Principles Of Classification Research Articles

Scientific concepts of development of public services in Ukraine

The scientific article is devoted to highlighting the state of scientific development of the provision of public services in Ukraine. The achievements of administrative scientists and approaches to understanding the categories of public and administrative services provided by public administration bodies are highlighted. The works of administrative scientists who paid considerable attention to the issue of providing administrative services in various spheres or to a specific subject were analyzed, namely: Bondarenko I.O. «Organizational and legal foundations of service provision in the field of customs affairs»; Bulkovskii T.O. «Quality management of the provision of administrative services by the Ministry of Internal Affairs of Ukraine; Vasylieva N.V. «Theoretical and methodological foundations of the formation and development of the system of providing administrative services at the regional level»; Voronyatnikov O.O. «Administrative and legal status of the State Migration Service of Ukraine»; Horbatyuk V.I. «Mechanisms for providing administrative services in the field of public finance management»; Dembytska S.L. «Legal principles of the provision of administrative services to the population of Ukraine by local self­government bodies»; Kostin O.Yu. «Administrative services provided by the State Border Service of Ukraine»; Mordvin I.A. «Administrative services of tax authorities of Ukraine»; V.V. Petovka «Legal regulation of the provision of administrative services in Ukraine»; Severin V.I. Administrative and legal principles of the provision of permit services by subjects of public administration; Skoromnyi Y.I. «Administrative services in agriculture of Ukraine; Sorokina I.S. «District State Administration as a Subject of Provision of Administrative Services»; Ter-Stepanyan O.V. «Administrative and legal principles of providing administrative services in the field of telecommunications and postal communication»; Khymychuk K.V. «Legal regulation of the provision of administrative services by the justice bodies of Ukraine», Pryimak L.I. «Public services in the activity of the State Tax Service of Ukraine: organizational and legal principles», etc. It was concluded that the attention of both scientists and practitioners is paid to the general principles of public service provision in Ukraine (the concept, essence of administrative services, principles of classification, quality assessment, etc.); the study of legal problems of public administration bodies has gained special development, the result of which is the introduction of a number of changes and additions to the current legislation that concerns the provision of public services; considerable attention is paid to the issue of providing public services in various areas or to a certain entity, etc. At the same time, the analysis of scientific publications, monographic studies, practical recommendations, as well as the results of the activities of public administration bodies on the provision of public services in Ukraine show that a complex scientific approach is needed to study the peculiarities of the provision of public services in Ukraine.

E-commerce and Mobile Application Development in the Sports Industry

In order to achieve user recommendations that best match their current contextual needs, the author proposes a mobile service QoS (Quality of Service) hybrid recommendation model based on sports user situational awareness. Cluster the users and service items covered by mobile users based on their location contextual information according to the classification principle of autonomous systems, forming a collaborative filtering and recommendation mechanism for mobile user service items; In response to the cold start problem of new users and new projects in traditional QoS recommendation methods, prediction and recommendation of missing QoS attribute preference values are based on User based and Item based CF; In response to the problem of difficult determination of mixed recommendation weights caused by massive data and uneven distribution of service QoS attribute values in mobile network environments, MapReduce based ant colony neural network weight training is used for CF mixed recommendation. Experimental results have shown that the Hadoop sports industry has improved the operational efficiency of algorithms and reduced the time for global QoS preference prediction; And by comparing the operation results of the 10% and 100% sub datasets, it can also be seen that the acceleration ratio of the algorithm will continuously improve with the increase of data volume, thereby improving the operational efficiency of the recommendation algorithm. It has been proven that the MapReduce based ant colony neural network weight training method significantly reduces the global computation time of the algorithm and improves the operational efficiency of the recommendation system.

