Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Evaluation of Internal Model Control for 3ϕ Grid-tied Solar PV Inverter

The control performance of the 3-phase grid inverter decides the quality and quantity of power transfer from solar panels to the utility grid. Therefore, performance enhancement of control techniques of grid-tied photovoltaic system has practical significance. The objective of this work is to enhance servo control of the grid-tied PV system using a multi-variable internal model control (IMC) technique. The design guidelines of IMC for the grid-tied photovoltaic system is clearly presented. The filter model of the voltage source inverter is employed to design the IMC. Experimental results are demonstrated for two conditions. In one of the conditions, cascaded control of DC-link capacitor voltage and grid current has been carried out. The current reference for the IMC is generated from the PI controller which controls the capacitor voltage. Maximum power of the PV is tracked by generating suitable reference voltage for the DC-link voltage control. In the other condition, only IMC performance is verified assuming constant DC source. The experimental setup is explained in detail and concluding remarks are presented.
The Methodology of Extraction and Analysis of Event Log Social Graph

Process mining allows extracting information on the company’s business processes from the event logs of its information systems, including the social resource graph. Such a social graph represents the communication of subjects (human resources) of various business process stages. The paper is focused on extracting a social graph from the ticket history of the customer support system, which stores the same actions of resources that have different business process functions. The paper presents the methodology and, as its realization, the package for extracting a social graph from the ticket history and analyzing the nature and quality of the communication between resources during the ticket solution. The package implements the functions of filtering the ticket history, extracting a social graph from it, social community detection in the extracted graph based on the structural similarity of the nodes and social graph leader identification. Two scenarios of applying the developed methodology are presented using the example of the ticket history of a large retailer.
A Power-Efficient Programmable DCNN Processor for Intelligent Sensing

Existing deep convolutional neural network (DCNN) processors are mainly designed for high-end applications such as autonomous vehicle, data center and smart phone where the design focus is the performance, while for intelligent sensing devices power efficiency are more important. In addition, programmability is important for DCNN processors to support different DCNN. We have proposed a power-efficient programmable DCNN processor dedicated for intelligent sensing devices and demonstrated it using FPGA. Several techniques have been proposed to improve the power efficiency. Implemented on a Xilinx VC707 FPGA board, It achieves a power efficiency of 31 Gops/W with peak performance of 487 Gops, which is better than several state-of-the-art DCNN processors.
Primary and Secondary Frequency Control of Low-Inertia Microgrids with Battery Energy Storage and Intermittent Renewable Energy Resources

This paper aims to present an accurate and meaningful model of primary and secondary frequency control of low-inertia microgrids (MGs), composed of battery energy storage, wind, and solar units. At the same time, the impact of the virtual synchronous generator (VSG) versus the PQ control method on the frequency variation of MG is discussed. Moreover, it is mathematically proven that power sharing occurs based on the reverse proportion of the droop coefficients of the diesel generator and the battery storage in the primary frequency control. However, when the secondary control loop is applied, the battery storage just provides power during transients and produces no power in the steady state. Simulation results show that the frequency response of the MG improves once the VSG control method is used for the inverters as well as confirming the power sharing ratio, having been mathematically proven.
Six-port Reflectometer with Kalman Filter Processing of Sensor Signals

In modern microwave measurements the six-port reflectometers and receivers are used in telecommunications, radiolocation, medicine, Internet of things. The signal processing is based on the algorithms and models that using the redundant information and is aimed at accuracy improving and uncertainty reducing. The Kalman filter is proven to be helpful in signals with errors processing. There was proposed a finite state machine (FSM) with such states as prediction and update for Kalman filter algorithm and its simulation with the help of field programmable gate array (FPGA).
Centimeter scale vertical heterogeneity in bacteria and chlorophyll a

The vertical distribution of bacterial abundance and chlorophyll a was examined at the centimeter scale with a high resolution sampler and the variation at this scale compared to the variation across the entire water column. Samples were taken near the mouth of Chesapeake Bay, USA, in the coastal Atlantic environment as part of the MECCAS (Microbial Exchanges and Couplings in Coastal Atlantic Systems) program. Bacterial abundance changed up to 5.1 times over the entire water column, while changing up to 3.5 times over 10cm. Chlorophyll a concentration changed up to 8 times over the entire water column and 5.1 times across the sampler. The high resolution of the sampler revealed steep gradients of bacterial abundance and chlorophyll a. Bacterial abundance changed up to 35 times m-', while the water column change was up to 2.1 times m-'. Chlorophylla changed up to 45 times m-', while the water column changes were less than 2 times m-' These changes and gradients over small scales indicate that at least under some conditions there are 'small2-scale processes occurring that may be important to plankton ecology, as has been suggested by some theoretical studies.
Fixed-Time Bipartite Containment of Multi-Agent Systems Subject to Disturbance

