Miracle-dz/UniFAR-FTUs huggingface dataset:
Extreme learning machine – radial basis function (ELM-RBF) networks for diagnosing faults in a steam turbine

ABSTRACT A fast and reliable fault diagnosis system for a steam turbine in thermal power plant is crucial. The system will detect and classify a potential or occurring fault, hence suitable precautions steps will be correctly determined, and unplanned breakdown will be prevented. This study proposes a new application of extreme learning machine-radial basis function networks (ELM-RBF) for steam turbine fault diagnosis system. ELM-RBF recently has been known for its extremely fast computation. The proposed system was tested with real fault historical data from a steam power plant in Jakarta. To evaluate the system performance, a comparison with backpropagation neural networks (BPNN) was conducted. Four scenarios using ELM-RBF and BPNN, with and without ReliefF for feature selection were designed. The results show high accuracy in almost all the scenarios tested. The BPNN shows better accuracy than ELM-RBF, however, ELM-RBF performs considerably faster computation than BPNN without significant decrease in accuracy.
Synchronous vibration suppression of magnetic bearing systems without angular sensors

Active Magnetic Bearing (AMB) levitates rotor by magnetic force without friction, and it can provide active control force to suppress vibration while rotating. Most of vibration suppressing methods need angular speed sensors to obtain rotating speed, but in many occasions, angular speed sensor is difficult to install or is difficult to guarantee reliability. This paper proposed a vibration suppressing strategy without angular speed sensor based on generalized integrator and frequency locked loop (GI-FLL) and phase shift generalized integrator (PSGI). GI-FLL and high-pass filter estimate frequency from control current, PSGI is applied to generate compensating signal. Firstly, model of AMB system expressed by transfer function is established and effect of centrifugal force is analyzed. Then, principle and process of vibration suppressing strategy is introduced. Influence of parameters are analyzed by root locus and bode diagram. Simulation results display the process of frequency estimation and performance of displacement. Experiments are carried on a test rig, results of simulations and experiments demonstrate the effectiveness of proposed vibration suppressing strategy.
Effect of Molecular Weight on Electro – Optic Switching Times of Liquid Crystal Polymer Doped with Ceo2 Nanoparticles

Nonlinearity liquid crystal cell which doped with Ferroelectric nanoparticles characters studied in this paper according to their eclectic absorption. Polysiloxin series with side chain liquid polymers used with side chain polymers and molecular weight ran This study consternate on polymers electro. optic characteristic with various molecular weights by using the mesogenic units and a stable polysiloxane. Suspension sensitivity or Electro-visual response to the mark of the used electric field, a real character to electric liquid crystals. The addition of CeO2, nanoparticles to polymer rise dielectric anisotropy and minimize response times. It observed that the voltages raised with molecular weight rising, and when intensity value rise, we gain voltage operating. Spectrometer used infrared measurements to locate the orientational order parameters, (S¯ ) for liquid crystals with a side series. The orientational order parameters (S¯ ) rely on temperature with various molecular weights and the threshold voltage.
Theoretical study the surface-enhanced Raman scattering spectra of Thiophenol absorbed on Ag nanoparticles

Surface-Enhanced Raman Scattering (SERS) is a powerful spectroscopic technique for highly sensitive molecular detection. It effectively improves the defect of low sensitivity of normal Raman spectra. So it is widely used in the area of surface science, analytical science, biological science and so on. Using Density Functional Theory (DFT) and Time- Dependent density functional theory (TD-DFT), the SERS spectrum has been simulated for biphenyl l-4, 4′-dithiol (BPDT, HS-(C6H4)2-SH), p-terphenyl-4, 4″-dithiol (TPDT, HS - (C6H4)3-SH )1, 4-benzenedithiol (BDT) absorbed on AuNPs and AgNPs. The SERS which aroused by C-C stretching mode is increasing with the benzene ring. Whereas, for the SERS of S-H bending vibrational mode, changing the position of S atom have little effect. The C-S stretching mode and S-H stretching mode are also little effect by the insert number.
Unsupervised Detection of Sybil Attack in Wireless Networks

