Transformation Tasks Research Articles

Criteria for the efficiency of enterprise management in the framework of digital transformation

The purpose of this article is to describe the essence and principles of a new methodological approach to the enterprise management system analysis in the context of digital transformation. The approach proposed by the authors is based on the key criteria for the effectiveness of any economic system management in the context of digital changes. The identified criteria for the effectiveness of management include such aspects as the enterprise digital transformation strategy development, which does not contradict the general course of development; the involvement of qualified employees in the field of digital management; ensuring the motivation of personnel to implement digital transformation; the introduction of a special modular digital platform that combines the necessary technologies, techniques; conducting systematic monitoring of the implementation of transformational tasks and goals. In this study, the authors used the method of theoretical generalization and comparison, scientific abstraction, logical, induction and deduction methods, etc. As a result of the study, it was concluded that it is expedient to apply the proposed methodological approach to the analysis of enterprise management by taking into account and evaluating the criteria for management effectiveness in the context of digital transformation, as well as calculating the integral index.

“Are we both right?” relations between theory of mind and epistemic humility in dyadic cooperative problem-solving in 5- to 9-year-old children

The current study addresses the question of the relation between ToM and children's epistemic humility - the tendency to acknowledge the limitations of one's knowledge while being open to another's input during socio-cognitive conflict regulation - in a cooperative problem-solving context. Sixty-four boys and girls between the ages of 5 to 9 years (32 same-gender dyads) were tested for their ToM with the Theory of Mind Test (TMT) and The Test of Emotion Comprehension (TEC), and for their epistemic humility through an analysis of their spontaneous verbal interactions during the resolution of a dyadic spatial transformation task. The results showed that children with higher levels of ToM more frequently showed more epistemic humility when faced with conflicting ideas, even when age was taken into account. The results are discussed in terms of the processes underlying socio-cognitive conflict regulation in peer cooperation and their educational implications.

Transforming Documents of the Austrian Nationwide EHR System into the OMOP CDM.

The Austrian nationwide EHR system ELGA can contribute valuable data for research due to its high volume of data and broad population coverage. In order to be applicable in international research projects, transformation to a standardized, research-oriented data model such as the OMOP common data model is essential. In this paper we describe our experience with the corresponding transformation task. Using Python scripts, we implemented a prototypical process that extracts, transforms, maps, and loads fully structured sections of ELGA documents to an OMOP database.

“His Is a Reverent Vandalism”: Alain Locke’s Aesthetics and Fugitive Democracy

Several contemporary scholars have embraced the aesthetic resources in the Black Radical Tradition for the purpose of revitalizing the democratic project. Ironically, however, many drawn to the radical potential of fugitive escape are concerned about flight or exodus from the democratic project itself resulting in a defense of politics that constricts the possible benefits of fugitive aesthetics for democratic life. This article draws on the work of Alain Locke, a key figure of the Harlem Renaissance, to suggest another way in which we might follow Black fugitive aesthetics. Through an engagement with Locke, I theorize the notion of a fugitive bearing associated with a set of sensibilities that we might cultivate in order to approach the task of democratic transformation as a “reverent vandalism.” This article also challenges dismissive readings of Locke’s aestheticism by closely reexamining his commitment to expressive autonomy in connection with his theory of democracy. His “New Negro” avant-garde, like the fugitive, remains tethered to political life even while seeking a freedom that is unavailable within the strictures of the standing normative order.

Evaluation of Carbon Emission and Carbon Contribution Capacity Based on the Beijing–Tianjin–Hebei Region of China

In 2020, during the 75th United Nations General Assembly, China proposed the goal of “achieving a carbon peak by 2030 and achieving carbon neutrality by 2060”. The Beijing–Tianjin–Hebei region is the largest carbon emission metropolitan area in China. Its energy consumption and carbon emission status means the Beijing–Tianjin–Hebei region shoulders the important task of low-carbon transformation. Only by achieving a “carbon peak” and “carbon neutrality” can the economy of the Beijing–Tianjin–Hebei region be sustainable. Based on this, we analyzed and comprehensively evaluated the complete ability of the Beijing–Tianjin–Hebei region to achieve carbon neutrality in 2008–2020, that is, carbon achievement ability, status, and intensity of energy consumption and carbon emissions based on the CRITIC weighting method. The results show that the energy consumption and carbon emission capacity of the Beijing–Tianjin–Hebei region have basically formed a step difference. On this basis, the evaluation of carbon contribution capacity also shows that Hebei Province, as a weak link in the coordinated development of the Beijing–Tianjin–Hebei region, bears the main responsibility of improving carbon contribution capacity and thus achieving the goal of carbon neutrality. This research study suggests that intra-regional cooperation should be strengthened to achieve coordinated development. At the end of the paper, policy suggestions for regional cooperation are given from four aspects: government, industry, low-carbon technology, and regional carbon sinks.

