Two-level Structure Research Articles

The Development of a Screening Tool for Chinese Disordered Gamers: The Chinese Internet Gaming Disorder Checklist (C-IGDC).

Despite the increasing research attention being paid to gaming disorder globally, a screening tool developed specifically for the Chinese population is still lacking. This study aims to address this gap by constructing a screening tool to assess Internet gaming disorder (IGD) symptomology, defined by the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), among Chinese gamers. Based on expert interviews and consultations, a focus group of gamers, a background literature review, and the IGD criteria proposed by the DSM-5, we developed the Chinese Internet Gaming Disorder Checklist (C-IGDC). This study evaluated its dimensional structure, reliability, validity, and screening efficacy with 464 Chinese past-year gamers (53% female; mean age = 19.84). The two-level structure of the 27-item C-IGDC showed a satisfactory model fit, acceptable reliability, as well as good validity via expected associations with Internet addiction, gameplay frequency, and depressive symptoms. The optimal screening cutoff score (≥20) was proposed to detect probable IGD cases. The C-IGDC is the first DSM-5-based, multidimensional IGD screening tool designed specifically for Chinese gamers. Further evaluation of the C-IGDC in epidemiological studies and clinical settings is recommended.

Small-Area Factors and Their Impact on Low Birth Weight-Results of a Birth Cohort Study in Bielefeld, Germany.

Introduction: The location of residence is a factor possibly contributing to social inequalities and emerging evidence indicates that it already affects perinatal development. The underlying pathways remain unknown; theory-based and hypothesis-driven analyses are lacking. To address these challenges, we aim to establish to what extent small-area characteristics contribute to low birth weight (LBW), independently of individual characteristics. First, we select small-area characteristics based on a conceptual model and measure them. Then, we empirically analyse the impact of these characteristics on LBW.Material and methods: Individual data were provided by the birth cohort study “Health of infants and children in Bielefeld/Germany.” The sample consists of 892 eligible women and their infants distributed over 80 statistical districts in Bielefeld. Small-area data were obtained from local noise maps, emission inventory, Google Street View and civil registries. A linear multilevel analysis with a two-level structure (individuals nested within statistical districts) was conducted.Results: The effects of the selected small-area characteristics on LBW are small to non-existent, no significant effects are detected. The differences in proportion of LBW based on marginal effects are small, ranging from zero to 1.1%. Newborns from less aesthetic and subjectively perceived unsafe neighbourhoods tend to have higher proportions of LBW.Discussion: We could not find evidence for negative effects of small-area factors on LBW, but our study confirms that obtaining adequate sample size, reliable measure of exposure and using available data for operationalisation of the small-area context represent the core challenges in this field of research.

The intraspecific structure of the Yellow-spotted ringlet Erebia manto (Denis & Schiffermüller, [1775]), with special reference to the bubastis group: an integration of morphology, allozyme and mtDNA data (Lepidoptera, Nymphalidae, Satyrinae)

Abstract. Present-day intraspecific diversity has largely been shaped by previous climatic events, but the spatial and temporal scales of differentiation processes in most species remain to be clarified. In Europe, the Pleistocene glacial cycles have generated population structures that remain especially evident in montane taxa. The intraspecific variation of the European subalpine Yellow-spotted Ringlet,Erebia manto(Denis & Schiffermüller, [1775]), shows a hierarchical, two-level structure that allows us to study intermediate stages of speciation. Morphologically, three subspecies clusters have been described in this butterfly: themanto,bubastisandvogesiacatype. An allozyme study previously revealed two genetic lineages within themantotype, and two within thevogesiacatype, but lackedbubastisrepresentatives. To further our knowledge of the intraspecific structure ofE. manto, we sampled all known and presumed intraspecific groups and sequenced 1,496 base pairs of the mitochondrial gene COI for 152 specimens from 15 localities. A median joining haplotype network, based on 40 parsimony informative sites, confirmed the four allozyme based lineages. Thebubastistype was acknowledged as a fifth genetic lineage, replacing themantotype populations in the southern part of the western Alps, and separated from it by a well-known zoogeographic borderline. We discuss how the present-day distributions, genetic relationships and timing of the differentiations align.

