Adequate Model Research Articles

Exploring Scoring Function Space: Developing Computational Models for Drug Discovery.

The idea of scoring function space established a systems-level approach to address the development of models to predict the affinity of drug molecules by those interested in drug discovery. Our goal here is to review the concept of scoring function space and how to explore it to develop machine learning models to address protein-ligand binding affinity. We searched the articles available in PubMed related to the scoring function space. We also utilized crystallographic structures found in the protein data bank (PDB) to represent the protein space. The application of systems-level approaches to address receptor-drug interactions allows us to have a holistic view of the process of drug discovery. The scoring function space adds flexibility to the process since it makes it possible to see drug discovery as a relationship involving mathematical spaces. The application of the concept of scoring function space has provided us with an integrated view of drug discovery methods. This concept is useful during drug discovery, where we see the process as a computational search of the scoring function space to find an adequate model to predict receptor-drug binding affinity.

Sustainable Approach to Metal Coin Canceling Methods, Using 3D Modeling and Finite Element Method Analysis

Over time, many minted coins were withdrawn from circulation, being replaced with new ones. The returned, obsolete metal coins were melted in order to ensure sustainable reuse of the alloy for other purposes. Between the withdrawal and melting, some of the metal coins were canceled by the destruction of their original shape and dimensions using adequate tools. The first part of this paper is focused on presenting some insights into the canceling method used on old Romanian nickel coins; also, some examples are presented. The introduction also includes a literature review in the field of coin manufacturing, covering subjects such as metal behavior under striking load and aspects of 3D modeling and FEM analysis as well as explaining some striking errors. The main purpose of this paper is to study the particularities of canceling methods applied to coins, which is conducted on relatively valuable collection metal pieces. In the second part of the paper, an adequate 3D model is computed for the canceling dies and the coin. Then, the assembled models are introduced, corresponding to each canceling case, consisting of the obverse and reverse canceling dies with coins inside them. For each model, the finite element analysis is realized and is achieved for different initial conditions. The final part of the paper presents the analysis results as well as the discussion and conclusions.

Возможности опосредствования решения арифметических задач третьеклассниками: процедура «комплектования»

<p>The purpose of our study was to reveal students’ typical difficulties when performing trivial transformations of several quantities, which are to be coordinated. We have designed two tasks that required to build up the "sets", and to keep their ratio. 97 third-graders from three secondary Moscow schools were recruited for this study. The participants solved the first task individually in the written form, and afterwards there were 25 couples randomly selected to solve the similar task jointly in an oral interview with the experimenter. The analysis of the results revealed the weakness and confusion of most of the surveyed students in solving such problems. The in-depth consideration of the written works and interviews allowed us to characterize the model means, used by students, as formal or meaningful. Among them, only the “portion-by-portion” measurement of two independent quantities, performed through drawing or using the counting material, provided by the experimenter, turned out to be effective. The study showed that the source of difficulties in solving problems related to “assembling sets” is the lack of adequate model mediation, and confirmed the relevance of considering the "assembling sets problem" in the general line of development of the number concept in primary mathematics education within the framework of V.V. Davydov's theory of learning activity.</p>

Некоторые особенности разрушения лопастей гребных винтов из нержавеющей стали для морских судов

The problematic issues of the operation of propeller blades made of stainless steel, using the example of the analysis of repairs and destruction of the propeller blade of the CPP nuclear container ship Sevmorput are discussed. The greatest danger to the operation of the vessel is the exhaustion of its load-bearing capacity by the blade at loads less than the maximum due to the appearance of surface and internal defects in the blades, further growth over time, due to improperly carried out repair work. To check the strength, calculations of the propeller in the nozzle were made at an average speed of 14.5 knots at a rotation speed of 100 rpm in the SolidWorks software package. By 3D modeling of the propeller, with the rotation of the blades at various angles, a picture of the distribution of pressures on the suction and discharge surfaces of the blades was obtained. Strength assessment showed that the processes that led to the blade breakage are based on the concentration of stresses caused by the presence of hidden defects. An analysis of the physical, mechanical and metallographic properties of the blade material with weld metal (used to correct casting defects and during repairs in operation) was carried out on samples made from a fragment of the root part of the damaged blade left after the break. The results of the study of micro-sections of the blade material made it possible to build an adequate physical model of fracture and formulate recommendations regarding the operation of stainless steel propellers of marine vessels.

