Processing Resources Research Articles

Enabling Natural Language Processing and AI Research in Low-Resource Languages: Development and Description of an Assamese UPoS Tagged Dataset

This paper describes in detail the Universal Parts of Speech (UPoS) tagged dataset for the Assamese language. PoS tagged dataset in a language is crucial for experimenting and creating resources for various Natural Language Processing (NLP) and AI research. With the growing usage of Universal Dependency standards, tagged dataset with Universal PoS tags are becoming very much essential for contemporary experiments in the NLP community. NLP research in Assamese, and Indo-Aryan language, is relatively new, and the language is considered a Low Resource language. The dataset of UPoS tagged Assamese text is created with an aim of contributing towards enriching this low resource language for NLP and AI tasks. The size of the dataset is 283506 tokens of Assamese vocabulary, against total 20280 sentences, tagged with 17 standard UPoS tags of core lexical categories. The raw data are taken from an open-source corpus originally tagged with BIS tagset. The original size of 453457 tokens against 29504 sentences, after subjected to data filtering, was reduced to this clean resource of 283506 tokens. Lexical categories mapping is done with linguistic expertise, from BIS to UPoS tagsets. Mapped pattern was used for a first-level conversion of BIS tags to UPoS tags. Linguistic validation is also performed with linguistic experts and inter annotator agreement/disagreements were recorded. Second level validation resulted in deciding on the agreement, producing the final version of the dataset. This Assamese UPoS tagged dataset is the first of its kind with UPoS annotations and shall serve a wider Assamese NLP research community for model training using Machine Learning/Deep Learning Techniques.

Extraction of Heterogeneous Features from Various Fruits and Classification of diseases using Deep Learning Techniques

The success of the farming business and the economy as a whole is directly tied to each individual farmer's output. Micro-level illnesses that emerge during fruit development may have a significant influence on output. There is an overwhelming variety of viruses, necessitating improvements in image processing and other informational resources before effective treatments can be proposed. Unparalleled approaches to segmenting, extracting characteristics, and classifying in order to identify overt conditions constitute a substantial portion of the research in this domain. However, in the event of larger and more in-depth datasets, machine learning and artificial intelligence technologies like deep learning and the like are necessary to discover illnesses that would otherwise be invisible to the human eye. Machine designers are not always sure what kinds of illnesses can be identified by mixing algorithms since there are so many possible combinations. In this paper we proposed a fruit disease detection and classification using deep convolutional neural network. The VGG-16 and VGG-19 both deep learning frameworks are utilized for detection of heterogeneous fruits. The both deep learning frameworks are archives 96.10% and 98.10% average accuracy on heterogeneous fruit dataset. In overall analysis the VGG-19 obtains higher accuracy with 200 epoch size which is higher the VGG-16 and other conventional machine learning and deep learning frameworks.

Hierarchy Analysis of Processes and Strategies for Strengthening Small and Medium Enterprises After New Normal in West Halmahera Regency

One of the main drivers of the economic sector to strengthen the economic foundation and support the community's economy. However, in the business process, there are many problems including not optimal use of local resources and relatively low quality of energy. In line with this description, the formulation of the problem is: How is the Hierarcki Process of supporting and inhibiting factors in the development of MSEs, and how the development strategy, business capital, labor, business networks, availability of raw materials, and the use of technology after the new normal, The purpose of this study is to see the extent of the role of MSMEs after the new normal in facing competitive entrepreneurial dynamics in facing economic competition. The research uses SWOT analysis, to see weaknesses, strengths, opportunities and challenges in economic problems in MSMEs. Then also use the AHP analysis tool, to explain how the process, infrastructure, and human resources in accessing technology information, and capital budgets as needed in addressing the dynamics of information technology in post-new zero economic needs with the target that all MSME actors can access needs both through fellow inter-district and provincial management partners and the role of banking, in providing capital in terms of regulations that ease the burden on MSMEs in West Halmahara Regency.