This paper studies the bipartite containment of multi-agent systems (MASs) subject to the bounded disturbance. Different from the subsistent related works on this topic, the settling time irrespective of initial value can be calculated in advance. Two distributed control algorithms are provided correspondingly for the first-order systems and second-order systems. Based on algebraic theory, properties of the Laplacian matrix and fixed-time stability theory, it shows that fixed-time bipartite containment can be achieved via the presented control law. Simulations are eventually employed to prove the correctness and effectiveness of theoretical results.
Modulation of nurr1 gene expression in mesencephalic dopaminergic neurones

The transcription factor/nuclear receptor Nurr1 is essential for the differentiation of midbrain dopaminergic neurones. Here we demonstrate that, during the ontogeny of rat ventral mesencephalon, nurr1 gene expression is developmentally regulated and its levels show a sharp peak between embryonic day E13 and E15, when most dopaminergic neurones differentiate. In addition, in primary cultures from embryonic rat mesencephalon, nurr1 gene follows a temporal pattern of expression comparable to that observed in vivo. We also report that exposure of embryonic mesencephalic cultures to depolarizing stimuli leads to a robust increase in nurr1 mRNA and protein. The depolarizing effect is also detected in mesencephalic cultures enriched in dopaminergic neurones by using a combination of bFGF and Sonic hedgehog. The latter further increases the number of dopaminergic neurones in these ‘expanded’ cultures, an effect abolished in the presence of anti‐Sonic hedgehog antibodies. Our data show that nurr1 gene is highly expressed in midbrain dopaminergic neurones in a sharp temporal window and that its expression is plastic, both in vivo and in vitro. In addition we show that Sonic hedgehog can direct dopaminergic differentiation in proliferating dopaminergic neuroblasts in vitro.
The effect of vitamin A supplementation in broodstock feed on reproductive performance and larval quality in Penaeus chinensis

The effect of feeding four semi-purified diets A1, A2, A3, A4, containing different vitamin A acetate levels 0, 20, 40, 60 mg kg−1 diet, respectively, on fecundity, egg hatching rate, larval survival rate and vitamin A content in eggs of Chinese shrimp (Penaeus chinensis) broodstock was compared with a fresh clam diet (control) in a 60-day feeding trial. The broodstock shrimp fed the diet with 60 mg kg−1 vitamin A acetate added exhibited significantly higher fecundity (P < 0.01). Hatching rate was highest with diet A4 (P < 0.05), whereas hatching rates were similar fed diets A1, A2, A3. Increasing levels of vitamin A in broodstock diet resulted in improvement in larval quality. The vitamin A levels in shrimp eggs from broodstock fed with diet A4 were higher compared with those from broodstock fed with diet A1, A2 (P < 0.01). The fecundity and hatching percentages were positively correlated with the vitamin A content in eggs in the present study. The results of this study showed that higher level of vitamin A in broodstock diet may have positive effects on fecundity and larval quality in P. chinensis.
Leveraging Shape, Reflectance and Albedo From Shading for Face Presentation Attack Detection

Presentation attack detection is a challenging problem that aims at exposing an impostor user seeking to deceive the authentication system. In facial biometrics systems, this kind of attack is performed using a photograph, video, or 3D mask containing the biometric information of a genuine identity. In this paper, we propose a novel approach to detecting face presentation attacks based on intrinsic properties of the scene such as albedo, depth, and reflectance properties of the facial surfaces, which were recovered through a shape-from-shading (SfS) algorithm. To extract meaningful patterns from the different maps obtained with the SfS algorithm, we designed a novel shallow CNN architecture for learning features useful to the presentation attack detection (PAD). We performed several experiments considering the intra- and inter-dataset evaluation protocols. The obtained results showed the effectiveness of the proposed method considering several types of photo- and video-based presentation attacks, and in the cross-sensor scenario, besides achieving competitive results for the inter-dataset evaluation protocol.
Fast Representative Sampling in Large-Scale Online Social Networks

