Empire State Research Articles

Subjects, aliens, and undesirables: Managing mobility and insecurity in the British Empire

How do states use migration control to track both foreign and domestic threats? This article approaches this question historically, examining colonial migration control policies employed by the British Empire in South Asia. Studying the imperial state reveals important antecedents for the contemporary migration regime and shows that empire states engaged in similar practices even as they were monitoring imperial rather than nation-state borders. Papers – in the form of identity documents such as passports that could be flagged – emerged as a critical tool in the quest to repress transnational solidarities and anti-colonial resistance. Examining the ways in which migration control was operationalized within the empire along with the logics guiding such regulation, this article argues that colonial migration control was deployed as a tool to target threats to the empire, not only those considered Britain’s enemies abroad but also British subjects and citizens. The pretext of collaboration with enemy aliens allowed the hardening of borders and an intensification in how they were policed. I argue that states use the justification of external or foreign threats to monitor internal or domestic threats. The colonial border did not merely serve as a tool to constrain the movement of enemy aliens or foreigners, but also to enable the surveillance of ‘undesirable’ British Indians who were considered suspicious. This article thus centers the imperial security perceptions that undergird the contemporary racialized migration regime and shows that these perceptions were crystallized at the height of empire.

Information structure of heterogeneous criticality in a fish school

Integrated information theory (IIT) assesses the degree of consciousness in living organisms from an information-theoretic perspective. This theory can be generalised to other systems, including those exhibiting criticality. In this study, we applied IIT to the collective behaviour of Plecoglossus altivelis and observed that the group integrity (Φ) was maximised at the critical state. Multiple levels of criticality were identified within the group, existing as distinct subgroups. Moreover, these fragmented critical subgroups coexisted alongside the overall criticality of the group. The distribution of high-criticality subgroups was heterogeneous across both time and space. Notably, core fish in the high-criticality subgroups were less affected by internal and external stimuli compared to those in low-criticality subgroups. These findings are consistent with previous interpretations of critical phenomena and offer a new perspective on the dynamics of an empirical critical state.

Advances in AI-based combination therapies versus standard treatments for heart failure in patients with advanced kidney injury

Background: Recent advancements in AI and Machine Learning (ML) can help manage kidney disease and heart failure by providing predictive analytics and personalized treatment plans, improving diagnostic accuracy, and assisting clinicians in controlling chronic disease symptoms like acute kidney injury (AKI) and Managing Heart Failure (HF). We evaluated AI and ML role in predicting AKI and managing HF patients and it involved analyzing various predictive models and technologies to improve early detection provide personalized treatment, and enhance overall patient outcomes in complex renal and cardiac conditions. Methodology: We conducted our search on databases Scopus, PubMed, and Web of Science. Empirical evidence and state of the art of cutting-edge literature and real-time field research was analyzed. Results: AI advancements in HF management for advanced kidney disease AKI include unsupervised machine learning for risk stratification, reinforcement learning for predictive modeling and dynamic management, and LLMs and chatbots for diagnostic support and patient education. New developments in AI and medical technology are predictive analytics for AKI, automated ultrasound interpretation, AI-enhanced dialysis monitoring, HF risk prediction, remote monitoring, and telehealth integration, and making personal treatment strategies according to patient needs. Innovations like wearable dialysis devices, bioengineered kidney tissues, ECMO, LVADs, TAVR, SGLT2 inhibitors, CRT, and nephroprotective drugs have also improved patient outcomes as these have enabled personalized care and early intervention strategies. Conclusion: It is possible to conclude that AI has revolutionized AKI and HF management. Novel technologies offer precise and adaptive care that strictly addresses individual patient needs.

