Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Transformer Modeling in Assessing Dielectric Failure Analysis

In this paper we analyze the possible causes of two dielectric failures that occurred in a hydropower s ubstation. To investigate the possible causes, the substation was modeled in detail and distinct transformer models were considered: a simple RLC circuit given by the manufacturer and fitted rational modeling of the transformer admittance matrix. The rational modeling was implemented as a multi-phase RLC circuit and a companion network in ATP. Accuracies issues regarding the realization of the rational modeling in ATP are als o presented. The obtained waveforms were further analyzed using a detailed winding model of the transformer. The results indic ated that higher overvoltages occurred across some parts of t he winding.
Miniature Inverted-Repeat Transposable Elements (MITEs) in the Two Lepidopteran Genomes of Helicoverpa armigera and Helicoverpa zea

Simple Summary Miniature inverted-repeat transposable elements (MITEs) are non-autonomous transposable elements that play important roles in genome organization and evolution. Helicoverpa armigera and Helicoverpa zea shows a high number of reported cases of insecticide resistance worldwide, having evolved resistance against pyrethroids, organophosphates, carbamates, organochlorines, and recently to macrocyclic lactone spinosad and several Bacillus thuringiensis toxins. In the present study, we conducted a genome screening of MITEs in the H. armigera and H. zea genomes using bioinformatics approaches, and the results revealed a total of 3570 and 7405 MITE sequences in the H. armigera and H. zea genomes, respectively. Among these MITEs, we highlighted eleven MITE insertions in the H. armigera defensome genes and only one MITE insertion in those of H. zea. Abstract Miniature inverted-repeat transposable elements MITEs are ubiquitous, non-autonomous class II transposable elements. The moths, Helicoverpa armigera and Helicoverpa zea, are recognized as the two most serious pest species within the genus. Moreover, these pests have the ability to develop insecticide resistance. In the present study, we conducted a genome-wide analysis of MITEs present in H. armigera and H. zea genomes using the bioinformatics tool, MITE tracker. Overall, 3570 and 7405 MITE sequences were identified in H. armigera and H. zea genomes, respectively. Comparative analysis of identified MITE sequences in the two genomes led to the identification of 18 families, comprising 140 MITE members in H. armigera and 161 MITE members in H. zea. Based on target site duplication (TSD) sequences, the identified families were classified into three superfamilies (PIF/harbinger, Tc1/mariner and CACTA). Copy numbers varied from 6 to 469 for each MITE family. Finally, the analysis of MITE insertion sites in defensome genes showed intronic insertions of 11 MITEs in the cytochrome P450, ATP-binding cassette transporter (ABC) and esterase genes in H. armigera whereas for H. zea, only one MITE was retrieved in the ABC-C2 gene. These insertions could thus be involved in the insecticide resistance observed in these pests.
Protein–Protein Binding Free Energy Predictions with the MM/PBSA Approach Complemented with the Gaussian-Based Method for Entropy Estimation

Here, we present a Gaussian-based method for estimation of protein–protein binding entropy to augment the molecular mechanics Poisson–Boltzmann surface area (MM/PBSA) method for computational prediction of binding free energy (ΔG). The method is termed f5-MM/PBSA/E, where “E” stands for entropy and f5 for five adjustable parameters. The enthalpy components of ΔG (molecular mechanics, polar and non-polar solvation energies) are computed from a single implicit solvent generalized Born (GB) energy minimized structure of a protein–protein complex, while the binding entropy is computed using independently GB energy minimized unbound and bound structures. It should be emphasized that the f5-MM/PBSA/E method does not use snapshots, just energy minimized structures, and is thus very fast and computationally efficient. The method is trained and benchmarked in 5-fold validation test over a data set consisting of 46 protein–protein binding cases with experimentally determined dissociation constant Kd values. This data set has been used for benchmarking in recently published protein–protein binding studies that apply conventional MM/PBSA and MM/PBSA with an enhanced sampling method. The f5-MM/PBSA/E tested on the same data set achieves similar or better performance than these computationally demanding approaches, making it an excellent choice for high throughput protein–protein binding affinity prediction studies.
Integrated Resource Management for Fog Networks

