Miracle-dz/UniFAR-FTUs huggingface dataset:
Short-term Load Forecasting for Distribution Substations Based on Residual Neutral Networks and Long Short-Term Memory Neutral Networks with Attention Mechanism

Electric load at distribution substation level has strong volatility due to various customer characteristics, making it challenging for short-term load forecasting. This paper proposes a novel short-term load forecasting method for distribution substations based on combined residual neutral networks (ResNet) and Long Short-Term Memory (LSTM) neutral networks with attention mechanism (ResNet-LSTM-Attention) where the advantages of ResNet and LSTM are combined together. A 34-layer ResNet is built to extract the latent features of data. Then, a two-layer LSTM is added to learn the time series characteristics of the extracted features, followed by an attention mechanism to selectively pay attention to the state of the hidden layer. The K-fold cross-validation is further adopted to make full use of data and improve the generalization ability of the model. Finally, the data set from North Carolina and the smart meter energy consumption data from the Low Carbon London project are employed to verify the validity of the method. Compared with traditional LSTM model, the proposed ResNet-LSTM-Attention method shows smaller mean absolute percentage error and better performance on load forecasting.
Research on Blind Source Separation of Transformer Vibration Signal Based on Full Convolution Time Domain Audio Separation Network

The vibration signal on the surface of the transformer tank is formed by mixing the winding and the iron core. Blind source separation of vibration signals can diagnose the operating status of windings and iron cores separately, and improve the accuracy of using vibration signals to diagnose transformers. However, due to the high correlation between the vibration signals of the winding and the iron core, traditional blind source separation algorithms such as FastICA based on the assumption of signal independence have certain limitations. In this paper, a fully convolutional time-domain audio separation network (ConvTasNet) based on a deep learning model is used to blind source separation of transformer vibration signals. The training set and verification set are used to iterate the network. The training is completed when the network loss value is iterated to the specified requirements. This will realize the separation of the vibration signal of the winding and the iron core. This paper has carried out simulation experiments to verify the algorithm. Compared with the traditional FastICA method, the separation effect has been improved. It can more accurately separate the vibration signals of different sources, and the fault diagnosis of transformer windings and iron cores based on vibration signals Promotion and application are of great significance.
Research on feature extraction method of power grid online data based on big data

The development of power simulation artificial intelligence technology needs massive open sample data. It is a trend to use the characteristics of online data to construct sample data. In order to solve the problem that the online data of power grid is rich in information, but the utilization rate of features is not high, aiming at the information features of generators, the LTTB dimension reduction and DBSCAN + L2 clustering methods are proposed, which reduce the complexity of feature extraction of time series data. The method is verified by the actual power grid data, and has achieved certain results.
Drive Control Strategy for Plug-in Hybrid Electric Vehicle System Based on Modular Multilevel Converter

A power conversion system for plug-in hybrid electric vehicles (PHEV) based on “back-to-back” modular multilevel converter(BMMC) is proposed to solve the problem that the traditional MMC-EV can only work with one motor. The power flow of BMMC system with two motors is complex and no one has ever studied it. The state of charge (SOC) balance control strategy of the integrated power converter is studied in various driving modes by this paper. Based on the bridge arm current control, this strategy combines the vector control of permanent magnet synchronous motor(PMSM) with the hierarchical SOC equalization control, realizes the high performance drive control of PHEV system, and high-efficiency power transfer and management among motor, generator and battery, and avoids the battery and energy loss caused by the redundant charge and discharge process. At the same time, it is convenient to switch freely between different driving modes. The correctness and feasibility of this control strategy are verified through RT-LAB real-time simulation.
Combination Prediction Method of Electric Vehicle Charging Load Based on Monte Carlo Method and Neural Network

In recent years, electric vehicles (EVs) have been widely used. A large number of EVs connected to the power grid will affect the economy and stable operation of the grid. Load prediction of EVs is the basis to solve the above problems. In practical application, the parameters of traditional model are difficult to obtain accurately and the calculation speed is slow. In order to solve this problem, a combination prediction method of electric vehicle charging load based on Monte Carlo method and neural network is proposed in this paper. Firstly, the Monte Carlo model is built to fit the electric vehicle charging load (EVCL) according to the user’s behavior characteristics. Then, the neural network is guided by the Monte Carlo model to learn the EVCL under different user behavior characteristics, and the mapping relationship from the basic data to the predicted load is established. Finally, the trained neural network model can realize the EVCL directly and quickly based on the basic data of EVs. The simulation results show that the proposed combined prediction method can realize the prediction of EVCL quickly. It is applicable to the daily total load prediction of large-scale electric vehicle cluster.
Mediating Role of Organizational Culture on the Association between Business Social Responsibility and Organization Performance in Nigeria

