Publications

The article studies the experience of automation of heating, ventilation, and air conditioning (HVAC) systems of buildings with regard to the technical capacities of the Internet of Things (IoT). Using the data from IoT devices maintains the set quality parameters throughout the entire operation period, which is achieved with the compensatory and predictive control algorithms. The objective of the research is to increase the HVAC control efficiency in smart buildings using the control system with the adaptation circuit, which proactively compensates any disturbances. The proper operation of the circuit requires accumulation of information of the venue during the operation period, which is used for building the transfer functions of the HVAC of the building. Continuous adaptation of the control system model to reality is a way to continuously optimize the adjustments of the regulation algorithm, ensuring effective operation of the local temperature regulation circuits. The capacities of the IoT controller-based control system and the generation of a compensatory-predictive control signal with the placement of the control algorithm in a “cloud” on a server are demonstrated with the indoor temperature control model. The simulation models of the indoor temperature changing processes are studied: the indoor temperature changing process model without a control system; model with a PI-regulator and disturbance compensation; the disturbance compensation model for the IoT controller-based control system. The structural and parametric identification of the model is carried out with the active experiment method

Background

In patients with psychotic disorders, sleep spindles are reduced, supporting the hypothesis that the thalamus and glutamate receptors play a crucial etio-pathophysiological role, whose underlying mechanisms remain unknown. We hypothesized that a reduced function of NMDA receptors is involved in the spindle deficit observed in schizophrenia.

Methods

An electrophysiological multisite cell-to-network exploration was used to investigate, in pentobarbital-sedated rats, the effects of a single psychotomimetic dose of the NMDA glutamate receptor antagonist ketamine in the sensorimotor and associative/cognitive thalamocortical (TC) systems.

Results

Under the control condition, spontaneously-occurring spindles (intra-frequency: 10–16 waves/s) and delta-frequency (1–4 Hz) oscillations were recorded in the frontoparietal cortical EEG, in thalamic extracellular recordings, in dual juxtacellularly recorded GABAergic thalamic reticular nucleus (TRN) and glutamatergic TC neurons, and in intracellularly recorded TC neurons. The TRN cells rhythmically exhibited robust high-frequency bursts of action potentials (7 to 15 APs at 200–700 Hz). A single administration of low-dose ketamine fleetingly reduced TC spindles and delta oscillations, amplified ongoing gamma-(30–80 Hz) and higher-frequency oscillations, and switched the firing pattern of both TC and TRN neurons from a burst mode to a single AP mode. Furthermore, ketamine strengthened the gamma-frequency band TRN-TC connectivity. The antipsychotic clozapine consistently prevented the ketamine effects on spindles, delta- and gamma−/higher-frequency TC oscillations.

Conclusion

The present findings support the hypothesis that NMDA receptor hypofunction is involved in the reduction in sleep spindles and delta oscillations. The ketamine-induced swift conversion of ongoing TC-TRN activities may have involved at least both the ascending reticular activating system and the corticothalamic pathway.

In the 1990's David Schmeidler and Itzhak Gilboa initiated the study of decision making under uncertainty in a completely new framework, without states but with data sets as the information on which to build choice behavior. While the first formulations of Case-Based Decision Theory (CBDT) aimed at applications in economic decision making, this theory which takes data as a primitive concept provides an alternative foundation for deriving beliefs and driving the choice of predictions. This opened a new perspective on old questions in statistics and artificial intelligence. In this review, we summarize these developments in Case-Based Decision Theory and highlight the immensely innovative nature of David Schmeidler's academic work.

We analyse the determinants of football fans’ happiness in the Russian Premier League using facial emotion recognition. We propose a new way of measuring subjective well-being and provide its empirical validation using sports data. Our sample consists of about 10,000 photos from football matches uploaded on the most popular social network in Russia during the seasons 2014/15–2017/18. The dataset of photos is analysed with the Emotion Recognition software, which takes a facial expression in an image as an input and returns the confidence across a set of emotions for each face in the image. Next we use multinomial logistic regression to identify the determinants of happiness. The results show that uncertainty and expectations are important drivers of football fans’ happiness. A win decreases the probability of being unhappy, and the effect becomes stronger for late rounds of a national championship. The change in happiness because of a home team win is stronger for males.