Правова заборона як метод обмеження політичної влади

The issue of legal prohibitions in national and European law has a pronounced interdisciplinary content. The modern Ukrainian environment, through which the legal values of European (Euro-Atlantic) and universal civilizations – principles, norms, institutions, sources of law, legal procedures, etc. – are “refracted” (“passed through”), is quite heterogeneous and contradictory, and this cannot but affect the peculiarities of Ukrainian law. These problems include, in particular, the absence, unlike in many European countries, of an explicit enshrinement of the principle of proportionality in the Constitution of Ukraine. This refers to the balance, proportionality of public interests and the interests of an individual in case of need to restrict his or her rights; excessive restrictions on human rights; distortion of the essence of the restricted rights; failure to comply with ECHR judgments at the “home level”; examples of risks when both the purpose and the means are inadequate to the specific situation requiring such a restriction, i.e., in an acceptable ratio to the weight and importance of the fundamental right. The author suggests the following areas of legal measures’ effectiveness: bringing regulations in line with international standards; further improvement of legislation; bringing restrictions of different levels (interstate, national, municipal) into the system; social justification and scientific validity of their establishment and use; increasing the legality and effectiveness of restrictions in the exercise of law; creation of real guarantees of lawful implementation of restrictive measures; legal education and legal education for all segments of the population. The prospects opened up for Ukraine in connection with its EU candidate status require not only further adaptation of Ukrainian legislation to the EU legal framework, continuation of reforms of the judicial system, law enforcement agencies, etc., but also a significant change in legal awareness, legal mentality and legal thinking of both ordinary Ukrainian citizens and, above all, public officials, judges, law enforcement officers, etc.Without such a change, these prospects may not be realized. It is stated that Ukraine’s heroic struggle against the Russian military invasion for freedom and independence significantly accelerated the formation of a full-fledged modern political Ukrainian nation. The war proved that an excessive focus on regional differences based on the principle of “what difference does it make” whether certain regions profess Soviet, national Ukrainian, or European values is futile. Of course, we are not talking about forming the same views on historical events or contemporary problems for all people, let alone the same type of thinking, which is what all totalitarian regimes have sought. The point is that, despite all the regional differences that are inherent in almost all states, the basic, fundamental values that define national identity should be common to all regions of the state. Modern history knows of no examples of democratic and legal states being built along different civilizational vectors. This is typical only for empire states, whose fate ultimately ends in the same way – in catastrophe.

MDEFC: Automatic recognition of human activities using modified differential evolution based fuzzy clustering method

In the present scenario, automatic Human Activity Recognition (HAR) is an emerging research topic, particularly in the applications of healthcare, Human Computer Interaction (HCI), and smart homes. By reviewing existing literature, the majority of the HAR methods achieved limited performance, while trained and tested utilizing unseen Internet of Things (IoT) data. In order to achieve higher recognition performance in the context of HAR, a new clustering method named Modified Differential Evolution based Fuzzy Clustering (MDEFC) is proposed in this article. The proposed MDEFC method incorporates an asymptotic termination rule and a new differential weight for enhancing the termination condition and improving this method’s ability in exploring the solution space of the objective function. The extensive empirical analysis states that the proposed MDEFC method achieved impressive recognition results with minimal training time by using both spatial and temporal features of the individual. The proposed MDEFC method’s effectiveness is tested on a real time dataset and an online Wireless Sensor Data Mining (WISDM) v1.1 dataset. The result findings demonstrate that the proposed MDEFC method averagely obtained 99.73 % of precision and 99.86 % of recall on the WISDM v1.1 dataset. Similarly, the proposed MDEFC method averagely obtained 93.46 % of f1-measure, 94.60 % of recall, and 93.88 % of precision on the real time dataset. These obtained experimental results are significantly higher in comparison to the traditional HAR methods.

Simultaneous Inference of Sea Ice State and Surface Emissivity Model Using Machine Learning and Data Assimilation

AbstractSatellite microwave radiance observations are strongly sensitive to sea ice, but physical descriptions of the radiative transfer of sea ice and snow are incomplete. Further, the radiative transfer is controlled by poorly‐known microstructural properties that vary strongly in time and space. A consequence is that surface‐sensitive microwave observations are not assimilated over sea ice areas, and sea ice retrievals use heuristic rather than physical methods. An empirical model for sea ice radiative transfer would be helpful but it cannot be trained using standard machine learning techniques because the inputs are mostly unknown. The solution is to simultaneously train the empirical model and a set of empirical inputs: an “empirical state” method, which draws on both generative machine learning and physical data assimilation methodology. A hybrid physical‐empirical network describes the known and unknown physics of sea ice and atmospheric radiative transfer. The network is then trained to fit a year of radiance observations from Advanced Microwave Scanning Radiometer 2, using the atmospheric profiles, skin temperature and ocean water emissivity taken from a weather forecasting system. This process estimates maps of the daily sea ice concentration while also learning an empirical model for the sea ice emissivity. The model learns to define its own empirical input space along with daily maps of these empirical inputs. These maps represent the otherwise unknown microstructural properties of the sea ice and snow that affect the radiative transfer. This “empirical state” approach could be used to solve many other problems of earth system data assimilation.