To address the problem of Sybil attacks in the wireless network, an unsupervised attack detection method using signal frequency bias distribution features is proposed. The method first estimates the signal frequency bias of the emitted signal of each wireless device and then calculates the signal frequency offset distribution characteristics, next it uses the DBSCAN clustering method to perform cluster analysis based on the distribution features. This method does not require prior learning of each device’s signal features and is not affected by the changing of channel status. Experiments based on the real-world environment and commercial hardware show that the proposed method can effectively detect the Sybil attack and achieve high accuracy in identifying malicious devices.
Exploratory and Predictive Analytics of User Preferences from Kaggle LEGO-Toys Datasets Using Spark ML

Apache Spark is an open-source distributed data processing framework. The paper presents a processing architecture for exploring and predicting user preferences using Apache Spark. The architecture is evaluated on LEGO-toys datasets of period 1949-2019 using the Spark Machine Learning (ML) algorithms. The large datasets analyzed consist of LEGO-toys parts, categories, themes and colour features. Spark ML algorithms are applied as (i) k-means analyses of clusters to identify commonalities in LEGO-toys themes and colours, (ii) classifications using the Support Vector Machines (SVMs), Naïve Bayes (NB) and Random Forest (RF) algorithms for theme-preference identification, and (iii) linear regression, decision tree regression, RF, and Gradient Boost for regression analyses to identify the colour-shift in user preferences. The paper elucidates the steps for analytics based on Spark. The results for exploratory and predictive analytics are presented. The evaluation metrics shows that the ensemble regression prediction is better when compared to other algorithms. The analytics give many interesting results. For example, LEGO company’s products have become more colourful (children preferences exhibiting colours spectral-shift and width), diversified and multifaceted over-the-time. The architecture helps in discovering future directions for the new designs in future LEGO products. The proposed architecture can be successfully employed in the related domain to predict product and user’s preferences.
Preparation of Microcrystalline Cellulose from Oil Palm Empty Fruit Bunch Fibre Using Steam-Assisted Acid Hydrolysis

The isolation of microcrystalline cellulose (MCC) from empty fruit bunch fibre (EFB-fibre) using acid hydrolysis through steam treatment (autoclave) followed by ultrasonication has been successfully established. The important parameter studied was the concentration of sulphuric acid (5%, 15% and 25%) at variable fixed reaction time and temperature. The resulting MCC was characterized using FTIR, TGA, XRD and SEM. FTIR transmission at 1163 cm-1 confirmed that the structure of cellulose was retained after undergoing acid hydrolysis. Thermal stability of MCC increased after being treated with H2SO4, which was determined using TG analysis. The morphological features were identified using Scanning Electron microscope (SEM), which showed the diameters of MCC to be in the range of 10 to 200 µm. The structural property of MCC was studied using X-ray diffraction (XRD) and the results showed that the MCC produced has crystallinity index of 72%. The results revealed that the parameters used tend to influence the physicochemical properties of MCC produced. Therefore, the MCC isolated from EFB fibres will be used as precursor for future EFB derived nanocellulose as well as a promising subject in nanocomposite research.
Prediction of Covid-19 Infection in Indonesia Using Machine Learning Methods

Currently, the world is experiencing a prolonged pandemic known as Covid-19. Many prediction models of Covid-19 have been developed by the governments to make the right decisions to control the outbreak. In Indonesia, there is also much research on the prediction of Covid-19 using machine learning methods, which provide the statistics to predict the total cases, the total deaths, the peak and the end of the pandemic. This paper investigates three prediction models: Gaussian Naive Bayes (GNB), and Support Vector Machine (SVM), and Decision Tree (DT) in predicting total cases and total deaths of Covid-19 in Indonesia. First, a preprocessing is applied to change the string data to the numerical dataset using a label encoder. Second, the models are trained using the Covid-19 Indonesia Time Series All Dataset (CITSAD) with 90% and 10% train/test split. The three models are then investigated to predict new cases and new deaths. The evaluation using the CITSAD of ten provinces in Indonesia shows that DT gives the highest accuracy of 93% and provides the fastest processing time of 48.4 seconds.
On the accuracy of soil survey in Queensland, Australia

