Miracle-dz/UniFAR-FTUs huggingface dataset:
Load Frequency Control of Two-region Power System based on Differential Evolution Algorithm

The load frequency control of the interconnected grid plays an important role in ensuring the safe and reliable operation of the grid, especially when new energy power generation is involved today, it is necessary to design a suitable controller and optimize the controller parameters so that the system frequency can be guaranteed even when the power system faces some random disturbances. Stability and stability of the exchange power of the tie line. Aiming at the optimization and tuning of load frequency controller parameters for two-region interconnected power grids, a controller parameter tuning scheme using differential evolution algorithm is proposed. This scheme uses the absolute value integral of minimizing the time multiplication error as the objective function, and uses the differential evolution algorithm to search to obtain the optimal load frequency controller parameters. And improved the differential evolution algorithm. A simulation model of the load frequency control system of the tworegion interconnected power grid is established in matlab/simulink. The simulation results show that the proposed algorithm can effectively maintain the stability of the system frequency.
Exploring the relationship between aggression and empathy: A case study in Vietnam

Background: Although aggression is a major predictor of antisocial behaviours in adolescents, it can be reduced by improving empathic concerns to victims. The current study investigated the relationship between empathy and aggressive behaviour in Vietnamese adolescents. It explored the impact of an intervention that emphasized self-control skills and perspectivetaking on aggressive adolescents in Vietnamese samples. Methods: A total of 1,677 adolescents from three regions of Vietnam took part in this study with the employment of the Reactive and Proactive Aggression Questionnaire, Basic Empathy Scale and Self-Control Questionnaire. Among these participants, 26 adolescents identified as having severe aggressive behaviour were selected to attend a pre-and-post intervention for ten weeks. Participants were assessed before and after a 10-week training course. Results: Findings revealed a negative relationship between aggression and empathy in Vietnamese adolescents. There was a statistically significant difference in aggressive behaviour and empathy scores in Vietnamese adolescents for several variables, including gender, academic performance, parents' occupation, and location. The intervention that focuses on self-control skills and perspective-taking has promising outcomes with aggressive adolescents. The obtained results add more to the literature about the role of empathy in reducing aggression in adolescents. 7 Conclusion: The current study was consistent with the previous studies with regard to the importance of empathic concern in reducing aggression. It also extended the evidence of the recommended intervention for aggression, that empathizes on self-control and empathy. Due to the negative association between aggression and empathy in adolescents, it calls for future research to discover other factors related to the aggression-empathy relationship that might help to reduce aggression in adolescents.
Per-Pixel Water and Oil Detection on Surfaces with Unknown Reflectance

Water and oil detection is important for machine vision applications such as visual inspection and robot motion planning. It is known that water absorbs near infrared light and oil absorbs near ultraviolet and blue light. Therefore, observing at the absorbed wavelengths, the apparent spectral reflectances of surfaces with water/oil are smaller than that without water/oil. We could detect water/oil based on the above absorption features by using a hyperspectral image, if the original spectral reflectances of surfaces are known. However, in general, the spectral reflectances of surfaces are unknown and spatially varying. In this paper, we propose a novel per-pixel water and oil detection method based on the Lambert-Beer's law and a low-dimensional linear model for spectral reflectance. We show that our method enables us to pixelwisely detect water and oil on surfaces with unknown and spatially-varying reflectance at high accuracy by using a hyperspectral image. The effectiveness of our proposed method is confirmed through a number of experiments using real hyperspectral images.
Mining Human Activity Patterns from Smart Home for Healthcare Applications

- Big data that are collected from the smart devices have been used to retrieve the human activity patterns to improve the health status of the people, as there is a lot of financial investment in the digital transformation as an effort to provide the healthier ecosystem for people. In these transformation millions of smart devices are equipped around the home, which provides a massive amount of refined and sorted data that can be used to analyze the health patterns of the human. In this research, the work mainly focus on analyzing the big data extracted from human activities in the smart home for frequent pattern mining, cluster analysis, prediction to measure and analyze the energy consumption changes and patterns based on the appliance to appliance level and appliance to time association, which is completely related to human activities
Control system with a non-parametric predictive algorithm for a high-speed rotating machine with magnetic bearings

This paper deals with research on the magnetic bearing control systems for a high-speed rotating machine. Theoretical and experimental characteristics of the control systems with the model algorithmic control (MAC) algorithm and the proportional-derivative (PD) algo - rithm are presented. The MAC algorithm is the non-parametric predictive control method that uses an impulse response model. A laboratory model of the rotor-bearing unit under study consists of two active radial magnetic bearings and one active axial (thrust) magnetic bearing. The control system of the rotor position in air gaps consists of the fast prototyping control unit with a signal processor, the input and output modules, power amplifiers, contactless eddy current sensors and the host PC with dedicated software. Rotor displacement and control current signals were registered during investigations using a data acquisition (DAQ) system. In addition, measurements were performed for various rotor speeds, control algorithms and disturbance signals generated by the control system. Finally, the obtained time histories were presented, analyzed and discussed in this paper
ENSURING COMPETITIVENESS OF AGRO-INDUSTRIAL ENTERPRISES BASED ON DEVELOPING THE HR STRATEGY