G2GT: Retrosynthesis Prediction with Graph-to-Graph Attention Neural Network and Self-Training

Retrosynthesis prediction, the task of identifying reactant molecules that can be used to synthesize product molecules, is a fundamental challenge in organic chemistry and related fields. To address this challenge, we propose a novel graph-to-graph transformation model, G2GT. The model is built on the standard transformer structure and utilizes graph encoders and decoders. Additionally, we demonstrate the effectiveness of self-training, a data augmentation technique that utilizes unlabeled molecular data, in improving the performance of the model. To further enhance diversity, we propose a weak ensemble method, inspired by reaction-type labels and ensemble learning. This method incorporates beam search, nucleus sampling, and top-k sampling to improve inference diversity. A simple ranking algorithm is employed to retrieve the final top-10 results. We achieved new state-of-the-art results on both the USPTO-50K data set, with a top-1 accuracy of 54%, and the larger more challenging USPTO-Full data set, with a top-1 accuracy of 49.3% and competitive top-10 results. Our model can also be generalized to all other graph-to-graph transformation tasks. Data and code are available at https://github.com/Anonnoname/G2GT_2.

PUNDIT: Pulmonary nodule detection with image category transformation.

Convolutional neural networks (CNNs) have achieved great success in pulmonary nodules detection, which plays an important role in lung cancerscreening. In this paper, we proposed a novel strategy for pulmonary nodule detection by learning it from a harder task, which was to transform nodule images into normal images. We named this strategy as pulmonary nodule detection with image category transformation (PUNDIT). There were two steps for nodules detection, nodule candidate detection and false positive (FP) reduction. In nodule candidate detection step, a segmentation-based framework was built for detection. We designed an image category transformation (ICT) task to translate nodule images into pixel-to-pixel normal images and share the information of detection and transformation tasks by multitask learning. As for references of transformation tasks, we proposed background consistency losses into standard cycle-consistent adversarial networks, which can solve the problem of background uncontrolled changing. A three-dimensional network was used in FP reductionstep. PUNDIT was evaluated in two datasets, cancer screening dataset (CSD) with 1186 nodules for cross-validation and (CTD) with 3668 nodules for external test. Results were mainly evaluated by competition performance metric (CPM), the average sensitivity at seven predefined FP rates. The CPM was improved from 0.906 to 0.931 in CSD, and from 0.835 to 0.848 inCTD. Experimental results showed that PUNDIT can improve the performance of pulmonary nodules detectioneffectively.

A Deep Learning Approach to Visualize Aortic Aneurysm Morphology Without the Use of Intravenous Contrast Agents.

Intravenous contrast agents are routinely used in CT imaging to enable the visualization of intravascular pathology, such as with abdominal aortic aneurysms. However, the injection is contraindicated in patients with iodine allergy and is associated with renal complications. In this study, we investigate if the raw data acquired from a noncontrast CT image contains sufficient information to differentiate blood and other soft tissue components. A deep learning pipeline underpinned by generative adversarial networks was developed to simulate contrast enhanced CTA images using noncontrast CTs. Two generative models (cycle- and conditional) are trained with paired noncontrast and contrast enhanced CTs from seventy-five patients (total of 11,243 pairs of images) with abdominal aortic aneurysms in a 3-fold cross-validation approach with a training/testing split of 50:25 patients. Subsequently, models were evaluated on an independent validation cohort of 200 patients (total of 29,468 pairs of images). Both deep learning generative models are able to perform this image transformation task with the Cycle-generative adversarial network (GAN) model outperforming the Conditional-GAN model as measured by aneurysm lumen segmentation accuracy (Cycle-GAN: 86.1% ± 12.2% vs Con-GAN: 85.7% ± 10.4%) and thrombus spatial morphology classification accuracy (Cycle-GAN: 93.5% vs Con-GAN: 85.7%). This pipeline implements deep learning methods to generate CTAs from noncontrast images, without the need of contrast injection, that bear strong concordance to the ground truth and enable the assessment ofimportant clinical metrics. Our pipeline is poised to disrupt clinical pathways requiring intravenous contrast.