The accuracy of soil survey is not well described in a consistent manner for either conventional or digital soil mapping in Queensland or more generally in Australia. Concepts of accuracy are often poorly understood and the rise of digital soil mapping has led to further terminology confusion for clients. Despite long-standing recommendations for derivation of accuracy statistics of soil surveys via statistically-based external validation, accuracy assessment by this method has been limited. Concepts for accuracy description (overall, producers and users accuracy) from the remote sensing discipline are applicable to soil survey and their use should be encouraged. An analysis of 12 published 1:50000 and 1:100000 soil surveys in Queensland revealed a 73% to 97% match between mapped polygonal and site data. This, in conjunction with accuracy standards for similar mapping disciplines and published soil survey accuracy assessments, leads us to recommend that a benchmark of 80% accuracy is realistic for all types of soil surveys. The adoption of a benchmark is however dependent upon further development and evaluation of accuracy assessment methods and standards, particularly in relation to minimum sample size and acceptance criteria. These outcomes will only be achieved if all surveys include accuracy assessment within the survey design.
Interests, Norms, Meanings: A Study of Rice Biotechnology in India

Agrarian environments have to be comprehended as being part of a biophysical and social environment that includes the urban and the nonurban, the arable and the nonarable, and other areas that are integrally linked to the world of agriculture and environment and their allied socioeconomic relations. This article examines the responses of rice biotechnologists located in selected Indian public agricultural institutes under the aegis of the State Agricultural University and the Indian Council of Agricultural Research on questions such as “How is GM (genetically modified) technology perceived by rice biotechnologists and under what limiting conditions is it being pursued in rice biotechnology research? Is there a consensus among rice biotechnologists over the application of GM technology? What are the complexities of the GM policy? and What are the implications of intellectual property rights on GM-based research and how scientists are responding to such institutional norms? ”
A Survey : Data Mining and Machine Learning Methods for Cyber Security

This survey paper describes the literature survey for cyber analytics in support of intrusion detection of machine learnings (ML) and data mining (DM) methods. Short ML/DM method tutorial details will be given. Documents representing each method were categorized, read and summarized based on the number of citations and significance of an evolving method. Since data is so important.
CMOS Implementation of a Novel High Speed 4:2 Compressor for Fast Arithmetic Circuits

This paper deals with the design and analysis of a new 4-2 compressor which can be used in high-speed multipliers. The proposed compressor features eliminated glitch at the output waveform. By optimum tuning of the width of the transistors, C out is produced more quickly, and therefore higher operating speeds can be achieved. The effect of process variation on the circuit has been studied and a new structure is proposed to overcome the effect of process variation by utilizing a reference voltage generator circuit. A 32 × 32-bit multiplier has been developed utilizing these compressors in order to evaluate the new compressor in a practical environment. The total multiplier circuit was simulated with HSPICE simulator (BSIM3v3 parameters) using CMOS standard cell library of 0.18 µm and the Layouts were extracted with Cadence Virtuoso v 5.1 Layout Plus tool. The simulation results represent 185 × 10−3 pj Power-Delay-Product (PDP) and 40 pj × ps Energy Delay Product (EDP) with 221 ps delay from input to output.
A Novel Specimen Produced by Additive Manufacturing for Pure Plane Strain Fatigue Crack Growth Studies

Fatigue crack growth is usually studied using C(T) or M(T) specimens with through-thickness cracks. The objective of the present study is to propose a cylindrical specimen with central crack, produced by additive manufacturing. This geometry allows to have pure plane strain state along the whole crack front, avoiding the complexities associated with corner points, crack shape, and variation of crack closure along crack front. Additionally, this geometry may be used to develop studies in vacuum, avoiding expensive vacuum equipment, since the air is not in contact with the crack front. Cylindrical specimens of Ti6Al4V titanium alloy were produced by Selective Laser Melting and tested at a stress ratio R = 0. Marking with overloads was the solution adopted to measure the length of the internal cracks. The fracture surfaces presented circular crack fronts and the da/dN-ΔK curves showed a great influence of atmosphere on fatigue crack growth. An average difference of 50% was found between the results in air and vacuum. Therefore, this geometry with internal crack is an interesting alternative to through-thickness geometries.
True blue: Temporal and spatial stability of pelagic wildlife at a submarine canyon

