Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Effect of Employer Offered Leave of Work on Participation in Continuing Vocational Education and Training – Investigating the Intention-Behavior Relation

The availability of time is a deciding factor for participation of adults in continuing vocational education and training (CVET). In view of the importance of time for participation, the present study investigates the impact of employer offered leave of work on employees’ participation behavior in CVET. Leave of work provides a specific timeframe for CVET by enabling the use of working time as learning time. The rationale of the intention-behavior relation as theorized by the theory of planned behavior provides the theoretical framework for the study. The theory allows the integration of individual and contextual factors (e.g., the work environment) in explaining individual behavior and the underpinning decision-making process. The theory conceptualizes time as an element of behavioral control that is required to act on an intention. Behavioral control is theorized to moderate the intention-behavior relation. Two modes of behavioral control are distinguished. We use employer offered leave of work as a proxy for actual behavioral control and the degree of perceived behavioral control regarding the availability of temporal resources to participate in CVET to investigate the theorized moderating role of behavior control on the intention-behavior relation. To test the hypotheses, two waves of panel data from the German National Educational Panel Study (NEPS) are used. Aiming at causal inferences, hybrid logit models are employed. We find that a participation intention is a significant predictor of CVET participation. However, the results provide no evidence regarding the theorized moderating role of actual behavioral control in terms of an employer offered leave of work on the intention-behavior relation. Furthermore, the results provide evidence that the degree of perceived behavioral control regarding the availability of temporal resources to participate in CVET does neither moderate the intention-behavior relation nor is a proxy for actual behavioral control. Finally, we discuss possible future developments of the theory of planned behavior by integrating action-theoretical assumptions from the value-expectancy theory.
A Hybrid Model for Power Consumption Forecasting Using VMD-Based the Long Short-Term Memory Neural Network

Energy consumption prediction is a popular research field in computational intelligence. However, it is difficult for general machine learning models to handle complex time series data such as building energy consumption data, and the results are often unsatisfactory. To address this difficulty, a hybrid prediction model based on modal decomposition was proposed in this paper. For data preprocessing, the variational mode decomposition (VMD) technique was used to used to decompose the original sequence into more robust subsequences. In the feature selection, the maximum relevance minimum redundancy (mRMR) algorithm was chosen to analyse the correlation between each component and the individual features while eliminating the redundancy between individual features. In the forecasting module, the long short-term memory (LSTM) neural network model was used to predict power consumption. In order to verify the performance of the proposed model, three categories of contrast methods were applied: 1) Comparing the hybrid model to a single predictive model, 2) Comparing the hybrid model with the backpropagation neural network (BPNN) to the hybrid model with the LSTM and 3) Comparing the hybrid model using mRMR and the hybrid model using mutual information maximization (MIM). The experimental results on the measured data of an office building in Qingdao show that the proposed hybrid model can improve the prediction accuracy and has better robustness compared to VMD-MIM-LSTM. In the three control groups mentioned above, the R 2 value of the hybrid model improved by 10, 3 and 3%, respectively, the values of the mean absolute error (MAE) decreased by 48.9, 41.4 and 35.6%, respectively, and the root mean square error (RMSE) decreased by 54.7, 35.5 and 34.1%, respectively.
Different Effects of Supervisor Positive and Negative Feedback on Subordinate In-Role and Extra-Role Performance: The Moderating Role of Regulatory Focus

As an important tool for supervisors to intervene subordinates’ work and influence their performance, supervisor feedback has gradually become a new academic research hotspot. In this study, we build and verify a theoretical model to explore the different effects of supervisor positive and negative feedback on subordinate in-role and extra-role performance, and the moderating role of regulatory focus in these relationships. With data from pairing samples of 403 Chinese employees and their direct supervisors, the results indicate that supervisor positive feedback is positively related to subordinate in-role and extra-role performance. Supervisor negative feedback is positively related to subordinate in-role performance and negatively related to subordinate extra-role performance. Regulatory focus of subordinate can moderate the influence of supervisor positive feedback on subordinate in-role and extra-role performance, but it cannot moderate the influence of supervisor negative feedback on subordinate in-role and extra-role performance. That means when subordinates have promotion focus, the influence of supervisor positive feedback on their in-role performance and extra-role performance was stronger than those with prevention focus. These results further enrich the research on the relationship between supervisor feedback and subordinate performance, especially the different effects of positive and negative feedback from supervisor on subordinate with different regulatory focus. All conclusions from the analyses above not only further verify and develop some previous points on supervisor feedback and subordinate performance, but also derive certain management implications for promoting subordinate in-role and extra-role performance from the perspective of supervisor positive and negative feedback.
Measurement of induction motor parameters by adaptive compensation for dead-time effect

