Miracle-dz/UniFAR-FTUs huggingface dataset:
Consensus-based Formation Control of Heterogeneous Linear Multi-agent Systems

This paper studies the formation control problem of heterogeneous linear multi-agent systems (MASs) with time-invariant relative position and angle constraints under a directed fixed communication topology. A consensus-based formation control protocol is proposed for the MAS based on the local information exchange between agents. First, we transform the formation control problem into the consensus problem. Then, the state linear transformation approach is used to further transform the consensus problem into the partial variable stability problem. Using the theory of partial variable stability, sufficient and necessary criteria are given to ensure the MAS realizes the required formation. In addition, the design process of the gain matrices in control protocol is given based on Lyapunov inequality and bilinear matrix inequality (BMI). Finally, a simulation example is presented to illustrate the validity of the theoretical results.
Review of the Impact Pathways of Biofortified Foods and Food Products

Biofortification is the process of increasing the concentrations and/or bioavailability of micronutrients in staple crops and has the potential to mitigate micronutrient deficiencies globally. Efficacy trials have demonstrated benefits of consuming biofortified crops (BFCs); and in this paper, we report on the results of a systematic review of biofortified crops effectiveness in real-world settings. We synthesized the evidence on biofortified crops consumption through four Impact Pathways: (1) purchased directly; (2) in informal settings; (3) in formal settings; or (4) in farmer households, from their own production. Twenty-five studies, covering Impact Pathway 1 (five studies), Impact Pathway 2 (three), Impact Pathway 3 (three), Impact Pathway 4 (21) were included. The review found evidence of an improvement in micronutrient status via Impact Pathway 4 (mainly in terms of vitamin A from orange sweet potato) in controlled interventions that involved the creation of demand, the extension of agriculture and promotion of marketing. In summary, evidence supports that biofortified crops can be part of food systems interventions to reduce micronutrient deficiencies in farmer households; ongoing and future research will help fully inform their potential along the other three Impact Pathways for scaling up.
Initial Study Using Electrocardiogram for Authentication and Identification

Recently, several studies have demonstrated the potential of electrocardiogram (ECG) to be used as a physiological signature for biometric systems (BS). We investigated the potential of ECG as a biometric trait for the identification and authentication of individuals. We used data from a public database, CYBHi, containing two off-the-person records from 63 subjects, separated by 3 months. For the BS, two templates were generated: (1) cardiac cycles (CC) and (2) scalograms. The identification with CC was performed with LDA, kNN, DT, and SVM, whereas a convolutional neural network (CNN) and a distance-based algorithm were used for scalograms. The authentication was performed with a distance-based algorithm, with a leave-one-out cross validation, for impostors evaluation. The identification system yielded accuracies of 79.37% and 69.84% for CC with LDA and scalograms with CNN, respectively. The authentication yielded an accuracy of 90.48% and an impostor score of 13.06% for CC, and it had an accuracy of 98.42% and an impostor score of 14.34% for scalograms. The obtained results support the claim that ECG can be successfully used for personal recognition. To the best of our knowledge, our study is the first to thoroughly compare templates and methodologies to optimize the performance of an ECG-based biometric system.
Exponential Bipartite Consensus of Linear Fractional-Order Multiagent Systems With Signed Directed Graph: An Output Feedback Approach

This paper aims to solve the exponential bipartite consensus problem of linear fractional-order multi-agent system (FOMAS) with signed directed graph. Unlike most of the existing works using the relative full state information of neighboring agents, a continuous observer-based output feedback bipartite consensus (OFBC) controller using only the relative output state information of neighboring agents is proposed to address the problem. Under the mild assumption that the signed directed graph has spanning tree, it is theoretically shown that the exponential bipartite consensus of FOMAS with the proposed continuous observer-based OFBC protocol can be achieved if the protocol parameters are selected suitably based on some linear matrix inequalities.
Development and Validation of Christian Ministers Psychopathological Scale (CMPS)

This paper describes the development and validation of a Christian Ministers Psychopathological Scale (CMPS) to facilitate early diagnosis of mental health challenges among Christian Ministers, especially in Nigeria. The study adopted a cross-sectional design and was carried out in four stages. The initial items were generated from an extensive review of literature and concerns raised from a focused group discussion among Christian ministers with Pentecostal affiliation. These concerns are perceived as possible predictors of mental health challenges among the ministers. Fifty-two (52) participants were randomly selected from Ede, Osun State, Southwestern Nigeria for the scale development, while four hundred and fifty-six (456) participants were randomly selected across the six geo-political zones in Nigeria. They responded to the online versions of the 15- item instrument (CMPS) and Depression Anxiety and Stress Scale (DASS-14) for the congruent validation. The observed KMO measure was 0.47, and a significant sphericity test was observed (X2=1258.495, df= .703, p = .000). All the 15 items extracted from the Principal Components Analysis PCA were within the very good discrimination values. The CMPS has a Cronbach's alpha of . 79. Congruent validity coefficient of (r =.368, p = .035); was observed between CMPS and DASS-14. Authors conclude that the CMPS is gender-sensitive, has acceptable psychometric properties and is recommended as a diagnostic tool for assessing psychopathological symptoms among Christian ministers.
Religious Violence and Twitter: Networks of Knowledge, Empathy and Fascination