Optimizing Random Forest for Predicting Thoracic Surgery Success in Lung Cancer Using Recursive Feature Elimination and GridSearchCV

Abstract. Lung cancer is one of the deadliest forms of cancer, claiming numerous lives annually. Thoracic surgery is a strategy to manage lung cancer patients; however, it poses high risks, including potential nerve damage and fatal complications leading to mortality. Predicting the success rate of thoracic surgery for lung cancer patients can be accomplished using data mining techniques based on classification principles. Medical data mining involves employing mathematical, statistical, and computational methods. In this study, the prediction of thoracic surgery success employs the random forest algorithm with recursive feature elimination for feature selection. The feature selection process yields the top 8 features. The 8 best features include 'DGN', 'PRE4', 'PRE5', 'PRE6', 'PRE10', 'PRE14', 'PRE30', and 'AGE'. Hyperparameter using GridSearchCV is then applied to enhance classification accuracy. The results of this method implementation demonstrate a predictive accuracy of 91.41%. Purpose: The study aims to develop and evaluate a Random Forest model with a Recursive Feature Elimination feature selection and applies hyperparameter GridSearchCV for predicting thoracic surgery success rate. Methods: This study uses the thoracic surgery dataset and applies various data preprocessing techniques. The dataset is then used for classification using the Random Forest algorithm and applies the Recursive Feature Elimination feature selection to obtain the best features. GridSearchCV is used in this study for hyperparameter. Result: The accuracy using the Random Forest algorithm and Recursive Feature Elimination feature selection with hyperparameters tuning GridSearchCV resulted in an accuracy of 91,41%. The accuracy was obtained from the following parameters values: bootstrap set to false, criterion set to gini, n_estimator equal to 100, max_depth set to none, min_samples_split equal to 4, min_samples_leaf equal to 1, max_features set to auto, n_jobs set to -1, and verbose set to 2 with 10-fold cross validation. Novelty: This study comparison and analysis of various dataset preprocessing methods and different model configurations are conducted to find the best model for predicting the success rate of thoracic surgery. The study also employs feature selection to choose the best feature to be used in classification process, as well as hyperparameter tuning to achieve optimal accuracy and discover the optimal values for these hyperparameters.

Practical guidelines of the International Working Group on the Diabetic Foot, European Society for Vascular Surgery, Society for Vascular Surgery on the prevention and management of diabetes-related foot disease 2023

Diabetes‑related foot disease results in a major global burden for patients and the healthcare system. The International Working Group on the Diabetic Foot (IWGDF) has been producing evidence‑based guidelines on the prevention and management of diabetes‑related foot disease since 1999. In 2023, all IWGDF Guidelines have been updated, based on systematic reviews of the literature and formulation of recommendations by multidisciplinary experts from all over the world. In addition, a new guideline on acute Charcot neuro‑osteoarthropathy was created. In this document, the IWGDF Practical Guidelines, we describe the basic principles of prevention, classification and management of diabetes‑related foot disease, based on the seven IWGDF Guidelines. We also describe the organizational levels to successfully prevent and treat diabetes‑related foot disease according to these principles and provide addenda to assist with foot screening. The information in these practical guidelines is aimed at the global community of healthcare professionals who are involved in the care of persons with diabetes. Many studies around the world support our belief that implementing these prevention and management principles is associated with a decrease in the frequency of diabetes‑related lower‑extremity amputations. The burden of foot disease and amputations is increasing at a rapid rate, and comparatively more so in middle to lower income countries. These guidelines also assist in defining standards of prevention and care in the these countries. Authors hope that these updated practical guidelines continue to serve as a reference document to aid healthcare providers in reducing the global burden of diabetes‑related foot disease.

Evaluation of geomorphological classification uncertainty using rough set theory: A case study of Shaanxi Province, China

AbstractGeomorphological classification is affected by classification principles, indicators, methods, and data resolution, which can lead to uncertainty in the results. Such uncertainty directly affects the quality and subsequent applications of geomorphological classification. To quantify and control the uncertainty, it is important to select an appropriate and effective method for evaluating the uncertainty of geomorphological classification. This study evaluated the uncertainty of geomorphological classification of Shaanxi Province at the ground‐feature class and image scales, which derived from rough set theory: rough entropy, approximate classification quality, and approximate classification accuracy. The three indicators helped effectively assess the uncertainty of geomorphological classification at multi‐scale and measured the degree to which different factors affected the uncertainty of geomorphological classification. The relative impacts of three factors on the uncertainty of classification decreased in the order of classification methods, data resolution, and classification indicators. This finding is helpful to objectively evaluate and control the uncertainty generated in the process and results of geomorphological classification, and can provide targeted reference and guidance for future geomorphological classification work, which is more conducive to decision‐making and application. At the same time, this study is also a beneficial supplement to the geomorphological research based on digital terrain analysis.