The paper investigated the relation between Business Social responsibility and Organization Performance with the mediation role of organization culture in SMEs in Nigeria. Furthermore, this investigation additionally looks at the intercession impact of hierarchical culture on the connection among BSR and execution. The indicators of BSR are trust, commitment and perceived ethics. A theoretical system was created dependent on surviving literary works and the creative model depends on these BSR builds information was gathered through hand conveyance technique by sending polls to 800 SMEs directors. Partial Least Square (PLS) calculation and bootstrap methods were utilized to test the examination’s speculations. The outcomes intimated the positive association between trust, perceived ethics and performance whereas there is negative relation between commitment and performance in Nigerian context. Furthermore, findings of mediation indicate that all the three hypotheses are significant. Therefore, significant positive effects of commitment, trust, and perceived ethics suggest that the variables are important in relation to performance. The outcome of this study provides significant contributions to both managers and researchers for further understanding on organizational culture and BSR effect on performance. As such, organizations should be encouraged to exhibit these social responsibilities for better performance. Improved performance of organizations can advance the social responsibility practices in organizations. Contributions, limitations, implications and necessary suggestions on the new areas of research are recommended and discussed in this research.
The Influence of Human Resource Management Practiceto Organizational Performance by Mediating Effect of Knowledge Management and Talent Management at Coal Mining Industry in Indonesia

The purpose of this research is to offer a model that measures the influence of human resource management to organizational performance by mediating effect of knowledge management and talent management at coal mining industry in East KalimantanProvince of Indonesia. The design used in this research is testing hypothesis by using structural equation modeling (SEM) with 127 samples. The data’s were collected from convenience samples of 10 biggest companies of coal mining industry in East Kalimantan Province being represented by assistant manager and above. This research shows a positive and significant relationship between human resource management and knowledge management to organizational performance and also relationship between knowledge management and talent management. This research also proved the mediating effect of knowledge management and talent management to improve the organizational performance but there was no direct affect of talent management to improve organizational performance.The managerial implication of this research is as a guidance for decision maker in the company or manager how to improve organizational performance in coal mining by implementing knowledge management and talent management. The novelty of this research is the research model refines overall understanding of the direct and indirect impact of human resource management to organizational performance on coal mining industry and moderating effect of knowledge management and talent management to improve it.
Disentangled representations of style and content for visual art with generative adversarial networks

We propose an approach to learning disentangled representations of style and content for generative modeling of visual art. By ﬁxing the style portion of the latent representation, we can generate diverse images in a particular style. Furthermore, we can ﬁx the content portion, examining a particular scene through the lens of a variety of styles. Our approach pairs generative adversarial networks with Siamese discriminators; samples from the real dataset consist of two works from the same artist
Repeated Lies? How Earnings Manipulators Guide Inve stors

This paper investigates the characteristics and con sequences of management guidance issued by firms that manipulate their mandatory financial rep orts. We find that firms manipulating earnings are more likely to issue earnings guidance during the misstatement period than before the manipulation and also in comparison to non-mani pulation firms. Further, the guidance issued by these firms is more in line with their ma nipulated earnings rather than with their restated true earnings. These findings are consist e t with the argument that firms manipulating earnings strategically use earnings guidance to con ceal their misstatements. Regarding the consequences, we find that earnings guidance delays the detection of accounting manipulations. However, when the earnings manipulations are detect ed, firms and managers issuing earnings guidance experience additional legal costs, as refl ected in the higher settlement amounts paid by these firms, and a higher likelihood that the conce r ed managers will be banned from serving as officers, directors, or accountants of a public com pany in the future. * We thank the Berg Center for Ethics and Leadership at the Katz Graduate School of Business, Universit y of Pittsburgh for research support. We thank the work shop participants at the University of Pittsburgh, the conference participants at the 2011 AAA meeting, and our discu s ant, Wendy Wilson, for their helpful comments. 1 Repeated Lies? How Earnings Manipulators Guide Inve stors O what a tangled web we weave When first we practice to deceive Sir Walter Scott (Marmion, 1808)
A Multi-Level Plagiarism Detection System Based on Deep Learning Algorithms

Summary With the high rate of online scientific publications and the accessibility to retrieval of information, has proved enormous problem of plagiarism. The techniques for detecting plagiarism are becoming increasingly advanced, whereas plagiarism of ideas still one of the greatest challenges. In this regard, some methods have been proposed in order to minimize the act of plagiarism of idea. In this paper, we propose a system of plagiarism detection of ideas based on Deep Learning Algorithms. The proposed approach deals with some problems encountered in detecting the plagiarism of ideas such as: loss of meaning or the difficulty of detection of semantic similarity between documents. Thus, our system consists of using in a first place doc2vec to have a vector representation of each sentence of a document and then we use the siamese LSTM to make learn our system that pair of documents is similar and finally we use the CNN algorithm to classify the different types of plagiarism.
Turnover Intention and Abusive Supervision and Management: Investigating the Role of Self-Identity and Future Work Self-Salience