Twitter analysis through data mining, text analysis, and visualization, coupled with the application of actor-network-theory, reveals a coalition of heterogenous religious affiliations around grief and fascination. While religious violence has always existed, the prevalence of social media has led to an increase in the magnitude of discussions around the topic. This paper examines the different reactions on Twitter to violence targeting three religious communities: the 2015 Charleston Church shooting, the 2018 Pittsburgh Synagogue shooting, and the 2019 Christchurch Mosque shootings. The attacks were all perpetrated by white nationalists with firearms. By analyzing large Twitter datasets in response to the attacks, we were able to render visible associations among actors across religions communities, national identities, and political persuasions. What this project revealed is that if we apply actor-network-theory and data visualization to look at networks created by human/non-human (text, computer, phone, meme, tweet, retweet, hashtag) actors, we can see that knowledge, empathy, and fascination drive communication around mass violence against religious communities.
Study on the effect of tailoring the hard copolymer polyurethane on the thermal, mechanical and electrical properties of hard copolymer polyurethane/multi-walled carbon nanotubes nanocomposites

This study focusses on the effect of the addition of multi-walled carbon nanotubes (MWCNTs) on the morphology and thermal, rheological and electrical properties of hard copolymer polyurethane (HCPU) using 1,5 pentane-diols as a chain extender, containing 85% of hard segment blocks. Step-growth polymerisation was used to synthesise a HCPU polymer matrix, followed by melt compounding to prepare the nanocomposites. SEM investigation of the neat HCPU structure and its nanocomposites indicated the uniform dispersion of MWCNTs within the polymer matrix; further TEM morphology investigation showed some agglomeration regions, attributed to the high MWCNT content (2 wt%). An XRD test confirmed a homogeneous distribution of MWCNTs by observing the remaining location of the highest peaks of MWCNTs and amorphous peaks of the HCPU matrix. Analysis of the melt rheology test results showed an improvement in complex viscosity combined with an increase in the G’ and G” of nanocomposites from those of pure HCPU due to the presence of MWCNTs. Finally, electrical conductivity of nanocomposites demonstrated a significant improvement in its value at high loading of MWCNTs from that of pure HCPU matrix.
Reinforcement Learning-Based Path Tracking With Application of Quadruped Robot

Path tracking is a hot issue in the control of robots, especially quadruped robots for their complexity in foot structure and walking gait. Pure-pursuit is widely used in vehicle path tracking, but most of the improvements on pure-pursuit only concern about tracking error. In this letter, we introduce a reinforcement learning-based method using Proximal Policy Optimization(PPO) to adaptively control the preview distance in pure-pursuit and the step length of quadruped robot according to the curvature of the road. Experiments show that we reached a balance in decreasing tracking error as traditional pure-pursuit while maintaining the running speed of quadruped robot.
Brand Preference of Professionals towards Choosing Smartphone in Nepal

It is tough to forecast or assess an individual preference, but it is vital for marketers since it represents an essential step in understanding consumer choice. It is critical to determine brand preference based on the awareness of the customer. The primary objectives of this study are to assess how professional consumers prefer brands in the choice of mobile phones. Business people, private jobholders, government personnel, and farmers are considered professionals for this study. Other goals are to explore the relationship between brand popularity, price, product attributes, social influence, marketing communication, and brand preference and examine the impact of independent variables on brand preference. Data were obtained from the 150 samples of professionals in Kathmandu valley through convenience sampling techniques. A total of 200 questionnaires were circulated, with 179 being collected. From the collected questionnaires, 29 were invalid, while 150 were valid and entered into SPSS 20. The data were analyzed through reliability, validity, descriptive statistics, correlation, and regression analysis. This study proved product attributes and price influenced the Smartphone purchase decision. So, the Samsung Smartphone is the first choice among professionals. It also confirmed the insignificant relationship between social influence and brand preference. Thus, the social context does not aid in purchasing a smart phone. The findings will support smart phone manufacturers in developing strategies based on consumer brand preferences.
Fake accounts detection on social media using stack ensemble system

