Method for an Effective Selection of Tools and Cutting Conditions during Precise Turning of Non-Alloy Quality Steel C45

The paper presents a constructing methodology for a modern approach to tools selection and solving the problem of assigning optimal cutting parameters for specific production conditions. The mathematical formulation determining the extreme values of the technological process optimality criteria is obtained. A system of technical and economic quality indicators for cutting tools is proposed. This system allows principles’ implementation of decentralization and interoperability “Industry 4.0” via finite element modeling of the cutting process based on solving the problem of orthogonal free cutting modeling. The proposed methodology further usage is possible by creating a standardized database on the parameters of the tool: the adhesive component of the friction cutting coefficient for processing of a specific pair of cutting and tool materials (or tool coating material) and the impacts of the cutting-edge radius on cutting efficiency of a particular material.

Multi-resource collaborative optimization for adaptive virtual machine placement

The unbalanced resource utilization of physical machines (PMs) in cloud data centers could cause resource wasting, workload imbalance and even negatively impact quality of service (QoS). To address this problem, this paper proposes a multi-resource collaborative optimization control (MCOC) mechanism for virtual machine (VM) migration. It uses Gaussian model to adaptively estimate the probability that the running PMs are in the multi-resource utilization balance status. Given the estimated probability of the multi-resource utilization balance state, we propose effective selection algorithms for live VM migration between the source hosts and destination hosts, including adaptive Gaussian model-based VMs placement (AGM-VMP) algorithm and VMs consolidation (AGM-VMC) method. Experimental results show that the AGM-VMC method can effectively achieve load balance and significantly improve resource utilization, reduce data center energy consumption while guaranteeing QoS.

The Fundamental of TCP Techniques

Strategies for prioritizing test cases plan test cases to reduce the cost of retrospective testing and to enhance a specific objective function. Test cases are prioritized as those most important test cases under certain conditions are made before the re-examination process. There are many strategies available in the literature that focus on achieving various pre-test testing objectives and thus reduce their cost. In addition, inspectors often select a few well-known strategies for prioritizing trial cases. The main reason behind the lack of guidelines for the selection of TCP strategies. Therefore, this part of the study introduces the novel approach to TCP strategic planning using the ambiguous concept to support the effective selection of experimental strategies to prioritize experimental cases. This function is an extension of the already selected selection schemes for the prioritization of probation cases.

Determinant of Covariance Matrix Model Coupled with AdaBoost Classification Algorithm for EEG Seizure Detection

Experts usually inspect electroencephalogram (EEG) recordings page-by-page in order to identify epileptic seizures, which leads to heavy workloads and is time consuming. However, the efficient extraction and effective selection of informative EEG features is crucial in assisting clinicians to diagnose epilepsy accurately. In this paper, a determinant of covariance matrix (Cov–Det) model is suggested for reducing EEG dimensionality. First, EEG signals are segmented into intervals using a sliding window technique. Then, Cov–Det is applied to each interval. To construct a features vector, a set of statistical features are extracted from each interval. To eliminate redundant features, the Kolmogorov–Smirnov (KST) and Mann–Whitney U (MWUT) tests are integrated, the extracted features ranked based on KST and MWUT metrics, and arithmetic operators are adopted to construe the most pertinent classified features for each pair in the EEG signal group. The selected features are then fed into the proposed AdaBoost Back-Propagation neural network (AB_BP_NN) to effectively classify EEG signals into seizure and free seizure segments. Finally, the AB_BP_NN is compared with several classical machine learning techniques; the results demonstrate that the proposed mode of AB_BP_NN provides insignificant false positive rates, simpler design, and robustness in classifying epileptic signals. Two datasets, the Bern–Barcelona and Bonn datasets, are used for performance evaluation. The proposed technique achieved an average accuracy of 100% and 98.86%, respectively, for the Bern–Barcelona and Bonn datasets, which is considered a noteworthy improvement compared to the current state-of-the-art methods.