Online Dynamic Hand Gesture Recognition Including Efficiency Analysis

Online dynamic hand gesture recognition is challenging mainly due to three reasons: (i) There is no indication when a gesture starts and ends in the video, (ii) performed gestures should only be recognized once, and (iii) the entire architecture should be designed considering the memory and power budget. In this paper, a two-level hierarchical structure consisting of a detector and a classifier is proposed which enables offline-working convolutional neural network (CNN) architectures to operate online efficiently by using sliding window approach. For efficiency analysis, different CNN architectures are applied to compare these architectures over offline classification accuracy, number of parameters and computation complexity. In order to evaluate the single-time activations of the detected gestures, we used Levenshtein distance as an evaluation metric since it can measure misclassifications, multiple detections, and missing detections at the same time. The performance of the approach is evaluated on two public datasets - EgoGesture and NVIDIA Dynamic Hand Gesture Datasets - which require temporal detection and classification of the performed hand gestures. ResNeXt-101 model achieves the state-of-the-art offline classification accuracy of 94.03% on EgoGesture benchmark and competitive results on NVIDIA benchmarks. In online recognition, we obtain very good performances with considerable early detections.

Normative and appearance performance-approach goal structures: Two-level factor structure and external linkages

While personal performance-approach goals (PAp goals) have already been successfully bifurcated into normative and appearance PAp goals, the same distinction has not yet been applied to performance-approach goal structures (PAp goal structures). The present study therefore aimed to (a) test the factorial two-level structure of PAp goal structures to establish whether a distinction between normative and appearance components is empirically supported, and (b) explore relations to achievement and approach-oriented achievement goals (mastery-approach goals, normative and appearance personal PAp goals). This study relied on a sample of 1,004 secondary school students (53.38% females, 49 classes). Results from multilevel confirmatory factor analyses revealed that a model with two separate normative and appearance PAp goal structure factors on the individual student level, and one overall PAp goal structure factor on the classroom level fit the data best. On the individual student level, normative PAp goal structures positively predicted achievement, whereas appearance PAp goal structures negatively predicted achievement. Each PAp goal structure type showed the strongest relation to the matching personal PAp goal, but no relations to mastery-approach goals were found. On the classroom level, the high associations between the overall PAp goal structure and personal PAp goals raised concerns on their empirical distinctiveness.

Blistering in Helium-Ion-Irradiated Zirconium, Aluminum, and Chromium Nitride Films

This work is devoted to studying blistering in ZrN, AlN, and CrN films formed by reactive magnetron sputtering. The surface morphology and cross-sectional microstructure of mononitride films after irradiation with He ions (energy 40 keV and doses of 3 × 1017−1.1 × 1018 cm–2) at room temperature are analyzed by scanning, atomic force, and transmission electron microscopy. The critical doses of blistering are determined for ZrN (6 × 1017 cm–2), AlN (5 × 1017 cm–2), and CrN (6 × 1017 cm–2) films. The high density of blisters in ZrN films leads to the merging of neighboring blisters (average size 0.75 μm) and the formation of large blisters (average size 1.35 μm). The blisters in the AlN films have a regular round shape (average size 1.7 μm). The СrN films are characterized by the presence of open blisters having a two-level structure: an upper blister with a diameter of 2−10 μm and a lower one with a diameter of 1.2 μm. As follows from the TEM results, 40‑keV He-ion irradiation of the films and their subsequent vacuum annealing leads to the formation of chains of radiation-induced pores filled with helium in the region of the projective ion range Rp. The formation of extended cracks is found to occur in the Rp region of ZrN, which is caused by interbubble fracturing due to high excess pressure in pores located at a depth close to Rp.

