Publications

Background and Purpose Despite the continuing efforts in multimodal assessment of the motor system after stroke, conclusive findings on the complementarity of functional and structural metrics of the corticospinal tract (CST) integrity and the role of the contralesional hemisphere are still missing. The aim of this work was to find the best combination of the motor system parameters, allowing classification of patients into three predefined groups of upper limb motor recovery.

Methods 35 chronic ischemic stroke patients (47 [26–66] y.o., 29 [6–58] months post-stroke) with only supratentorial lesion and unilateral upper extremity weakness were enrolled. Patients were divided into three groups depending on the upper limb motor recovery. Non-parametric statistical tests and regression analysis were used to investigate the relationships among structural and functional motor system parameters, probed by diffusion tensor imaging (DTI) and transcranial magnetic stimulation (TMS). In addition, stratification rules were tested, using a decision tree classifier to identify parameters explaining motor recovery.

Results Fractional anisotropy (FA) ratio in the internal capsule (IC) and absence/presence of motor evoked potentials (MEPs), were equally discriminative of the worst motor outcome group (96% accuracy). MEP presence diverged for two investigated hand muscles. Concurrently, for the three recovery groups’ classification, the best parameter combination was: IC FA ratio and Fréchet distance between the contralesional and ipsilesional CST FA profiles (91% accuracy). No other metrics had any additional value for patients’ classification.

Conclusions This study demonstrates that IC FA ratio and MEPs absence are equally important markers for poor recovery. Importantly, we found that MEPs should be controlled in more than one hand muscle. Finally, we show that better separation between different motor recovery groups may be achieved when considering the whole CST FA profile.

In this paper, we rethink the corporate digital divide, a phenomenon not studied in detail in prior research. Motivated by innovation-diffusion, competence-based and skill-biased technical change theories, we hypothesize that all digital technologies’ innovations must be supported by demand for related skills and should be integrated into an innovation cycle. This research is conducted using a vast dataset of 1000 large Russian firms observed over ten years, with information collected from open internet-based sources and processed through content analysis. Among the key findings, the digital-innovation cycle has been explored and visualized, by identifying the most probable period of these innovations and their further diffusion. The digital-divide concept has been explicated by examining data on the relative dynamics of digital skills demanded by the same companies during the period of investigation. The empirical results deliver an interesting insight and encourage us to rethink the corporate digital divide through causality between competency accumulation and digital technological shifts. That, in turn, identifies the conditions necessary for the prediction of demand shocks in relation to digital competencies in labor markets.

Research on individual differences in the fields of chronobiology and chronopsychology mostly focuses on two – morning and evening – chronotypes. However, recent developments in these fields pointed at a possibility to extend chronotypology beyond just two chronotypes. We examined this possibility by implementing the Single- Item Chronotyping (SIC) as a method for self-identification of chronotype among six simple chart options il- lustrating the daily change in alertness level. Of 2283 survey participants, 2176 (95%) chose one of these op- tions. Only 13% vs. 24% chose morning vs. evening type (a fall vs. a rise of alertness from morning to evening), while the majority of participants chose four other types (with a peak vs. a dip of alertness in the afternoon and with permanently high vs. low alertness levels throughout the day, 15% vs. 18% and 9% vs. 16%, respectively). The same 6 patterns of diurnal variation in sleepiness were yielded by principal component analysis of sleepiness curves. Six chronotypes were also validated against the assessments of sleep timing, excessive daytime sleepi- ness, and abilities to wake or sleep on demand at different times of the day. We concluded that the study results supported the feasibility of classification with the 6 options provided by the SIC.

This study models the impact of renewable energy consumption on economic growth. Data on renewable energy consumption and real GDP usually have different frequencies, and commonly used methods of data aggregation lead to information loss. In order to avoid the problem, the method of temporal disaggregation was used in our paper. Further, we implemented the vector error correction model to define the type of causal relationship between variables. As a result, our analysis has indicated a positive relationship between renewable energy consumption and economic growth in the long-run period, whereas in the short-run period, economic growth has been found to cause renewable energy consumption. Therefore, the conservation hypothesis has been confirmed in our work.

The article is devoted to the description of intelligent information retrieval system development according to industry standards based on the Stanford CoreNLP tool. The description of the subject area, design, and main stages of system development are presented: development of extracting information module from industrial standards, implementation of a web service using the Flask framework, and a client web application on React JS. The use of the developed system by engineers and software developers will make it possible to effectively manage the definition base of industrial standards, understand them correctly and observe them in accordance with the chosen field of knowledge.

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

This paper examines the issue of employee discrimination after a political crisis: the annexation of Crimea. The annexation, which resulted in a political crisis in Russian-Ukrainian relations, is a setting which allows us to test if a bilateral political issue caused employee discrimination. We use a quasi-experimental approach to examine how the political crisis influenced participation in major sports leagues in Russia and Ukraine. The results show that the employment conditions significantly worsened since the Crimea crisis started.

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.

The article explores the possibility of increasing the loyalty of the youth audience - generation Z - to regional brands using the example of the Perm State Academic Opera and Ballet Theater named after P.I. Tchaikovsky. The authors substantiated the use of the SERVQUAL methodology as a key method for studying the influence of the main branding indicators on attracting the attention of young people and increasing their loyalty to the region of residence. Using the SERVQUAL methodology, the authors undertook a study and assessment of the impact of the theater brand on young consumers - representatives of the Z generation.

This study covers the application of asymmetric accuracy metrics in the daily retail sales prediction problem. The paper is focused on the empirical validation of an accuracy metric derived from the newsvendor model. We scrutinize the accuracy metric’s advantages and describe its properties. This paper uses two main accuracy metrics: quantile-weighted and mean absolute error. We compare the economic effect of accuracy metrics for different models trained on food retail chain data. The results show that the asymmetric loss based on quantile-weighted absolute loss leads to lower business costs than the mean absolute error. The research is of interest to store managers, inventory management specialists, and logistics specialists in retail and restaurant chains. Our findings provide a better understanding of how to implement forecasting methods in order to obtain more accurate sales predictions that meet practitioner expectations.

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.  

Firms are increasingly turning towards new-age technologies such as artificial intelligence (AI), the internet of things (IoT), blockchain, and drones, among others, to assist in interacting with their customers. Further, with the prominence of personalization and customer engagement as the go-to customer management strategies, it is essential for firms to understand how to integrate new-age technologies into their existing practices to aid seamlessly in the generation of actionable insights. Towards this end, this study proposes an organizing framework to understand how firms can use digital analytics, within the changing technology landscape, to generate consumer insights. The proposed framework begins by recognizing the forces that are external to the firm then lead to the generation of specific capabilities by the firm. Further, the firms capabilities can lead to the generation of insights for decision-making that can be data-driven and/or analytics-driven. Finally, the proposed framework identifies the creation of value-based outcomes for firms and customers resulting from the insights generated. Additionally, we identify moderators that influence: (a) the impact of external forces on the development of firm capabilities, and (b) the creation of insights and subsequent firm outcomes. This study also identifies questions for future research that combines the inclusion of new-age technologies, generation of strategic insights, and the achievement of established firm outcomes.

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.