Decision Level Research Articles

Color crowding considered as adaptive spatial integration.

Crowding is the inability to recognize an object in clutter, classically considered a fundamental low-level bottleneck to object recognition. Recently, however, it has been suggested that crowding, like predictive phenomena such as serial dependence, may result from optimizing strategies that exploit redundancies in natural scenes. This notion leads to several testable predictions, such as crowding being greater for nonsalient targets and, counterintuitively, that flanker interference should be associated with higher precision in judgements, leading to a lower overall error rate. Here we measured color discrimination for targets flanked by stimuli of variable color. The results verified both predictions, showing that although crowding can affect object recognition, it may be better understood not as a processing bottleneck, but rather as a consequence of mechanisms evolved to efficiently exploit the spatial redundancies of the natural world. Analyses of reaction times of judgments shows that the integration occurs at sensory rather than decisional levels.

TMODINET: A trustworthy multi-omics dynamic learning integration network for cancer diagnostic

Multiple types of omics data contain a wealth of biomedical information which reflect different aspects of clinical samples. Multi-omics integrated analysis is more likely to lead to more accurate clinical decisions. Existing cancer diagnostic methods based on multi-omics data integration mainly focus on the classification accuracy of the model, while neglecting the interpretability of the internal mechanism and the reliability of the results, which are crucial in specific domains such as precision medicine and the life sciences. To overcome this limitation, we propose a trustworthy multi-omics dynamic learning framework (TMODINET) for cancer diagnostic. The framework employs multi-omics adaptive dynamic learning to process each sample to provide patient-centered personality diagnosis by using self-attentional learning of features and modalities. To characterize the correlation between samples well, we introduce a graph dynamic learning method which can adaptively adjust the graph structure according to the specific classification results for specific graph convolutional networks (GCN) learning. Moreover, we utilize an uncertainty mechanism by employing Dirichlet distribution and Dempster-Shafer theory to obtain uncertainty and integrate multi-omics data at the decision level, ensuring trustworthy for cancer diagnosis. Extensive experiments on four real-world multimodal medical datasets are conducted. Compared to state-of-the-art methods, the superior performance and trustworthiness of our proposed algorithm are clearly validated. Our model has great potential for clinical diagnosis.

Quality-aware fuzzy min-max neural networks for dynamic brain network analysis and its application to schizophrenia identification

Dynamic functional connections (dFCs) refer to the observed phenomenon that functional connectivity changes over a short time, which has become a pioneering method in the diagnosis of brain diseases. However, current dynamic brain network analysis methods are insufficient to address the fuzzy information of brain networks caused by the quality issues of rs-fMRI data, and the uncertainty arising from inconsistent data quality across different windows due to missing values and noise in individual windows. These results in unreliable integration of multiple windows. To this end, this paper proposes a dynamic brain network analysis method based on quality-aware fuzzy min–max neural networks (QFMMNet). The individual window of dFCs is treated as a view, and we define three convolution filters to extract features from the brain network under the multi-view learning framework, thereby obtaining multi-view evidence for dFCs. We design the multi-view fuzzy min–max neural networks (MVFMM) based on fuzzy sets to deal with the fuzzy information of the brain network, which takes evidence as input patterns to generate hyperboxes and serves as the classification layer of each view. A quality-aware ensemble module is introduced to deal with uncertainty, which employs Dempster-Shafer Theory (D-S theory) to directly model the uncertainty and evaluate the dynamic quality-aware weighting of each view. At the decision level of model, a weighted fusion strategy is employed for multi-view fusion based on the dynamic quality-aware weighting, thereby the quality of each view is fully considered to improve the trustworthiness of the decision. We verified the effectiveness of QFMMNet through comparison with advanced algorithms on three real schizophrenia datasets, and the results show that QFMMNet significantly improved the classification performance of brain disease diagnosis tasks. The accuracy on the Huaxi, COBRE, and Xiangya datasets reached 87.14%, 86.67%, and 85.27%, respectively.

Invasive species management: The case of pink salmon in Iceland

The combination of warming waters and increased human activity in the Arctic creates increasingly favorable conditions for the arrival of invasive species to Arctic and sub-Arctic aquatic ecosystems. Invasive species threaten biodiversity and can impact local communities and industries that rely on healthy ecosystems. In Iceland, the pink salmon (Oncorhynchus gorbuscha), native to Pacific waters, has been found in rapidly increasing numbers since 2017. This research aimed to explore the institutional response to the arrival of pink salmon in Iceland. Documents such as international agreements and Icelandic laws and policies were analyzed for similarities, differences, and knowledge gaps related to pink salmon management. In addition, semi-structured interviews (n=10) explored experiences, knowledge, and opinions of individuals in different levels of decision-making, biological research, angling tourism, and nature conservation. Results show that reactions to the arrival of pink salmon are limited to registration of catches. No widespread research has been initiated and knowledge of basic ecology and potential impacts are lacking. Furthermore, no organized coordinated management responses are in place. Because of the growing importance of invasive species management around the world, this research adds novel insight into opportunities for improvement of invasive species management in Iceland through the development of recommendations for formalized procedures and risk assessments.