Development of hardware and software platform for collection and processing of operational data of agricultural equipment

The article denotes to the issues of the use of modern digital technologies for the development of mobile monitoring system for technical condition of the agricultural machinery for predicting the resource and planning the maintenance. (Research purpose) The research purpose is developing a structure and hardware and software tools for creating a platform for collecting and processing operational data of agricultural machinery. (Materials and methods) Based on modern microcontroller technology, authors created a two-level structure of the hardware and software platform, a scheme of first-level modules designed to collect data from sensors and pre-process it, a scheme of a second-level module for collecting information from first-level modules, structuring it and transmitting it to a remote server via a GPRS channel for storage, processing and analysis. The article presents the developed database structure and site layout on a remote server to control incoming information and graphical representation of the dynamics in measured parameters. (Results and discussion) The created platform has been installed on the Kioti CK22 tractor for experimental research and demonstrated at the agro-industrial exhibition-forum “Field Day of the Ryazan region-2019” on July 19, 2019. (Conclusions) With relatively low hardware costs, the developed platform has sufficient functional and computational resources to implement mobile monitoring of the technical condition of units and aggregates of agricultural machines and their compliance with technological modes of soil treatment. The universal structure of the developed platform can serve as a basis for digitalization of many other objects of agricultural production in such industries as animal husbandry; crop production; accounting, storage and processing.

Asymmetric Gaussian confinement potential and decoherence effect on polaron in quantum disk with electromagnetic field

Selecting the asymmetric Gaussian potential to describe the electron that is confined in a quantum dot, the ground and first excited state energy and wave function of the system are derived by using the Pekar-type variational method, and the two-level structure required for a qubit is constructed. The influence of the electron–phonon (EP) coupling constant on the probability density of the electron in the superposition state and the period of the oscillation of a qubit with electromagnetic field are investigated. By studying the effect of the dispersion coefficient, the EP coupling constant and the electromagnetic field on the spontaneous emission rate of the LO phonon, the influence of material parameters and the electromagnetic field on the decoherence of the qubit in quantum dots is revealed, and the theoretical scheme of suppressing the decoherence of qubit in quantum dots by phonon effect is enriched and perfected.

Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System

Based on the collected weather data from the agricultural Internet of Things (IoT) system, changes in the weather can be obtained in advance, which is an effective way to plan and control sustainable agricultural production. However, it is not easy to accurately predict the future trend because the data always contain complex nonlinear relationship with multiple components. To increase the prediction performance of the weather data in the precision agriculture IoT system, this study used a deep learning predictor with sequential two-level decomposition structure, in which the weather data were decomposed into four components serially, then the gated recurrent unit (GRU) networks were trained as the sub-predictors for each component. Finally, the results from GRUs were combined to obtain the medium- and long-term prediction result. The experiments were verified for the proposed model based on weather data from the IoT system in Ningxia, China, for wolfberry planting, in which the prediction results showed that the proposed predictor can obtain the accurate prediction of temperature and humidity and meet the needs of precision agricultural production.

Procurement decisions over multiple periods under piecewise-linear shortage costs and fixed capacity commitments

Abstract We study optimal procurement in a case where the buyer must match supply against uncertain demand using a combination of low-cost order-in-advance procurement contracts and a high-cost real-time balancing mechanism. The procurement contracts have a two-level structure in which the commitment to procure a fixed quantity for multiple periods has a lower unit-price than period-specific commitments. Moreover, the balancing mechanism implies a salvage value for unused supply and piecewise-linear shortage costs: small shortages (relative to the total quantity procured) are balanced with a lower unit cost than larger shortages. Minimizing procurement costs results in a stochastic non-linear multivariate optimization problem, which can be interpreted as a generalization of the classic newsvendor model. We derive the optimality conditions for this problem and show how they can be utilized to obtain a cost minimizing procurement strategy by solving a series of single variable equations. The model is then applied to support procurement decision making of a pulp & paper company that procures natural gas worth tens of millions of euros annually. This allows us to highlight some key practical implications of our work.

