Miracle-dz/UniFAR-FTUs huggingface dataset:
Emotional Analysis Model for Social Hot Topics of Professional Migrant Workers

Text makes up a large portion of network data because it is the vehicle for people's direct expression of emotions and opinions. How to analyze and mine these emotional text data has become a hot topic of concern in academia and industry in recent years. The online LDA (Latent Dirichlet Allocation) model is used in this paper to train the social hot topic data of professional migrant workers on the same time slice, and the subtopic evolution and intensity are obtained. The topic development is divided into four categories, and the classification model is created using SVM (Support Vector Machine). Instead of decision makers, a virtual human with sensibility and rationality is built using a hierarchical emotional cognitive model to solve multiobjective optimization problems interactively. It analyzes human body structure and emotional signals, and then combines them with visual and physiological signals to create multimodal emotional data. An example is used to demonstrate the effectiveness of the proposed model.
European airline delay cost reference values

The objective of this report is to provide users with updated reference values for the cost of delay to European airlines. It updates previous reporting (appended herewith) based on 2010 as the reference year. This new report presents costs for the reference year 2014 and extends the range of aircraft covered from 12 to 15, generated by a fully updated version of the model.
Automatic Fake News Detection based on Deep Learning, FastText and News Title

As a range of daily phenomena, Fake News is quickly becoming a longstanding issue affecting individuals, public and private sectors. This major challenge of the connected and modern world can cause many severe and real damages such as manipulating public opinion, damaging reputations, contributing to the loss in stock market value and representing many risks to the global health. With the fast spreading of online misinformation, checking manually Fake News becomes ineffective solution (not obvious, difficult and takes a long time). The improvement of Deep Learning Networks (DLN) can support with high degree of accuracy and efficiency the classical processes of Fake News spotting. One of the keys improvement strategies are optimizing the Word Embedding Layer (WEL) and finding relevant Fake News predicting features. In this context, and based on six DLN architectures, FastText process as WEL and Inverted Pyramid as News Articles Pattern (IPP), the present paper focuses on the assessment of the first news article feature that is hypothesized as affecting the performances of fake news predicting: News Title. By assessing the impact that the Embedding Vector Size (EVS), Window Size (WS) and Minimum Frequency of Words (MFW) in News Titles corpus can have on DLN, the experiments carried out in this paper showed that the News Title feature and FastText process can have a significant improvement on DLN fake news detection with accuracy rates exceeding 98%. Keywords—Fake news; automatic detection; deep learning; FastText; news title
Flexible Object Manipulation

Flexible objects are a challenge to manipulate. Their motions are hard to predict, and the high number of degrees of freedom makes sensing, control, and planning difficult. Additionally, they have more complex friction and contact issues than rigid bodies, and they may stretch and compress. In this thesis, I explore two major types of flexible materials: cloth and string. For rigid bodies, one of the most basic problems in manipulation is the development of immobilizing grasps. The same problem exists for flexible objects. I have shown that a simple polygonal piece of cloth can be fully immobilized by grasping all convex vertices and no more than one third of the concave vertices. I also explored simple manipulation methods that make use of gravity to reduce the number of fingers necessary for grasping. I have built a system for folding a T-shirt using a 4 DOF arm and a fixed-length iron bar which simulates two fingers. The main goal with string manipulation has been to tie knots without the use of any sensing. I have developed single-piece fixtures capable of tying knots in fishing line, solder, and wire, along with a more complex track-based system for autonomously tying a knot in steel wire. I have also developed a series of different fixtures that use compressed air to tie knots in string. Additionally, I have designed four-piece fixtures, which demonstrate a way to fully enclose a knot during the insertion process, while guaranteeing that extraction will always succeed.
A Study on the Perception of Public Space in Displaced Relocation