In coastal systems, marine-protected areas (MPAs) have been shown to increase the diversity, abundance, and biomass of wildlife assemblages as well as their resilience to climate change. The effectiveness of pelagic MPAs is less clear, with arguments against their establishment typically based on the highly mobile nature of pelagic taxa. We used mid-water stereo-baited remote underwater video systems (stereo-BRUVS) and spatial predictive models to characterize the pelagic wildlife assemblage at the head of the Perth Canyon, one of the largest submarine canyons in Australia, over a 7-yr period (2013 – 2019). The total number of unique taxa and mean values of taxonomic richness, abundance, fork length, and biomass demonstrated strong interannual stability, although mean taxonomic richness and abundance were signi ﬁ cantly lower in 2018 relative to other years. Seasonal variability was absent in 2016, but in 2018, taxonomic richness and abundance were three times greater in the Austral spring than in the autumn. Some mobile megafauna were only recorded at the Perth Canyon Marine Park (PCMP) in the autumn, suggesting a seasonal component to their occurrence. The ﬁ ne-scale distribution of pelagic taxa at the canyon head was largely stable over time, with many areas of higher relative probability of presence located outside protected zones. Despite a degree of variability that may relate to the effect of the El Ni ~ no Southern Oscillation on the Leeuwin Current, the PCMP assemblage demonstrates a relatively high degree of spatiotemporal stability. Stronger protection of the PCMP (IUCN II or higher) would potentially improve conservation outcomes for many species of pelagic wildlife.
Temperature tolerance of a hybrid III-V/Si distributed feedback semiconductor laser with a large quality factor

The sensitivity of a hybrid distributed feedback semiconductor (DFB) laser heterogeneously integrated onto silicon (Si) is extensively characterized in the presence of external optical feedback at different bias and temperature conditions. The unique modal engineering approach of the device allows the light generated in the III-V material to be stored in the low-loss Si region to significantly enhance the quality (Q) factor of the cavity resonator. This design leads to an increased temperature tolerance of the laser without impacting the transmission efficiency even under the most severe feedback conditions. At a temperature of T = 35◦C, the laser continuous to unveil optimal performance and exhibits feedback insensitivity when externally modulated at 10 Gbps transmission over a 10 km fiber coil. The study presented here demonstrates the ability of a high-Q laser to achieve floor-free transmission at different operating conditions with a power penalty degradation no greater than 1.5 dB. The prolonged transition to the coherence collapse regime at a much higher reflection level evidenced by this device when compared to its III-V counterparts in addition to its ability to withstand perturbations associated with temperature variations and unintentional back-reflections delivers a step forward towards isolator-free applications. This work suggest that this type of semiconductor lasers can serve as a promising solution for the development of compact and reliable photonic integrated circuits (PICs).
Multiple Regression, Correlation and Path Analysis of Gall Midge Incidence, Yield and Yield Components in Rice (Oryza sativa L.) Hybrids