A Review and Analysis of Evaluation Practices in VIS Domain Applications.

This paper presents a review and analysis of evaluation practices within the visualization and visual analytics (VIS) domain, with a focus on domain application work accepted at the IEEE VIS conference from 2018 to 2022. Through the analysis of 140 pertinent papers, we establish a detailed classification principle for evaluation practices, using the Who, When, What, and How indicators. This principle covers facets such as analysis methods, targets, scenarios, participant expertise, and stages of occurrence. By systematically categorizing the application domains presented in these works, we apply our established classification principle to discern and categorize the evaluation practices within them, identifying the prevailing characteristics and trends. The paper explores the variety of evaluation methods employed across different application domains and observes the distinctions in their usage. In conclusion, we provide insights and highlight concerns for conducting evaluations in upcoming domain application research. Our findings are intended to inform and guide subsequent studies in a similar context. All information about the reviewed papers, the coding results, and the detailed data analysis process can be accessed at https://github.com/narrating-complexity/vis_evaluation_analysis.

Application of finite screw method for multi-mode mechanism classification and determination

To enrich the theoretical system of multi-mode mechanisms, a classification method and a determination method are proposed in this paper. From the perspective of configuration transformation, the multi-mode mechanisms are divided into two types: one based on lockable joints and the other based on the principle of bifurcated motion. Furthermore, the mechanisms based on the principle of bifurcated motion are categorized into two types: one based on the variable mobility branch and the other based on constraint singularity generated by branches. The principles of classification are expounded and the determination method is developed. The proposed classification and the determination methods of the multi-mode mechanism provide new insights for their analysis.

Algorithm of surgical treatment for diaphyseal defects of the forearm bones due to gunshot injuries

Introduction In the current system of providing medical aid to wounded servicemen, along with the conservative primary surgical treatment and minimally invasive extrafocal fixation, high-tech surgical interventions of considerable complexity with the use of additive and tissue-engineering technologies have been coming to the forefront. It is necessary to determine their place in the current algorithm of limb bone defect management, which was the substantiation of our study.The purpose of the study was to improve the algorithm for selecting a treatment method for patients with associated gunshot defects of the forearm based on the literature and clinical observations.Materials and Methods We analyzed scientific articles in PubMed and Scientific Electronic Library (eLIBRARY.ru) platforms, published from 2004 to 2024, on the basis of which we could refine the algorithm of treatment method selection for patients with associated gunshot defects of the forearm. The developed algorithm was used to treat 178 patients with gunshot fractures of the forearm.Results The review of the literature established the main provisions and principles that are applied in the reconstruction of the forearm with an associated defect. When choosing the method of bone defect management, a great number of authors tend to build a “reconstructive ladder”, moving from less severe (one bone) and extended defects (small defect up to 2 cm) to more complex (both bones) and massive defect (more than 10 cm). Upon having considered the revealed regularities, we improved the algorithm of surgical treatment of the latter, which is based on two classification principles: defect extention and location. Reconstruction of the forearm as a dynamic system after diaphyseal fractures requires consider the state of the radioulnar joint. The function of the latter depends on the length ratio of the radius and ulna bones. Therefore, we substantiated small (up to 2 cm) forearm bone defects that can be managed by simple surgical methods. Another fundamental addition to the algorithm was the allocation of a patients’ group with a defect of one forearm bone and a fracture of the other bone (defect-fracture); this combination allows avoiding complex surgical methods for reconstruction and use segment shortening.Discussion The treatment of associated forearm defects is challenging, the choice of reconstruction technique remains uncertain, and the required consensus is lacking. Several forearm reconstruction techniques are available, yet there is no reliable evidence of their effectiveness in terms of treatment time, complications, reoperations, and functional recovery.Conclusion The algorithm proposed for the treatment of extensive gunshot-associated defects of the forearm allows us to consider the change in the anatomy, make a surgical plan based on the reconstruction vector, and select optimal surgical techniques.