The focus of the New Urbanization Strategy and Rural Revitalization Strategy has shifted from the material needs of residents to their needs for a better life and the improvement of quality of life. Based on the concept of quality of life, taking three remote relocation and resettlement communities in Chang'an District of Xi'an as an example, the evaluation index system of community public space is constructed. The research shows that: there are differences in residents' perceptions of public space comfort, security and pleasure dimensions in three communities before and after relocation and resettlement, and the perception order of community public space dimensions was the same after relocation and resettlement, but the use of accessibility, night lighting, pedestrian flow, community atmosphere and parking, and parking space factors differed significantly.
IMPACT OF IMPROVED CROP PRODUCTION TECHNOLOGIES ON ENHANCING BARLEY AND WHEAT PRODUCTIVITY UNDER RAIN-FED AREAS OF JORDAN

Thirty seven on-farm trials in 2001/02 and 2002/03 growing seasons were conducted to demonstrate the beneficial effects of improved crop production technologies (ICPTs) on the productivity of barley and wheat over farmers' practices in arid and semi-arid regions of Jordan. The ICPTs were compared with the farmers’ method in an area of one hectare in each of the farmers’ fields for each treatment (i.e. ICPTs versus farmers' practices). The results showed significant differences for grain and straw yield and an increase in harvest index, indicating the positive influence of ICPTs on the farm economic return. Compared to farmers’ practices, ICPTs resulted in an increased mean income of 128.02 and 228.13 US$ ha -1 for barley with a benefit-cost ratio of 1.98 and 1.79 for the application of ICPTs compared to 1.80 and 1.38 for farmers' practices in 2001/02 and 2002/03 growing seasons, respectively. Similarly, the mean income obtained from the ICPTs application in wheat was 182.27 and 133.84 US $ ha -1 more than that resulted from farmers practices with a benefit-cost ratio of 3.25 and 2.18 for the ICPTs compared to 3.0 and 1.86 for farmers' practices in the first and second growing season, respectively. This additional income could substantially benefit small-scale farmers and improve their livelihoods in Jordan. The results from the current study indicate the potential benefits of ICPTs in enhancing barley and wheat yields, and as consequence increase net returns in the arid and semi-arid regions of Jordan.
Mechanical behavior of bionic drag reduction needle during puncturing process

Objective To design a kind of bionic drag reduction needle and investigate its mechanism of painless injection.Methods Based on the analysis of mosquito's mouthpart structure,two types of bionic drag reduction injector needles,sawtooth needle and stripe needle,were designed with the painless feature as mosquito's blood-sucking action.The injection processes of normal smooth needle and two bionic drag reduction injector needles were simulated by using explicit kinetic software LS-DYNA,and analyzed the pain-relief mechanism of bionic drag reduction needles.Results Through contrast and analysis on the stress contour,internal energy curves and resistance curves of both the smooth needle and bionic needles during the puncturing process in soft tissue,the bionic needle was found to have obvious drag reduction effects.The drag reduction rate of the saw-tooth needle and stripe needle was 23.48% and 31.57%,respectively.Conclusions The bionic needles can minimize the friction resistance to achieve the painless injection by drag reduction,and its effect is determined by the structure and shape of the bionic needle.
S-Fertilizer (Elemental Sulfur) Improves the Phytoextraction of Cadmium through Solanum nigrum L.