In today’s world, social media has spread widely, and the social life of people have become deeply associated with social media use. They use it to communicate with each other, share events and news, and even run businesses. The huge growth in social media and the massive number of users has lured attackers to distribute harmful content through fake accounts, leading to a large number of people falling victim to those accounts. In this work, we propose a mechanism for identifying fake accounts on the social media site Twitter by using two methods to preprocess data and extract the most effective features, they are the spearman correlation coefficient and the chi-square test. For classification, we used supervised machine learning algorithms based on the ensemble system (stack method) by using random forest, support vector machine, and Naive Bayes algorithms in the first level of the stack, and the logistic regression algorithm as a meta classifier. The stack ensemble system was shown to be effective in achieving the best results when compared to the algorithms used with it, with data accuracy reaching 99%.
Construction of power grid digital twin model based on GAN

With the proposal of the new goal of energy reform in the 14th Five-year Plan, it has become inevitable that more and more renewable energy and new loads will be added to the power grid. The high complexity of the power grid and the difficulty of management and control will become the urgent problem to be solved. With the support of the "Digital China" strategy, it will be a new methodology to solve the above problems to realize the digitization of power grid with digital twin technology as the core. Based on the current energy reform, this paper analyzes the necessity and importance of the current power grid digital transformation. This paper expounds the digital picture of power grid supported by digital twin technology, proposes and constructs the digital model architecture of power grid based on digital twin technology, and based on its realization, puts forward the idea of constructing power grid digital twin by using GAN generative adversarial theory.
Feedback control strategies for quadrotor-type aerial robots: a survey

Control of aerial robots is a popular research field as applications with different payloads lead to a variety of flight missions. Quadrotor-type unmanned systems are one such example considered in this paper. The performance in any flight experiment depends strictly on the chosen feedback control scheme, which is the core issue addressed in the paper. A number of approaches have been reported in the literature and this paper presents a survey of these schemes with an in-depth discussion of recent research outcomes. A detailed performance evaluation of the controllers, namely proportional-integral-derivative control, sliding mode control, backstepping control, feedback linearization-based control and fuzzy control schemes, are presented. Due to the popularity of the quadrotor-type aerial vehicles, the contribution of the current work is to provide an in-depth guide to the autopilot designers of quadrotor-type unmanned aerial vehicles.
Molecular and Serological Detection of Leptospira spp. in Italian Tortoises (Testudo spp.)

Abstract Leptospirosis is a worldwide zoonosis, characterized by a complex ecological cycle, including the environment, wild and domestic animals, and humans. It is also considered a water-borne infection. Although a solid body of data is available for more conventional hosts, limited information is available on neglected species such as reptiles, especially Testudinidae, both captive and free-ranging. Molecular and serological tests were performed to detect Leptospira spp. in Testudo spp. in Italy. Leptospiral DNA was detected using cloacal swabs collected from 116 individuals (54 free-ranging and 62 captive tortoises) by polymerase chain reaction (PCR). Unfortunately, because of the presence of PCR inhibitors, swabs from only 50 animals were eligible for analysis. A serological microagglutination test was performed on 121 Testudo spp. (7 free-ranging and 114 rescued tortoises). Sixteen individuals were tested using both assays, and the remainder were tested with one or the other. Ten out of 50 (20%) amplifiable cloacal swabs were positive for Leptospira DNA, the identity of which was confirmed by sequencing two amplicons. Two out of four repeatedly sampled animals only tested positive in the autumn. Seroprevalence was relatively low (0.8%, 1/121), at low titer (1:100). No positivity was recorded among free-ranging animals. Although these results do not provide a complete epidemiological snapshot of leptospirosis in chelonians, they suggest that tortoises are susceptible to pathogenic Leptospira. Further data are needed to determine the role of the captive environment as a possible predisposing factor and to assess the role of these reptiles in the ecology of leptospirosis.
Dynamic SLAM System Using Histogram-based Outlier Score to Improve Anomaly Detection

It is well known that the traditional visual SLAM systems have performed well in a relatively ideal environment, but for the dynamic environment, there are some problems in the instance segmentation algorithm such as incomplete segmentation or wrong segmentation which will result in outliers. Aiming at this problem, this paper proposed a dynamic SLAM system using Histogram-based Outlier Score (HBOS) to improve the reprojection residual constraint to increase the robustness in removing outliers. In this method, HBOS method is used to process the reprojection residual, and the feature association data obtained after the segmentation mask processing is further filtered to improve the robustness. This method can combine the semantic segmentation network with traditional geometric methods to solve the data association problem caused by the wrong information generated from the dynamic objects. To verify the effectiveness of the proposed method, the experiment is carried out on the public TUM data set and the results show that the proposed method improves the accuracy of SLAM positioning in the dynamic environment.
Fake accounts detection system based on bidirectional gated recurrent unit neural network