Obtaining secondary students’ perceptions of instructional quality: Two-level structure and measurement invariance

Students' perceptions of instructional quality have become an important information source for teachers' professional development. This requires knowledge of the structure of these perceptions, their validity, and generalizability. To this end, we conducted a study with 15,005 German 512 grade students from 690 classrooms in three different school types and three different grade levels. Assuming three basic dimensions of instructional quality with 7 facets, we investigated the factorial structure of students' perceptions with two-level confirmatory factor analyses as well as their generalizability with two-level measurement invariance analyses. Our results confirmed the postulated factorial structure and strict invariance across subject groups, school types, and grade levels. We confirmed the same structure in teachers' assessments of their instructional quality that were positively correlated with the students' assessments. As such, these findings shed light on the structure, validity, and generalizability of students’ perceptions of instructional quality.

DGS-HSA: A Dummy Generation Scheme Adopting Hierarchical Structure of the Address

With the increasing convenience of location-based services (LBSs), there have been growing concerns about the risk of privacy leakage. We show that existing techniques fail to defend against a statistical attack meant to infer the user’s location privacy and query privacy, which is due to continuous queries that the same user sends in the same location in a short time, causing the user’s real location to appear consecutively more than once and the query content to be the same or similar in the neighboring query. They also fail to consider the hierarchical structure of the address, so locations in an anonymous group may be located in the same organization, resulting in leaking of the user’s organization information and reducing the privacy protection effect. This paper presents a dummy generation scheme, considering the hierarchical structure of the address (DGS-HSA). In our scheme, we introduce a novel meshing method, which divides the historical location dataset according to the administrative region division. We also choose dummies from the historical location dataset with the two-level grid structure to realize the protection of the user’s location, organization information, and query privacy. Moreover, we prove the feasibility of the presented scheme by solving the multi-objective optimization problem and give the user’s privacy protection parameters recommendation settings, which balance the privacy protection level and system overhead. Finally, we evaluate the effectiveness and the correctness of the DGS-HSA through theoretical analysis and extensive simulations.

STREAMLINED SOLUTIONS TO MULTILEVEL SPARSE MATRIX PROBLEMS

We define and solve classes of sparse matrix problems that arise in multilevel modelling and data analysis. The classes are indexed by the number of nested units, with two-level problems corresponding to the common situation, in which data on level-1 units are grouped within a two-level structure. We provide full solutions for two-level and three-level problems, and their derivations provide blueprints for the challenging, albeit rarer in applications, higher-level versions of the problem. While our linear system solutions are a concise recasting of existing results, our matrix inverse sub-block results are novel and facilitate streamlined computation of standard errors in frequentist inference as well as allowing streamlined mean field variational Bayesian inference for models containing higher-level random effects.

CURRENT TRENDS IN THE DEVELOPMENT OF LEGAL REGULATION OF VAT IN THE PEOPLE’S REPUBLIC OF CHINA (ADOPTION OF TAX LEGISLATION AND REDUCTION OF THE TAX BURDEN)

The article is devoted to the legal aspects of improving VAT in the People’s Republic of China. In fact, this is the first attempt to comprehensively study the VAT law, which has passed a nationwide discussion and is awaiting adoption by the highest government body of the PRC. Changes in national taxation models in the face of increasing turbulence in the global economy highlight the topic of this work, since taking into account the main trends in the legal regulation of value added taxation of the world’s largest VAT economy is important for understanding the possibilities for developing such a system in the Russian Federation. The subject of research is the transformation of regulatory legal regulation of taxation in the PRC on the basis of progressive improvement of tax legislation. As the immediate practical tasks to be solved by the country’s leadership in this sphere of public relations, the reduction of the tax burden of the economy and the re-registration of tax law institutions were noted. The research methodology is based on the use of system analysis and dialectic techniques. The studied financial and legal object is considered as a structurally designed description of a complex dynamic system in its movement and development. In the framework of comparative law, an attempt was made to identify the basic laws of the legal evolution of the normative regulation of VAT. Attention is drawn to the fact that the main characteristics of the new Chinese VAT correspond to the system of conceptual principles put forward by the OECD, among which the leading role is played by the principle of neutrality of this type of taxation. In the final part of the work, conclusions are drawn that the proposed tax law of the PRC has a two-level structure, including a wide range of reference norms and by-laws. At the same time, comprehensive measures undertaken by the leadership of the People’s Republic of China in the field of legal regulation of VAT strengthen the country’s socio-economic development opportunities, stimulating entrepreneurs to expand their production.