Automatic mapping of winter wheat planting structure and phenological phases using time-series sentinel data

The precise extraction of winter wheat planting structure holds significant importance for food security risk assessment, agricultural resource management, and governmental decision-making. This study proposed a method for extracting the winter wheat planting structure by taking into account the growth phenology of winter wheat. Utilizing the fitting effect index, the optimal Savitzky–Golay (S–G) filtering parameter combination was determined automatically to achieve automated filtering and reconstruction of NDVI time series data. The phenological phases of winter wheat growth was identified automatically using a threshold method, and subsequently, a model for extracting the winter wheat planting structure was constructed based on three key phenological stages, including seeding, heading, and harvesting, with the combination of hierarchical classification principles. A priori sample library was constructed using historical data on winter wheat distribution to verify the accuracy of the extracted results. The validation of fitting effect on different surfaces demonstrated that the optimal filtering parameters for S–G filtering could be obtained automatically by using the fitting effect index. The extracted winter wheat phenological phases showed good consistency with ground-based observational results and MOD12Q2 phenological products. Validation against statistical yearbook data and the proposed priori knowledge base exhibited high statistical accuracy and spatial precision, with an extracting accuracy of 94.92%, a spatial positioning accuracy of 93.26%, and a kappa coefficient of 0.9228. The results indicated that the proposed method for winter wheat planting structure extracting can identify winter wheat areas rapidly and significantly. Furthermore, this method does not require training samples or manual experience, and exhibits strong transferability.

Passenger Route and Departure Time Guidance Under Disruptions in Oversaturated Urban Rail Transit Networks

The urban rail transit (URT) system attracts many commuters with its punctuality and convenience. However, it is vulnerable to disruptions caused by factors such as extreme weather and temporary equipment failures, which greatly affect passengers’ journeys and diminish the system’s service quality. In this study, we propose targeted travel guidance for passengers at different space–time locations by devising passenger rescheduling strategies during disruptions. This guidance not only offers insights into route changes but also provides practical recommendations for delaying departure times when required. We present a novel three-feature four-group passenger classification principle, integrating temporal, spatial, and spatiotemporal features to classify passengers in disrupted URT networks. This approach results in the creation of four distinct solution spaces based on passenger groups. A mixed integer programming model is built based on individual level considering the first-in-first-out rule in oversaturated networks. In addition, we present a two-stage solution approach for handling the complex issues in large-scale networks. Experimental results from both small-scale artificial networks and the real-world Beijing URT network validate the efficacy of our proposed passenger rescheduling strategies in mitigating disruptions. Specifically, when compared to scenarios with no travel guidance during disruptions, our strategies achieve a substantial reduction in total passenger travel times by 29.7% and 50.9%, respectively, underscoring the effectiveness in managing unexpected disruptions.

Research and design of automatic cutting machine for propellant NDSI-2K and FCG-2.B41M

This article introduces the results of research on a new solution in the process of cutting and selecting propellants used for the double-based tubular propellant production line of Factory Z195/General Department of Defense Industry. The traditional method is usually manual, using the foot pedal force of the worker through a mechanical drive system to pull the cutting knife down to cut the propellant. It takes a lot of time, labor and product quality is not high. After cutting it is necessary to lathe the ends of propellant tube to meet technical requirements. With the new solution, the propellant is cut automatically, quickly, safely and accurately and the selection of the propellant is carried out through a weighing conveyor system based on the principle of classification by weight. The entire working process is controlled by a sensor systemand PLC programerthrough a monitoring computer located in the control room. Processing data can be stored and periodically exported for report purposes.

The portals of the facades of buildings of the late 19th and early 20th centuries in Kyiv