The Multilevel Factor Structure of Social Cohesion, Mutual Efficacy, and Informal Social Control: A Case for Practice-Informed Research.

Collective efficacy is the process by which social cohesion is activated as informal social control. Mutual efficacy, the perceived capability of the community, mediates the relationship between the two constructs. Interventions based on collective efficacy have a positive impact on individuals but are limited in their ability to affect the broader community. A possible explanation for this finding is that community-level theories operate differently at the individual and neighborhood levels. The present study contributes to the literature by testing the multilevel factor structure of social cohesion, mutual efficacy, and informal social control. Findings suggest that multiple-factor structures demonstrate adequate model fit. However, the three-factor model is most consistent with social work theory and practice. Social workers can foster constructive dialogue to build social cohesion, authentically engage residents to build mutual efficacy, and train residents in skills necessary to institute informal social control.

Resonant entanglement of photon beams by a magnetic field

In spite of the fact that photons do not interact with an external magnetic field unless nonlinearity of QED is taken into account, the latter field may indirectly affect photons in the presence of a charged environment. This opens up an interesting possibility to continuously control the entanglement of photon beams without using any crystalline devices. We study this possibility in the framework of an adequate QED model. In an approximation it was discovered that such entanglement has a resonant nature, namely, a peak behavior at certain magnetic field strengths, depending on characteristics of photon beams direction of the magnetic field and parameters of the charged medium. Numerical calculations illustrating the above-mentioned resonant behavior of the entanglement measure and some concluding remarks are presented.

STUDY OF THE INTERNET OF THINGS NETWORK CONSTRUCTION TASKS

The emergence and development of the Internet of Things stimulates the development of telecommunications and computing technologies. The current state of this process and its prospects allow the inclusion of a large number of devices connected to communication networks. This leads to the need to select an adequate model and methodological apparatus that allows working with such a quantity. The article discusses the concept of the IoT, which determines that any devices or things can now interact with each other at any time at any point in space.

Target flow deviations on the cardiopulmonary bypass cause postoperative delirium in cardiothoracic surgery-a retrospective study evaluating temporal fluctuations of perfusion data.

Postoperative delirium is common, costly, and associated with long-term morbidity and increased mortality. We conducted a cohort study to assess the contribution of cardiopulmonary bypass to the development of postoperative delirium by means of algorithm-based data processing. A database was compiled from three datasets of patients who underwent cardiac surgery between 2014 and 2019: Intensive care unit discharge files, cardiopulmonary bypass protocols, and medical quality management records. Following data extraction and structuring using novel algorithms, missing data was imputed. Ten independent imputations were analyzed by multiple logistic regression with stepwise deletion of factors to arrive at a minimal adequate model. Postoperative delirium was diagnosed in 456/3163 patients (14.4%). In addition to known demographic risk factors and comorbidities like male sex, age, carotid disease, acute kidney failure and diabetes mellitus, cardiopulmonary parameters like total blood volume at the cardiopulmonary bypass (AOR 1.001; CI 1.1001 to 1.002) were independent predictors of postoperative delirium. Higher values of the minimal blood flow were associated with a lower risk of postoperative delirium (AOR 0.993; CI 0.988 to 0.997). Flow rates at least 30% above target did emerge in the minimal adequate model as a potential risk factor, but the confidence interval suggested a lack of statistical significance (AOR 1.819; 95% CI: 0.955 to 3.463). Cardiopulmonary bypass data processing proved to be a useful tool for obtaining compact information to better identify the roles of individual operational states. Strict adherence to perfusion limits along with tighter control of blood flow and acid-base balance during cardiopulmonary bypass may help to further decrease the risk of postoperative delirium.