The goal of the research is to stipulate the role of personnel in ensuring the competitiveness of agro-industrial enterprises and to define basic areas of its development. It is required to formulate essential provisions of the HR strategy in the agrarian sector to bring the latter to the leading positions in the national economy. The article shows that the formation of the competences of agro-industrial enterprise employees becomes a source of its competitive advantages. It shows the interrelation of the competitive personnel with the other elements of the competitiveness of the agro-industrial enterprise. It is determined that the development of the personnel of agro-industrial enterprises requires the relevant diversified support. The leading areas of the HR strategy of the development of the agroindustrial enterprises’ personnel have been defined. In the context of these areas, the mechanisms whose joint functioning will allow improving the components of the competitiveness of the agro-industrial enterprises’ personnel have been characterized.
The growth and biofilm formation of Enterococcus faecalis in ethanol extract of Citrus aurantiifolia Indonesian species

Context: Enterococcus faecalis is reported as a biofilm bacterium involved in the pathogenesis of tooth root canal infection. The ability to grow and form biofilms is the reason these bacteria are difficult to be eliminated. Aims: To analyze the antibacterial effect of Citrus aurantiifolia against the growth and biofilm formation of E. faecalis. Methods: This study used E. faecalis ATCC 29212 and ethanol extract of lime peel (Citrus aurantiifolia). The E. faecalis growth was assessed by spectrophotometry, and biofilm formation was examined with 1% violet crystals also visualized by microscope (magnification 400×). Results: Citrus aurantiifolia extract can inhibit the growth and biofilm formation of E. faecalis with varying quantities. Based on period incubations, a concentration of 6.25% had a better ability to suppress the growth of E. faecalis (<300 CFU/mL). The 75% concentration was the highest among other levels to inhibit the biofilms formation of E. faecalis that assumed the higher concentration of the extract, the most significant increase in inhibition of the biofilm formation of E. faecalis. Growth and biofilm formation of E. faecalis showed linearity in line with a concentration of 6.25% (p<0.05: 0.000) with a relatively stable relationship (r=0.506). Conclusions: Citrus aurantiifolia extract can inhibit the growth of E. faecalis that is in line with the inhibiton of the biofilm formation.
Navigation Density Prediction of ConvLSTM Model based on Multi-features

The waterborne traffic density in the field of intelligent transportation is nonlinear, unstable and fluctuating, etc. So the accurate prediction of traffic density is faced with challenges. In addition, waterway transportation is an important way of logistics and trade in China. Compared with road transportation, waterway transportation has higher safety and lower cost, so it is particularly important for us to predict the accuracy of waterway traffic density. In this paper, a multi-feature graph convolution and long and short time memory network model is proposed to solve the spatio-temporal correlation problem of traffic flow data and realize the prediction of traffic density in water transportation. The model uses graph convolution operation to extract spatial features and short and long time memory network to extract temporal features. Three related features of ship's navigable density, ship's average speed and ship's density are introduced to achieve more accurate traffic volume prediction. Experimental verification of the model was carried out on the real AIS data set. Through multiple groups of comparative experiments, the experimental results showed that the proposed model was better than the baseline LSTM network and the single-feature graph convolution LSTM model in predicting the navigable density of water transportation, with the accuracy increased by 25% and 13% respectively.
Managing Heparin induced Thrombocytopenia in patients who are undergoing Left Ventricular Device Placement procedure and ultimately Heart transplant

Heparin induced thrombocytopenia (HIT) is a complicated disease that arises from an autoimmune attack against platelets. Heparin that is administered in a patient with HIT forms a complex with Platelet factor-4 (PF4), which is found on the surface of platelets. This complex leads to a Type III Hypersensitivity reaction where IgG antibodies bind to the Heparin/PF4 complex. The platelet-IgG complex subsequently becomes a target for destruction by the spleen. Platelet destruction eventually results in thrombocytopenia five to ten days following heparin exposure. The severity of the resultant thrombocytopenia can range from bruise-like discoloration of the skin all the way to hemorrhagic stroke in the most severe cases. Fragments of the destroyed platelets can further induce the activation of additional platelets, which lead to a thrombotic state. As a result of the recurrent thromboses, patients can develop a deep venous thrombosis (DVT) which can progress into a pulmonary embolism in severe cases. Full understanding of this phenomenon is essential in management of patients who develop this condition.
A STUDY ON FINANCIAL PERFORMANCE OF SELECTED FOOD PROCESSING COMPANIES IN INDIA