People differently rank factors they see as important when making decisions with potential environmental impact. Both in a survey and theoretically, we investigate the relative importance of monetary and non-monetary factors and how and whether attitudes to uncertainty affect this ranking. Risk and ambiguity aversion explains the ranking of decision factors that involve uncertainty about future outcomes but often uncertainty is neglected: e.g. possible changes to standards and regulations are seen as a factor of uncertainty, while data manipulation by car manufacturers, as manifested in the recent Dieselgate scandal, is not seen as such. Responses in our sample overwhelmingly reject consumers’ indifference with respect to environmental issues, however when it comes to individual consumption choices, these are strongly dominated by monetary factors. Our data highlight an important role of institutions as collective commitment devices to help promote proenvironmental behaviour.  

The common approach to predict the price of residential property is the hedonic price model and its extension to the case of spatial autoregression. The hedonic approach models the dependence between the price and internal characteristics of an apartment, house characteristics and external characteristics. To account for the unobserved quality of the surrounding environment price model includes factors of spatial price correlation, where the distance is usually measured as the distance in geographic space. Determining the price the seller focuses not only on the observed and unobserved factors of the apartment, house and its environment but also on the prices of similar marketed objects which can be selected both by geographic proximity and by characteristics similarity. In this paper, we use ensemble clustering approach to measure objects proximity and test that the proximity of objects in the characteristics space along with spatial correlation explains the significant variation in prices that in turn leads to an improvement of predictive ability of the model.

Purpose. This study suggests an alternative to confirmatory content analysis (CA) and empirically demonstrates that explorative CA enables new insights into the mechanism of intellectual capital (IC) disclosure. In so doing, this research contributes to both methodological and empirical advancements in IC disclosure research.

Design/methodology/approach. Employing the assumptions of positive accounting theory and taking book value of intangible assets as a reference, our research design utilizes well-established text-mining (TM) tools based on a least absolute shrinkage and selection operator regression. We assume that the degree of cohesion between officially disclosed and evaluated intangible assets on balance sheets and those contextually delivered in narrative form may affect how IC is ultimately disclosed in annual reports.

Findings. Our main finding is in line with the results and criticism of previous studies. We show that companies do not extensively disclose IC in their annual reports. However, some narrative forms for IC disclosure are identified and confirmed by several robustness checks.

Research limitations/implications. First, the findings provide internal validity only for large US enterprises. These firms have similar, well-structured reporting requirements. This analysis might be enriched by an examination and a comparison of different institutional contexts, such as emerging countries. Second, following previous studies, annual reports serve as the source of data. Consequently, the findings are relevant only for mandatory and voluntary disclosure of IC, mitigating the relevance of this study for contexts of involuntary disclosure.

Originality/value. This study make two contributions. First, we add to the empirical literature by offering one more piece of evidence on whether and, if so, the extent to which companies disclose IC in their annual reports. Second, we provide a further examination of confirmatory CA by proposing a number of statistically validated codes and tokens that are indicators of IC communication by companies.

Using Major League Soccer as a unique dataset, this study examines the direct andindirect role of coaches’ experience in determining team performance. Inspired by labormarket studies, we applied traditional indicators of team salary structure and, unlikeprevious studies, empirically test the hypothesis that coach experience affects the way inwhich team salary distribution inuences performance. Our results suggest that coacheswith experience as professional soccer players improve team performance directly butworsen the negative effect of a skewed s alary distribution. Moreover, experience as aplayer is more important than coaching experience. (JEL D3, J3, M5)

Purpose – Video games are considered as a leisure activity that makes being unemployed more attractive than before. In this study, the authors use eSports prizes as a proxy for the popularity of video games to analyze its influence on total and youth unemployment. Design/methodology/approach – The authors develop a theoretical model and empirically test it using the total prize money won by representatives of a country in a given season in eSports tournaments, via a panel regression model with the country-year as a unit of observation. The data set includes information about 191 countries between 2000 and 2015. Findings – The authors’ results of regression analysis show a positive influence of the popularity of video games on the unemployment rate. In addition, the authors analyze this effect for countries with different levels of income and labor productivity. The authors found a significant inverse relationship between income level and the effect of the popularity of video games on total and youth unemployment. Originality/value – While previous studies rely mostly on self-reported data, the authors suggest a new approach to measure video game popularity. This paper contributes to existing knowledge with empirical evidence on how leisure activities affect unemployment at the country level.  