ГИБКИЕ МЕТОДОЛОГИИ В УПРАВЛЕНИИ КОМАНДОЙ РАЗРАБОТЧИКОВ IT-ПРОЕКТА

One of the important issues in the implementation of the strategy for the socio-economic development of the country and regions is the problem of managing the intellectual resources of companies. In modern conditions of economic development, the intellectual resources of companies are becoming increasingly important. The key task of enterprises in the IT field is to increase the efficiency of ongoing business processes. Problems related to the organization and management of teamwork are becoming increasingly important. Choosing the optimal approach to team management in a certain situation plays a significant role in the development of companies. [1]. The performance of software development companies largely depends on the correctness of the chosen software development methodology.
 
 The purpose of the study is to study approaches to managing the intellectual resources of companies, as well as choosing the optimal approach to managing a team of IT project developers. The article presents the results of a study of existing methodologies, and also makes an attempt to form an optimal and adequate model for managing a team of IT project developers.

Spatiotemporal Variations in Biophysical Water Quality Parameters: An Integrated In Situ and Remote Sensing Analysis of an Urban Lake in Chile

This study aims to develop and implement a methodology for retrieving bio-optical parameters in a lagoon located in the Biobío region, South-Central Chile, by analyzing time series of Landsat-8 OLI satellite images. The bio-optical parameters, i.e., chlorophyll-a (Chl-a, in mg·m−3) and turbidity (in NTU) were measured in situ during a satellite overpass to minimize the impact of atmospheric distortions. To calibrate the satellite images, various atmospheric correction methods (including ACOLITE, C2RCC, iCOR, and LaSRC) were evaluated during the image preprocessing phase. Spectral signatures obtained from the scenes for each atmospheric correction method were then compared with spectral signatures acquired in situ on the water surface. In short, the ACOLITE model emerged as the best fit for the calibration process, reaching R2 values of 0.88 and 0.79 for Chl-a and turbidity, respectively. This underlies the importance of using inversion models, when processing water surfaces, to mitigate errors due to aerosols and the sun-glint effect. Subsequently, reflectance data derived from the ACOLITE model were used to establish correlations between various spectral indices and the in situ data. The empirical retrieval models (based on band combinations) yielding superior performance, with higher R2 values, were subjected to a rigorous statistical validation and optimization by applying a bootstrapping approach. From this process the green chlorophyll index (GCI) was selected as the optimal choice for constructing the Chl-a retrieval model, reaching an R2 of 0.88, while the red + NIR spectral index achieved the highest R2 value (0.79) for turbidity analysis, although in the last case, it was necessary to incorporate data from several seasons for an adequate model training. Our analysis covered a broad spectrum of dates, seasons, and years, which allowed us to search deeper into the evolution of the trophic state associated with the lake. We identified a striking eight-year period (2014–2022) characterized by a decline in Chl-a concentration in the lake, possibly attributable to governmental measures in the region for the protection and conservation of the lake. Additionally, the OLI imagery showed a spatial pattern varying from higher Chl-a values in the northern zone compared to the southern zone, probably due to the heat island effect of the northern urban areas. The results of this study suggest a positive effect of recent local regulations and serve as the basis for the creation of a modern monitoring system that enhances traditional point-based methods, offering a holistic view of the ongoing processes within the lake.