In this paper, we consider integrated resource management for fog networks inclusive of intelligent energy perception, service level agreement (SLA) planning and replication-based hotspot offload (RHO). In the beginning, we propose an intelligent energy perception scheme which dynamically classifies the fog nodes into a hot set, a warm set or a cold set, based on their load conditions. The fog nodes in the hot set are responsible for a quality of service (QoS) guarantee and the fog nodes in the cold set are maintained at a low-energy state to save energy consumption. Moreover, the fog nodes in the warm set are used to balance the QoS guarantee and energy consumption. Secondly, we propose an SLA mapping scheme which effectively identifies the SLA elements with the same semantics. Finally, we propose a replication-based load-balancing scheme, namely RHO. The RHO can leverage the skewed access pattern caused by the hotspot services. In addition, it greatly reduces communication overheads because the load conditions are updated only when the load variations exceed a specific threshold. Finally, we use computer simulations to compare the performance of the RHO with other schemes under a variety of load conditions. In a word, we propose a comprehensive and feasible solution that contributes to the integrated resource management of fog networks.
Social Media Recommender Systems (SMRS): A Bibliometric Analysis Study 2000–2021

The increasing popularity of social media resources such as blogs, bookmarks, chatrooms, forums and video portals in recent years has attracted diverse users. Following the rise of the Internet, online content has become overloaded, prompting the introduction of recommender systems on social media. As a result, research on the dynamic growth of recommender systems in social media has gained significant traction since the year 2000. Social media recommender systems (SMRS) utilize multiple recommendation fields such as item, user, location, tag, event, tour and game in searching for preferred recommended information. Thus, young research fellows, academic scholars and practitioners must understand the need for SMRS to be complemented with recommendation fields. This requirement underlines the significance of a bibliometric analysis that focuses on social media based on existing publications. Hence, using the Web of Science (WoS) database, this study aimed to gather statistical information on SMRS to help researchers acquire an extensive understanding of such systems. The analysis was conducted by identifying SMRS-related publications and scientometric indicators to assess the growth rate, including the relative growth rate (RGR), doubling time (DT) and the field-normalized citation score (NCSf)—for citation analysis. Overall, this bibliometric study provides relevant measures for comparing and improving the citation rate of publications for new researchers.
IncFIB-4.1 and IncFIB-4.2 Single-Replicon Plasmids: Small Backbones with Large Accessory Regions

Purpose To establish a typing scheme for IncFIB replicon and to dissect genomic features of IncFIB-4.1/4.2 single-replicon plasmids. Methods A total of 146 representative fully sequenced IncFIB-replicon-containing plasmids were selected to construct a phylogenetic tree of repBIncFIB sequences. A collection of nine IncFIB-4.1/4.2 single-replicon plasmids from China were fully sequenced here and compared with the first sequenced IncFIB-4.1/4.2 single-replicon plasmids from GenBank to dissect their genomic diversity. Results In this study, a repB sequence-based scheme was proposed for grouping IncFIB replicon into seven primary types and further into 70 subtypes. A collection of nine IncFIB-4.1/4.2 single-replicon plasmids were fully sequenced here and compared with the first sequenced IncFIB-4.1/4.2 single-replicon plasmids from GenBank. These 11 plasmids had small backbones and shared only three key backbone markers repB together with its iterons, parABC, and stbD. Each plasmid contained one large accessory region (LAR) inserted into the backbone, and these 11 LARs had significantly distinct profiles of mobile genetic elements (MGEs) and resistance/metabolism gene loci. Antibiotic resistance regions (ARRs; the antibiotic resistance gene-containing genetic elements) were found in seven of these 11 LARs. Besides resistance genes, ARRs carried unit or composite transposons, integrons, and putative resistance units. IncFIB-4.1/4.2 single-replicon plasmids were important vectors of drug resistance genes. This was the first report of three novel MGEs: In1776, Tn6755, and Tn6857. Conclusion Data presented here provided a deeper insight into diversity and evolution of IncFIB replicon and IncFIB-4.1/4.2 single-replicon plasmids.
The Correlation Between Students’ Speaking Skills And Self-Directed Learning In Virtual English Community