Growing importance of human resources places the role of managers at the core of company efficiency. However, there are studies that demonstrate the efficiency of teams without a manager, so-called self-managed teams, is higher comparing with managed teams. Thus, despite the focus on managerial efficiency in the economic literature, the issue of whether a team needs amanager is far from settled. In this paper, we use a quasiexperimental setting from e-Sports (competitive video gaming) to understand whether the hiring a manager is of benefit to team performance. The empirical part of the study is based on endogenous switching regression model. This method allows investigating what performance of self-managed team would be if it will have a manager and vice versa. The dataset includes the information of prize money and features of top e-Sports teams in Counter-Strike: Global Offensive (e-Sports discipline) from 2013 to 2017. The main finding of this study is that managed teams perform better than self-managed ones but this is not due to the manager.

The most common tools to understand perception of food products are hall tests, surveys and observations. However, these approaches require large samples to get reliable results and they are rather costly and time-consuming. Furthermore, they are also highly expert-dependent and rely on the assumption that study participants can express their preferences consciously and explicitly. In our paper, we suggest an electroencephalography- based (EEG) approach to evaluate perceived product similarity in a cross-modal taste-visual task. We tested two potential neurometrics measured from Fz electrode: the amplitude of the N400-like evoked response potentials (ERP) and the power of induced gamma oscillations during 400-600 ms period after visual stimulus presentation. Both metrics showed a strong correlation with the perceived similarity scores at both individual and group levels; however, N400-like amplitude had greater inter-subject variability making it less suitable for practical applications. The results based on the power of induced gamma oscillations (N=18) could be compared to traditional hall-tests (N=200) and may potentially reveal subtle differences in food perception that can not be captured in the hall-tests.

The study explores the relationships between employee burnout, work-family balance,and organizational dissent. These relationships were tested in an under-researched and culturaly unique context, Russia. Data collected from 232 full-time employees in the Prm region were analysed using multiple regression analysis. Analysis revealed that employee burnout is negatively related to articulated dissent and positively related to latent dissent.

In this paper, we address several aspects of applying classical machine learning algorithms to a regression problem. We compare the predictive power to validate our approach on a data about revenue of a large Russian restaurant chain. We pay special attention to solve two problems: data heterogeneity and a high number of correlated features. We describe methods for considering heterogeneity — observations weighting and estimating models on subsamples. We define a weighting function via Mahalanobis distance in the space of features and show its predictive properties on following methods: ordinary least squares regression, elastic net, support vector regression, and random forest.

This study investigates whether economic freedom of a region drives firm performance. Despite the large number of papers about the relationship between economic freedom and growth, there is still little evidence on the role of economic freedom in performance of individual firms. We address this gap in the literature using hierarchical linear modelling, allowing us to investigate regional differences in company level performance. The dataset consists of information about 1096 companies combined with the Index of Economic Freedom for Russian regions during the period 2004 – 2014

The paper discusses the problems of preventing harmful information spreading in social Networks. Social networks are widespread nowadays and are used not only for managers and marketers propagation of advertising, promotion of goods, but also by attackers to spread harmful information. Thus, there is a need to counter the attackers. This paper presents simulation tools and several features that contribute to the successful application for modeling social networks and examine different strategies preventing rumors and harmful information spreading. The authors cite an example of a simulation model for identifying intruders in a social network, software tools and the results of simulation experiments.

The language-oriented approach is becoming more and more popular in the development of information systems, but the existing DSM platforms that implement this paradigm have significant limitations, including insufficient expressive capabilities of the models used to implement visual model editors for complex subject areas and limited abilities to transform visual models. Visual languages are usually based on graph models, but the types of graphs used have certain limitations, such as insufficient expressiveness, the complexity of representing large-dimensional models and operation executions. For creating a tool that does not have the described constraints, development of a new formal model is needed. HP-graphs can become a solution for this problem. It is not only possible to create new visual languages for diverse domains based on them, but also to develop efficient algorithms to perform different operations on models constructed using these languages. The HP-graph definition is given and the justification of the expressive power of the proposed model is presented, the main operations for HP-graphs are described. The chosen graph formalism combines the capabilities of different types of graphs to represent visual models and allows creating a flexible model editor for the DSM platform, to implement effective algorithms of performing operations, in particular, model transformations.