In order to improve the measurement precision of induction motor parameters at standstill,a novel method by adaptive compensation for dead-time effect in inverter control was proposed.Proportional resonant(PR) controller was introduced to adaptively compensate the dead-time effect of alternating current.The PR phase locked loop and the feature of dead-time effect were implemented to relieve the voltage wave distortion by dead-time compensation.The estimation of dead-time effect was used to reduce the error caused by nonlinear factor.The experimental results show the proposed method reduces the harmonics of current and voltage 8.92% and 7.65% separately by comparing with traditional method.The amplitude and phase of voltage are compensated and the fluctuation of voltage amplitude is less than uncompensated one.It is validated that the proposed method improves the measurement precision of parameters at least 6.4%.
RISK MODEL OF APPLICATION OF LIFTING METHODS

The article discusses the main provisions (methods, risk models, calculation algorithms, etc.) of theissue of organizing the protection of personal data (PD), based on the application of anonymizationprocedure. The authors reveal the relevance of the studied problem based on the tendency of thegeneral growth of informatization and the further development of the Big Data technology. Thiscircumstance leads to the need to use the so-called risk approach based on calculating the risk of PD asa probabilistic assessment of the amount of possible damage that the owner of the data resource mayincur as a result of a successfully carried out information attack. For this purpose, the article describesan algorithm for calculating the risk of PD and proposes a risk model of the depersonalizationprocedure, which considers confidentiality problems arising both as a result of unauthorized accessand as a consequence of planned data processing. To describe the risk model of the anonymizationprocedure, the types of attacks on the confidentiality of personal data, anonymization metrics andequivalence classes are analyzed, as well as the attacker's profiles and data distribution scenarios.Thus, the choice of a risk model for the depersonalization procedure was justified, and calculations forthe generated synthetic set of PDs were presented. As a conclusion, it should be noted that the modelof anonymization risk assessment proposed and tested on synthetic data makes it possible to abandonthe concept of guaranteed anonymized data, introducing certain boundaries for working with risks andbuilding a continuous process for assessing PD threats, taking into account the constantly growingvolume of stored and processed information.
Reducing bacterial antibiotic resistance by targeting bacterial metabolic pathways and disrupting RND efflux pump activity

Antibiotic resistance is a significant issue for the medical community, worldwide. Many bacteria develop drug resistance by utilizing multidrug resistant or MDR efflux pumps that can export antibiotics from bacterial cells. Antibiotics are expelled from bacteria by efflux pumps a part of the resistance nodulation division (RND) family. Types of RND efflux pumps include the AcrAB-TolC tripartite protein pump. There are an excessive number of antibiotic compounds that have been discovered; however, only a few antibiotics are effective against MDR bacteria. Many bacteria become drug resistant when sharing genes that encode MDR efflux pump expression. MDR efflux pump encoding genes are incorporated into plasmids and then shared among bacteria. As a consequence, advancements in genetic engineering can sufficiently target and edit pathogenic bacterial genomes for perturbing drug resistance mechanisms. In this perspective and review, support will be provided for utilizing genetic modifications as an antimicrobial approach and tool that may effectively combat bacterial MDR. Ayhan et al. found that deleting acrB, acrA, and tolC increased the levels of antibiotic sensitivity in Escherichia coli. Researchers also found that glucose, glutamate, and fructose all induced the absorption of antibiotics by upregulating the gene expression of maeA and maeB that is a part of the MAL-pyruvate pathway. Therefore, the current perspective and review will discuss the potential efficacy of reducing antibiotic resistance by inhibiting genes that encode efflux protein pump expression while simultaneously upregulating metabolic genes for increased antibiotic uptake.
Comparative Study of Indonesia and Malaysia Stock Portfolio Performance