Optimizing Task Scheduling in Cloud Data Centres with Dynamic Resource Allocation Using Genetic Algorithm (TSOGA)

Nowadays, Massive business applications are increasingly giving attention to cloud computing data centres because of its high potential, adaptability, and efficiency in supplying several sources of both software and hardware to support networked consumers. The criteria for autonomy of virtual machines necessitate a flexible resource allocation strategy for Virtual Machines (VMs) .The majority of resource utilization models were inaccurate, making it impossible to determine the virtual machine's energy usage directly from the hardware. Due to the size of modern data centres and the constantly changing character of their resource supply, efficient scheduling solutions must be developed to oversee these resources and meet the objectives of both cloud service providers and cloud customers. Hence an algorithm called Task Scheduling Optimization based Genetic Algorithm (TSOGA) has been proposed to dynamically allocate the resources in pursuit of scheduling the tasks in cloud data centers. The proposed module initially focuses on task scheduling process, followed by optimized running time of task execution. For data centres with dynamic resource allocation, the goal of TSOGA is to efficiently assign jobs to resources while minimizing execution time and optimizing resource utilization. Thus, to manage the data centres while achieving high levels of efficiency in resource allocation, we constructed a virtual node for our research. Incorporation of Genetic Algorithm is to determine an ideal or nearly ideal schedule for carrying out tasks using the available resources while taking into account a variety of restrictions and goals, such as minimizing execution and waiting time of task during dynamic scheduling process and efficient resource utilization.

Enhancement of Cryptocurrency Mining through Artificial Intelligence for Workstation Systems

For workstation computers, it is highly recommended to use AI-powered bitcoin mining to maximise processing time and resource allocation. Conventional PC mining systems have a number of difficulties, such overheating, system availability algorithms, and controlling power consumption in relation to bitcoin production. The study has introduced an AI board called LSAI48266x in order to encounter the traditional PC mining systems. It uses intelligent algorithms to solve the problems existence with traditional PC based crypto mining. Even in the absence of human workers at the workstation PCs, LSAI48266x detects the best times to mine cryptocurrency by incorporating a variety of variables. The goal of the suggested solution is to minimise total electricity use while optimising CPU power utilisation. Using AI to increase computer resource efficiency during idle times is the major goal of the idea, which also aims to reduce excessive power consumption, preserve system dependability, and increase income.

Synthesis of MOF-5/polyethersulfone (PES) mixed matrix membranes for enhancing membrane filtration performance in polyphenol purification

The various apple products industries produce a large amount of apple residue, which is easily fermented, causes environmental pollution, and its disposal cost is high, but is rich in nutrients, such as polyphenols. Polyphenols can be purified to realize high-value deep processing of apple pomace and to promote energy reuse of food waste. In this study, the highly selective purification of polyphenols was achieved by membrane filtration using prepared Metal-organic framework (MOF)-5/PES mixed matrix membranes with apple peels as raw material. The polyethersulfone mixed matrix membrane was loaded with MOF-5 by the phase inversion method, and their structural and physicochemical properties were characterized by scanning electron microscopy (SEM), and X-ray diffraction (XRD). Zeta potential and specific surface area of MOF-5 particles were measured, as well as the water contact angle and anti-fouling properties of the mixed matrix membrane were analyzed. It was confirmed that the membrane loaded with MOF-5 showed better hydrophilicity and mechanical properties compared with the pristine polyether sulfone membrane. Under practical conditions, the increased hydrophilicity could enhance the anti-fouling properties of membranes, which would improve the flux recovery ratio of membranes. In addition, the prepared MOF-5/PES mixed matrix membrane was applied to the purification of polyphenols, showing excellent purification performance of polyphenols. In particular, the purity of polyphenol after membrane filtration could reach 70.45% when the additional amount of MOF-5 was 10%. This research provides a method to prepare MOF-5/PES mixed matrix membranes, which effectively solves the problem of unstable and unsatisfactory purification effect of commercially available membranes, promotes the development of new materials in membrane science, and realizes high-value deep processing and comprehensive resource development of food waste using membrane filtration.

Thermal Imaging for Burn Wound Depth Assessment: A Mixed-Methods Implementation Study.