The compositional features were considered and the portals of the facades of buildings of the late 19th and early 20th centuries were classified. in Kyiv. Their symbolism, structure and structural construction, stylistics and characteristic architectural and decorative features were studied. The purpose of the study: to investigate and analyze the compositional, stylistic and semantic features of the portals of the facades of buildings of the late 19th and early 20th centuries. in Kyiv, to develop the principles of classification of portals and conduct their classification. Methodology. The research was conducted on the basis of the following methods: empirical, theoretical and empirical-theoretical. The empirical method includes observation, photo-fixation, graphic sketches and constructions, comparisons and generalizations. Theoretical techniques include: going from the abstract to the concrete, abstraction, concretization, identification and separation. Most of the work was carried out using empirical and theoretical methods. The results. Photographs, graphic sketches and classification of building portals of the late 19th and early 20th centuries were carried out. in Kyiv based on the developed compositional-constructive and stylistic principles of classification. It was found that the portals are located mainly on the main compositional axes of the facades of historical buildings, namely on the main vertical divisions, which are highlighted by risalites, bay windows, attics, towers and often changed scale and shape of windows. The role of the portals in the overall composition of the building and the problem of violation of the compositional integrity of the facades due to the replacement or destruction of individual parts and elements of the portals have been revealed. In particular, as a result of unsuccessful repairs and renovations in some buildings of the historical center, the entrance doors were replaced with faceless, rough, unscaled ones, which distorted not only individual facades, but also entire sections of the urban environment. The scientific novelty and practical significance of the research lies in the identification of the compositional and semantic features of the portals of the facades of Kyiv buildings of the late 19th and early 20th centuries, as well as in the detailed analysis of the compositional structure, constructive and stylistic components of the portals. A scientific novelty is the developed classification of the portals of the historical buildings of Kyiv. The research will contribute to the deepening of theoretical and practical knowledge about the peculiarities of Kyiv portals of the specified period, which can be used in the restoration and reconstruction of buildings of the late 19th - early 20th centuries. in Kyiv. The developed classification of portals will be a useful educational reference material for students - future architects and designers who are interested in the peculiarities of Kyiv's historical buildings.

An analysis of laser distance measuring by different laser rangefinders

Laser rangefinders have emerged as indispensable tools in various fields, enabling accurate distance measurements through the utilization of laser technology. This paper provides a comprehensive overview of laser rangefinders, including the principle of laser rangefinders, classification, practical applications, and calibration methods. Different types of laser rangefinders are discussed based on their operating principles, while the various fields in which laser rangefinders are widely used are highlighted. The importance of accurate calibration is also discussed in depth, and the various techniques used to calibrate laser rangefinders are explored. In addition, accuracy and measuring range of each kind of laser rangefinder will also be discussed. This paper aims to provide a comprehensive understanding of laser rangefinders, including their classification, practical applications, and calibration methods. It also highlights the importance of accurate distance measurements in various industries and the role of laser rangefinders in meeting these requirements.

A supervised learning algorithm based on the quasi-Bayesian method for the probability density functions and application for medical data

This study proposes a new supervised learning algorithm for probability density functions (PDFs) and effectively applies it to medical images. The proposed algorithm is demonstrated step by step, illustrated with a numerical example, and proofed the convergence. This algorithm contributes significantly to the field of recognition in four key areas. The first contribution is the improvement of determining prior probabilities by establishing a method based on the fuzzy relationship between each classified PDF and groups within the training set through cluster analysis technique. The next contribution involves developing a new measure to evaluate the level of similarity between the classified PDFs and the considered groups. Another contribution is the establishment of a new classification principle, quasi-Bayes, for PDFs. The final contribution of this study is the application of the proposed algorithm to both numerical and image data, where objects are represented as representative PDFs. Practical applications on various medical datasets with different characteristics have demonstrated the outstanding advantages of the proposed algorithm over other methods, including traditional statistics, machine learning, and deep learning approaches, based on metrics such as ACC, AUC, F1-Score, and One-way ANOVA test. Specifically, experimental results of the Skin cancer data show that the proposed algorithm achieved an ACC index of 98.024%, higher than other methods, including ResNet-50, Inception ResNet V2, and CNN with 97.33%, 97.815%, and 80.702%, respectively. Similarly, the proposed algorithm also obtained notable results for other indices such as AUC and F1-Score. Additionally, we also obtained similar results for the Brain tumour dataset.

Obstetric Antiphospholipid Syndrome: Insights on the Diagnosis, Treatment, and Hot Issues