This study examines the performance of the stock portfolios of Indonesian and Malaysian Manufacturing companies using the Wealth Added Index (WAI) measurement from Stern Value Management. The research was carried out by accessing financial reports from the Indonesian stock exchange website (www.IDX.co.id.) and the Malaysia Stock Exchange (bursamalaysia.com). In this study, the analysis method was carried out in two ways: descriptive analysis and inferential analysis. The descriptive analysis describes each of the variables studied. In contrast, inferential analysis shows the relationship between each variable obtained through data processing. The method of estimating the research results uses a complete panel data regression analysis (balanced panel data) with the Stata SE 14 device to answer the research questions. The study results prove that voluntary disclosure has a positive effect on stock portfolio performance in both Indonesia and Malaysia. The disclosure of internet financial reporting has a positive effect on stock portfolio performance in Indonesia and Malaysia, and the third disclosure of Human Resources Accounting has a positive effect on performance. Stock portfolio in both Indonesia and Malaysia. The implication of this research proves that voluntary disclosure, internet financial reporting disclosure, and human resource accounting disclosure have a role in stock portfolio performance. Disclosure of financial statements must be well organized. A new standard of disclosure of financial statements is needed that accommodates voluntary disclosures, disclosures of internet financial reporting, and Human Resources Accounting disclosures.
RISK ASSESSMENT OF DAM CONSTRUCTION PROJECTS USING DELPHI METHOD AND MULTI- CRITERIA DECISION MAKING TECHNIQUES (TOPSIS) AND SHANNON ENTROPY MODELS

One of the factors which can lead to economically cost-effective project is effective risk management and its factors. Dam construction projects spending considerable time and cost and complex spatial conditions are more risky than other construction projects. This study determines the importance of risks and their ranking in dam construction projects and then prioritizes various projects based on risks using Delphi and TOPSIS. In order to assess the risk of projects, it is necessary to first identify and prioritize risk parameters in the projects and then determine the rank of risk factors by TOPSIS and calculate their effect on the projects. The results of this study show that the least risky project can be selected and run by prioritizing the criteria, calculating relative weight of each criterion and assessing the risk parameters.
ADCoC: Adaptive Distribution Modeling Based Collaborative Clustering for Disentangling Disease Heterogeneity from Neuroimaging Data

Conventional clustering techniques for neuroimaging applications usually focus on capturing differences between given subjects, while neglecting arising differences between features and the potential bias caused by degraded data quality. In practice, collected neuroimaging data are often inevitably contaminated by noise, which may lead to errors in clustering and clinical interpretation. Additionally, most methods ignore the importance of feature grouping towards optimal clustering. In this paper, we exploit the underlying heterogeneous clusters of features to serve as weak supervision for improved clustering of subjects, which is achieved by simultaneously clustering subjects and features via nonnegative matrix tri-factorization. In order to suppress noise, we further introduce adaptive regularization based on coefficient distribution modeling. Particularly, unlike conventional sparsity regularization techniques that assume zero mean of the coefficients, we form the distributions using the data of interest so that they could better fit the non-negative coefficients. In this manner, the proposed approach is expected to be more effective and robust against noise. We compared the proposed method with standard techniques and recently published methods demonstrating superior clustering performance on synthetic data with known ground truth labels. Furthermore, when applying our proposed technique to magnetic resonance imaging (MRI) data from a cohort of patients with Parkinson's disease, we identified two stable and highly reproducible patient clusters characterized by frontal and posterior cortical/medial temporal atrophy patterns, respectively, which also showed corresponding differences in cognitive characteristics.
A 1-cycle 2GHz Bufferless Router for Network-on-chip

Recently, bufferless router, which does not need buffers, has become a low-cost solution for Network-on-Chip. To improve the performance of the bufferless router, a 1-cycle high-performance bufferless router was proposed for Network-on-Chip. The router used a simple permutation network instead of the serialized switch allocator and the crossbar to achieve high performance. Compared with the virtual channel router and the baseline bufferless router, the proposed bufferless router can achieve the frequency of 2 GHz with small area cost under TSMC 65 nm technology. Simulation results under both synthetic and application workloads demonstrate that the proposed bufferless router achieves much less average packet latency than the virtual channel router and other bufferless routers.
The Influence of Fan Economic Marketing on Enterprises in the Current Market

With the progress of the times, the Internet is becoming more and more developed. All kinds of media emerge in endlessly. Gradually, fans also begin to appear as a group with great commercial value“Words such as "carrying goods", "endorsement ambassador" and "XXX recommendation" began to become the key to brand traffic and sales. In the era of fan economy, the economic model with idol as the core has become popular. The innovative and developed marketing means has also become an important stroke affecting enterprises. The research theme of this time is what impact the marketing means of fan economy has on enterprises and even society in the domestic market. For example, it plays a vital role in the change of marketing means of enterprises and plays an innovative role in the sales channel of the market.
Combined scheduling of electricity and heat in a microgrid with volatile wind power