Background: Implementing innovations emerging from clinical research can be challenging. Thermal imagers provide an accessible diagnostic tool to increase the accuracy of burn wound depth assessment. This mixed-methods implementation study aimed to assess the barriers and facilitators, design implementation strategies, and guide the implementation process of thermal imaging in the outpatient clinic of a burn centre. Methods: This study was conducted between September 2022 and February 2023 in Beverwijk, The Netherlands. Semi-structured interviews with burn physicians guided by the Consolidated Framework for Implementation Research (CFIR) were conducted to identify barriers and facilitators. Based on the barriers, implementation strategies were developed with the CFIR-ERIC Matching Tool, and disseminated to support the uptake of the thermal imager. Subsequently, thermal imaging was implemented in daily practice, and an iterative RE-AIM approach was used to evaluate the implementation process. Results: Common facilitators for the implementation of the thermal imager were the low complexity, the relative advantage above other diagnostic tools, and benefits for patients. Common barriers were physicians' attitude towards and perceived value of the intervention, the low compatibility with the current workflow, and a lack of knowledge about existing evidence. Six implementation strategies were developed: creating a formal implementation blueprint, promoting adaptability, developing educational materials, facilitation, conducting ongoing training, and identifying early adopters. These strategies resulted in the effective implementation of the thermal imager, reflected by a >70% reach among eligible patients, and >80% effectiveness and adoption. Throughout the implementation process, compatibility, and available resources remained barriers, resulting in low ratings on RE-AIM dimensions. Conclusions: This study developed implementation strategies based on the identified CFIR constructs that impacted the implementation of a thermal imager for burn wound assessment in our outpatient clinic. The experiences and findings of this study could be leveraged to guide the implementation of thermal imaging and other innovations in burn care.

Pre-service teachers’ perceptions about the contributions of field work and construction of a physical model to modelling aquifers

Teachers are crucial to implement innovative activities in the classroom and to make them effective for their students’ learning. Teachers’ beliefs have been found to condition the extent to which and the way in which they carry out certain activities. This study analyses the beliefs of 73 pre-service teachers (two cohorts) about two resources (fieldwork and physical model) used in an aquifer modelling sequence in which they had participated. Their perceptions are compared with the hypothetical purposes of the resources in a modelling process. The data analyzed were the written reflections at the end of the activities. Both resources were rated very positively. Practically all participants referred to contributions to learning and many made metacognitive reflections. The results show that both resources met the expectations about their contribution to modelling. The implications for future design of modelling activities are discussed.

Exploring the nexus between corporate digitalisation and environmental sustainability: The moderating role of slack resources

This paper investigated if high digitalisation exposure allows companies to reduce their environmental impacts. Further, the study also deepened the critical role of available slack resources by analysing their possible moderating effects on the relationship between the firm's higher digitalisation exposure and environmental performance. Adopting the unbalanced panel data approach, the authors performed 5 years fixed-effects regression analysis on a sample of 5,481 non-financial listed companies' data derived from the Refinitiv ESG universe, as well as considering the annual constituents lists of the STOXX® Global Digitalisation index. Findings showed digitalisation positively affects specific environmental performance. Moreover, results highlighted that available slack resources negatively moderate the relationship between the firms' higher digitalisation exposure and the sustainable management of resources in internal business and procurement processes. Accordingly, slack resources could stress the profit maximisation logic penalising corporate sustainability.

Balanced or imbalanced, that is the question: The effect of R&D/marketing power structure on new product development time

Accelerating new product development (NPD) to gain a first-mover advantage is crucial in today's competitive business landscape. This paper aims to examine the effective power structure in NPD projects through the lens of two divergent theoretical perspectives: Information Processing Theory (IPT) and Resource Dependence Theory (RDT). While IPT suggests the benefit of a balanced decision-making power among various subunits, RDT advocates for a concentrated decision power for faster consensus and efficiency. Our research specifically investigates whether a balanced or imbalanced R&D/marketing power structure is more conducive to accelerating NPD time. This focus stems from the recognition that the interplay between R&D and marketing is critical in shaping NPD outcomes, though we acknowledge the broader context of multifunctional collaboration in NPD, including aspects like manufacturing. The paper tests two competing hypotheses: one advocating a balanced R&D/marketing power structure as per IPT, and the other supporting a dominant R&D/marketing power structure in line with RDT. We surveyed project leaders, R&D, and marketing personnel from 126 NPD projects across 87 Taiwanese firms. The empirical results significantly endorse the balanced R&D/marketing power structure hypothesis, suggesting that a harmonious collaboration between these two key subunits can expedite NPD processes. Our findings contribute to both academic research and managerial practice by offering nuanced insights into optimizing power structures within NPD teams, particularly highlighting the efficacy of a balanced R&D/marketing approach in the context of the prevalent theories.