Obstetric antiphospholipid syndrome (OAPS) is an autoimmune disorder associated with various pathological pregnancies, such as recurrent miscarriage, stillbirth, severe pre-eclampsia and severe placental insufficiency. The persistent presence of antiphospholipid antibodies (aPLs) is the most important laboratory characteristic of OAPS. OAPS severely affects the reproductive health of women of childbearing age in China. Reports indicate that approximately 9.6% stillbirths, 11.5% severe pre-eclampsia, and 54% recurrent miscarriages are associated with OAPS or aPLs. However, the pathogenesis of OAPS remains unclear. Previously, thrombosis at the maternal-fetal interface (MFI) was considered the main mechanism of OAPS-related pathological pregnancies. Consequently, the use of low molecular weight heparin and aspirin throughout pregnancy was recommended to improve outcomes in OAPS patient. In recent years, many studies have found that thrombosis in MFI is uncommon, but various inflammatory factors are significantly increased in the MFI of OAPS patients. Based on these findings, some clinicians have started using anti-inflammatory treatments for OAPS, which have preliminarily improved the pregnancy outcomes. Nevertheless, there is no consensus on these second-line treatments of OAPS. Another troubling issue is the clinical diagnosis of OAPS. Similar to other autoimmune diseases, there are only classification criteria for OAPS, and clinical diagnosis of OAPS depends on the clinicians' experience. The present classification criteria of OAPS were established for clinical and basic research purposes, not for patient clinical management. In clinical practice, many patients with both positive aPLs and pathological pregnancy histories do not meet the strict OAPS criteria. This has led to widespread issues of incorrect diagnosis and treatment. Timely and accurate diagnosis of OAPS is crucial for effective treatment. In this article, we reviewed the epidemiological research progress on OAPS and summarized its classification principles, including: 1) the persistent presence of aPLs in circulation; 2) manifestations of OAPS, excluding other possible causes. For the first point, accurate assessment of aPLs is crucial; for the latter, previous studies regarded only placenta-related pregnancy complications as characteristic manifestations of OAPS. However, recent studies have indicated that adverse pregnancy outcomes related to trophoblast damage, such as recurrent miscarriage and stillbirth, also need to be considered in OAPS. We also discussed several key issues in the diagnosis and treatment of OAPS. First, we addressed the definition of non-standard OAPS and offered our opinion on defining non-standard OAPS within the framework of the 2023 American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) APS criteria. Then, we discussed the advantages and disadvantages of different aPL testing methods, emphasizing that harmonizing results across platforms and establishing specific reference values are keys to resolving controversies in aPL testing results. We also introduced the application of non-criteria aPLs, especially anti-phosphatidylserine/prothrombin antibody (aPS/PT) and anti-β2 glycoprotein Ⅰ domain Ⅰ antibody (aβ2GPⅠDⅠ). Additionally, we discussed aPL-based OAPS risk classification strategies. Finally, we proposed potential treatment methods for refractory OAPS. The goal is to provide a reference for the clinical management of OAPS.

Accurate wire rope defect MFL detection using improved Hilbert transform and LSTM neural network

ABSTRACT Wire rope is playing a significant role in various engineering fields. Although a great number of non-destructive testing methods are applied to wire rope defect detection, the results are greatly influenced by the signal noises and the quantitative defect detection of wire rope is consequently limited. Therefore, a new accurate and quantitative wire rope defect magnetic flux leakage (MFL) recognition method based on improved Hilbert transform and long short-term memory (LSTM) neural network is proposed. The theoretical background of the proposed signal processing models and preliminary results, as well as the influence of the main model parameters are analyzed first. Then, different wire rope defect classification principles through LSTM neural network and performance evaluation based on the improved signal processing and machine learning combined method are presented. Finally, the defect inspection and classification comparison results between the proposed method and other deep learning neural networks and pattern recognition strategies are given, which manifest that the proposed method has higher classification accuracy and shorter running time for five different wire rope defects under various servicing conditions, and is promising in accurate defect inspection for multi-scenario wire rope. Additionally, the main conclusions, disadvantages of the proposed method are summarized.

Data-Driven Policy Learning Methods from Biological Behavior: A Systematic Review

Policy learning enables agents to learn how to map states to actions, thus enabling adaptive and flexible behavioral generation in complex environments. Policy learning methods are fundamental to reinforcement learning techniques. However, as problem complexity and the requirement for motion flexibility increase, traditional methods that rely on manual design have revealed their limitations. Conversely, data-driven policy learning focuses on extracting strategies from biological behavioral data and aims to replicate these behaviors in real-world environments. This approach enhances the adaptability of agents to dynamic substrates. Furthermore, this approach has been extensively applied in autonomous driving, robot control, and interpretation of biological behavior. In this review, we survey developments in data-driven policy-learning algorithms over the past decade. We categorized them into the following three types according to the purpose of the method: (1) imitation learning (IL), (2) inverse reinforcement learning (IRL), and (3) causal policy learning (CPL). We describe the classification principles, methodologies, progress, and applications of each category in detail. In addition, we discuss the distinct features and practical applications of these methods. Finally, we explore the challenges these methods face and prospective directions for future research.