Online social networks (OSNs) have become important platforms for efficiently connecting people and promoting information dissemination, which is of great importance to our social life and society. However, due to privacy concerns, and access limitations, it is difficult to obtain the whole network of OSNs for analysis, so it is critical to have a representative subgraph. Yet due to the same reasons, we are in lack of the original network as the ground truth which poses great challenges on evaluating sampling methods on the performance on unbiasedness, let alone representativeness. Thus uniform sampling (UNI) [Gjoka et al. 2010] was proposed to obtain an unbiased nodal property distribution as of the original network to evaluate the degree of bias of other methods. Yet UNI sampling suffers from its low efficiency, and the representativeness and connectivity of the obtained subgraph, which is formed by the sampled nodes and connections between them, are rarely studied. We propose an adaptive UNI sampling (adpUNI) method to overcome previously mentioned disadvantages of UNI by dividing the userID space into several intervals, whose sampling probability adaptively changes based on its target rate. By adding its neighbors of the targeted node into the sample set (adpUNI+N), we can further improve the performance on sampling efficiency and obtain a more connective and representative subgraph. When applied to Sina Weibo and Twitter, our methods over-perform other classical methods on sampling efficiency, and always have a better performance on connectivity and representativeness than UNI sampling. And we also find that an unbiased sample doesn’t guarantee a more representative subgraph.
Analysis of Using of Fractal Signals for Noise Immune Information Transmission Systems

This paper analyses the signals with broadband spectrum in order to identify the possibility of their use in information transmission systems in complex electromagnetic circumstances. The possibility of practical implementation of signals in the systems of information transmission and control of signal parameters with the purpose of obtaining high-dimensional signaling space is analyzed. Comparative statistical and correlation properties of different types of self-similar signals: fractal splines, broadband signals, and fractal Brownian noise type signals.
Hourly Prediction of Load using Edge Intelligence over IoT

Energy is the lifeblood of modern societies. In the past decades, due to the increases in population and comfort demands of people, energy consumption and associated CO2 emissions increased rapidly in the world. The next hour’s energy prediction would help in facilitating future energy management, planning, and conservation in the industries and community setups. Data-driven models provide a practical approach to energy consumption prediction. This paper offers an Internet of Things (IoT) based Solution by running a data-driven model between edge and cloud layer to predict the energy consumption of a building in real-time. To achieve a real-time response from the system, the Machine Learning (ML) inference algorithm runs in the edge node to predict the next hour energy demand of the building. A Microgrid IoT setup is established in the authors’ organization for experimental purposes. This paper provides a substantial review of the four main ML approaches, including linear regression, multilayer perceptron, SMO Regression and Support Vector Machine (LibSVM) which have commonly been applied in forecasting and improving building energy performance. Considering the performance measures like Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), this paper concludes that SVM performance is better in terms of prediction accuracy among all others. In terms of prediction time, it is concluded that the edge node takes less time for real-time prediction compared to the cloud.
Highly increased TNF sensitivity of tumor cells expressing the yeast Δ9-desaturase gene

L929 and WEHI tumor cell lines were genetically modified to constitutively express the Saccharomyces cerevisiae Ole 1 gene, coding for the delta 9-desaturase enzyme. These cells exhibit an increased ratio of monounsaturated fatty acids in their membrane phospholipids paralleled by an overall decrease in the membrane molecular order and a highly increased tumor necrosis factor-alpha (TNF) sensitivity. The TNF-alpha signaling cascade involves events, like receptor clustering and cleavage of membrane constituent lipid molecules by phospholipases, which are influenced by the physical state of cellular membranes. We discuss the possible involvement of non-bilayer forming lipids in the control of signaling mechanisms leading to TNF cytotoxicity.
Impact Research of Sound Vibration Frequencies on the Laser Beam Response of the Most Common Ukrainian Glass

The elemental composition of glass samples on an X-ray fluorescence analyzer and radiation transmission spectrograms depending on the laser wavelength has been considered in the article. Power reflection coefficients of the glass samples at different vibrational frequencies have been discovered. The model of window glass oscillation under the action of an acoustic wave has been identified. The oscillation amplitude of window glass under the action of an acoustic wave has been estimated. It has been established that the oscillation amplitude of the window glass increases depending on the type and thickness of the glass.
Desweign and Synthesis of Chassis of Automated Seed Sowing Robot for BT Cotton Seed

Cotton, one of the most important fiber crop, is the backbone of the Indian textile industry with a share of 60% according to the confederation of Indian Industries, 2018. In terms of global production, cotton is the foremost fiber crop. Current estimates for world production are about 25 million tons annually, accounting for 2.5% of the world's cultivable land. The top cotton producing countries include China, India and the United states respectively. India hosts 1/3rd of the global acreage of cotton but accounts for less than 1/5th of the global production. In India, over 6 million farmers have their lives dependent on cotton and about 40-50 million people are working in cotton trade and its processing. Cotton being the major cash crop of India, plays a dominant role in the industrial and agricultural economy of the country. But Cotton cultivators in India are marred by several problems, some of which are: non-availability of labor, high wage rate and inadequate technical know-how. This paper focuses upon our first step towards automating an important activity of agriculture i.e. sowing. And the first step involves the design and development of the chassis of the automated seed sowing robot (For BT cotton seed).
Statistical Analysis of Yield Trials by AMMI and GGE