Transformation of the education system in the context of spatial development of the region (on the example of the Republic of Crimea)

The article considers the features of the development of the education system in the Republic of Crimea as the main factor of socio-spatial development of the region. The authors have analysed the dynamics of the main indicators of the education system at three levels: general education, secondary vocational and higher education. The considered main indicators are as follows: geographical accessibility of educational institutions for the population, their provision with information and technical means of educational process and human resources. The analysis of human resources was carried out in the context of those categories of personnel who directly implement and provide the educational process. The study revealed the presence in educational institutions of managers and teaching staff without higher education. The article compares the number of external part-time and full-time employees in the categories under study, compares the results with the number of students. It is demonstrated that in the conditions of the tendency of growth in the number of external collaborators and reduction of full-time employees in the system of higher education the number of students who voluntarily left education is growing. The article also considers the dynamics of growth of the wage fund for these categories of employees, and compares the average wage per person by type of activity (education) in the Southern Federal District in relation to other federal districts. It is revealed that the level of remuneration of education employees in the Southern Federal District in 2021 is at the level of 2016 in comparison with other districts. According to the indicators of the personnel structure by the ratio of full-time employees and external part-timers, trend lines have been built to identify the trends of changes in their number. For some categories of employees the forecast for two years was made to identify probabilistic quantitative changes in the staffing of the education system. It is substantiated that the solution of personnel issues will lead to the preservation and increase in the number of students, improve the quality of education and human development in the region. The paper presents conclusions on the results of the study. On the basis of the constructed trends and conclusions the authors formulate specific recommendations to eliminate the identified problems in the education system.

Factors Contributing to Environmentally Friendly Business Practices in SMEs (Small and Medium Enterprises) With a Focus on Process Performance and Resources: Study of Local Culinary SMEs in North Kuin Village, South Kalimantan, Indonesia

Environmentally friendly business practices (PBR) are often overlooked by small and medium enterprises (SMEs), even though they are proven to provide competitive advantages, reduce risks, encourage environmental sustainability, and increase organizational efficiency. This research analyzes the factors that influence PBR in local culinary SMEs, with a focus on process performance (PP) and SME resources. This research uses a quantitative approach with structural equation modeling (SEM) to analyze the influence of process performance (PP) and SME resources (fundamental resources (FR), upgrading capability (UC), and dynamic capabilities (DC)) on PBR in local culinary SMEs in North Kuin Village, South Kalimantan. The research results show that only fundamental resources (FR) and improvement capabilities (UC) influence PBR in local culinary SMEs. SMEs with access to resources and the ability to increase capacity have more potential to implement PBR.

Olfactory Categorization Is Shaped by a Transmodal Cortical Network for Evaluating Perceptual Predictions.

Creating and evaluating predictions are considered important features in sensory perception. Little is known about processing differences between the senses and their cortical substrates. Here, we tested the hypothesis that olfaction, the sense of smell, would be highly dependent on (nonolfactory) object-predictive cues and involve distinct cortical processing features. We developed a novel paradigm to compare prediction error processing across senses. Participants listened to spoken word cues (e.g., "lilac") and determined whether target stimuli (odors or pictures) matched the word cue or not. In two behavioral experiments (total n = 113; 72 female), the disparity between congruent and incongruent response times was exaggerated for olfactory relative to visual targets, indicating a greater dependency on predictive verbal cues to process olfactory targets. A preregistered fMRI study (n = 30; 19 female) revealed the anterior cingulate cortex (a region central for error detection) being more activated by incongruent olfactory targets, indicating a role for olfactory predictive error processing. Additionally, both the primary olfactory and visual cortices were significantly activated for incongruent olfactory targets, suggesting olfactory prediction errors are dependent on cross-sensory processing resources, whereas visual prediction errors are not. We propose that olfaction is characterized by a strong dependency on predictive (nonolfactory) cues and that odors are evaluated in the context of such predictions by a designated transmodal cortical network. Our results indicate differences in how predictive cues are used by different senses in rapid decision-making.

Knowledge, Attitude and Usage of Information and Communication Technology (ICT) and Digital Resources in Pre-Service Teachers

It is important to understand information and communication technology (ICT) and digital resources in classroom teaching and learning processes. Pre-service teachers’ attitudes towards ICT and digital resources in teaching and learning processes are important for positive attitudes in students towards these tools. The study employs a mixed methods study targeted teachers who have completed their internship training to understand the knowledge, attitude and usage of ICT and digital resources tries to find a correlation between these three facets. It employs a questionnaire with validated tools for measuring attitude, knowledge and usage of ICT and Digital Resources and the results are tabulated. Using statistical analysis of mean and standard deviation, the study affirms a positive correlation between Knowledge, attitude and usage of ICT and Digital Resources by Pre-Service teachers.