Joint estimation of sea ice and atmospheric state from microwave imagers in operational weather forecasting

AbstractSatellite‐observed microwave radiances provide information on both surface and atmosphere. For operational weather forecasting, information on atmospheric temperature, humidity, cloud, and precipitation is inferred directly using all‐sky radiance data assimilation. In contrast, information on the surface state, such as sea‐surface temperature (SST) and sea‐ice concentration (SIC), is typically provided through third‐party retrieval products. Scientifically, this is a sub‐optimal use of the observations, and practically it has disadvantages such as time delays of more than 48 h. A better solution is to estimate the surface and atmospheric state jointly from the radiance observations. This has not been possible until now, due to incomplete knowledge of the surface state and the radiative transfer that links this to the observed radiances. A new approach based on an empirical state and an empirical sea‐ice surface emissivity model is used here to add sea‐ice state estimation, including SIC, to the European Centre for Medium‐range Weather Forecasts atmospheric data assimilation system. The sea‐ice state is estimated using augmented control variables at the observation locations. The resulting SIC estimates are of good quality and they highlight apparent defects in the existing OCEAN5 sea‐ice analysis. The SIC estimates can also be used to track giant icebergs, which may provide a novel maritime application for passive microwave radiances. Further, the SIC estimates should be suitable for onward use in coupled ocean–atmosphere data assimilation. There is also increased coverage of microwave observations in the proximity of sea ice, leading to improved atmospheric forecasts out to day 4 in the Southern Ocean.

FORTIFICATION LINE OF THE GRAND DUCHY OF LITHUANIA: A NEW STAGE OF DEFENSE ARCHITECTURE OF THE EASTERN EUROPE SPACE

The theme of the history of medieval defensive architecture in the Northern Black Sea region becomes extremely relevant in the realities of the modern history of Ukraine, when the Russian invaders try to capture the southern lands of Ukraine and defend the idea of belonging of these territories to Russia with pseudo-historical concepts of «Russian peace». The results of the historical and archaeological study of the South of Ukraine, especially in recent decades, demonstrate other circumstances of the development of the South. They make it possible to highlight the main trends of the evolution of the region in direct continuity with the previous history of Russia in new historical circumstances. This is the time when the Kyiv principality became part of the Grand Duchy of Lithuania, Russia and Zhemaitia, the borders of which reach the Black Sea coast in the late 14th — early 15th century. Here the turbulent activity of the Grand Duke of Lithuania Vytautas unfolds in the construction of fortresses, customs houses, the arrangement of crossings and port facilities. The defensive architecture of the Southern region of the Grand Duchy of Lithuania, Russia and Zhemaitia takes place during the formation of a new map of Eastern Europe, with the presence of Lithuania, the formation of early Ukraine on the heritage of Russia, the formation of the Crimean Tatar people and its statehood, the active activity of the Principality of Moldavia in connection with the presence and influences of Genoa, contacts with Poland, influence of Ottoman Empire and other states of that time. It was the time of construction of the castle fortification, which belonged to the first stage of adaptation of military affairs to the artillery of the contemporary world, which covers the time of the 14th— 16th centuries. The comparative castellology gave the possibilities to study some regularity in the typological model of fortresses. This method was used in the course of study of fortresses of Vitovt’s fortification line, such as Tiagin fortress. It allows establishing the common features of the planning structure of the fortification architecture of the Northern Black Sea region — a new stage of evolution, which was characterized by stone castle architecture. In turn, they met the needs of the stage of development of artillery at the end of the 14th and the beginning of the 16th centuries. Medieval Europe, in which the influences of various cultures of the East and the West intersect, new defense opportunities and prospects are developing.

Self-control and Problematic Social Media Use: A Meta-Analysis

Failure to exercise self-control is one of the leading causes of substance and behavioral addictions. Problematic social media usage (PSMU), a type of behavioral addiction, has become an increasingly serious problem with a significant impact on the lives of individuals of all generations. Therefore, it is crucial to understand the underlying mechanism of PSMU to effectively address the issue. This study performed a meta-analysis, which systematically synthesized existing research in the field, to establish the current empirical state of the relationship between self-control and PSMU. The study included 57 independent samples that met the inclusion and exclusion criteria (N = 29.576). The analysis results indicated a moderate negative association between self-control and PSMU (r = -.29). Furthermore, the moderator analysis outcomes suggested that the association between self-control and PSMU did not differ significantly based on variables such as developmental period, educational stage, geographic region, and publication status. The study’s findings are expected to inspire future investigations and therapeutic approaches.

