% are steroids, and 7.71% are cromolyn. Meds were overlooked a portion of time by 45.2% of the kids, and 52.8% attempted to escape to taking medication. Arrangements of follow-up consideration were kept by 69% of those given an arrangement, by an expected 60.0% of the individuals who were advised explicitly to require an arrangement. Just a single third of guardians report that they had the option to fend off the youngster from realized asthma triggers about constantly. After the subsequent, the huge changes are seen. Adherence to asthma-the executives program includes various territories: prescription, arrangement keeping, avoidance, and applying a crisis strategy. A barrier to Adherence may exist in one or every one of the four of these areas, prompting ineffectual control of asthma. The patient education improving the patient-doctor organization and furthermore improve Adherence.
A new perspective on Gaussian shadow rate term structure models∗

We propose a new approach to estimating shadow rate term structure models. This modifies the Joslin, Singleton, and Zhu (2011) factor rotation technique to allow for the zero lower bound using the Wu and Xia (2016) discrete-time closed-form approximation of the Black (1995) model. Compared with the standard approach based on the extended Kalman filter, our approach significantly improves convergence and greatly reduces the computation time. It has the added advantage of producing more robust estimates of the lower bound parameter and the path of the shadow rate. We apply the shadow rate model to recent U.S. data and show that expected inflation and unemployment gap are important unspanned macro factors that drive term premiums, as in Joslin, Priebsch, and Singleton (2014). JEL classification: G12, C13.
Determination of Gender from Various Measurements of the Humerus

Objective To study different measurements of the humerus for the determination of sex Study design A cross-sectional study Place and duration of study The Anatomy Department of Nishtar Medical University and Hospital from June 1, 2018, to May 31, 2019. Methodology Six measurements, including maximum length of the humerus (MLH), maximum diameter of the head of the humerus (MDH), vertical diameter of the head of the humerus (VDH), transverse diameter of the head of the humerus (TDH), epicondylar breadth (EB), and condylar breadth (CB) were calculated for 122 male humeri and 52 female humeri. These variables were compared between both genders using the student’s t-test. Wilks’ lambda test was applied. The demarking point of all these variables was defined as the average of the means of male and female measurements. Correctly identified cases were calculated in the male group, the female group, and the whole study group. Data analysis was done in SPSS v.23. 0 (IBM Corp, Armonk, NY). P>0.05 was statistically insignificant. Results The differences of MLH, MDH, VDH, TDH, EB, and CB were statistically significant between both the genders (p<0.001). The accuracy of MLH was 85% in the total population. The accuracy of MDH, VDH, and TDH was 76%, 85%, and 76% in the total population, respectively. EB and CB correctly identified 75% and 78% of all the bones. Conclusion This study shows that maximum humeral length and the vertical diameter of the humeral head are the two most important measurements with the highest accuracy for the determination of gender from the skeletal remains of the human body in the South-East Asian population.
Repellent and fumigant toxic potential of three essential oils against Ephestia kuehniella

Essential oils, when used as bio-insecticides in the control of insect pests of stored grains have shown specificity and variation in the potentiality of their mode of action. In the present study, three essential oils extracted from three aromatic plants of different families, white wormwood (Artemisia herba alba, Asteraceae), oregano (Origanum vulgare, Lamiaceae) and rue (Ruta montana, Rutaceae), were evaluated for their repellent and fumigant toxic potential against the flour moth larvae, Ephestia kuehniella (Lepidoptera, Pyralidae), under laboratory conditions. The essential oils extraction was done by the hydrodistillation method. The repellent activity was carried out in Petri dishes using a filter paper treated with different oil dilutions (25, 75, 100, 120, 130, 150 µL/mL). The fumigant toxicity was determined on three concentrations (50, 130, 150 µL/L air). Two plants were shown to be repellent against the E. kuehniella larvae. Origanum oil was the most repellent with 67% of repellency rate followed by Artemisia oil (46%) at 120µL/mL after 2 hours of exposure. The oil of R. montana had an attractant activity against the larvae and was the most toxic with 56.7% of larval mortality in the first 24 hours. The median lethal concentrations (LC50) recorded were 11.6, 175.4 and 1100.0 µL/L air for the plant oils R. montana, O. vulgare and A. herba alba, respectively. R. montana and O. vulgare essential oil are shown to be efficient with high toxic and repellent properties against E. kuehniella larvae. Their specific potential could be integrated in the selection of the best bioinsecticides for the optimum protection of stored grain.
Clustering-based approximation of multi-agent systems defined on weighted directed graphs