This study was conducted to investigate correlation between students' speaking skills and self-directed learning in a virtual English community. This quantitative research involved 20 students who joined the speaking community at a public university in Surabaya. It used speaking rubric and a questionnaire as instrument data and was then analyzed by using SPSS 26.0 for Windows. The result of this study was the students included moderate self-directed learning users. The highest mean score was self-management (M=3.79) and followed by self-motivation (M=3.64) and self-monitoring (M=3.34). In terms of speaking skills, the students were good speakers in every speaking aspect. Sequentially, the best speaking aspect mastered was vocabulary and followed by grammar & accuracy, pronunciation, and fluency & coherence. Through correlation analysis, the use of self-directed learning was highly correlated with the students' speaking skills (r=0.669). Thus, it could be said that applying self-directed learning is a helpful way for the students' speaking skills. Keywords: Speaking skills, self-directed learning, English community
EVALUATION OF WATER DELIVERY EFFICIENCY IN IRRIGATION CANAL UNDER EXISTING MANAGEMENT STRATEGY USING HYDRAULIC MODEL

Managing water distribution in irrigation canals aims timely and in needed quantity implementation of supplies of water to irrigated area without excessive spillage i.e. at reduced conveyance loss and adequate water use for irrigation of the crops. This article analyzed existing management practice of water distribution in irrigation canal in view of efficiency. The study was carried out for an existing main irrigation canal. The operational water loss was evaluated by indirect quantitative approach. Hydraulic simulation model of the irrigation canal was created using hydraulic software HEC-RAS. Changes in hydraulic conditions due to existing structures along the canal course were taken into account in the model. The water operational loss was determined by simulation with HEC-RAS model in steady state conditions. The model was used to study the influence of the operating conditions on the size of loss. Results obtained show the influence of the management practice of the water distribution and operation conditions and maintenance of the canal on the magnitude of operational loss.
Divergences of the RLR Gene Families across Lophotrochozoans: Domain Grafting, Exon–Intron Structure, Expression, and Positive Selection

Invertebrates do not possess adaptive immunity but have evolved a variety of unique repertoires of innate immune sensors. In this study, we explored the immune diversity and specificity of invertebrates based on the lophotrochozoan RLRs, a major component in antiviral immune recognition. By annotating RLRs in the genomes of 58 representative species across metazoan evolution, we explored the gene expansion of RLRs in Lophotrochozoa. Of note, the N-terminal domains of lophotrochozoan RLRs showed the most striking diversity which evolved independently by domain grafting. Exon–intron structures were revealed to be prevalent in the domain grafting of lophotrochozoan RLRs based on an analysis of sibling paralogs and orthologs. In more than half of the cases, the mechanism of ‘exonization/pseudoexonization’ led to the generation of non-canonical N-terminal domains. Transcriptomic studies revealed that many non-canonical RLRs display immune-related expression patterns. Two of these RLRs showed obvious evidence of positive selection, which may be the result of host defense selection pressure. Overall, our study suggests that the complex and unique domain arrangement of lophotrochozoan RLRs might result from domain grafting, exon–intron divergence, expression diversification, and positive selection, which may have led to functionally distinct lophotrochozoan RLRs.
Chromosomal Engineering through CRISPR– Cas Technology: A Way Forward