Human capital study: experience of empirical testing of complex tools

This article approbates the Russian-language version of an interdisciplinary toolkit for human capital resources research. The Russian-language toolkit for measuring systematically organized human resources, which are consistent with social capital theory and intensively developing typology of psychological capital resources in the version of A. Moses, F. Lutens and V. Avoglio, is verified for the first time, determining the article’s scientific novelty. The toolkit has a two-level structure: general (external to the organization) and specific (internal) resources. Contemporary human capital theory assumes that people have different knowledge, skills, and abilities that can be successfully applied in solving work problems. The definition of social capital includes the concepts of trust, embeddedness, social support, and social exchange. The definition of psychological capital integrates four sub-indices: efficiency (based on trust and confidence), hope, optimism, and resilience. The empirical basis of the study includes the results of a mass sociological survey (N = 1 261). The sample represents the working population of the Tyumen Region 18 over years old (as of 2020). This has required using the methods of correlation and factor analysis. The results show the interrelationships and latent factors of the multidimensional construction of human, social and psychological capital in connection with concepts of trust and social well-being. The authors have identified the features of the Russian structure of human capital. The confidence in self-efficiency, as well as self-reliance, resilience, optimism, and belief in being in demand at work most strongly explain the variations in human capital in the Russian case. Thus, psychological capital is built into the concept of labor motivation, since it reflects willingness to work systematically, as well as employees’ perception of their socio-psychological characteristics, optimal to perform current and strategic work tasks. The social capital builds up in a multidimensional structure, when generalized trust and the strength of social contacts at work are almost orthogonal to each other.

A hierarchical parallel evolutionary algorithm of distributed and multi-threaded two-level structure for multi-satellite task planning

The aim of multi-satellite task planning is to study how to distribute limited resources of satellites and payloads and execution time for observation missions to be completed within a limited set of available satellites so as to best satisfy the observational demand. Aiming at the shortcomings of the current study on multi-satellite task planning, this paper proposes a hierarchical parallel evolution algorithm framework which is based on a distributed and multi-threaded two-level structure. It adopts parallel communication flow and task distribution strategy of multi-machine, multi-core and two-level structure. The distributed parallel evolution model work among multi-machines, whereas the multi-threaded parallel evolution model work among multi-cores to reduce the communication overhead of the parallel system while maintaining the global optimisation of the algorithm. The result of the experiment showed that the multi-satellite task planning evolutionary optimisation model established in the paper is effective. Subsequently, it was proven that the hierarchical parallel-evolving algorithm proposed by the paper can greatly cut down the time consumed for the evolution and improve the algorithm solving efficiency, which can effectively solve both the multi-satellite task planning issue and optimisation problems in other fields. It is thus of important use value.