The War on Tigray: Geopolitics and the Struggle for Self-Determination

Tigray continues to be a unique case among ancient kingdoms, Westphalia nation-states, post-colonial Africa, and post-Cold War national liberation struggles. It is one of the oldest civilizations (Aksumite Kingdom) and the historical, cultural, and political soul of ancient and modern Ethiopia. But it was made to be an ‘oppressed nation struggling for regional autonomy and survival from genocidal aggression’ by the empire state of Ethiopia and the ‘garrison state’ of Eritrea. It also survived Egyptian and Mahdist expansionists, and Italian colonialism, though it lost ‘Bahre-Negash’ (Eritrea) to Italy due to the Wuchale Agreement (or Treaty of Wichale) in 1889. Eritrea was handed over to Italian colonialists by Menelik II of Shewa-Amhara in order to weaken Tigray’s geopolitical power base. Tigray was also the hotbed of the first Woyane rebellion in 1943 which sparked the idea of ‘land to the Tiller, fair taxation, and self-rule,’ and the Tigray People’s Liberation Front (TPLF) led the second Woyane rebellion (1975–91) that restructured the empire state of Ethiopia into a federation with the 1995 constitution. Since 2018, after 27 years of the Ethiopian People’s Revolutionary Democratic Front (EPRDF)- led federal experiment, Tigray again became a victim of a ‘genocidal war’ led by the Ethiopian federal army, Amhara forces, Ethiopian regional forces, and Eritrean army, supported by UAE and Turkey drones, and the Somalian army. The international community and the African Union supported Abiy Ahmed and Issaias Afewerki’s genocidal war in the name of “maintaining the territorial integrity of Ethiopia” by neglecting the principles of Responsibility to Protect (R2P), people’s self-determination rights, and the prevention of genocide. Tigray, therefore, is a rare case in the vicious cycle of violence. The Tigrayan ‘two-year’ survival war (from November 2020 to November 2022) against those who practice genocide was destined to break Tigray’s historically vicious cycle of violence perpetrated by Ethiopia and Eritrea. The paper aims to investigate the historical, geopolitical, and security reasons that have trapped Tigray into facing the post-2020 genocidal war.

The Spirit of New York: Defining Events in the Empire State's History by Bruce W. Dearstyne (review)

The Spirit of New York: Defining Events in the Empire State's History by Bruce W. Dearstyne (review)

Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis

Accurate state-of-health (SOH) estimation is critical for reliable and safe operation of lithium-ion batteries. However, reliable and stable battery SOH estimation remains challenging due to diverse battery types and operating conditions. In this paper, we propose a physics-informed neural network (PINN) for accurate and stable estimation of battery SOH. Specifically, we model the attributes that affect the battery degradation from the perspective of empirical degradation and state space equations, and utilize neural networks to capture battery degradation dynamics. A general feature extraction method is designed to extract statistical features from a short period of data before the battery is fully charged, enabling our method applicable to different battery types and charge/discharge protocols. Additionally, we generate a comprehensive dataset consisting of 55 lithium-nickel-cobalt-manganese-oxide (NCM) batteries. Combined with three other datasets from different manufacturers, we use a total of 387 batteries with 310,705 samples to validate our method. The mean absolute percentage error (MAPE) is 0.87%. Our proposed PINN has demonstrated remarkable performance in regular experiments, small sample experiments, and transfer experiments when compared to alternative neural networks. This study highlights the promise of physics-informed machine learning for battery degradation modeling and SOH estimation.