Creation of pea recombinant lines with increased level of adaptability to drought conditions

Introduction. Peas are among the most common legumes in Ukraine, so the creation and introduction into production of new varieties has a significant economic effect. The main purpose of its breeding is to create highly adaptable to biotic and abiotic factors varieties, the yield of which would be stable for many years. Matcrials and mcth0ds. Field studies were conducted in the central zone of Odessa region during 2018–2021. Control nursery, ecological, preliminary and competitive testing were sown in plots 10 m long with a seeder “Klen-1.5C”, sowing of breeding and collection nurseries was carried out manually in a wide rows with 45 sm. In the process of vegetation conducted phenological ob- servations, the necessary accounting and evaluation. Plots of control, ecological, preliminary and competitive testing were harvested with a breeding combine “Sampo-130”, plants of selection and collection nurseries were tied into sheaves and then threshed on a thresher. Seed yield was calculated at 14% humidity. Rcsults and discussi0n. Under arid condi- tions in 2018–2020, the yield of all varieties and breeding lines was low, which indicates the need to strengthen breeding research to improve the adaptive characteristics of the new source material. Analysis of the results of our own research and a large volume of literature sources show that the creation of varieties with a more stable level of productivity will take place gradually through the consistent accumulation of positive gene loci using complex stepwise crosses and large volumes of hybrid populations of early generations, and effective selection highly adaptive forms. The network of ecological tests is of great importance. Under optimal moisture conditions, the yield of the best varieties reaches 5.0 t / ha, and in some recombinant lines exceeds this value. The identification of donors and sources of drought and heat resistance among the world gene pool is becoming increasingly important. It is especially important to identify the genetic loci of these traits by genetic molecular research methods.

Motor Skills Development: Peculiarities of Programmed Teaching of Acrobatic Exercises to Girls Aged 15

The purpose of the study was to determine the peculiarities of programmed teaching of a cartwheel to girls aged 15. Material and methods. The study participants were 20 girls aged 15. The children and their parents were fully informed about all the features of the study and gave their consent to participate in the experiment. To solve the tasks set, the following research methods were used: study and analysis of scientific and methodological literature; pedagogical observation, timing of training tasks; pedagogical experiment, methods of mathematical statistics, factor analysis, nearest neighbor analysis. Results. The analysis of similarities revealed that the program components are interrelated. Series of training tasks ІV, V and VI have the highest correlation. Series of training tasks І, ІІІ and IV are combined into one group and provide conditions for teaching girls aged 15 the cartwheel. Conclusions. The study obtained factor models of the teaching program, which explain 70.645% and 68.468% of the variation of results. The models characterize the two-factor structure of the teaching program where training tasks are closely connected with one another, which indicates their effective selection. It was found that the tasks of series I, II and III, and the tasks of series III, IV and V are most connected with one another, which confirms the combined nature of the method of algorithmic instructions. Optimization of the number of repetitions of the entire exercise (series of tasks VI) depends on the amount of exercises of the second and fifth series of training tasks.

Graphical analysis to determine the stability and adaptability of yield characteristics of dwarf cashew trees

The cashew culture provides jobs and boosts the economy of the Northeast region and is therefore of great socioeconomic importance. In genetic improvement programs, the existence of an interaction between genotypes and environments has been observed, making studies of adaptability and stability essential for effective selection. Thus, the objective was to study the performance of early dwarf cashew clones using the GGE Biplot in three agricultural years (2016–2018). The experimental design was in randomized blocks with 25 treatments (clones) and three replications. The plot consisted of four plants spaced 8 × 8 m apart. The variables evaluated were nut yield throughout the harvest (PRC), average nut weight (PMC), and percentage of nuts pierced (PCF). The clones most characterized as ideotypes were T25, T14, T7, T8, T2, T10, T15, and T22 for PRC; T12, T1, and T16 for PMC; and T5, T24, T21, and T8 for PCF, with coincidence for T8 in PC and PCF. Clones with values above the general average, with emphasis on stability, were T14 and T2 for PRC; T12, T1, T16, T8, and T22 for PMC; and T5 and T22 for PCF. The years with test characteristics, that is, those with average environmental factors for all years, were 2016 for PRC and 2018 for PCF, with no test year for PMC; the every years were more discriminating to PCF, with the exception of 2016 for PRC.