Business‐like approaches are applied more and more widely in nonprofit organization contexts, and theaters are no exception. Revenue generation, customer segmentation, and personalized marketing are becoming the key managerial concerns. Our study focuses on two relevant aspects of theater attendees' behavior. We examine visitors' willingness‐to‐pay (WTP) for theater seats (to derive revenue drivers), and its difference between two segments – single and couple visitors (to uncover the social motivation effect). These aspects taken together have never been previously studied in the nonprofit marketing context. We model WTP using the actual purchase data from Perm Opera and Ballet Theatre in Russia. Unlike most marketing studies which use stated preference for WTP evaluation, we employ the revealed preference approach. The results verify that single and couple visitors may be treated as separate segments, allowing for personalized promotion and other marketing decisions.

This paper considers a dynamic ventilation system of the underground mining. The research is relevant from the point of view of safety and energy saving of mining operations, since the process of ventilation of underground mining companies consumes from 30 to 50% of all company electricity. Existing methods of ventilation control often do not ensure rational energy consumption, as they do not take into account the dynamics of air distribution and changes in environmental parameters. The proposed method includes basic algorithms for calculating the interrelationship of physical parameters of general natural draught between the trunks. The method includes: calculation of the draught’s power; calculation of productivity and the choice of the required mode of operation for the main fan unit (MFU) considering the inertia of the ventilation system; dynamic calculation of the control signal on the fan unit taking into account the impact of the general natural draught. The method is focused on the implementation of the TICK stack used to create IoT applications as part of the Cyber-Physical System (CPS) for ventilation based on the InfluxData platform. The proposed CPS architecture consists of four subsystems: physical object subsystem, IoT network and computing infrastructure - ICT infrastructure, digital twin, user interface. CPS architecture provides processing of data from energy meters, controllers and air environment parameters, implemented in on-line and off-line calculation units

This research was aimed at analyzing the moderating role of region on the impact of internal and external sources of knowledge on product innovation from a multilevel perspective. This study has made a contribution to the knowledge and innovation management field for small and medium-sized enterprises (SMEs), by analyzing the utilization of internal and external sources of knowledge in rapidly changing environments, such as the Russian business context, with consideration given to regional disparity. Empirical estimations are carried out on the basis of more than 700 Russian manufacturing SMEs, observed in 2018 within the framework of the project, “Factors of Competitiveness and Growth of Russian Manufacturing Enterprises”. Internal and external sources of knowledge were identified through latent variables and a method of hierarchical linear modeling was applied, considering firm-level data nested within different regions. The results obtained, show that in Russian SMEs, when considering the moderation role of the region, internal and external knowledge have a positive impact on product innovation. Moreover, external knowledge contributes more by comparison to internal knowledge. Meanwhile, the region context conditions the strength of the innovation effect for both knowledge sources. The significance of regional conditions in transforming internal and external sources of knowledge into product innovation, requires specific policy elaboration at regional level. Moreover, the dominating role of external knowledge sources for product innovation in SMEs, proves the necessity of specific policy elaboration with regard to the knowledge-sharing infrastructure connecting different business units.

In this paper we apply social network analysis to study the boards of directors of 107 large listed Russian firms between 2009 and 2014. Traditional corporate governance metrics, such as demographic characteristics, experience or multiple directorships, confirm a previously established positive trend towards greater independence and better qualification of the boards of Russian firms. We also find a decrease in the centrality of directors, which corroborates the diminishing concentration of power of some directors. The most connected firms have a specific profile since they are larger, have lower market valuations, and stronger ties with government (both due to higher proportions of government owned shares and a greater number of directors who are former politicians). Our findings also demonstrate that the boards of financial institutions are less connected, whereas political and independent directors are more centralized.

This paper studies the influence of parental involvement in the educational process on the educational achievements of Russian students and their educational strategies, such as studying in high school and successful admission to university. We argue that the patterns of parental involvement represent a link between the formal (school) and informal (family) educational institutions and can have a beneficial effect on academic performance and contribute to the choice of the educational pathway to higher education. Based on data from the longitudinal study ‘Trajectories in Education and Careers’, it was shown that the results of school state examinations are positively associated with the active participation of parents in school meetings, the employment of tutors (except for the Unified State Exam score in mathematics), and the provision of additional literature for the child. A negative relationship was found between homework control and student success. In general, the factor of ‘rational’ (not excessive) involvement is positively associated with educational achievement and educational choice, which may indicate the non-linear nature of the relationship. Parental involvement itself depends on the family characteristics, such as mother’s education, family income and the number of books at home. In addition, family has a positive impact on educational success and educational strategies, and high school characteristics are especially important for the results of the Unified State Exam and the university choice.