This thesis deals with multi-agent systems where the information flow between the agents is represented by a weighted directed graph. We consider a model reduction technique for multi-agent systems that preserves the network structure of the original model. We extend existing results for undirected graphs to the case of directed graphs. We will see that the reduced-order model again represents a multi-agent system de fined on a weighted directed graph and that consensus is preserved. Furthermore, we will give an explicit expression for the relative approximation error that is involved in the model reduction.
Perceived organizational justice, trust, and OCB: A study of Chinese workers in joint ventures and state-owned enterprises

In this study, we investigate the relationships of perceived organizational justice, trust, and organizational citizenship behavior (OCB) among Chinese workers in joint ventures (JVs) and state-owned enterprises (SOEs). We develop a model that considers distributive justice and procedural justice as antecedents of trust in organization, and interactional justice as an antecedent of trust in supervisor. These two types of trust are expected to affect workers’ OCB. We hypothesize that distributive justice has a stronger effect on trust in organization in SOEs than in JVs, while procedural justice has a stronger effect on trust in organization in JVs than in SOEs. Besides, the effect of trust in supervisor on OCB is hypothesized to be stronger in JVs than in SOEs. We analyze data collected from 295 and 253 supervisorsubordinate dyads in a JV and a SOE respectively, and the results support our hypotheses. The theoretical and practical implications of this study are discussed.
The basal autonomic balance and during isometric exercise in young people with different cardiovascular reactivity

Introduction: the autonomic nervous system plays an important role in cardiovascular readjustments to exercise. In cardiovascular hyperreactivity there is a greater sensitivity of the sympathetic system to different stressors. Objective: to determine the characteristics of cardiac autonomic control in young adults with different degrees of cardiovascular reactivity at rest and during isometric exercise. Material and Methods: the sample consisted of 97 individuals of both sexes, it was divided into three groups; normorreactives, hyperreactive and with hypertensive response according to the pressor response to the sustained weight test. All individuals underwent a variability study of heart rate at rest and during the isometric test. Variables in the frequency domain were studied: HF, LF, resting LF/HF ratio and the parameters of the Poincare diagram SD1, SD2 and the SD1/SD2 ratio at rest and during exercise. Results: At the basal state, hyperreactive individuals and with a hypertensive response had a sympathetic predominance over cardiac function and a lower variability of heart rate. During isometric exercise the values of the SD1 and SD2 axes decreased in all groups and the SD1/SD2 ratio decreased in normorreactive individuals and with hypertensive response, but it was only significantly in the hyperreactive ones. Conclusions: In individuals with cardiovascular hyperreactivity, an autonomic imbalance is already present in the basal state and there is a reduction in autonomic vagal modulation during exercise that may contribute to the development of hypertension.
Operationalizing Trumbo’s Principles of Bivariate Choropleth Map Design

Trumbo’s (1981) ideas on bivariate choropleth design have been underexplored and underutilized. He noted that effective map design (including color selection) is directly informed by the intended goal or use of the map (i.e., what questions might the map answer), and he identified three common spatial relationships that can be displayed by a bivariate choropleth: inverse relationships, a range of one variable within another, and direct relationships. Each is best suited to answering different map readers’ questions. Trumbo also suggested sample color palettes to focus the map reader’s attention on pertinent data. In consultation with Trumbo, we extended his ideas, first by creating focal models that illustrate his three spatial relationships. We then constructed sample maps to examine each of the focal models, and finally compared each model by mapping the same two data sets (of obesity and inactivity). We investigated the visual differences in each of the resulting maps, and asked spatial questions regarding the relationships between obesity and inactivity. Our work validates Trumbo’s ideas on bivariate choropleth map design, and we hope our focal models guide cartographers towards making color choices by linking their map purpose to the appropriate focal model.
Practices, Challenges, and Prospects of Big Data Curation: a Case Study in Geoscience