Soil contamination with toxic heavy metals [such as cadmium (Cd)] is becoming a serious global problem due to the rapid development of the social economy. This study was carried out to assess the beneficial role of two different kinds of (S)-fertilizer in the phytoremediation of Cd contaminated soil through Solanum nigrum L. Gypsum (Gyp) and Elemental sulfur (ES) was applied alone and in combination with different ratios (0, 100:0, 0:100, 50:50 mg kg−1) accompanied by different Cd levels (0, 25, 50 mg kg−1). After seventy days of sowing, plants were harvested for determination of growth, physiological characteristics, oxidants and antioxidants, along with Cd uptake from different parts of the plant. Cd toxicity significantly inhibited growth, physiology and plant defence systems, and also increased Cd uptake in the roots and shoots of Solanum nigrum L. The application of Gyp 100 mg kg−1 boosted plant growth and physiology along with oxidants and antioxidants activity as compared to ES 100 mg kg−1 alone, and combine application of GYP+ES 50 + 50 mg kg−1. The application of ES 100 mg kg−1 showed an effective approach to decreasing Cd uptake as compared to Gyp 100 mg kg−1. Overall results showed that the combined application of GYP+ES 50 + 50 mg kg−1 significantly enhanced the phytoremediation potential of S. nigrum in Cd contaminated soil. Thus, it is highly recommended to apply the combined application of GYP+ES for phytoremediation of Cd contaminated soil.
Comparison between Deterministic and Stochastic formulations of Particle Swarm Optimization, for Multidisciplinary Design Optimization.

Particle Swarm Optimization (PSO) is having a growing space in the optimization community, mainly due to its appreciable qualities of fast initial progress, reduced computational cost and parallel structure, suitable for High Performance Computation (HPC) platforms. Original formulation includes some random coecients, so that a statistical analysis of the solution is often needed. To avoid the latter situation, Deterministic Particle Swarm Optimization (DPSO) has been introduced: removing all the random coecients the DPSO is a deterministic algorithm, so that a single run is considered to evaluate the success of the algorithm. In this paper, a comparative study between PSO and DPSO is reported, in order to investigate the performance of DPSO versus PSO.
Security in SDN networks and their applications

Introduction: The review article is the product of the research on Security in SDN networks and their applications, developed at the District University in 2020, presenting the latest advances, that have been made in security. Problem: The security weaknesses that SDN networks have had, due to being a new architecture. This has not allowed traditional networks to be replaced. Objective: To carry out a review of the state of the art of SDN networks, focusing research on the security of the control layer and its advances. Methodology: The descriptive method is implemented, consulting databases such as Scopus, IEEE and ScienceDirect, using the following search criteria: SDN networks, security in SDN networks, applications with SDN networks and OpenFlow protocol. It is shown as a research sample: the Asian, European and American continents with years of research from 2014 to 2020. Results: Great advances have been made in terms of security for SDN networks, which allows us to see an early solution to the weaknesses that it currently faces. Conclusion: SDN networks will solve all the challenges they face and will be consolidated as a solid and reliable architecture. Originality: an important focus is taken on the security of SDN networks and the great development that has occurred in this regard is evident. Limitations: SDN networks are a new architecture, so their development has been very little and advances in security have been significantly affected.
Energy Autonomous Two-Way Repeater System for Non-Line-of-Sight Interrogation in Next Generation Wireless Sensor Networks

In this effort, we present a fully energy autonomous two-way repeater system for use in nonline-of-sight (NLOS) interrogation of wireless sensor networks. The proposed system is based on a pair of tunnel diode reflection amplifiers, taking advantage of the existence of a negative differential resistance region that exists when the device is biased at a low voltage in the quantum tunneling region. This enables the development of an ultralow-cost, ultralow-power means to provide amplification. The proposed system demonstrates an ability to extend the total read range of semipassive radio frequency identification (RFID) tags by 4.55 times while consuming over 1000 times less power than comparable technologies, a notable improvement on previously published work. The system shows an ability to be customized to fit a wide array of different applications where NLOS interrogation and the read range of semipassive RFID tags pose a challenge.
Remote Sensing and Machine Learning Modeling to Support the Identification of Sugarcane Crops

