Miracle-dz/UniFAR-FTUs huggingface dataset:
Photovoltaic‐battery powered grid connected system using multi‐structural adaptive circular noise estimation control

Correspondence Yashi Singh, Electrical Department, IIT Delhi, Hauz Khas, New Delhi, 110016, India. Email: iyashi.singh@gmail.com Abstract The intermittent nature of renewable energy sources results in an interruptible and inadequate power. The integration of the battery energy storage to renewable energy sources provides reliable and continuous power to the loads. Further, the high penetration of renewable energy sources raises the power quality problems in the grid. This work deals with performance improvement of PV-battery energy storage based energy conversion system achieved through implementing a multi-structural adaptive circular noise estimation control for its grid connected mode. The control is developed for the grid side voltage source converter, which provides the power quality improvement and automated transition from grid connected mode to standalone mode or vice-versa. It provides multi-functional features such as harmonics extraction, DC offset elimination, and power quality improvement in PV-battery energy storage based energy conversion system even at nonlinear loading condition in grid connected mode. In standalone mode, the amplitude and waveform of load voltage are controlled sinusoidal by the voltage control. Performance of PV-battery energy storage based energy conversion system is studied to validate the acceptability of these controls according to the IEEE-519 standard.
The concentration of enteric methane from cattle fed different fibre level

The study aimed to predict the concentration on cattle’s enteric methane fed with different crude fibre (CF) content. Twenty-four male Bali cattle were fed with three different rations of A (low CF), B (medium CF) and C (high CF). At the end of the study, the rumen fluid was taken at 0 and 3 h after feeding, analysed for partial VFA to predict the concentration of enteric methane formed. The experiment design was a completed randomized design with three treatments and eight replications. Prediction of methane concentration at 0 and 3 h and their increase were not differed among treatments. At 0 h, the predicted methane concentrations of treatments A, B and C (mean ± SEM) were 12.59 ± 0.561 mmol L−1, 9.53 ± 1.737 mmol L−1 and 9.06 ± 1.041 mmol L−1, respectively. While at the 3 h were 16.64 ± 1.19 mmol L−1, 14.2 ± 1.052 mmol L−1 and 16.24 ± 1.495 mmol L−1, respectively. The increasing methane concentration up to 3 h was 42.74 ± 16.895%, 79.39 ± 16.332% and 58.00 ± 11.120%. It was concluded that the difference in fibre ration content had not affected the concentration of methane up to 3 h after feeding.
Understanding the effects of school catchment areas and households with children in ethnic residential segregation

Abstract Households with children have been suggested to play a key role in ethnic residential segregation. One possible mechanism is that school district boundaries affect their segregation patterns, but direct evidence on this is scarce. This study investigates the role of school catchment areas for ethnic residential segregation among different types of households in the city of Helsinki, Finland, using individual-level register-based data covering the complete population of the city between 2005 and 2014. The analyses consist of three steps: a description of ethnic segregation among different types of households with segregation indices, an analysis of mobility flows between school catchment areas, and a boundary discontinuity analysis of the causal effects of the boundaries of catchment areas on the mobility of different types of Finnish-origin households. The analyses show that ethnic segregation is stronger among households with children than among childless households and the residential mobility of higher-income Finnish-origin households with children is particularly affected by the school catchment area boundaries.
THEORY THROUGH ARGUMENT: APPLYING ARGUMENT MAPPING TO FACILITATE THEORY BUILDING

ABSTRACT This paper proposes an extension of Fletcher and Huff’s (1990) argument mapping technique, which focuses on an examination of individual arguments, to depict the broader argumentative terrain of a theoretical discourse. To illustrate the utility of argument mapping as a literature review technique, we demonstrate an application of this approach to the domain of Technology Frames of Reference (TFR) research. We then compare the insights derived from this approach to those derived from a more traditional literature review of this domain. Findings of this comparison suggest that an argument-centred approach could contribute the following new insights into a theoretical domain: (1) provide a means of weighting contributions to a theoretical discourse by depicting these contributions in terms of levels of argumentation; (2) identify new “types” of argument-centred gaps in the literature; (3) offer a structural and progressive view of theory development in a particular domain that could be used to could be used to link and compare insights derived from competing theoretical lenses; and (4) propose a closer linkage between theory testing and theory building by focusing the testing on “arguable” aspects of a theoretical claim.
Simulation of parallelization options for the computational image formation algorithm in the synthetic aperture radar for oil spill detection

The article discusses the methods of parallelization of the computational image formation algorithm in radar stations designed to detect oil spills. The methods are based on parallel calculations. The methods for modeling the image formation process, the method for extracting information dependencies and the method for scaling and distributing subtasks were developed. The technique for analyzing the efficiency of the parallelization process and evaluating the algorithm stability was suggested, and the computational experiment was conducted. Its results were evaluated.
Temporal variability and driving factors of the carbonate system in the Aransas 
Ship Channel, TX: A time-series study