Food processing industry is considered as a sunrise industry in the world because of its large potential for growth and socio economic impact. Food processing industry has enormous significance in India’s development because of the vital linkages and synergies it promotes between the two pillars of our economy i.e., industry and agriculture. The study was based on secondary data and three companies namely Brittania Industries Limited, Nestle India Limited and Glaxo SmithKline Consumer Healthcare Limited were purposively selected. The study was carried out for the period of five years i.e. from 2013 to 2017. The liquidity position leverage position was improved for all the three companies and turnover ratios were higher. Financial position was improved for all the three companies from 2013 to 2017.
Towards News Aggregation in Russian: a BERT-based Approach to News Article Similarity Detection

This paper presents neural models developed for the Dialogue Evaluation 2021: Russian News Clustering shared task. The participants were offered the news clustering task, but solutions based on the classiﬁcation were also accepted. We applied a classiﬁcation approach and implemented a hard-voting ensemble of pretrained language models ﬁne-tuned on the ofﬁcial competition dataset. Our solution ranked third among 17 teams at the Russian News Clustering shared task in both private and public leaderboards and obtained 95.73% of F1-score (private LB) and 96.5% (public LB).
Quantifying Soil Moisture Impacts on Water Use Efficiency in Terrestrial Ecosystems of China

Soil moisture (SM) significantly affects the exchange of land surface energy and the stability of terrestrial ecosystems. Although some conclusions have been drawn about the effects of SM on the ecosystem water use efficiency (WUE), the influence mechanism and the quantitative assessment framework of SM on WUE are still unclear. This study provides an analysis framework for the feedback relationship between SM and WUE based on the dependence of the evaporation fraction on SM and output datasets from remote sensing and the Global Land Data Assimilation System. The results show that the range of WUE of terrestrial ecosystems of China was 0.02–19.26 g C/kg H2O in the growing season with an average value of 1.05 g C/kg H2O. They also show a downward trend in 43.99% of the total area. In the evapotranspiration (ET) pathway, SM negatively affected WUE, and the sensitivity coefficient ranged from −18.49 to −0.04. In the net primary production (NPP) pathway, the sensitivity coefficient ranged from −68.66 to 43.19. Under the dual effects of the ET and NPP pathways, the influence of SM on WUE was negative in 84.62% of the area. Variation in SM led to significant WUE variability. Generally, the percentage change in WUE (ΔWUE) ranged from 0% to 190.86%, with an average value of 28.02%. The maximum ΔWUE ranged from 0% to 758.78%, with an average value of 109.29%. The WUE of forest ecosystems showed strong resistance to SM variation, whereas that of non-forest vegetation was more sensitive to SM variation. This analytical framework provides a new perspective on the feedback relationship between WUE and SM in terrestrial ecosystems.
Implementation Word2Vec for Feature Expansion in Twitter Sentiment Analysis Naufal Adi N1, Erwin Budi Setiawan2 1,2Informatic, School of Computing, Telkom University 1naufaladin@student.telkomuniversity.ac.id, 2erwinbudisetiawan@telkomuniversity.ac.id*

Abstract Twitter is a microblog-based social media site launched on July 13, 2006. In March 2020, 476.696 tweets about the government policy in COVID-19 spread on Twitter were captured by the Institute for Development of Economics and Finance (Indef). Government policy has a standard meaning, namely a decision systematically made by the government with specific goals and objectives relating to the public interest, whether carried out directly or indirectly. Sentiment analysis analyzes people’s opinions, sentiments, evaluations, attitudes, and emotions from written language. In this decade, Sentiment Analysis is has become a trendy research area. The purpose of this paper is to focus how to implement word2vec using similarity word as a feature expansion for minimize the vocabulary mismatch in Twitter Sentiment Analysis using “word embeddings”. This research contains 11.395 tweets for a dataset, where the dataset will be used in two classifications: Support Vector Machine Algorithm and Artificial Neural Network Algorithm. The output of Word2Vec will be used for feature expansion in this research, where the algorithm of expansion will check in each row in the corpus where has a similarity vector with that word and will replace the word with the similarity of this words if the value is 0. The dataset in Feature Expansion is using 142.545 articles from Indonesian media. The result of this research is ANN is better than SVM, where the ANN without feature expansion gets 68.89 % and using feature expansion gets 72.58 %. For SVM, the final accuracy without feature expansion is 63.95 %, and using feature expansion gets 68.56 %. This research proves that feature expansion can improve the final accuracy.
Integer Formulation for Computing Transaction Aggregation to Detect Credit Card Fraud