Numerical algorithm for estimating a conditioned symmetric positive definite matrix under constraints

SummaryWe present RCO (regularized Cholesky optimization): a numerical algorithm for finding a symmetric positive definite (PD) matrix with a bounded condition number that minimizes an objective function. This task arises when estimating a covariance matrix from noisy data or due to model constraints, which can cause spurious small negative eigenvalues. A special case is the problem of finding the nearest well‐conditioned PD matrix to a given matrix. RCO explicitly optimizes the entries of the Cholesky factor. This requires solving a regularized non‐linear, non‐convex optimization problem, for which we apply Newton‐CG and exploit the Hessian's sparsity. The regularization parameter is determined via numerical continuation with an accuracy‐conditioning trade‐off criterion. We apply RCO to our motivating educational measurement application of estimating the covariance matrix of an empirical best linear prediction (EBLP) of school growth scores. We present numerical results for two empirical datasets, state and urban. RCO outperforms general‐purpose near‐PD algorithms, obtaining ‐smaller matrix reconstruction bias and smaller EBLP estimator mean‐squared error. It is in fact the only algorithm that solves the right minimization problem, which strikes a balance between the objective function and the condition number. RCO can be similarly applied to the stable estimation of other posterior means. For the task of finding the nearest PD matrix, RCO yields similar condition numbers to near‐PD methods, but provides a better overall near‐null space.

Impact of Goods and Service Tax on MSME Sector: A Study Using Artificial Neural Network and Multivariate Analysis of Variance

Micro, small and medium enterprises (MSME) is one the vital sector of the developing economy. Changes in fiscal policy directly impact this sector the most. The present article aims to examine the impact of the recent Indian indirect system tax overhaul, that is, Goods and Service Tax (GST), on the MSME sector. We have employed multivariate analysis of variance and multi-layer perceptron neural network on the extensive data set for 60 months from April 2015 to March 2020. The empirical findings state that a significant increase has been observed in the MSMEs’ export, revenue generation and gross domestic product in the post-GST era. The findings may aid the emerging economies that are planning to alter tax systems to boost the MSME sector at the international level. JEL Codes: H20, L00, B22, C01, C45

Quantification of sectoral impact of COVID-19 on Indian economy: an application of economy-wide accounting framework

PurposeCOVID-19 has affected the economies adversely from all sides. The sudden halt in production has impacted both the supply and demand sides. It calls for analysis to quantify the impact of the reduction in economic activity on the economy-wide variables so that appropriate steps can be taken. This study aims to evaluate the sensitivity of various sectors of the Indian economy to this dual shock.Design/methodology/approachThe eight-sector open economy general equilibrium Global Trade Analysis Project (GTAP) model has been simulated to evaluate the sector-specific effects of a fall in economic activity due to COVID-19. This model uses an economy-wide accounting framework to quantify the impact of a shock on the given equilibrium economy and report the post-simulation new equilibrium values.FindingsThe empirical results state that welfare for the Indian economy falls to the tune of 7.70% due to output shock. Because of demand–supply linkages, it also impacts the inter- and intra-industry flows, demand for factors of production and imports. There is a momentous fall in the demand for factor endowments from all sectors. Among those, the trade-hotel-transport and manufacturing sectors are in the first two positions from the top. The study recommends an immediate revival of the manufacturing and trade-hotel-transport sectors to get the Indian economy back on track.Originality/valueThe present study has modified the existing GTAP model accounting framework through unemployment and output closures to account for the impact of change in sectoral output due to COVID-19 on the level of employment and other macroeconomic variables.

OOA-modified Bi-LSTM network: An effective intrusion detection framework for IoT systems

Currently, the Internet of Things (IoT) generates a huge amount of traffic data in communication and information technology. The diversification and integration of IoT applications and terminals make IoT vulnerable to intrusion attacks. Therefore, it is necessary to develop an efficient Intrusion Detection System (IDS) that guarantees the reliability, integrity, and security of IoT systems. The detection of intrusion is considered a challenging task because of inappropriate features existing in the input data and the slow training process. In order to address these issues, an effective meta heuristic based feature selection and deep learning techniques are developed for enhancing the IDS. The Osprey Optimization Algorithm (OOA) based feature selection is proposed for selecting the highly informative features from the input which leads to an effective differentiation among the normal and attack traffic of network. Moreover, the traditional sigmoid and tangent activation functions are replaced with the Exponential Linear Unit (ELU) activation function to propose the modified Bi-directional Long Short Term Memory (Bi-LSTM). The modified Bi-LSTM is used for classifying the types of intrusion attacks. The ELU activation function makes gradients extremely large during back-propagation and leads to faster learning. This research is analysed in three different datasets such as N-BaIoT, Canadian Institute for Cybersecurity Intrusion Detection Dataset 2017 (CICIDS-2017), and ToN-IoT datasets. The empirical investigation states that the proposed framework obtains impressive detection accuracy of 99.98 %, 99.97 % and 99.88 % on the N-BaIoT, CICIDS-2017, and ToN-IoT datasets, respectively. Compared to peer frameworks, this framework obtains high detection accuracy with better interpretability and reduced processing time.