Abstract. Estuaries are complex systems with substantial heterogeneity in water chemistry, including carbonate chemistry parameters such as pH and partial pressure of CO2 (pCO2), because of the diversity of co-occurring biogeochemical processes. To better understand estuarine acidification and air-sea CO2 fluxes from estuaries, it is important to study baseline variability and driving factors of carbonate chemistry. Using both discrete bottle sample collection (2014–2020) and hourly sensor measurements (2016–2017), we explored temporal variability, from diel to interannual scales, in the carbonate system (specifically pH and pCO2) at the Aransas Ship Channel located in northwestern Gulf of Mexico. Using other co-located environmental sensors, we also explored the driving factors of that variability. Both sampling methods demonstrated significant seasonal variability at the location, with highest pH (lowest pCO2) in the winter and lowest pH (highest pCO2) in the summer. Significant diel variability was also evident from sensor data, but the time of day with elevated pCO2/depressed pH was not consistent across the entire monitoring period, sometimes reversing from what would be expected from a biological signal. Though seasonal and diel fluctuations were smaller than many other areas previously studied, carbonate chemistry parameters were among the most important environmental parameters to distinguish between time of day and between seasons. It is evident that temperature, biological activity, and tide level (despite the small tidal range) are all important controls on the system, with different controls dominating at different time scales. The results suggest that the controlling factors of the carbonate system may not be exerted equally on both pH and pCO2 on diel timescales, causing separation of their diel or tidal relationships during certain seasons. Despite known temporal variability on shorter timescales, discrete sampling was generally representative of the average carbonate system and average air-sea CO2 flux on a seasonal and annual basis based on comparison with sensor data.
Using CNN to Predict the Resolution Status of Bug Reports

Bug tracking systems (BTS) are a resource for receiving bug reports that help to improve software applications. They usually contain reports reported by the end-users or developers. Bug Reports contain some suggestions, complaints, etc. The problem is that every submitted bug report is not accepted to implement. Mostly bug reports are rejected because they are incomplete, duplicate, expired, etc. while only a few are accepted to implement. But the developers have to check every bug report manually that needs many resources (i.e. labour, money, time). In this study, we proposed a convolutional neural network (CNN) based approach to automatically classify bug reports as accepted and rejected. Results show that the proposed approach achieves the highest performance as compared to closely related works.
Impact of Implant Design and Bone Properties on the Primary Stability of Orthodontic Mini-Implants

This study investigated the correlation between bone characteristics, the design of orthodontic mini-implants, the pull-out force, and primary stability. This experimental in vitro study has examined commercial orthodontic mini-implants of different sizes and designs, produced by two manufacturers: Tomas-pin SD (Dentaurum, Ispringen, Germany) and Perfect Anchor (Hubit, Seoul, Korea). The total number of 40 mini-implants were tested. There are two properties that are common to all tested implants—one is the material of which they are made (titanium alloy Ti-6Al-4V), and the other is the method of their insertion. The main difference between the mini-implants, which is why they have been selected as the subject of research in the first place, is reflected in their geometry or design. Regardless of the type of implant, the average pull-out forces were found to be higher for a cortical bone thickness (CBTC) of 0.62–0.67 mm on average, compared to the CBTC < 0.62 mm, where the measured force averages were found to be lower. The analysis of variance tested the impact of the mini-implant geometry on the pull-out force and proved that there is a statistically significant impact (p < 0.015) of all three analyzed geometric factors on the pull-out force of the implant. The design of the mini-implant affects its primary stability. The design of the mini-implant affects the pulling force. The bone quality at the implant insertion point is important for primary stability; thus, the increase in the cortical bone thickness increases the value of the pulling force significantly.
A review on training and leadership development: its effectiveness for enhancing employee performance in Indian construction industry

The second largest contributor to Indian GDP is the construction industry. It is the largest employing industry having huge economic potential which was valued over $126 billion in 2016 and it continues on a steady growth path. It is expected that the value of the real estate and construction market will increase seven-fold by 2028. Growth is likely to be underpinned by stable government support for infrastructure development, as well as through expanded private sector involvement. However, the industry suffers from inefficiencies that result in time and cost overrun in their projects. Some of the potential causes of inefficiencies originate from the underdeveloped labour market and poor organizational structures that operate in the industry. An exhaustive list of factors found to drive the industry to its inefficient operation resulting invariably to failures. If we put all these factors in one basket, they reduce to one single word; “lack of training of workforce and poor management”, whether it refers to the skilled, semi-skilled or unskilled labour force and/or the poor project management from the organizational side of the project. The present study extensively reviews problems of construction company that originate from the labour market.
Review on Crop Prediction Using Deep Learning Techniques