The Additive Main effects and Multiplicative Interaction (AMMI) model, Genotype main effects and Genotype X Environment interaction (GGE) model, and Principal Components Analysis (PCA) are singular value decomposition (SVD) based statistical analyses often applied to yield-trial data. This paper presents a systematic comparison, using both statistical theory and empirical investigations, while considering both current practices and best practices. Agricultural researchers using these analyses face two inevitable choices. First is the choice of a model for visualizing data. AMMI is decidedly superior, not for statistical reasons, but rather for agricultural reasons. AMMI partitions the overall variation into genotype main effects, environment main effects, and genotype X environment interactions. These three sources of variation present agricultural researchers with different challenges and opportunities, so it is best to handle them separately, while still considering all three in an integrated manner. Second is the choice of a member of a given model family for gaining predictive accuracy. AMMI, GGE, and other SVD-based model families are essentially equivalent, but best practices require model diagnosis for each individual dataset to determine which member is most predictively accurate. Making these two choices well allows researchers to extract more usable information from their data, thereby increasing efficiency and accelerating progress.
Losses from cattle faeces during chemical analysis

The effects of drying temperature and method of sample digestion on the determination of nitrogen and dry matter in the faeces of cattle grazing pastures of two different nitrogen contents were examined. Nitrogen loss varied from 8 to 20 per cent depending on the temperatures and associated length of drying time. The variation in the loss between samples from animals grazing the two pastures was small, tending to be higher for the samples taken from animals grazing a higher nitrogen pasture. Sample digestion by the H2SO4-H2O2 method was found to be as satisfactory as the micro-Kjeldahl method. Drying at 100°C for 24 hours gave the least loss of nitrogen. This procedure in conjunction with the H2SO4-H2O2 , digestion is proposed as a routine method for determination of nitrogen in faecal material.
A Catalogue of Agile Smells for Agility Assessment

Background: The Manifesto for Agile Development has already inspired many software development methods such as Scrum, XP, and Crystal Reports. However, being “agile” is not trivial and only a few companies are capable of mastering so-called agile practices. Failure to apply the agile approach properly can do more harm than good and may jeopardize the benefits of an agile method. Thus, evaluating an organization’s ability to apply agile practices using an agility assessment tool is critical. Aims: In this paper, we extend the metaphor of code smell and introduce the term agile smell to denote the issues and practices that may impair the adoption of the agile approach. The focus of the paper is defining and validating a catalogue of agile smells that can support agility assessment. Method: A literature review and a survey were conducted to identify and confirm the characterization of agile smells. Once identified, the agile smells were organized in a structured catalogue. Results: The literature review found 2376 references published between 2001 and 2018. We selected 55 papers for full consideration and identified 20 agile smells. The survey consulted 20 participants to determine the relevance of the selected agile smells. Conclusion: We have identified a set of 20 agile smells that were ranked according to their relevance. For each smell, we proposed at least one strategy to identify the smell’s presence in real projects. The catalogue can be used by companies to support the assessment of their agility ability.
Combining Multimodal DNN and SigPid technique for detecting Malicious Android Apps

Google announced that they are 2.7 billion active Android users in the world. There are different categories of mobile apps available, based on development nature and different categories, for example apps based on development nature are native apps, hybrid apps, web apps and apps based on different categories are gaming, business, educational, lifestyle, entertainment and utility etc. Due to its scalable growing and open source nature it is easily vulnerable to hackers or attackers to steal our information and inject malware apps into our mobile system. In recent days, a new malware app called Joker was introduced in the mobile market and infected 24 apps in the Google store, which mainly focus on extracting money and credential information from the users without their knowledge. Many scientific researches are undergoing to identify android malicious apps using machine learning classification approach. In this paper, the deep neural network algorithm is chosen to detect Android Malware by extracting features from the Android Manifest file and java API modules then fed into Deep Neural network (DNN) [10] classifier to detect whether the apps as malicious or benign app. This paper combines the Sigpid [1] (Significant Permission Identification) technique which is given as a feature in Multimodal DNN. SigPid was evaluated as a high probability of detecting malware apps using Permissions. The goal is to implement an efficient framework for detecting the Android Malware apps using Machine learning techniques which can be used in App store where all the applications are deployed in cloud and also in Mobile Antivirus app, to detect the harmfulness of the app.