The breeding of crops is dependent on the potential to interrupt or maintain genetic links between characteristics, and the availability of genetic variability. CRISPR-Cas is a new genome-editing technique that has made it possible for breeders to introduce regulated and site-specific genetic diversity while simultaneously improving qualities with high efficacy. The existence of genomic linkage is a barrier in transferring desirable features among domesticated species from their wild counterparts. One way to address this issue is to create mutants with deficiencies in the meiotic recombination machinery, thereby enhancing global crossover frequencies between homologous parental chromosomes. Although this seemed to be a promising approach at first, thus far, no crossover frequencies could be enhanced in recombination-cold regions of the genome. Consequently, attempts have been made to induce site-specific DSBs in both somatic and meiotic plant cells by utilizing CRISPR–Cas techniques to achieve preset crossovers among homologs. Nonetheless, this method has not yielded significant heritable homologous crossings which were recombination-based. Lately, CRISPR–Cas has been used to achieve hereditary chromosomal rearrangements (CRs), including translocations and inversions, in plants. This method allows for the development of megabase CRs by DSB repair through non-homologous end-joining after insertion of DSBs in somatic plant cells. This technique may potentially make it possible to restructure genomes on a more global scale, culminating in the creation not just of synthetic plant chromosomes, but also that of new plant species.
Research on the Prediction of the Operational Risk Field of Intelligent Vehicles Based on Dual Multiline LiDAR

To effectively evaluate the risk situation between intelligent vehicles and surrounding traffic participants in complex scenes, a complex traffic environment perception technology based on dual multiline light detection and ranging (LiDAR) is proposed in this work. The vehicle motion state is predicted by fusing the multiview characteristics of point cloud timing and multitarget interaction information, and the risk assessment model is constructed via artificial potential field theory. The real-time point cloud information is used to obtain the time-sequence bird’s-eye view and range image. The improved VGG19 network model is used to extract the time-sequence high-level abstract combined features in the multiview scene. The constructed time-sequence feature vector is used as the input data of the attention mechanism, and the attention-bidirectional long short-term memory (Attention-BiLSTM) model is used for training to form the desired input-output mapping relationship. The motion state of the target vehicle can therefore be updated, and the static and dynamic risk fields of traffic participants surrounding the vehicle can be established based on artificial potential field theory, thereby allowing for the evaluation of the operational risk of the intelligent vehicle. The results of experiments demonstrate that the prediction effect of the target vehicle state parameters via the use of the proposed model is better than that of other compared models, and the prediction effect of the risk field of intelligent vehicle operation based on the multiview point cloud features and vehicle interaction information is good.
Effects of Dynamic Stiffness of Rotor Bearing on Rotordynamic Characteristics

The calculations of rotordynamic characteristics for an aeroengine were performed respectively by the static stiffness,dynamic stiffness and engine finite element model for the rotordynamics characteristics affected by dynamic stiffness of the rotor bearing.The various calculation results were compared and analyzed.The reasons of drastic change of the dynamic stiffness of rotor bearing were analyzed.The effects of the dynamic stiffness of rotor bearing and the local vibrations of various casings on the engine vibration could be analyzed by the engine model.
Willingness to Receive COVID-19 Booster Vaccine: Associations between Green-Pass, Social Media Information, Anti-Vax Beliefs, and Emotional Balance