One of the main concerns of agricultural financing institutions is to make sure the loans they grant are used for the stated objective when the loan was requested. Specifically, when Banco Agrario de Colombia grants loans for crop farmers, it schedules verification visits to the cultivation sites to check if the crop stipulated in the loan agreement exists and assess its health. These visits are challenging to make due to the number of visits over vast areas that they need to schedule, lack of trained personnel, and difficulty of access. This article proposes a software tool, based on a machine learning model for processing free satellite imagery, to support the bank’s identification of non-compliant crops with the investment plan before making field visits, minimizing the loss of investment by focusing on those areas to prioritize the visits. Sugarcane along the department of Boyacá, Colombia was chosen as the case of study. Free access satellite imagery through the Colombian Data Cube (CDCol) was used and machine learning models were applied on them to classify the land and predict the presence of the crop, a Random Forest model achieved an overall F1-score of 91% using Landsat-8 imagery and a K-nearest Neighbors model achieved an overall F1-score of 98% using Sentinel-2 imagery.
Environmental Messages Promote Plant-Based Food Choices: An Online Restaurant Menu Study

Food production accounts for a quarter of all greenhouse gases, making shifting people’s diets toward lower carbon foods a critical strategy for reducing emissions. This study finds that displaying thoughtfully framed environmental messages on restaurant menus can significantly increase customers' uptake of lower carbon, plant-rich dishes. WRI finds that the two most effective descriptive messages doubled the chance that a consumer would order a vegetarian menu item. These themes are “small changes can make a big difference” and “join a movement of people choosing foods with less impact on the climate. ” ​ Restaurants and food businesses should use these findings to increase sales of lower carbon menu items while helping consumers choose foods that fit a climate-friendly lifestyle. While the WRI study was done online with more than 6,000 participants, the findings can be adapted and tailored to a wide variety of retail and food service contexts. ​ More research and real-world learnings will further our base of knowledge. This study, however, shows that adding environmental messaging can be an easy, cost-effective and promising way for companies to see impact and shift consumer choices toward more climate-friendly options. ​
A new robust parameter estimation approach for multinomial categorical response data with outliers and mismeasured covariates

Abstract Being confronted with multinomial categorical response data often occurs in the medical, health, social sciences and other fields. However, the data in practice are subject to measurement error (ME) or outliers due to some unavoidable reasons. There are few studies dealing with problems of ME and outliers in the data with unordered and multi-classified responses simultaneously. To address the effects of ME and outliers in multinomial logistic models, which are most suitable for analyzing categorical outcome variables, we propose novel approaches. Firstly, we develop the extensively weighed corrected score method to estimate parameters in multinomial logistic models with ME. Furthermore, to tackle the problem of that how to obtain parameter estimations when the ME and outliers occur simultaneously, we develop a new robust method, namely the robust extensive weighted corrected score method. Some simulation studies are conducted to evaluate the estimators’ finite sample performance. It has been verified that these estimators are consistent and asymptotically normally distributed under general conditions. They are easy to compute, perform well and stably and are robust against the normality assumption for the ME. The proposed methods are also applied to two real datasets to illustrate the practicability of methods.
Channel limitation of 1-D wire random network for transparent conducting electrodes application

1-D wire random network is a promising ITO substitute for flexible transparent conducting electrodes. However, the sheet resistance (Rs) of the 1-D network drastically increases when the network is patterned into narrow channels in electronic applications such as touch screen panel and light emitting diodes. In this work, the undesirable Rs increment of the 1-D wire random network is demonstrated based on Monte Carlo simulation, and critical factors that determine the channel width limitation are investigated. Moreover, aligned wire configuration in the random network is proposed in order to alleviate the constraint on the Rs of the narrow channel.
Analysis Of A 3-level-vsi Neutral-point-control For Fundamental Frequency Modulated Svc-applications