Evaluation of low‐grade waste heat resources in coking and sintering processes of Chinese iron and steel enterprises

AbstractThe amount of waste heat resources produced by iron and steel industry is huge and various. Due to the lack of consideration of the lowest discharge temperature of the low‐grade waste heat and the difficulty in recycling, the classification of these resources is still ambiguous, which makes the total amount of this kind of resource is huge, but few has been effectively recycled. The industry standard of China “Detection and evaluation method for low grade waste heat from the process of iron and steel enterprises” (YB/T 4786‐2019), classifies the low‐grade waste heat resources by energy level analysis. Based on the field investigation and on‐site tests of coking and sintering process in Baosteel, the low‐grade waste heat resources are analyzed and evaluated by employing this industry standard. The category distribution and total quality of low‐grade waste heat resources are obtained. It is proved that the standard is accurate for the classification of low‐grade waste heat resources and precise for the range division. The implementation of it is of practical significance to solve the problems such as the assessment of total waste heat etc.

Flare: An FPGA-Based Full Precision Low Power CNN Accelerator with Reconfigurable Structure.

Convolutional neural networks (CNNs) have significantly advanced various fields; however, their computational demands and power consumption have escalated, posing challenges for deployment in low-power scenarios. To address this issue and facilitate the application of CNNs in power constrained environments, the development of dedicated CNN accelerators is crucial. Prior research has predominantly concentrated on developing low precision CNN accelerators using code generated from high-level synthesis (HLS) tools. Unfortunately, these approaches often fail to efficiently utilize the computational resources of field-programmable gate arrays (FPGAs) and do not extend well to full precision scenarios. To overcome these limitations, we integrate vector dot products to unify the convolution and fully connected layers. By treating the row vector of input feature maps as the fundamental processing unit, we balance processing latency and resource consumption while eliminating data rearrangement time. Furthermore, an accurate design space exploration (DSE) model is established to identify the optimal design points for each CNN layer, and dynamic partial reconfiguration is employed to maximize each layer's access to computational resources. Our approach is validated through the implementation of AlexNet and VGG16 on 7A100T and ZU15EG platforms, respectively. We achieve an average convolutional layer throughput of 28.985 GOP/s and 246.711 GOP/s for full precision. Notably, the proposed accelerator demonstrates remarkable power efficiency, with a maximum improvement of 23.989 and 15.376 times compared to current state-of-the-art FPGA implementations.

ФОРМИРОВАНИЕ ЦИФРОВОЙ ГРАМОТНОСТИ УЧИТЕЛЕЙ В КЫРГЫЗСТАНЕ

Digital technologies are bringing more and more changes to various areas of human activity, including education. In the modern information society, digital literacy is becoming an integral part of education throughout the world. Due to the rapid development of technology and the increasing use of digital tools and resources in the educational process, teachers need to have the skills to work with them. In Kyrgyzstan, the problem of developing teachers’ digital literacy to improve the quality and expand access to school education is also relevant. This article analyzes educational policy on developing teachers’ digital literacy. The article discusses the results and experience of introducing modern digital learning technologies based on a competency-based approach in the Kyrgyz State Academy of Physical Culture and Sports named after B.T. Turusbekov. The pedagogical conditions for the formation of professional competence are discussed. The results of a survey among teachers of CIS countries’ universities to determine the level of development of digital skills in working on information platforms are presented.

The Use of Interactive Teaching Tools in the Kazakh Language Lessons of Primary Classes

Today, the goal of the school is not only to give new knowledge, values and personal meanings, but also to reveal the abilities of growing younger students. The interest in interactive methods and tools stems from the need to improve modern practice. Interactive teaching methods provide solutions to educational problems. The issue of the introduction and further use of electronic resources in the educational process is one of the most important in modern conditions. The article discusses the main aspects of the use of electronic educational resources in Kazakh language lessons in primary school. The analysis of the readiness of future primary school teachers to use active means in the educational process is carried out. The general theoretical and methodological basis of the research problem: the scientific works on the use of interactive tools in the educational process are considered, the specifics of interactive methods and the theoretical significance of the research are revealed-pedagogical conditions for the use of interactive learning tools in the development of speech skills of younger schoolchildren. At the same time, in order to teach the subject «Kazakh language» in primary school, based on the results of the pedagogical practice of future primary school teachers in secondary school, the identification of the main contradictions between the new requirements for the development of students' speech skills and the insufficiently studied possibilities of using interactive learning tools in Kazakh language lessons in the practical implementation of these requirements is stated. In a comparative analysis of the results of the study, it was found that with the help of interactive learning tools, the positive dynamics of the presented tasks was noted, the data on the results of the research stages are represented by diagrams, figures.