The aims of the present investigation were (i) to determine psychological relapses of COVID-19 booster vaccine; (ii) to identify the determining factors affecting willingness to receive COVID-19 vaccine; and (iii) to study the relationship among emotional characteristics (anxiety, stress, depression, optimism), social media information, and the mandatory political choices (i.e., green-pass) in Croatian people. A cross-sectional online survey was conducted for 1003 participants (median age: 40 years) from Croatia during December 2021. Results showed a significant association between vaccinated and unvaccinated participants in all sociodemographic variables, except for gender (p = 0.905). For psychological variables, significant differences were found only for levels of optimism (p < 0.001). People with a postgraduate degree (OR: 2.25, [1.14–4.46], p = 0.020) and PhD (OR: 1.97, [95% CI: 1.01–3.52], p = 0.021) had higher odds of being vaccinated than participants with high school diplomas. Additionally, participants seeking information on TV and radio (OR: 2.35, [1.71–3.23], p < 0.001) or from general practitioner (OR: 2.53, [1.78–3.61], p < 0.001) had higher odds of being vaccinated. Conversely, participants seeking information on social networks (OR: 0.36, [0.27–0.49], p < 0.001), general internet/blogs forums (OR: 0.34, [0.22–0.52], p < 0.001), and from friends or acquaintances (OR: 0.66, [0.48–0.91], p = 0.011) had lower odds of being vaccinated. Additionally, results showed that information policies have failed to fully convince the population to vaccinate and that depression (p = 0.491), anxiety (p = 0.220), and stress (p = 0.521) were not determining factors leading to the decision to receive COVID-19 vaccine. Most of the vaccinated participants perceived the green-pass as potentially useful. In contrast, most unvaccinated participants believed that the green-pass is a form of discrimination and not useful (88%). Further and broader research into possible reasons for continuing or undertaking vaccination is needed. It is recommended to introduce a measure of conformism that represents a change of attitude, belief, or behavior in a narrower sense.
Innovation Efficiency Evaluation of China’s High-Tech Industry considering Subindustry with a Parallel Slack-Based Measure Approach

Although the performance estimation of technological innovation activities in the Chinese high-tech industry has been discussed continuously in prior literature, few studies analyze the imbalance development of subindustries and regional heterogeneity. Therefore, this study develops a parallel slack-based measure data envelopment analysis approach to estimate the innovation efficiency of the high-tech industry from regional and industrial perspectives. Compared with prior research, the proposed SBM-DEA model can be used to identify the inefficiencies caused by the subindustries via considering internal subindustries as parallel subunits. The proposed model is applied in the Chinese high-tech industry between 2011 and 2014. Empirical results reveal three critical findings. First, there exists an improving potential for innovation efficiency. Second, significant disparities in innovation efficiency are observed at the industrial level and regional level. Third, the inefficiency of the high-tech industry mainly stems from the low performance of the electronic equipment and communication equipment subindustry and computer and office equipment subindustry. Some suggestions for enhancing innovation efficiency are also proposed.
An empirical study on the various stock market prediction methods

Investment in the stock market is one of the much-admired investment actions. However, prediction of the stock market has remained a hard task because of the non-linearity exhibited. The non-linearity is due to multiple affecting factors such as global economy, political situations, sector performance, economic numbers, foreign institution investment, domestic institution investment, and so on. A proper set of such representative factors must be analyzed to make an efficient prediction model. Marginal improvement of prediction accuracy can be gainful for investors. This review provides a detailed analysis of research papers presenting stock market prediction techniques. These techniques are assessed in the time series analysis and sentiment analysis section. A detailed discussion on research gaps and issues is presented. The reviewed articles are analyzed based on the use of prediction techniques, optimization algorithms, feature selection methods, datasets, toolset, evaluation matrices, and input parameters. The techniques are further investigated to analyze relations of prediction methods with feature selection algorithm, datasets, feature selection methods, and input parameters. In addition, major problems raised in the present techniques are also discussed. This survey will provide researchers with deeper insight into various aspects of current stock market prediction methods.
Using fNIRS to Verify Trust in Highly Automated Driving

Trust in automation is crucial for the safe and appropriate adoption of automated driving technology. Current research methods to measure trust mainly rely on subjective scales, with several intrinsic limitations. This empirical experiment proposes a novel method to measure trust objectively, using functional near-infrared spectroscopy (fNIRS). Through manipulating participants’ expectations regarding driving automation credibility, we have induced and successfully measured opposing levels of trust in automation. Most notably, our results evidence two separate yet interrelated cortical mechanisms for trust and distrust. Trust is demonstrably linked to decreased monitoring and working memory, whereas distrust is event-related and strongly tied to affective (or emotional) mechanisms. This paper evidence that trust in automation and situation awareness are strongly interrelated during driving automation usage. Our findings are crucial for developing future driver state monitoring technology that mitigates the impact of inappropriate reliance, or over trust, in automated driving systems.