Banks assume great costs caused by fraudulent credit card transactions. With the development of new means of payment and the virtualization of banking services, fraud is becoming more and more diﬃcult to detect and its frequency is growing at a dizzying rate. Currently available defense tools are not enough to counter fraud in continuous evolution. In this work, we present an integer formulation to compute functions of transactions data that turn to be useful for detecting behavioral patterns present in past fraudulent events. Our formulation allows the aggregation of transactions over any set of features, in addition to ﬁltering transactions that verify structured requirements. We test our model with real-world data from a French bank, using SAT solvers. Numerical results obtained on several instances show the eﬀectiveness of our approach.
Proactive disruption impact assessment in manufacturing supply networks

ABSTRACT Companies are increasingly exposed to the risk profile of their supply network and managing this risk is gaining in importance in a tightly interconnected global and competitive business environment. More frequent and more severe supply disruptions have led to a growing interest in assessing their potential consequences. Therefore, in this paper four supply disruption indicators are derived to proactively assess the potential financial impact of supply disruptions. The stochastic nature of potential supply disruptions is modelled by adopting a Monte-Carlo and Value-at-Risk simulation approach: Quantitative, objective data and qualitative, subjective estimates by managers are combined to improve proactive risk-related decision-making at a company level. The developed methodology for deriving the supply disruption indicators is applied to a case study in a German manufacturing company in order to quantitatively assess differences in the disruption risk structures of various supply relations. The results of the study show that a Value-at-Risk approach based on Monte-Carlo simulation can be effectively adopted to assist supply managers in proactively identifying suitable risk mitigation strategies.
Withdrawal Interval Estimation of Doxycycline in Yellow Catfish (Pelteobagrus fulvidraco) Using an LC-MS/MS Method Based upon QuEChERS Sampling Preparation

Limited information on residue depletion of doxycycline (DC) is available in yellow catfish (Pelteobagrus fulvidraco) using the determination of LC-MS/MS based upon a rapid and simple method of sample preparation. This study collected plasma and tissue samples of yellow catfish at pre-determined time points following 3-day consecutive oral administration of DC at 20 mg/kg. The samples were prepared using a QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe) approach. The limit of detection and the limit of quantitation were 2.5 and 5 µg/kg or µg/L, respectively, for DC in plasma and tissues (e.g., muscle + skin, liver, kidney, and gill). The recoveries of DC ranged from 67.2% to 86.2%. The decision limit (CCα) and the detection capability (CCβ) were from 106.2 to 127.8 μg/kg or μg/L. The withdrawal times of DC in muscle + skin were estimated to be 22 d based on the guidelines in China and Europe and 27 d based on Japan’s standard. Overall, this study not only provides an efficient method to rapidly determine the DC concentrations in fish-derived tissues but also provides important information on the safety assessment of DC in aquatic animal-derived food products.
Titanium dioxide ( respirable fraction ) MAK Value Documentation , supplement – Translation of the German version from 2019

The German Commission for the Investigation of Health Hazards of Chemical Compounds in the Work Area has re-evaluated titanium dioxide [13463-67‐1]. Titanium dioxide causes lung tumours in rats at high concentrations. In 2009, titanium dioxide was evaluated and, because a maximum concentration at the workplace (MAK value) could not be established, it was classified in Carcinogen Category 3A. Titanium dioxide is a biopersistent granular dust and, in a later evaluation, inhalation studies with titanium dioxide were used to derive the general threshold limit value for granular biopersistent dusts. Therefore, the respirable fraction of titanium dioxide dust is reclassified in Carcinogen Category 4 and a MAK value of 0.3 mg/m3 ×material density is established for the respirable fraction according to the general threshold value for biopersistent granular dusts. Reclassification in Carcinogen Category 4 is justified because the lung tumours are regarded as a consequence of the inflammatory mechanism of action, for which a threshold can be defined. Direct genotoxic effects appear to be of subordinate relevance for the carcinogenicity of biopersistent granular dusts. By analogy with the biopersistent granular dusts, Peak Limitation Category II is established for titanium dioxide with an excursion factor of 8. Since titanium dioxide is not systemically distributed and accumulates only locally in the lungs, damage to the embryo or foetus is unlikely when the MAK value is not exceeded. Titanium dioxide is classified accordingly in Pregnancy Risk Group C. Titanium dioxide is not expected to be a sensitizer and is not taken up via the skin in toxicologically relevant amounts. The MAK Collection for Occupational Health and Safety 2020, Vol 5, No 1 1 MAK Value Documentations — Titanium dioxide MAK value (2018) 0.3 mg/m3 R × material densitya) Peak limitation (2018) Category II, excursion factor 8 Absorption through the skin –