Agriculture is the very important sector of each country, where the gross domestic pay relies on it. The outcome of the agriculture or crop management was completely based on the end yield and the market rate. The complete factor of the crop yield depends on timely monitoring and suggestion. Artificial intelligence gives a way to monitor the crop and to predict the yield in an automatized outcome. The study has been made on the deep learning and its hybrid techniques such as Artificial neural network, deep neural network and Recurrent neural network. It helped to identify how the technology of artificial intelligence helps to improve the crop yield. The research study clearly gives the idea and need of recurrent neural network and hybrid network in the field of agriculture. It also shows how it outperforms the other networks such as artificial neural network and convolutional neural network. The results were analyzed and the future perspectives were drawn with the obtained outcome.
Preparation and Characterization of Electrically Conductive Polymer Nanocomposites with Different Carbon Nanoparticles

Carbon nanoparticles possess a combination of high electrical and thermal transport properties, as well as low density and different morphologies that make them a good choice to reinforce plastics. Polymer nanocomposites offer great expectations for new and unexpected applications due to the possibility of changing their electrical/thermal behavior by adding nanoparticles while retaining the flexibility and processability of plastics. The possibility of electrical and thermal conduction in a polymer matrix with low amounts of nanoparticles brings opportunity for high demanding applications such as electrical conductors, heat exchangers, sensors, and actuators. Polyolefin nanocomposites offer a significant challenge due to their insulative nature and low affinity for carbon nanoparticles; due to the latter, new production tendencies are proposed and investigated.
An average consensus‐based power‐sharing among VOC‐based distributed generations in multi‐bus islanded microgrids

This study proposes a hierarchical control structure for load power-sharing among distributed generations (DG) in a multi-bus microgrid (MG). The proposed control structure uses an average consensus-based distributed protocol (CDP) for the secondary control layer and virtual oscillator control (VOC) as a primary controller. The proposed method can nullify the power-sharing inaccuracy caused by the mismatches of the equivalent impedances seen from the outputs of DGs. The eigenvalue-based small-signal analysis is used to analyse the secondary controller and communication time-delay on system stability. The results of the stability analysis are used to design secondary controller and time-delay compensator. The validity of the proposed method is veriﬁed by both ofﬂine simulations in Matlab/Simulink and control hardware-in-the-loop (CHiL) using a real-time Opal-RT platform.
Attack Sample Generation Algorithm Based on Dual Discriminant Model in Industrial Control System

The research of intrusion anomaly detection in industrial control system is facing the problem of few attack samples. This paper proposes an attack data sample generation algorithm based on dual discriminant model. Firstly, a few attack samples are generated by multiple attacks based on the original data set. Secondly, the sparse matrix algorithm is used to expand the data set, and the original data features are distributed to each extended data to ensure the authenticity of the seed data. Finally, a large number of attack samples are generated by the dual discriminant model to complete the sample expansion. The experimental results of Mississippi SCADA natural gas pipeline data set and an industrial control system data set show that, based on keeping the data in a reasonable range, the algorithm can obtain an effectively expanded sample data set. SVM, Random Forest and XGBOOST are used to classify the dataset. The results show that AUC index is better than the negative sample data set generated by traditional GAN algorithm, and is similar to the initial negative sample index.
Synthesis of nanoparticles composed of silver and silver chloride for a plasmonic photocatalyst using an extract from a weed Solidago altissima (goldenrod)

Phytosynthesis of nanomaterials is advantageous since it is economical, ecofriendly, and simple, and, what is more, in the synthetic protocols, nontoxic chemicals and biocompatible materials are used. Here, a green synthetic methodology of nanoparticles (NPs) composed of silver (Ag) and silver chloride (AgCl) NPs is developed using a leaf extract of Solidago altissima as a reducing agent for the first time. Utilization of a terrestrial weed for the synthesis of Ag and AgCl NPs is a novel environmentally friendly approach considering that no toxic chemicals, external halide source, or elaborate experimental procedures are included in the process. The optical properties and elemental compositions of as-synthesized Ag and AgCl NPs are well characterized, and the degradation of an organic dye, i.e., rhodamine B (RhB), is investigated using the Ag and AgCl NPs. We find that degradation of RhB is effectively achieved thanks to both surface plasmon resonance and semiconductor properties of Ag and AgCl NPs. The surface-enhanced Raman scattering and antibacterial activities are also examined. The present approach to the synthesis of NPs using a weed may encourage the utilization of hazardous plants for the creation of novel nanomaterials.
Compact wide band antenna for millimetric communications