Morpho-molecular Characterization of Maize Inbred Lines Accelerating Parental Selection for Hybridization

Characterization of genetic diversity is the foundation step for crop improvement, which provides a basis for analyzing combining ability and heterosis of inbred genotypes during a hybridization program. An investigation was carried out at the field laboratory of the Genetics and Plant Breeding department in Bangabandhu Sheikh Mujibur Rahman Agricultural University, to elucidate the genetic architecture by evaluating 12 morphological and 4 molecular (SSR) markers within 52 diverse S1 genotypes, and to assess the relationship of molecular and morphological GD. An almost equal amount of PCV and GCV coupled with high heritability and genetic advance for the traits cob weight (gm), NKPC, and NKPR lead to the selection of promising genotypes based on these characters. Correlation coefficient and scatter plot matrix established a positive and strong relationship of KL (mm), KW (mm), and KT (mm) with 100 kernel weight (gm) suggesting the importance of kernel morphology. Mahalanobis D2 statistics revealed the highest inter-cluster distance between I and II. The percentages of molecular variance within the population and among the population were 76% and 14 %, respectively. The optimum K-value was 5. Heatmap relying on molecular GD exposed MMIL-28, MMIL-54, and MMIL-96 as the most diverse lines. SHE analysis hypothesized the increase of richness and diversity over time. Less correlation between the divergence generated from morphological traits and molecular markers suggested that the morphological variation may be determined by environmental factors and also by genetic factors. A strategy for the effective selection of predicting parental lines for a future hybridization program was developed.

Effective selection of countries in sub-Saharan Africa for new market entry by independent wind power producers

When independent power producers (IPPs) assess new market entry opportunities, subjective decision making can result in an unfavourable outcome. Multi-criteria decision analyses (MCDA) objectify the decision process and help to achieve better results. The aim of this study is to determine and rank the most important criteria for market entry and then determine which selected countries in sub-Saharan Africa are most favourable for wind IPPs. A combination of MCDA methods was used to rank seven countries. Nineteen criteria, identified in the literature reviewed, were included in the analyses. In the first phase of the study an industry expert survey was conducted and the analytical hierarchy process (AHP) was used to rank the criteria in order of importance. In the second phase, a preference ranking organization method for enrichment evaluation (PROMETHEE) was employed to rank the countries from most to least favourable for IPP market entry. The expert survey and AHP showed that political and economic criteria are more important than technical and social criteria. The PROMETHEE model ranked South Africa followed by Ethiopia as the most favourable markets for wind IPPs to enter. These countries have strong natural wind resources but only South Africa offers incentives specifically for on-grid renewable energy. The methods used in this study are not restricted to the wind industry and can be expanded to different technologies and industries to assist with decision making.

COMORBID CONDITIONS IN OBESITY IN CHILDREN AND ADOLESCENTS AND MODERN TECHNOLOGIES OF THEIR DIAGNOSIS

The review presents modern views of not only the long-known comorbid conditions and obesity in children and adolescents, including arterial hypertension, dyslipidemia, steatohepatosis, impaired carbohydrate tolerance and type 2 diabetes, but also less studied comorbid associations with overweight in childhood: ectopic deposition of fatty tissue, reproductive and psychological disorders, orthopedic problems and disorders of the gastrointestinal tract. It is emphasized that only a comprehensive interdisciplinary approach to the observation and treatment of obesity can help in the development and implementation of a personalized and therefore more effective selection of therapeutic and preventive interventions.