Comprehensive Review of the Determination and Reduction of the Minimum Miscibility Pressure during CO2 Flooding.

With the increasing oil demand, more attention has been paid to enhancing oil recovery in old oil fields. CO2 flooding is popular due to its high oil displacement efficiency and ability to reduce greenhouse gas emissions. Laboratory experiments and on-site application cases have shown that the minimum miscibility pressure has a greater impact on CO2 flooding than other factors. If the reservoir pressure is below the minimum miscible pressure, then there is CO2 immiscible flooding. Both theoretical analysis and experimental results show that the recovery rate of CO2 miscible flooding is 2-5 times higher than that of immiscible flooding. If the reservoir pressure is increased by water flooding before CO2 injection, it is easily limited by the physical property parameters. Therefore, accurately determining and effectively reducing the minimum mixing pressure has become the focus of research. Currently, there are two types of methods for determining the minimum miscible pressure: experimental and theoretical methods. The experimental method is generally considered more accurate, including the slim tube test, rising bubble apparatus, and vanishing interfacial tension, etc. However, it is worth noting that the minimum miscibility pressure is dynamically changing, and there will be high economic costs if measured repeatedly through experimental methods during reservoir development. Therefore, it is recognized that the minimum mixing pressure can be determined at any time using theoretical calculation of initial data, which will reduce economic and time costs to a high degree. In this paper, the theoretical calculation method is divided into empirical correlation, state equation, and artificial intelligence algorithm. The techniques for reducing the minimum miscibility pressure can be classified into two categories: miscible solvents and surfactant methods. The miscible solvent method can be further divided into monocomponent and polycomponent methods. This paper compares the advantages and disadvantages of the existing techniques for measuring and reducing MMP and selects the best method.

The Influence of Ethical Leadership on Organizational Citizenship Behavior with Employee Engagement and Work Meaningfulness as a Mediator for Employees PT. Bank X in Surabaya

The goal of this study is to analyze the relationship ethical leadership and employee organizational citizenship behavior (OCB) mediated by employee engagement and meaningfulness of work at PT. Bank X Surabaya. Grounded in social exchange theory, this quantitative research involved a sample of 162 employees from PT. Bank X Surabaya. Data collection via online questionnaires and analyzed via Partial Least Square (PLS). Empirical findings state that ethical leadership has an insignificant positive impact on employee OCB, but has a significant impact on the escalation of employee engagement and meaningfulness of work. Employee engagement and work meaningfulness have a significant positive impact on employee OCB. Both variables mediate the relationship between ethical leadership and employee OCB. Practical implications show creating ethical leadership is critical to encouraging employee OCB. However, the study is limited by its focus solely on bank employees, potentially restricting the generalizability of the findings.

Pioneers of Modern Education: A Comparative Study Between the Ottoman Empire and Other States

Pioneers of Modern Education: A Comparative Study Between the Ottoman Empire and Other States

The crisis of the professional identity of the teacher in the context of the formation of a new information and communication educational paradigm: Aspect of research and publication activity

The problem of the study - the genesis of the problem of the crisis of professional identity of a teacher in the context of the formation of a new information and communication educational paradigm in the second half of the twentieth century - early twenty-first century. The analysis of the scientific and pedagogical literature of the second half of the twentieth century - the beginning of the twenty-first century demonstrates that the transition to a new paradigm of education was marked by the emergence of a crisis of professional identity, which is also emphasised by the ambiguity of definitions of the concept of professional identity. In the modern scientific and pedagogical discourse, a teacher is seen as a person with numerous roles. The scientific, theoretical and philosophical foundations for considering research activities and publication activity as key indicators of the level of professional identity of a teacher are identified and substantiated. An empirical study of the opinions of practicing teachers in Poland, Slovakia, Ukraine and Sweden on research and publication activity is carried out. The results of the international empirical research state the presence and reveal specific manifestations of the professional identity crisis.