An optimization model is developed for scheduling electricity and heat production in a microgrid under a day-ahead market environment considering the operation constraints and the volatility of wind power generation.The model optimizes the total operation costs from energy and heating consumption,meanwhile considers the minimization of the actual flow deviation at the point of common coupling(PCC) from scheduled values.The stochastic nature of wind output is represented by different scenarios with the help of scenario generation and reduction techniques.The model is finally formulated into a mixed-integer programming(MIP) problem.Numerical simulations present the efficacy of the proposed model for day-ahead scheduling of a microgrid with wind penetration under the deregulated environment.
Res2Fusion: Infrared and Visible Image Fusion Based on Dense Res2net and Double Nonlocal Attention Models

Infrared and visible image fusion intends to generate a synthetic image with superior scene representation and better visual perception. The existing deep learning-based fusion methods merely make use of the convolution operation to extract features with a local receptive field, without fully considering their multiscale and long-range dependency characteristics, which may fail to preserve some essential global context from source images. To this end, we develop a novel and efficient fusion network based on dense Res2net and double nonlocal attention models, termed Res2Fusion. We introduce Res2net and dense connections into the encoder network with multiple available receptive fields, which is used to extract the multiscale features, and can retain as much meaningful information as possible for fusion tasks. In addition, we develop double nonlocal attention models as a fusion layer to model long-range dependencies on the local features. Specifically, these attention models can refine feature maps obtained by the encoder network to more focus on prominent infrared targets and distinct visible details. Finally, the comprehensive attention maps are used to generate a fused result through the simple decoder network. Extensive experiments demonstrate that the proposed method can simultaneously retain highlighted infrared targets and rich visible details and transcends other state-of-the-art fusion methods in terms of subjective and objective evaluation. The corresponding code is publicly available at https://github.com/Zhishe-Wang/Res2Fusion.
A Novel Model for Predicting Machining Error in Peripheral Milling Process

Investigations on the prediction and controlling of machining error in milling process are the key issues for machining quality control.They are of great significance to ensure productivity and accuracy for machining process.Based on cutter deflection and cutter edge trajectories,a novel modeling method for predicting the machining error in peripheral milling process with flexible cutter is presented.Key ideas of this method can be listed as follows.The cutter deflection is calculated based on the predicted cutting force.The cutter edge trajectories are solved in light of circular approximation.The machining error is determined by searching for the minimal value of all cutter edge trajectories.Compared with the existing methods,the remarkable feature of the presented method lies in two aspects.On the one hand,the generation mechanism of machining error is well revealed in the modeling procedure.On the other hand,since the influences of cutter edge trajectory on machining error are concerned,the prediction result can illustrate the surface topography.The validity of the presented model is experimentally demonstrated by a series of cutting tests.
Learning Users’ Visual Preferences for Improving Recommendations

Sequential recommender systems (SRSs) aim to predict the next item interest to a user by learning the users’ dynamic preferences over items from the sequential user-item interactions. Most of existing SRSs make recommendations by only modeling a user’s main preference towards the functions of items, while ignoring the user’s auxiliary visual preference towards the appearances and styles of items. Although visual preference is less significant than the main preference, it may still play an important role in most of users’ choice on items. On the one hand, a user often prefers to choose the item which matches her/his visual preference well from multiple items with the same function. For example, a lady may choose one clothes whose style suits her best from multiple clothes with the same function. On the other hand, some particular users (e.g., young girls) are usually very concerned about the appearances of some special items (e.g., clothes, jewelry). Therefore, the overlook of modeling users’ visual preference may generate unsatisfied recommendations which can not match a user’s various types of preferences and thus reduce the consumption experience. To address this gap, in this paper, we propose modeling users’ visual preferences to improve the performance of sequential recommendations. Specifically, we devise a coupled Double-chain Preference learning Network (DPN) to jointly learn a user’s main preference and visual preference as well as the interactions between them. In DPN, one chain is for modeling a user’s main preference by taking the IDs of items as the input and the other chain is for modeling the user’s visual preference by taking appearance images of items as the input. Finally, the two types of preferences are carefully integrated with an attention module for the next item prediction. Extensive experiments on two real-world transaction datasets show the superiority of our proposed DPN over the representative and state-of-the-art SRSs.
Optimal power allocation in OFDM-based cognitive radio systems

The primary user(PU) suffers interference from both in-band subcarriers and out-band subcarriers' sidelobes of secondary users(SU) in OFDM-based cognitive radio systems.An optimal power allocation scheme was developed to protect the PUs' performance while efficiently exploiting the spectrum opportunities.The scheme was obtained by solving a convex optimization problem.The effects of the subcarrier side lobe are included unlike in the iterative partitioned water-filling algorithm.Simulations show that the optimal power allocation scheme maximizes the channel capacity while satisfying the PUs' interference power constraints.