Perceived military to civilian transition-development of conceptual framework and scale for Ex-servicemen

Perceived Military to Civilian Transition is the subjective experience of transition from military institution to the civilian institution due to voluntary or involuntary retirement from military service reflected by changes in vocation, social, health and family domains of life. This study aims to develop a psychometric tool for evaluating Perceived Military to Civilian Transition. Three studies were conducted by cross-sectional surveys (Study 1 N = 25, Study 2 N = 82, Study 3 N = 400) using the purposive snowball sampling among Ex-Servicemen in India. Study 1–16 Items were generated on 5-point rating scale. Study 2-Items were reduced to 9, based on psychometric scrutiny. First order EFA reveals three latent factors reflecting changes in vocation and health (3 items), family (2 items), and social domain (4 items). Second order EFA confirms presence of one higher order factor. Study 3-CFA performed using ULS estimation method for Higher order model provides empirical support for the PMCT scale having adequate model fit Chi.Sq. = 142.29, df = 24, GFI = 0.99, AGFI = 0.98, NFI = 0.98, RFI = 0.97, SRMR = 0.05. The 9-item scale possesses satisfactory internal consistency reliability alpha (Study 2 = 0.80, Study 3 = 0.77) and composite reliability omega (Study 2 = 0.79, Study3 = 0.77). This paper contributes a reliable and valid psychometric tool for measuring Perceived Military to Civilian Transition for Indian Ex-servicemen.

Construction and validation of the Scale of Willingness for Emotional Learning in a Chilean sample

This study aimed to build and validate a self-reported measure to assess the construct willingness for emotional learning to study individual differences in the development of emotional competences. This scale was validated by expert raters and then administered to a sample of 320 adults, mostly from northern Chile. An adequate model fit of the theoretical structure was demonstrated using a confirmatory factor analysis (CFA). The internal structure of the scale consists of two dimensions: (a) willingness to learn about the subjective aspects of emotions (feelings) and (b) external aspects of emotions (expression and recognition). They showed positive correlations with the three dimensions of perceived emotional intelligence and negative and positive associations with the three dimensions of alexithymia. The scale was also found to have adequate levels of internal consistency. Theoretical and practical implications of using the Scale of Willingness for Emotional Learning in the Chilean adult population are discussed.

Integral evaluation of thyroid lymphatic region in phytocorrection of hypothyroidism consequences

To date, the integrative involvement of thyroid lymphatic region structures in providing tissue homeostasis in hypothyroidism and during rehabilitation remains a poorly understood problem. The use of phytotherapy is attractive for reducing the effects of hypothyroidism. This issue requires scientific justification during the rehabilitation phase.Material and methods. The work was performed on 160 white Wistar rats using light and electron microscopy according, data of morphometry and statistical matrix analysis. An adequate model of hypothyroidism was created by mercazolil administration. After the mercazolil withdrawal, we investigated the reversibility of thyroid and regional lymph node changes during the rehabilitation period (on 7, 14, 21 days) on the background of bioactive phytocomposition intake and without it. Phytocomposition from laminaria and medicinal plants of Siberia was used at a dose of 0.2 g/kg for three weeks after the cancellation of mercazolil.Results and discussion. The study showed a unidirectional change in thyroid and lymph node structure with a preferential decrease in indicators in hypothyroidism. The value of the total normalized index for the lymphatic region (lymphothyroostat) is negative during the recovery period without phytocorrection. The index has a negative value initially and up to 14 days and becomes positive only by the end of 21 days of the study reflecting the inertia of the structural response of organs. Phytotherapy provides high rationed index rates for lymphothyrostat. The index becomes positive starting at day 14. There is an intensification of compensatory and adaptive reactions at all levels of lymphothyrostat organization, starting with ultrastructural. The lymph node contributes significantly to the provision of homeostasis of the lymph region of the thyroid gland.Conclusions. Reorganization of the lymph node and thyroid gland is interconnected in hypothyroidism. The active role of the lymph node in providing tissue homeostasis in the lymph region of the thyroid is apparent. Thyroid and lymph node function can be enhanced by phytotherapy. Phytocomposition reduces the effects of hypothyroidism. It is a kind of modifier of thyroid and lymph node structure and function

Construction of Evaluation System for English Translation Teaching in Colleges and Universities under Cross-cultural Perspective