The purpose of this study is to examine the linkage of abusive supervision and turnover intentions of employees of pharmacy in Thailand milieu by focusing on mediation of self-identity and moderation of future work selfsalience. Destructive leaders have been characterized as abusiveness: they practice hostile verbally and non-verbally reactions towards employees. Furthermore, the major objective is to compare the findings of manufacturing and services context of pharmacy to suggest research patterns. The findings of the present study reported that abusive supervision and turnover intentions are positively associated while abusive supervision and self-identity have negative relationship and self-identity and turnover intention also negatively correlated with each other in pharmacy in Thailand. Furthermore, self-identity mediated in the relationship of abusive supervision-turnover intents in services context and has no mediation effect in the context of pharmacy in Thailand. This study is first of its kind, it provides new insights to understand abusiveness at the workplace in manufacturing and services contexts of pharmacy in Thailand.
How a Conversational Agent Might Help Farmers in the Field

In this position paper, we look at how conversational search technology might be used to help farmers meet their increasingly demanding information needs. Agriculture has become more data-driven as a plethora of resources are available for farmers in making growing decisions. However, accessing, interpreting, collating and contex-tualising all these resources is a real impediment for farmers in the field. We posit that conversational search offers an attractive solution to this problem. In this paper, we categorise the unique information needs of farmers and explain some of the problems and challenges that these create from a search perspective. We show why conversational search offers a unique solution to these problems. The key components of a hypothetical conversational agent that meets farmers information needs are presented. Finally, we highlight how the agricultural domain actually offers an interesting and fruitful playing field for research on conversational search agents and encourage further work in this area
DAMAGE POTENTIAL OF ROOT-KNOT NEMATODE (Meloidogyne incognita Chitwood) POPULATION DENSITY ON PLANT GROWTH PARAMETERS RELATED TO PLANT AGE OF MUNG BEAN (Vigna radiata (L.) Wilczek)

Mung bean (Vigna radiata (L.) Wilczek) is an economically important leguminous crop in Asia, where root knot disease caused by Meloidogyne incognita (RKN) is a major constraint to mung bean production. Despite its significance in mung bean production, the effects of RKN on the growth, yield, and rhizobial nodulation in mung bean have rarely been investigated. Moreover, the influence of time of RKN infection i.e. size of galls and giant cell formation is not known in relation to plant age of mung bean. This research investigated the effect of initial population density of RKN inoculated at different ages of the susceptible mung bean cultivar “Kamphaeng Saen 2” (KPS2) on the growth, yield, rhizobial nodulation, size of galls, and giant cell formation. The impact on growth, yield and rhizobial nodulation of different nematode densities and plant ages were determined compared to a control. The initial population density levels (100, 500, 1,000, 2,000 and 4,000 infective second-stage juveniles (J2s) of M. incognita) were negatively correlated with growth, yield (except root weight) and rhizobial nodule parameters. In contrast, plant age at inoculation was positively correlated with these parameters at each inoculum level. Additionally, the sizes of root galls and giant cells of young mung bean plants infected with RKN were larger than for older plants.
AN EFFICIENT VLSI IMPLEMENTATION OF APPROXIMATE MULTIPLIER FOR EDGE DETECTION ALGORITHM

Approximate computing is an emerging trend in VLSI design aimed at reducing the overall efficiency in terms of area, power and delay. This technique trades off accuracy for better performance and can be employed in fault tolerant applications like multimedia processing. In this paper, we propose three variants of 16x16 multipliers designed using approximate 4:2 compressors and 15:4 compressors. The design focuses on trade off of accuracy for area reduction and faster computation. To examine the performance of the multiplier in typical fault resilient application, Sobel edge operator is used. The acceptable results from the edge detection algorithm proves the niche that approximate computing has found for itself in the domain
Foreign Remittance and Economic Performance of Nigeria

The study examines the influence of foreign remittances on the economic performance of Nigeria using time series data for the period 1986-2018. The time-series data was sourced from the World Bank. Error Correction Model (ECM) technique was adopted to determine the impact of foreign remittances on economic performance in Nigeria and how the exchange rate mediates the relationship. From the empirical examination, the paper posited that a reduction in the exchange rate is required for the foreign remittances to effectively influence the performance of the economy of Nigeria. The study, further, concluded that while the foreign remittances improve the performance of the economy of Nigeria, the exchange rate is harming it. The paper, therefore, recommends that the Government of Nigeria needs to overvalue naira and make it stable to boost the effect of the foreign remittances on the performance of Nigerian economy as well as collaborate with the money transfer agents to reduce the cost of transferring money to Nigeria and offer incentives like tax exemption for the Nigerians in diaspora when they invest certain portion of their foreign earned money. This strategy might encourage the Nigerians in diaspora to channel their resources into the investment ventures that could transform the performance of the economy of Nigeria.
Implementation of the National Early Warning Score Tool in the Acute Care Setting