A single-layer ultra-wideband microstrip patch antenna loaded with an asymmetrical U-shaped slot for millimetre wave application is presented in this paper. The antenna is coaxial fed, and it is implemented on a Roger 5880 substrate with a relative dielectric constant of 2.2, thickness 3.15 mm, and loss tangent 0.0009. It operates over the frequency in the Ka-band from 31 to 37 GHz in the microwave spectrum. The results of the simulated reflection coefficient, radiation pattern, and maximum gain of the antenna are in fair agreement with the measured ones. Moreover, the measured reflection coefficient is carried out in the labs. The gain of The peak value of 7.4 dB is obtained at 34 GHz.
Optimization Extraction of Indigofera tinctoria L. using Microwave-assisted Extraction

Dyes are many used in textiles, food, drink, and cosmetics. In the textile industry, the demand for dye is increasing, especially in natural dye. One of the natural dye that is often used in those products is Indigofera tinctoria L. due to its various potential. However, the time extraction needed is very long in the conventional extraction process. Because of that, some researchers have proved that microwave-assisted extraction takes a short time and high yield extraction obtained because microwave radiation is transmitted to the plant matrix that increasing solubility of solvent, and decreasing surface tension. Correlated with that, Indigofera tinctoria L. dye extraction using microwave-assisted extraction has been analyzed by modeling with response surface methodology with Box-Behnken Design (BBD) with parameters of ratio feed to solvent (0.02-0.1 g/ml), temperature (50-60°C) and extraction time (50-90 minutes). The obtained model could represent the extraction process and showed there an interaction among parameters on the yield, but ratio F/S had the most significant effect. Besides, the difference of optimization model obtained at optimum condition process extraction at ratio feed to solvent 0.02, temperature 60°C, and 50 minutes. The actual yield is 4.976% is closed to the predicted yield which the value is 4.85%. The compounds and similarity of the sample were analyzed using FT-IR and the similarity compound of indigo is 39.11%.
The Relationship Between the Electromagnetic Field Amplitude and its Frequency in a Single Photon

The current theory in physics states that amplitude, namely the amplitude of the electric and magnetic fields, is continuous. It is proposed in this paper that these two types of amplitude can be quantized. This study reports that the amplitude of the electromagnetic field is discrete. Two methods were used to generate an equation of the discrete amplitude of the electromagnetic field, namely the Maxwell’s theory of Electromagnetic Field and the Einstein’s theory of the photoelectric effect. According to the results of the theoretical analysis, the lowest amplitude of the electric field and the lowest amplitude of the magnetic field depend on the magnitude of frequency of the electromagnetic field. The study concludes that in a photon containing an electromagnetic field amplitude equal to the E-Goen or B-Goen constant is multiplied by the square of its frequency.
Evaluation of the potential anti-alcoholism activity of chitosan grafted with gallic acid

In order to broaden the application of chitosan (CS) in the anti-alcoholism, gallic acid (GA) was grafted onto the CS molecular chain by 1-(3-dimethylaminopropyl)-3-ethyl carbodiimide hydrochloride (EDC)-mediated coupling, and the structure of gallic acid grafted chitosan (CS-GA) was characterized by using UV-Vis spectroscopy (UV-Vis) and Fourier transform infrared spectroscopy (FTIR). The potential anti-alcoholism activity of CS-GA was preliminarily evaluated with ethanol adsorption rate and 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging rate. The results showed that GA was grafted onto the CS successfully and CS-GA was obtained. CS-GA could adsorb a large amount of alcohol and reach a peak within 15 min after contact with alcohol in vitro. The DPPH radical scavenging ability of CS-GA was 57.5%, which was 2.4 times of CS. In summary, CS-GA had vigorous potential anti-alcoholism activity and is expected to be developed as a promising antidote.
Analyze of shape memory wires behavior under external solicitation using finite elements analysis (FEA)

Nitinol shape memory alloys present many applications in the industrial areas based on exceptional memory effect properties. The behavior of a shape memory alloy (superelastic effect) underwire form (four diameters: 1, 1.5, 2 and 2.5 mm and three lengths: 75, 150 and 375 mm) at drawing was analyzed using finite element analysis (FEA). The wire shape was C letter. The experimental temperature test, in a first step, was 23 °C (room temperature) and we analyze the appliance of four solicitation forces in order to determine the specific deformations and tensions. In second simulation step we apply a higher temperature, respectivelly 80 °C, in order to analyze the behavior of the element through transformation phase from martensite to austenite. The simulation provides information on the total deformation, equivalent stress, and equivalent elastic deformation of the intelligent elements under external solicitations pointing the appearance of maximum and minimum solicitation areas.