Abstract The construction of the evaluation system of English translation teaching in colleges and universities under the cross-cultural perspective to improve the quality of translation education. The focus is on integrating the LMBP neural network algorithm and quantitative judgment indexes to provide an adequate evaluation model for English translation teaching. The study adopts the neural network algorithm based on LMBP, combined with the quantitative processing methods of “AHP+DEA” and “Entropy Assignment Method+Euclidean Distance Method”, to systematically construct and empirically analyze the evaluation indexes of teaching. It is found that teaching construction, application and effect are the core indicators of evaluation. Regarding teaching construction, classroom content integrity (correlation coefficient 0.428) and learning resources integrity (correlation coefficient 0.439) are the key factors. Teaching application analysis showed that hybrid teaching positively correlated with student achievement (r=0.569). Conducting effectiveness analysis showed that student discussion and in-class performance were significantly and positively correlated with teaching evaluation (p<0.005). These results prove the effectiveness and usefulness of the constructed evaluation system.

Genotyping of SNPs in bread wheat at reduced cost from pooled experiments and imputation

Key messagePooling and imputation are computational methods that can be combined for achieving cost-effective and accurate high-density genotyping of both common and rare variants, as demonstrated in a MAGIC wheat population.The plant breeding industry has shown growing interest in using the genotype data of relevant markers for performing selection of new competitive varieties. The selection usually benefits from large amounts of marker data, and it is therefore crucial to dispose of data collection methods that are both cost-effective and reliable. Computational methods such as genotype imputation have been proposed earlier in several plant science studies for addressing the cost challenge. Genotype imputation methods have though been used more frequently and investigated more extensively in human genetics research. The various algorithms that exist have shown lower accuracy at inferring the genotype of genetic variants occurring at low frequency, while these rare variants can have great significance and impact in the genetic studies that underlie selection. In contrast, pooling is a technique that can efficiently identify low-frequency items in a population, and it has been successfully used for detecting the samples that carry rare variants in a population. In this study, we propose to combine pooling and imputation and demonstrate this by simulating a hypothetical microarray for genotyping a population of recombinant inbred lines in a cost-effective and accurate manner, even for rare variants. We show that with an adequate imputation model, it is feasible to accurately predict the individual genotypes at lower cost than sample-wise genotyping and time-effectively. Moreover, we provide code resources for reproducing the results presented in this study in the form of a containerized workflow.

Automation Model Development for School Reaccreditation of Early Childhood Education

As stated in the Research and Development Objectives 2020-2024 by BSKAP, it was agreed on the need to implement a correct and credible quality monitoring and evaluation system. For Primary and Secondary Education, it is determined that 100% of education units will carry out Competency Assessment (AK) and Character Surveys (SK) starting in 2021 by first developing and preparing relevant measuring tools along with indicators of AK-SK readiness in the future, its implementation in 2021 while trying implement the overall accreditation process automation policy. Therefore, performance assessment in these units requires other proxies of learning indicators which are considered to have functions equivalent to competency assessments and character surveys. Instead of direct field visits, if correct, mathematical modeling can be performed to derive measurement proxies derived from the PPA or IPV variables or a combination of both. Automation modeling has been applied to approximately 5,000 school samples by applying three alternative methods, namely Principal Component Analysis (PCA), Partial Least Square (PLS) and Confirmatory Factor Analysis (CFA). PCA modeling was successfully used on 49 predictors without a response variable (Y); ii) PLS modeling was successfully applied to 49 predictors involving response variables; iii) CFA modeling has been successfully carried out on PPA and IPV one by one, because the combined modeling has not succeeded in producing an adequate model in the form of goodness of fit. Keywords: accreditation, assessments, early childhood education, statistical modeling

Interaction model of forestry enterprises with society and environment

The article presents the results of a study on the interaction between timber enterprises, society, and the environment. Currently, a business's effectiveness is determined by its ability to resolve conflicts between social, environmental, and economic spheres. The purpose of this work was to obtain and substantiate an adequate model of enterprise interaction with society and the environment. The main method used was analysis. Several models were developed to describe the interaction of the studied objects. The text provides a justification for existing conflicts in the work of timber enterprises. The development of technological aspects is proposed as a direction to transition the forest industry to a qualitatively new level of functioning. Several proposed key transformations can contribute to the transition to a balanced interaction model between the enterprise and the environment. The model of interaction between timber enterprises, society, and the environment is developed, highlighting areas for both increase and decrease. The study concludes that a balanced policy, oriented towards achieving sustainable results over time, is necessary for timber enterprises to achieve long-term success. It is important to consider the interests of future generations and the surrounding society, as this approach will only become more important in the future.