Online social networks have become the most widely used medium to interact with friends and family, share news and important events or publish daily activities. However, this growing popularity has made social networks a target for suspicious exploitation such as the spreading of misleading or malicious information, making them less reliable and less trustworthy. In this paper, a fake account detection system based on the bidirectional gated recurrent unit (BiGRU) model is proposed. The focus has been on the content of users’ tweets to classify twitter user profile as legitimate or fake. Tweets are gathered in a single file and are transformed into a vector space using the GloVe word embedding technique in order to preserve the semantic and syntax context. Compared with the baseline models such as long short-term memory (LSTM) and convolutional neural networks (CNN), the results are promising and confirm that using GloVe with BiGRU classifier outperforms with 99.44% for accuracy and 99.25% for precision. To prove the efficiency of our approach the results obtained with GloVe were compared to Word2vec under the same conditions. Results confirm that GloVe with BiGRU classifier performs the best results for detection of fake Twitter accounts using only tweets content feature.
A Shallow Spiking Neural Network for P300 Classification

P300 is widely used in brain-computer interface (BCI) systems due to its high bit rates. Fast and accurate classification of P300 is the key to ensure the performance of P300-based brain-computer interface (BCI). In this paper, a shallow spiking neural network (SNN) model based on ANN-to-SNN method is proposed for P300 detection. Firstly, a shallow convolutional neural network (CNN) is pre-trained, and then its weight is transferred to the equivalent SNN for final recognition. The effect of the model is verified based on the public dataset provided by ITBA University. We compare the accuracy of CNN and SNN on the test dataset. The results show that equivalent SNN shows the comparable performance as the CNN, which proves the effectiveness.
Design and verification of RPN Neutron Source Test Robot

Nuclear instrument system (RPN) source test is a key test before the first loading of nuclear power plant core, which directly affects the availability of measuring channel during loading and the efficiency of loading work. An automatic test method of neutron source for nuclear instrument based on robot technology is proposed by studying the traditional test method of neutron source carried manually. The engineering application shows that this method can greatly improve the efficiency of nuclear instrument source test and reduce the requirement of test manpower, and at the same time can minimize the radiation dose of neutron source operator, which has good economic and social benefits.
COVID Impact to Equity Margin Loans—A Practical Approach to Measure Risk with the Client Behavior Assumptions

Equity margin loan deals are loans to counterparties secured by collateral in the form of equities. Credit exposure arises when the equity collateral falls below the value of loan. During the life of the deal, the counterparty has to give more shares (or cash) to banks or brokerage houses in case of a fall in the share price, so that the level of the collateral amount stays approximately the same and does not deteriorate. The objective of this paper is to attempt to review the risks associated to margin loan deals and propose some suggestions to factor in certain margin call delays or liquidation uncertainties under the stressed COVID-19 situation. Moreover, we also showed a sample practical approach to measure the tail risk of margin loan which included the counterparty’s likely behavior under stress and used a callable bond pricing logic to assess the likely time and moral hazard problem of the counterparty to walk away from margin loans.
VECTOR: Very Deep Convolutional Autoencoders for Non‐Resonant Background Removal in Broadband Coherent Anti‐Stokes Raman Scattering

the input to a lower dimensional latent representation without losing critical information. The decoder learns to reconstruct the input from the compressed representation. We also introduce skip connection bypasses from the encoder to the decoder, which beneﬁts the reconstruction performance for deeper networks. We conduct abundant experiments to compare our proposed VECTOR to previous approaches in the literature, including the widely applied Kramers-Kronig method, as well as two another recently proposed methods that also use neural networks.
Effects of Endogenous Non-Starch Nutrients in Acorn (Quercus wutaishanica Blume) Kernels on the Physicochemical Properties and In Vitro Digestibility of Starch

The present study investigated the multi-scale structure of starch derived from acorn kernels and the effects of the non-starch nutrients on the physicochemical properties and in vitro digestibility of starch. The average polymerization degree of acorn starch was 27.3, and the apparent amylose content was 31.4%. The crystal structure remained as C-type but the relative crystallinity of acorn flour decreased from 26.55% to 25.13%, 25.86% and 26.29% after the treatments of degreasing, deproteinization, and the removal of β-glucan, respectively. After the above treatments, the conclusion temperature of acorn flour decreased and had a significant positive correlation with the decrease in the crystallinity. The aggregation between starch granules, and the interactions between starch granules and both proteins and lipids, reduced significantly after degreasing and deproteinization treatments. The endogenous protein, fat, and β-glucan played key roles in reducing the digestibility of acorn starch relative to other compounds, which was dictated by the ability for these compounds to form complexes with starch and inhibit hydrolysis.