A unified classification system for HIV-1 5' long terminal repeats.

The HIV-1 provirus mainly consists of internal coding region flanked by 1 long terminal repeats (LTRs) at each terminus. The LTRs play important roles in HIV-1 reverse transcription, integration, and transcription. However, despite of the significant study advances of the internal coding regions of HIV-1 by using definite reference classification, there are no systematic and phylogenetic classifications for HIV-1 5' LTRs, which hinders our elaboration on 5' LTR and a better understanding of the viral origin, spread and therapy. Here, by analyzing all available resources of 5' LTR sequences in public databases following 4 recognized principles for the reference classification, 83 representatives and 14 consensus sequences were identified as representatives of 2 groups, 6 subtypes, 6 sub-subtypes, and 9 CRFs. To test the reliability of the supplemented classification system, the constructed references were applied to identify the 5' LTR assignment of the 22 clinical isolates in China. The results revealed that 16 out of 22 tested strains showed a consistent subtype classification with the previous LTR-independent classification system. However, 6 strains, for which recombination events within 5' LTR were demonstrated, unexpectedly showed a different subtype classification, leading a significant change of binding sites for important transcription factors including SP1, p53, and NF-κB. The binding change of these transcriptional factors would probably affect the transcriptional activity of 5' LTR. This study supplemented a unified classification system for HIV-1 5' LTRs, which will facilitate HIV-1 characterization and be helpful for both basic and clinical research fields.

Modeling of transportation and technological processes in automated systems

The purpose of the study is to create a problem-oriented model of transport and technological processes in automated systems. To achieve this goal, it is necessary to develop methodological aspects of creating an automated modeling system. The content of the basic principles of problem-oriented modeling in the study of an integrated automated system for managing stocks and transport and technological processes is revealed in the paper. At the same time, methodological aspects of creating automated modeling systems, namely, a conceptual mathematical model for the analysis and standardization of technical and economic indicators are considered. The functioning of logistics systems, namely, system modeling (supply — transport) is also considered. The problems of interaction and relationships, optimization of resource flows, and increasing the efficiency of management decisions making have been solved. It is shown that modeling resource flows involves adjustments through iterative redistribution of interregional and intraregional supplies among suppliers and consumers. Combining product items into one delivery leads to savings in stocks formation costs by reducing the number of orders. To create effective automated control systems, it is necessary to study in detail the primary object — the logistics and technical support system itself. The creation of applied mathematical and software is a promising direction for solving this problem and developing it as an automated modeling system. In this regard, when creating such automated control systems, it is most advisable to use a model-oriented approach, which is based on the principles of system analysis, standardization, classification and modularity. Thus, it is shown that the use of the problem-oriented approach to creating an automated modeling system makes it possible to reduce the labor intensity of its development and implementation, to provide the opportunity to unify algorithms for solving a problem, but also to apply standard methods for processing experimental results.

Useful but overused? The “plate model” as a food educational tool in home economics

ABSTRACT The plate model is widely used to promote healthy eating. Despite extensive adoption in dietary guidelines, the model’s role in food educational practices is scarcely studied. The present study aimed to explore the plate model as a food educational tool in the school subject Home Economics (HE). Use of the plate model was investigated with Rogoff’s three planes of analysis as a framework. The research question was: “How is the plate model used in a food educational practice, considering institutional, interpersonal, and personal planes?” Data from video-recorded classroom observations, focus groups, interviews, and text documents were analyzed. The data were generated through a case study, with twelve students and two HE-teachers followed over the course of a school year. Results show how the plate model was framed as the right way of eating. Nutritionally “proper” food was described, with the model functioning as a bridge linking (abstract) nutritional content with (concrete) dishes. This way of using the model might hamper understandings of fundamental principles for nutritional classification. Moreover, those who do not eat plate model-type dishes may be wrongly judged. Supported by empirical events, this study shows how the plate model can be useful – but also overused – in food education.