This paper presents a new method to control the neutral-point (NP) of a 3-level-VSI in SVC-applications with fundamental frequency modulation (FFM) or optimized pulse patterns. In a first step the systems equations for a 3-level-VSI connected to a symmetrical, ideal sinusoidal load are derived. This includes a general equation for the NP-current. Based on this, investigations on the self-balancing abilities are performed. It is shown that, in the presence of a resistance in the system, the 3-level VSI is self-balancing for all pulse patterns symmetrical to a quarter of a period and for all power factors on the AC-side. However, in high power applications the self-balancing is very slow due to very small resistances in the system. This is verified by SABER simulations for an SVC-application. In a further step, a NP-control principle for fundamental frequency modulated SVC-applications is deduced on a simplified model. The validity of this scheme for a wide operation range is verified by mathematical analysis and simulation results with Matlab. In a further step, this analysis is extended by including all harmonics of the 3 level-VSI output currents, which shows their influence on the control scheme. Simulation results with Matlab and SABER proof the validity of the theoretical investigations. Finally the control method is adapted to PWM-SVC-applications.
MEMS switches

Microelectromechanical system (MEMS) microswitches are receiving increasing attention, particularly in the RF community. Low power consumption, low insertion loss, high isolation, excellent linearity, and the ability to be integrated with other electronics all make microswitches an attractive alternative to other mechanical and solid-state switches. This article features a newly developed, surface micromachined, electrostatically actuated direct metal-to-metal microswitch which can be used in applications from dc through microwave.
Estimating the production function for the Brazilian industrial sector: A Bayesian panel VAR approach

Abstract The scope of this paper is to estimate the production function for the Brazilian industrial sector from a longitudinal panel of the industrial sector (Annual Industrial Survey produced by the Institute of Geography and Statistics—PIA/IBGE—and the Ministry of Labour and Employment’s Annual Relation of Social Information—RAIS/MTE—ranging from 1996 until 2005) through a Bayesian Vector Autoregressive (BVAR) approach. This new method adds to the empirical industrial organization another way to estimate the demand, avoiding cumbersome calculations. It gives the possibility of analysing not only the dynamic relationships among the variables but also the shocks through the impulse response function (IRF). Additionally, it gives the opportunity to analyse the industry sector’s productivity by minimizing the problem of endogeneity and therefore it also sheds some light on the trend of this variable throughout the period abovementioned.
Reducing the Computational Complexity of Wide-Area Backup Protection in Power Systems

Wide-area backup protection (WABP) refers to the identification of the faulted line amongst candidate lines by using sparse PMU measurements. This letter proposes a technique for reducing the computational complexity of WABP methods that employ the superimposed-circuit concept. A proposition is presented and proved to justify that the system of equations pertaining to every candidate line can be directly obtained from the bus impedance matrix of the pre-fault power system. This is in contrast with existing WABP methods requiring the establishment of as many distinct bus impedance matrices as the number of candidate lines in the power system. The applicability and effectiveness of the proposed technique are verified by conducting more than 20,000 simulations on the IEEE 39-bus test system.
Towards a Strategic IT GRC Framework for Healthcare Organizations

The rapidly changing healthcare market requires healthcare institutions to adjust their operations to address regulatory, strategic, and other risks. Healthcare organizations use a wide range of IT systems producing large amounts of sensitive and confidential data. However, few tools are available to measure the data governance activities of healthcare institutions and align healthcare data management with legislation. The Governance, Risk, and Compliance (GRC) Model focused on integrating that ability to achieve organizational goals. The demand for corporate governance is crucial for protecting the healthcare system from risks. An adaptation of a modified version that includes strategy, processes, technology, people, as well as legal and business requirements was developed to analyze the factors affecting IT GRC implementation in healthcare organizations. Although about 48% of participants reported that their organizations implemented IT GRC programs, 16% stated that they are considering implementing IT GRC programs soon. In almost 71% of healthcare organizations, IT governance, risk management, and compliance are integrated. Among the factors influencing the implementation of IT GRC programs in Saudi healthcare organizations, legal context ranked as the most critical, followed by process, strategy, then technology, business, and finally, people contexts. This study shows that healthcare organizations must assess various factors for the effective implementation of IT GRC activities. Keywords—Information technlogy; strategic; healthcare; governance; compliance