THE POSSIBILITIES OF USING PARALLEL PROGRAMMING USING PYTHON

This article explores the development of parallel software applications using the Python programming language. Parallel programming is becoming increasingly important in the information technology world as multi-core processors and distributed computing become more common. Python provides developers with a variety of tools and libraries for creating parallel applications, including threads, processes, and asynchronous programming. This topic covers the basics of parallel programming in Python, including the principles of thread and process management, error handling, synchronization mechanisms, and resource management. He also considers asynchronous programming using the asyncio library, which allows you to efficiently handle asynchronous tasks. In addition, this topic raises issues of optimization and profiling of parallel applications, as well as explores distributed parallel programming using third-party libraries and frameworks. He also emphasizes the importance of testing and debugging in the context of parallel programming. Research and experiments in parallel programming using Python help developers create high-performance and efficient applications that can effectively use multi-core systems and distributed computing. This article offers an in-depth study that examines how Python is suitable for teaching parallel programming to inexperienced students. The results show that there are obstacles that prevent Python from maintaining its advantages in the transition from sequential programming to parallel.

FEATURES OF THE FOREIGN STUDENTS TRAINING IN THE ELECTIVE DISCIPLINE "QUALITY SYSTEMS IN PHARMACY"

The pharmaceutical industry is rapidly developing and implementing new innovative approaches and legal regulation of the drugs quality management system, which includes a set of process methods and resources that are necessary for quality management. Therefore, the mastery by students of the specialty 226 "Pharmacy, Industrial Pharmacy" of the elective discipline "Quality Systems in Pharmacy" is an important issue of their professional training which will help them to master the methodology of quality management, normative-legislative and organizational-economic aspects of quality control of medicinal products at pharmaceutical enterprises. The elective discipline "Quality Systems in Pharmacy" is closely connected to some other disciplines, such as “Pharmacy and Industrial Technology of Drugs”, and “Technology of Cosmetic Drugs”. Training takes place according to the credit-module system in accordance with the basic requirements of the Bologna Convention.
 The main tasks of the elective discipline "Quality Systems in Pharmacy" are: providing the students with general knowledge about the quality indicators of medicines depending on the stage of their life cycle by fulfilling the requirements of good pharmaceutical practices and developing documentation in accordance with their requirements; formation of practical skills related to the rules and requirements for the creation, manufacture, research, distribution, sale, and storage of medicines in accordance with the requirements of good pharmaceutical practices operating in the European Union; improvement of the level of general and scientific development of students, formation of their outlook and perspective as modern qualified specialists in the field of pharmacy.
 The aim of the research is to study the peculiarities of the training of foreign students of the specialty 226 "Pharmacy, Industrial Pharmacy" in the Ivano-Frankivsk National Medical University (IFNMU) in the elective discipline "Quality Systems in Pharmacy". The subject of study of the discipline "Quality Systems in Pharmacy" is the methodology of pharmaceutical development, the components of the quality assurance system of medicines, including the requirements of the Guidelines of good practices in pharmacy for each stage of the life cycle of a medicinal product. The discipline is usually chosen by the well formed specialists.
 Teaching the elective discipline "Quality Systems in Pharmacy" at the specialty 226 "Pharmacy, Industrial Pharmacy" of IFNMU is necessary for students to thoroughly master the quality management system of medicines, it contributes to the optimization of the educational process and the improvement of the new principle of problem-oriented education of students and its control. This discipline orients students to the importance of the following issues: ensuring the quality of medicines at all stages of their life cycle; regulatory activities in the field of quality control of medicines; implementation of medicines; optimization and monitoring of pharmaceutical quality systems processes. This discipline forms the important competencies, knowledge, skills, abilities, and aspirations of students for self-development, while their teacher also develops and improves in the professional field together with students. Taking into account the trend of the European development of the pharmaceutical industry, the quality management system of medicines also should be harmonized with European standards.