The present investigation was carried out to understand the interrelationship and degree of dependence of grain yield on its components and gall midge incidence and to elucidate their relative importance. The experiment was conducted using 42 rice hybrids developed by crossing 6 CMS lines and seven testers in Line X Tester mating design, their parents and four checks. Observations were recorded on gall midge incidence, grain yield and fourteen component characters. Correlation coefficient analysis suggested that grain yield per plant had highly significant and positive association with panicle length (rp = 0.4600**, rg = 0.5545**) followed by number of grains per panicle (rp = 0.4219**, rg = 0.5125**) and number of productive tillers per plant (rp = 0.4006**, rg = 0.4942**) and negative non significant association with gall midge incidence (rp =-0.1154, rg =0.1430). Panicle length had the highest positive direct effect (0.9688) followed by the number of grains per panicle (0.5748). Gall midge incidence showed a negative direct effect at the phenotypic level and a positive direct effect at the genotypic level on the grain yield per plant. Further, it showed indirect negative effects via plant height, number of productive tillers per plant, number of grains per panicle, spikelet fertility, hulling percentage, milling percentage, kernel length and kernel breadth as Original Research Article Begum et al.; CJAST, 40(2): 33-45, 2021; Article no.CJAST. 65381 34 revealed from path analysis. The result of multiple linear regression exhibited that only the number of productive tillers per plant and the number of grains per panicle contributed significantly towards grain yield per plant. The step-wise regression analysis revealed that the panicle length, number of grains per panicle and number of productive tillers per plant were the most important characters having R=0.6505 and thus, could explain 42.31% of the total variation of grain yield. Gall midge incidence contributed to only 0.95% of the variation for grain yield, which might be due to the low incidence or resistance of the hybrids to the gall midge.
Social graph convolutional LSTM for pedestrian trajectory prediction

Understanding the movement of pedestrians and predicting their future trajectory can be very important in intelligent transportation systems because accurate pedestrian trajectory prediction will improve the level of autonomous driving technology and reduce trafﬁc accidents. The authors address this problem with a social graph convolutional long short-term memory neural network architecture by considering the movement information of each pedestrian and its interaction with neighbours. Speciﬁcally, the authors use a graph to model pedestrian walking state where nodes denote the pedestrian movement information, and edges represent the interactions between pairwise pedestrians. An end-to-end architecture that combines a sequence-to-sequence model with a graph convolutional network to learn the movement features and interaction features is used. To capture the interaction inﬂuence on different pedestrians, an emotion gate to reﬁne the learned features and ﬁlter out useless information is introduced. A companion loss function to increase the ability of the network to capture ‘walking in groups’ behaviour is further proposed. Through experiments on two public datasets (ETH and UCY), the authors prove that our method outperforms the previous methods.
Pt3Ti (Ti19@Pt60)-Based Cuboctahedral Core–Shell Nanocluster Favors a Direct over Indirect Oxygen Reduction Reaction

Developing a highly efficient catalyst with lower Pt content for the oxygen reduction reaction (ORR) is highly sought for fuel cell applications. The potential applicability of a cuboctahedral core–shell (Ti19@Pt60) nanocluster (NC) toward ORR activity has been investigated and compared with that of a pure Pt NC (Pt79). The energetic stability, thermal stability, and dissolution limit of Ti19@Pt60 NCs has been investigated for their possible synthesis and practical usages. Thermodynamic and kinetic parameters are explored to find out the most favored ORR pathway and product selectivity on the Ti19@Pt60 NC. Rate-determining steps (*O2 activation and *OH formation) are highly improved over the Ti19@Pt60 NC with respect to the cuboctahedral Pt NC (Pt79), pure metal (Pt, Pd, and Ag), and alloy (Pt3M; M = Ni, Co, Ti) based catalysts. Our detailed investigation reveals that the *O2-induced structural changes favor direct *O2 dissociation on the Ti19@Pt60 NC surface. Further, we find that a dual mechanism (ligan.
Polycyclic aromatic hydrocarbons and metals in mosquito coil and coil ash: are the concentrations alarming?

Unmanaged mosquito coil ash could be regarded as a possible source of metal and PAHs contamination in households but such risk is generally overlooked. To address this issue, polycyclic aromatic hydrocarbons (PAHs) and metals were estimated in the bottom ash of a few popular mosquito coils marketed in India. The detected concentration range of individual PAHs in coil ash samples was 8 (benz(a)anthracene and acenapthylene) to 2925 ng g−1 ash (phenanthrene) while metal concentration ranged from 0.3 (As) to 28,366 µg g−1 ash (Fe). Metal concentration ranged from 0.1 (Cd) to 3193.0 (Fe) µg g−1 in the unburnt coils. There was substantial magnification in metal concentration in coil ash over coil. The study indicated that mosquito coil ash could be an important source of metal and PAH contamination in households and hence should be carefully managed.