Open and persistent access to past, present, and future scientifc data is fundamental for transparent and reproducible data-driven research. The scientifc community is now facing both challenges and opportunities caused by the growingly complex disciplinary data systems. Concerted efforts from domain experts, information professionals, and Internet technology experts are essential to ensure the accessibility and interoperability of the big data. Here we review current practices in building and managing big data within the context of large data infrastructure, using geoscience cyberinfrastructure such as Interdisciplinary Earth Data Alliance (IEDA) and EarthCube as a case study. Geoscience is a data-rich discipline with a rapid expansion of sophisticated and diverse digital data sets. Having started to embrace the digital age, the community have applied big data and data mining tools into the new type of research. We also identify current challenges, key elements, and prospects to construct a more robust and future-proof big data infrastructure for research and publication for the future, as well as the roles, qualifcations, and opportunities for librarians1information professionals in the data era. Received 06 May 2019 ~ Accepted 11 September 2019 Correspondence should be addressed to Suzhen Chen, Cataloging Department, University of Hawaiʻi at Mānoa Library, 2550 McCarthy Mall, Honolulu, Hawaii 96822. Email: suzhen@hawaii.edu The International Journal of Digital Curation is an international journal committed to scholarly excellence and dedicated to the advancement of digital curation across a wide range of sectors. The IJDC is published by the University of Edinburgh on behalf of the Digital Curation Centre. ISSN: 1746-8256. URL: http://www.ijdc. net/ Copyright rests with the authors. This work is released under a Creative Commons Attribution Licence, version 4.0. For details please see https://creativecommons.org/licenses/by/4.0/ International Journal of Digital Curation 2020, Vol. 14, Iss. 1, 275–291 275 http:11dx.doi. org110.22181ijdc.v14i1.669 DOI: 10.22181ijdc.v14i1.669 276 | Practices, Challenges and Prospects of Big Data Curation doi:10.2218/ijdc.v14i1.669
Opinion Mining using Hybrid Approach on Big Data: A Perspective

The growing adoption of social media networks as a platform for involvement opinions on different aspects emerged the sentiment analysis and opinion mining as a vigorous research area. In recent times, the sentiment analysis on Twitter has concerned considerable attention due to its many applications in various aspects of our lives. Opinion mining, also identified as Sentiment analysis, plays a vital role in this process. It is the study of emotions i.e. Sentiments, Expressions that are stated in natural language. Natural language techniques used to extract emotions from unstructured data. There are several techniques, used to analyse the data. The objective of this paper is to provide the overview of Sentiment Analysis, Applications and a previous works and techniques in the field of Natural Language Processing. Keywords— Sentiment Analysis, opining mining, SVM (Support Vector Machine), Rainforest, Hybrid Approach
Analysis of transported pollution and haze-related diseases via HYSPLIT Trajectory Modelling in the urbanized area of Johor, Malaysia

Exposure to the haze pollutants may cause a diverse range of health impacts including symptoms that lead to treatment-seeking behavior, outpatient and emergency department visits, and hospitalization for moderate and severe cases and mortality for the worst-case scenarios. This study is carried out as an effort to understand how the haze occurrence that has been plaguing South East Asia affect the health of the citizen in Malaysia, particularly in Johor due to its close proximity to Sumatra which has been identified as one of the hotspots for frequent forest fires. The study is done in Larkin and Pasir Gudang where health cases caused by poor air quality such as conjunctivitis, URTI and asthma have been identified while HYSPLIT model indicates the trajectory of the wind at different heights to help us understand how wind movement during the selected time of the year causes haze formation. The findings of this study showed that low wind heights below 2000m AGL in September cause particular matter accumulation over Malaysia as the wind over Malaysia. In turn, this causes high API reading which leads to higher conjunctivitis, URTI and asthma cases. The investigation of the exposure and health risks of the air pollutants through a risk-based approach will definitely be beneficial in providing new information about the correlation between health risks and air pollution in Johor, which may also have implications on the environmental and social policies.
Multi-seasonal Composites from Landsat Data

12 Increasing attention is being directed at mapping the fractional woody cover of savannahs using 13 Earth-observation data. In this study, we test the utility of Landsat TM/ ETM-based spectral-temporal 14 variability metrics for mapping regional-scale woody cover in the Limpopo Province of South Africa, 15 for 2010. We employ a machine learning framework to compare the accuracies of Random Forest 16 models derived using metrics calculated from different seasons. We compare these results to those 17 from fused Landsat-PALSAR data to establish if seasonal metrics can compensate for structural 18 information from the PALSAR signal. Furthermore, we test the applicability of a statistical variable 19 selection method, the recursive feature elimination (RFE), in the automation of the model building 20 process in order to reduce model complexity and processing time. All of our tests were repeated at 21 four scales (30, 60, 90, and 120 m-pixels) to investigate the role of spatial resolution on modelled 22 accuracies. 23 Our results show that multi-seasonal composites combining imagery from both the dry and wet 24 seasons produced the highest accuracies (R= 0.77, RMSE=9.4, at the 120 m scale). When using a single 25 season of observations, dry season imagery performed best (R=0.74, RMSE=9.9, at the 120 m 26