МОДЕЛИРОВАНИЕ ПЕРВИЧНОЙ ЭМИССИЙ АКЦИЙ АКЦИОНЕРНОГО ОБЩЕСТВА

At present, in contrast to lending and investment services, models for raising additional capital for a company by issuing shares and then placing them on the securities market are becoming more popular. This article discusses the model of issuing shares for a joint-stock company (JSC) and the subsequent purchase of their traders. The model has a two-level hierarchical structure, where the leading party is the JSC, and the lead, in turn, is the shareholders. The company determines the number of shares and their issue price. Depending on the total capitalization of the company and the nominal share price, the company’s revaluation coefficient (P/BV) is formed. This coefficient affects the General mood of shareholders in the market, who use the sale or purchase of shares to change the total capitalization of the company. The price for the current time period consists of the algebraic sum of the price for the previous time period and the total capitalization, thus, through changes in the total turnover of funds, shareholders are able to influence the share price. The main income for a shareholder is the difference between the purchase and sale of shares, as well as the payment of its dividends. For the company, the task is to maximize profits by buying shares on the stock exchange, as well as minimize losses when selling them. After describing the target functions and applying the simulation method, the optimal issue price for a fixed number of shares was found for the company. Unfortunately, the market does not lend itself to accurate forecasts due to the large influence of the human factor. Very often, shareholders can act against the rational and most profitable strategy. Despite this, this model will help to approximate the behavior of players in the stock market in subsequent development, thereby facilitating the study of price movements on the stock exchange.

The Moderated Mediating Effect of Business Ethics towards Firm Performance

The objective of this study is to investigate the relationship among manager ethical leadership, organisation ethical culture, corporate ethics program and both firm performances: ROA and corporate governance reputation. By exploring the: 1) direct effect; 2) mediating effect; 3) moderated mediating effect or conditional indirect effect, that have influences on firm performances of 84 listed firms on the stock exchange of Thailand, which is represented by 785 participants. For analysis and comparison of the two-level factor structure of the conceptual model, each stage of effect is tested by PROCESS (model 1, 4, and 14), whereas an overall moderated mediating model is tested by structural equation model (SEM). It was found that the relationship between manager ethical leadership and both firm performances through organisation ethical culture is positive when an organisation implements a strong ethics program. Furthermore, based on estimation, SEM uses maximum likelihood while PROCESS uses ordinary least square regression. It was also found that both estimators provide exactly regression weight, but the statistical inferential tests are slightly different. Thus, this study offers the alternative best-fit estimator for another moderated mediating model testing.

Interacting Multiple Model UAV Navigation Algorithm Based on a Robust Cubature Kalman Filter

To improve the precision and robustness of Unmanned Aerial Vehicle (UAV) integrated navigation systems, this paper presents an Interacting Multiple Model (IMM) navigation algorithm based on a Robust Cubature Kalman Filter (RCKF) with modified Zero Velocity Update (ZUPT) method assistance. This algorithm has a two-level fusion structure. At the bottom level, the Global Positioning System/Inertial Navigation System (GPS/INS) integrated navigation model and the Dynamic Zero Velocity Update/Inertial Navigation System (DZUPT/INS) integrated navigation model are established by modifying the Zero Velocity Update (ZUPT) method. Subsequently, the RCKF algorithm adopts a robust factor to weaken the influence of measurement outliers on the filter solution. At the top level, the estimation results of the GPS/INS integrated navigation model and the DZUPT/INS integrated navigation model are fused by the IMM algorithm. In addition to enhancing the robustness of filter estimation in the presence of measurement outliers, the proposed navigation algorithm also corrects navigation errors with ZUPT method assistance. Simulation and experimental analyses demonstrate the performance of the proposed navigation algorithm for UAVs.

Receding Horizon Reference Governor for Implementable and Optimal Powertrain-Aware Eco-Driving

This paper presents an adaptive, two-level control structure that makes it possible to implement a numerical optimization algorithm for eco-driving in real time. The reference governor in the higher level is characterized by a low, flexible sampling rate and adopts a receding horizon for preview and optimization. The optimization algorithm thereby finds the energy-minimizing solution, based on Pontryagin’s minimum principle (PMP), for traveling the selected route segments without colliding with the preceding vehicle. The tracking control in the lower level compensates for errors due to modeling inaccuracies and unmodeled disturbances. The hierarchical structure can accommodate different types of numerical solvers and control schemes that apply to various vehicle powertrain configurations. A large-scale simulation study using real-world route data with high-fidelity powertrain models validates the proposed control structure and its online implementation.