Conflictology and the Law «On Mediation»

Modern realities in a rapidly changing world are condensing events with conflict potential, which will certainly affect the prospects for the further development of mankind as a whole, as well as in relation to individual states and social groups. Such phenomena as social disunity, racial and ethnic intolerance, terror, often resulting in dramatic social and interethnic conflicts, actualize the need to develop the institution of mediation. In October 2021, at a meeting of the Assembly of the People of Kazakhstan, President Kassym-Jomart Tokayev noted «It is necessary to actively use the capabilities of public legal institutions, in particular, mediation». As the next priority task, the Head of State noted «that for ethnomediation in our conditions the format of professional negotiators is the most effective».The January events of last year exposed a number of social problems, including the gap between the poor and the rich. In his speech, the Head of State emphasized that «the country has changed, and the attitude of Kazakhstan to power, the economic system and life in general has also changed. And now there is a strategic task of a deep transformation of the entire social structure. In the discourses of modern foreign and domestic experts, questions about the problems of world development through the escalation of social conflicts are more often raised. Based on this, we consider it important that real reforms should contribute to meeting the security interests of modern Kazakhstan. This requires further formation and systematization of the institution of mediation in the socio-cultural life of Kazakhstan.

МЕТОДИЧЕСКИЕ АСПЕКТЫ ОБУЧЕНИЯ МАТЕМАТИКЕ С ИСПОЛЬЗОВАНИЕМ ИНФОРМАЦИОННЫХ И СКВОЗНЫХ ТЕХНОЛОГИЙ

Digital transformation affects all aspects of educational and professional activities. An urgent task of digital transformation of education is access to digital and end-to-end technologies. Modern digital and end-to-end technologies provide an innovative approach to learning, put forward new digital tools for the development of the university, promote the exchange of accumulated knowledge and experience. The use of digital technologies in modern education has become an affordable and reliable tool that contributes to improving the daily work of high-tech industries and service companies. This article is devoted to the review of new opportunities of digital transformation in education. Attention is paid to the role of information and end-to-end technologies, digital tools in teaching mathematical disciplines. The necessity of including these technologies in the educational process in connection with the demand for their application in modern professional activity is substantiated. On the basis of information and digital end-to-end technologies, the authors updated the main blocks of the work program of the discipline “Discrete Mathematics and mathematical Logic” In the fund of evaluation tools, practice-oriented tasks were developed. Taking into account: analysis and integration of scientific and methodological literature, internet resources, experience of scientific and pedagogical activity, the authors have improved and tested the methodology for the formation of digital competencies of students of the 02.03.02 Fundamental Informatics and Information Technology training direction.

Digital transformation of the agro-industrial complex as a transition to highly efficient production

Modern digitalization opportunities are very broad and cover the entire agricultural sector. The paper considers the potential of digital transformation of production, the goals and economic benefits of digitalization for agricultural producers. The authors studied the four main directions of digitalization of production processes in the agro-industrial complex. The financial support of the project of digitalization of agriculture, the structure of funding in the dynamics over a number of years, including at the expense of the region were analyzed. The paper identifies the problems of digitalization of small businesses, the digital divide between urban and rural areas. The authors formulated the main external and internal tasks of the digital transformation of the agro-industrial complex, which will provide a highly efficient production in the future.

Об основных направлениях единой государственной денежно-кредитной политики на 2023 год и период 2024 и 2025 годов: мнение экспертов Финансового университета

The subjects of the research are tasks, principles, measures and tools, mechanism and factors of implementation of the unified state monetary policy for the medium—term period 2023-2025, taking into account the current macroeconomic situation. The purpose of the work is - based on the examination of the report published by the Bank of Russia "The main directions of the unified state monetary policy for 2023 and the period 2024 and 2025" to identify the problems of the formation and implementation of monetary policy in the medium term in order to successfully achieve the task of transformation of the Russian economy. The report of the Bank of Russia states that the continued targeting of inflation in the medium term as a monetary policy regime increases the efficiency of the use of factors of production in the economy. However, it is noted that the contribution of monetary policy to the economic development of Russia should be more significant due to its conditionality with the process of structural adjustment of the economy and the activation of internal growth factors. Special attention is paid to the problems of modern principles of monetary policy, the effects of its transmission mechanism, the formulation of scenarios of macroeconomic development. Conclusions are drawn that the provisions of the report of the Bank of Russia "The main directions of the unified state monetary policy for 2024 and the period 2024 and 2025" deserve a positive assessment, taking into account the comments and recommendations made by the experts of the Financial University.