Practice Problem: The organization arbitrarily called a rapid response or code blue call based on abnormal vital signs or intuition and needed a new process to identify early recognition of patient deterioration. PICOT: For adult inpatients in an acute care setting in a large healthcare system (P), will implementation of the National Early Warning Score (NEWS) across the organization (I) compared to data from the last fiscal year where NEWS was not used (C) decrease code blue calls (O) within 8-weeks (T)? Evidence: Eleven high quality studies met the inclusion criteria and found that the NEWS is a validated track and trigger tool, which promotes early detection of patients’ clinical deterioration and more accurate rapid response calls. Intervention: Staff fulfilled virtual training on completion of the NEWS tool within the computerized patient record system. Chart audits were conducted to measure compliance with the number of times the NEWS tool was used to trigger an event, and the number of times the trigger was missed. Outcome: The result of the two-tailed paired samples t-test was not statistically significant for rapid response calls. However, the clinical significance of NEWS implementation was that there was an increase in rapid responses and a decrease in code blue responses post NEWS
EFFECTS OF ETHANOL LEAF EXTRACT OF EREMOMASTAX SPECIOSA (AFRICAN BLOOD TONIC) ON FEMALE REPRODUCTIVE HORMONES OF ALBINO WISTAR RATS

Eremomastax speciosa is known to exhibit a wide range of biological activities because of the presence of tannins, saponins, alkaloids, flavonoids and cardiac glycosides. This work evaluates the effects of Eremomastax speciosa on reproductive hormones in female rats using standard analytical methods. The phytochemical screening and the antioxidant activities of Eremomastax speciosa leaves were also evaluated using standard methods. Twenty-five matured virgin female rats were divided into five groups of five rats each; groups 1 and 2 were the normal and positive controls and were given distilled water and the standard drug respectively while groups 3, 4 and 5 were administered the ethanol extract of the plant in graded dosages of 400, 200, and 100mg/kg respectively. The phytochemical screening reviewed the presence of alkaloids, tannins, saponins, terpenes and flavonoids while, the leaves showed a significant free scavenging activities with IC50 90μg/ml. The hormonal result showed that the extract caused a dose dependent significant increase (p<0.05) in estrogen, prolactin, progesterone and FSH when compared with the normal control. However, there was a significant decrease (p<0.05) in serum LH levels also in comparison with the normal control. In conclusion, the ethanol leaf extract of Eremomastax speciosa suggested an increase in the reproductive enhancing hormones which might increase the reproductive activities of the female rats.
AN EFFICIENT VLSI IMPLEMENTATION OF EDGE DETECTION ALGORITHM FOR IMAGE PROCESSING APPLICATION

: Edge detection is that the core research area among different fields such as; image processing. Computer vision, machine learning, pattern recognition. In object detection, the primary obligatory step is to work out the perimeters of an object during a better way that's further used for processing. The feature vector comprises nothing but key point description, which provides the data of edges. Edge detection is that the key to success and is somehow or the opposite passionate about it. The proposed Sobel edge detection algorithm uses approximation methods to interchange the complex operations; pipelining is used to cut back the latency. Finally, this algorithm is implemented by Verilog HDL. The mix is formed with Prewitt and Laplacian algorithm as one architecture by similar methodology compared with the previous hardware architecture for edge detection, the proposed architecture requires fewer Area, Delay & Power Efficient.
Analysis of body shape variation in Glossogobius giuris (Hamilton 1882) sampled from Lake Mainit, Philippines, using geometric morphometrics

Fluctuating Asymmetry (FA) is a tool to assess morphological differences from different bioindicator species. This study was conducted to evaluate the body shape variation of Glossogobius giuris sampled from Lake Mainit, Philippines, using geometric morphometrics. A total of 60 specimens was collected with 30 each sex. Landmarking techniques were established and applied to the fish morphology. Samples were subjected to tps Util and Symmetry and Asymmetry Geometric Data (SAGE) software to identify the body shape analysis. Among the factors analyzed, Procrustes ANOVA results shown a highly significant difference (P<0.0001) in the female and male samples and thus, indicating a shape differences between the sexes. While Principal Component Analysis (PCA) displayed a higher percentage in female samples (83.1614%) than of the male (79.3984%) denoting body shape variations between the populations indeed, females are highly inclined to perturbations since they need a buffer from various environmental requirements and primarily through the reproduction process. Thus utilizing geometric morphometric enables to determine morphological variations of species among and within the taxa.