A basic epistemic logic and its algebraic model

In this paper we propose an algebraic model for a modal epistemic logic. Although it is known the existence of algebraic models for modal logics, considering that there are so many different modal logics, so it is not usual to give an algebraic model for each such system. The basic epistemic logic used in the paper is bimodal and we can show that the epistemic algebra introduced in the paper is an adequate model for it.

Development of the ontological model of situation management of projects based on SCRUM under risky conditions

Projects in the field of information technology implemented on the basis of Scrum are quite often carried out under the influence of sharply changing circumstances both in the project itself and in the environment, which are characterized as risky. Therefore, the processes related to the situational project management, which is the object of this study, become important. For projects, it is necessary not only to identify the current situation but also to formally present it in order to evaluate and determine rational ways of achieving project goals under risky conditions, which is the task of this study. Crisis situations in projects that arise under risky conditions are quite difficult to predict and take appropriate preventive measures in time. In difficult cases of establishing a cause-and-effect relationship between factors, we have unstructured data of various types and nature. Therefore, to solve this task, it is necessary to apply a combined approach, based on a complex combination of situational management methods, formalized, intelligent methods and expert methods, as well as ontological engineering. This will make it possible to build an adequate decision-making model under risky conditions, taking into account the influence of external and internal environmental factors. An ontological model of situational project management based on Scrum is proposed, which will ensure the interconnection and consistency of decisions in accordance with the situation under risky conditions. The proposed model of the situation provides a formalized description and assessment of the situation under risky conditions in real time. The result of the application of this model is an increase in the efficiency of projects due to compliance with time limits, reduction of overspending of resources, as well as adaptation to rapidly changing circumstances and adequate response

Analysis of methods of classification of electronic messages based on neural network models

In the article, the creation of a mechanism for detecting and classifying messages is considered, with an assessment of how effectively different neural networks work and can recognize and classify different types of electronic messages, including phishing attacks, spam, and legitimate messages. A preliminary analysis of incoming messages has been performed, encompassing their headers, text, and other relevant attributes. For instance, in the case of emails, these attributes could be the 'subject' and 'sender' of the message. Methods for data preparation and processing have been reviewed, including text vectorization, noise removal, and normalization, to be utilized in training neural networks. Message tokenization has been performed by transforming them into a numerical format while considering the selection of features. For text messages, it is crucial to execute both tokenization and text vectorization. The model training was performed on the test data with prior splitting into two parts: 80% for training and 20% for testing. The training set is utilized for training the model, while the test set is used to evaluate its effectiveness. The peculiarity of the class structure of the data, namely the uniformity of the distribution of classes, is considered. In this case, spam occurs less frequently than legitimate messages, so class balancing techniques such as random deletion of redundant examples, upsampling, and subsampling were applied to ensure adequate model training. Optimization of network parameters was performed, by researching the optimal parameters of neural networks, such as the number and size of layers, activation functions, and optimization of hyperparameters to achieve the best performance. Hyperparameter optimization includes determining optimal settings for neural networks, such as layer size, activation functions, learning rate, and other parameters. The effectiveness was assessed by comparing the results and performance of various classification methods based on neural networks using metrics such as precision and F1-score. It was determined how well the methods can avoid misclassifications where legitimate messages are mistakenly identified as spam, and vice versa. A comparison of the methods' effectiveness in processing a large volume of messages in real time was conducted. An analysis of different architectures of neural network models was performed. Based on the analysis, it was revealed how effectively different neural network models can recognize and classify messages as spam.