High-frequency Normalizing Flow for Image Rescaling.

It is desirable to develop efficient image rescaling methods to transmit digital images with different resolutions between devices and assure visual quality. In image downscaling, the inevitable loss of high-frequency information makes the reverse upscaling highly ill-posed. Recent approaches focus on joint learning of image downscaling and upscaling (e.g., rescaling). However, existing methods still fail to recover satisfactory high-frequency signals when upscaling. To solve it, we propose high-frequency flow (HfFlow), which learns the distribution of high-frequency signals during rescaling. HfFlow is an overall invertible framework with a conditional flow on the high-frequency space to compensate for the information lost during downscaling. To facilitate finding the optimal upscaling solution, we introduce a reference low-resolution (LR) manifold and propose a cross-entropy Gaussian loss (CGloss) to force the downscaled manifold closer to the reference LR manifold and simultaneously fulfill recovering missing details. HfFlow can be generalized to other scale transformation tasks such as image colorization with its excellent rescaling capacity. Qualitative and quantitative experimental evaluations demonstrate that HfFlow restores rich high-frequency details and outperforms state-of-the-art rescaling methods in PSNR, SSIM, and perceptual quality metrics.

Human-computer interaction for virtual-real fusion

Human-computer interaction for virtual-real fusion

Smart Resource Management: An Innovative Way of Balancing Materials, Energy, and Waste for a Green Future

In an era defined by environmental consciousness and resource scarcity, the concept of smart resource management has emerged as a linchpin in achieving a sustainable and green future. This abstract explores the multifaceted landscape of resource utilization, energy management, and waste minimization, collectively shaping our journey toward a more ecologically responsible world. The manuscript delves into the critical dimensions of this paradigm shift, examining innovative practices and technologies across diverse sectors. From precision agriculture optimizing crop yields with minimal environmental impact to the integration of renewable energy sources reshaping our energy landscape, the narrative unfolds . This highlights the profound impact of circular economy principles, which prioritize resource conservation and recycling. These principles, coupled with resource-sharing platforms, redefine how we perceive and utilize materials, fostering economic growth while reducing waste. Amidst these transformative tasks, challenges and barriers to implementation come to light. The abstract acknowledges these obstacles and explores emerging technologies and trends that promise to surmount them. Ultimately, the abstract underscores the imperative of smart resource management in the pursuit of a green future. It encapsulates the essence of a global movement where sustainability, economic prosperity, and environmental stewardship converge, offering hope and inspiration for generations to come.

A Survey on Learning Objects' Relationship for Image Captioning.

Image captioning is a challenging modality transformation task in computer vision and natural language processing, aiming to understand the image content and describe it with a natural language. Recently, the relationship information between objects in the image has been investigated to be of importance in generating a more vivid and readable sentence. Many types of research have been done in relationship mining and learning for leveraging into the caption models. This paper mainly summarizes the methods of relational representation and relational encoding in image captioning. Besides, we discuss the advantages and disadvantages of these methods and provide commonly used datasets for the relational captioning task. Finally, the current problems and challenges in this task are highlighted.

Formation of development programs with multi-purpose projects at ferrous metallurgy enterprises

The improvement of management mechanisms for the formation and calendar planning of development programs is the most important direction for improving the productivity (achievement of goals) and efficiency (reduction of the amount of resources consumed) of the activities of metallurgical companies. Currently, it is necessary to ensure the mobilization of companies’ assets to solve the tasks of their sustainable development. The task of forming a program for the development of a metallurgical enterprise (company) is considered. The program includes several different areas of development: improvement of existing business processes (sales, supply, production, repair of equipment, etc.), production technologies of various stages (production of coke, agglomerate, cast iron, steel, rolled products), implementation of digital transformation tasks, etc. Each of the directions of the development program contains projects described by effect, size of investments, changes in the expenditure items of operating budget related to the costs of operating those systems and processes that the project is aimed at improving, as well as an indicator describing the risk of project implementation. One of the directions of the development program may include multi-purpose projects, the implementation of which leads not only to changes in its own performance indicators, but also to changes in the performance indicators of projects of other (non-multi-purpose) directions of such development program. The case is considered when management of the development program includes the management of the overall budget and achievement of overall goal of the program (the maximum effect from implementation of all projects). At the same time, project risk management and changes in the operating budget are implemented at the level of project portfolio management of individual program areas (there are no restrictions on risks and changes in the operating budget common to the development program). The stated formalizations of the problems, their decomposition and composition schemes, and the developed procedures for solving individual subtasks are based on the provisions and methods of the theories of system analysis and a new section of discrete mathematics (network programming).

ETHNOGRAPHIC ASPECT IN THE CONTENT OF FUTURE SPECIALISTS’ TRAINING AND ETHNOCULTURAL ACTIVITIES

The purpose of the article is to analyze the prospects for the ethnographic aspect usage as a component of the content of higher education and ethno cultural activities of future specialists. Certain aspects of the study of disciplines from the standpoint of Ukrainian ethnology are highlighted. The importance of using materials from Ukrainian ethnology for the spiritual development and education of youth is emphasized. It is emphasized that understanding the formation of national self-identification as a result of the appropriate provision of the professional training process is one of the most significant competencies of the 21st century. The possibilities of ethnology for updating the content of teaching professional disciplines for future specialists in the humanitarian field have been identified.
 The issue’s relevance is explained by the need to form a young person’s worldview based on national and universal spiritual values, respect for the achievements of national culture, and the use of rich folk traditions in the course of ethno cultural activities. One of the main tasks of reforming the modern education system is to provide a solution to the issue of the formation of civil society as a community ready to participate in the processes of state formation and to protect democratic values, the territorial integrity of the country since without the affirmation of democratic values in the consciousness of the individual, an active civic position formation, moral responsibility for the fate of the country, education of beliefs, feelings of love and respect for the cultural and historical past of Ukraine, solving the task of modern transformation of Ukrainian society is not possible.
 Ethnology is considered to be an integrative component of the content of education in secondary and higher education institutions; the need for a more systematic approach to the content of the educational material of Ukrainian-cultural disciplines, which contain a powerful formative and educational potential, which contributes to the process of self-awareness of the Ukrainian people, the establishment of Ukraine as a political nation and an independent state, is emphasized.

Cortical and cerebellar structural correlates of cognitive-motor integration performance in females with and without persistent concussion symptoms

ABSTRACT Introduction Fifteen percent of individuals who sustain a concussion develop persistent concussion symptoms (PCS). Recent literature has demonstrated atrophy of the frontal, parietal, and cerebellar regions following acute concussive injury. The frontoparietal-cerebellar network is essential for the performance of visuomotor transformation tasks requiring cognitive-motor integration (CMI), important for daily function. Purpose We investigated cortical and subcortical structural differences and how these differences are associated with CMI performance in those with PCS versus healthy controls. Methods Twenty-six age-matched female participants (13 PCS, 13 healthy) completed four visuomotor tasks. Additionally, MR-images were analyzed for cortical thickness and volume, and cerebellar lobule volume. Results No statistically significant group differences were found in CMI performance. However, those with PCS demonstrated a significantly thicker and larger precuneus, and significantly smaller cerebellar lobules (VIIIa, VIIIb, X) compared to controls. When groups were combined, volumes of both the cerebellar lobules and cortical regions were associated with CMI task performance. Conclusion The lack of behavioral differences combined with the structural differences may reflect a compensatory mechanism for those with PCS. In addition, this study highlights the effectiveness of CMI tasks in estimating the structural integrity of the frontoparietal-cerebellar network and is among the first to demonstrate structural correlates of PCS.

Impact of Public Environmental Concerns on the Digital Transformation of Heavily Polluting Enterprises.

China is currently facing the arduous tasks of energy conservation, emission reduction and structural transformation, making it of great significance to study the digital transformation of heavily polluting enterprises. As an important informal regulatory system, public environmental concerns affect corporate environmental behavior by increasing external environmental pressure. This study uses the data of listed companies in China's heavily polluting industries from 2012 to 2020 and Baidu Index data to analyze how public environmental concerns affect the digital transformation of heavily polluting enterprises. This study finds that public environmental concerns can significantly promote the digital transformation of heavily polluting enterprises. For non-state-owned, green image and high-tech enterprises, the impact is even more obvious. Furthermore, based on the structural and hierarchical perspective of enterprise digital transformation, we find that public environmental concerns significantly promote digital technology application. This study puts forward some suggestions for government departments to formulate environmental protection regulations, enterprises to fulfill their green responsibilities and the public to participate in environmental governance.