Clinical decision making and moral distress among intensive care units nurses in Iran

BackgroundIntensive care units are often presented as environments where ethical issues are common and decisions can determine the life or death of patients, and these units have unique challenges due to critical health care. In these units, the relationship between the medical team and the patient’s relatives, their refusal of treatment, informed consent causes the nurses to have conflict in their decision making, therefore, this study aims to determine the level of clinical decision-making and moral distress and the relationship between them in intensive care units nurses.MethodsThis cross-sectional study was conducted with a descriptive-analytical approach in 2023 in Gorgan city. The number of 198 nurses in the intensive care units of Gonbad Kavos hospitals in the north of Iran were investigated and evaluated using the Corley Moral Distress questionnaire (2002), and Laurie’s Clinical Decision questionnaire (2001) in 2023. Independent T-test and anova analysis of variance were used for bivariate analysis. The significance level in this study was considered 0.05.ResultsThe results of the study showed that the mean and standard deviation of clinical decision making was 60.98 ± 10.25 (analytical-systematic level) and moral distress was 92.2 ± 23.61 (moderate level). There was a statistically significant relationship between clinical decision-making and nurses’ moral distress (P < 0.001 and r = 0.370). The moral distress score had a statistically significant relationship with marriage, employment status, education level, age, work experience and duration of employment in the special department. Also, the clinical decision score had a statistically significant relationship with employment status, education level, age and work experience.ConclusionAccording to these results, it seems that more attention should be paid to the moral distress of nurses in intensive care units, and it is necessary to improve their decision-making towards intuitive interpretation, which is done by designing and compiling training programs and workshops for nurses can be done, so that we can provide optimal nursing services to the patients of this department.

Escape: an optimization method based on crowd evacuation behaviors

Meta-heuristic algorithms, particularly those based on swarm intelligence, are highly effective for solving black-box optimization problems. However, maintaining a balance between exploration and exploitation within these algorithms remains a significant challenge. This paper introduces a useful algorithm, called Escape or Escape Algorithm (ESC), inspired by crowd evacuation behavior, to solve real-world cases and benchmark problems. The ESC algorithm simulates the behavior of crowds during the evacuation, where the population is divided into calm, herding, and panic groups during the exploration phase, reflecting different levels of decision-making and emotional states. Calm individuals guide the crowd toward safety, herding individuals imitate others in less secure areas, and panic individuals make volatile decisions in the most dangerous zones. As the algorithm transitions into the exploitation phase, the population converges toward optimal solutions, akin to finding the safest exit. The effectiveness of the ESC algorithm is validated on two adjustable problem size test suites, CEC 2017 and CEC 2022. ESC ranked first in the 10-dimensional, 30-dimensional tests of CEC 2017, and the 10-dimensional and 20-dimensional tests of CEC 2022, and second in the 50-dimensional and 100-dimensional tests of CEC 2017. Additionally, ESC performed exceptionally well, ranking first in the engineering problems of pressure vessel design, tension/compression spring design, and rolling element bearing design, as well as in two 3D UAV path planning problems, demonstrating its efficiency in solving real-world complex problems, particularly complex problems like 3D UAV path planning. Compared with 12 other high-performance, classical, and advanced algorithms, ESC exhibited superior performance in complex optimization problems. The source codes of ESC algorithm will be shared at https://aliasgharheidari.com/ESC.html and other websites.

'For Youth by Youth': Distributive Leadership in Action With a Youth Codesign Team.

Integrated youth services are an emerging delivery model in Canada that addresses siloed and fragmented youth mental health and other services. Youth engagement is viable for developing integrated youth services when purposefully built. However, it is not always clear how youth are involved in service transformation as decision-makers, and it requires an exploration of how to work with youth authentically and intentionally in the codesign process. This study reflects on the development of HOMEBASE, a network of integrated youth service delivery in Saskatchewan, Canada, and documents the process of actively and authentically engaging with youth through distributive leadership in the codesign process. Youth are actively and eagerly willing to participate in the codesign process of developing integrated services when there is a shared responsibility, and they are authentically involved and informed within the decision-making process. This requires time to form trust, build relationships and provide youth with low-pressure environments to foster healthy debates. By utilizing a distributive leadership approach, the Youth Codesign Team has been engaged in various levels of decision-making. By following these guiding principles, policymakers, youth development workers and researchers can engage youth in meaningful ways to improve the design and development of integrated care. Five youths from the HOMEBASE Provincial Youth Co-Design Team collaborated in writing this article based on their experiences of being engaged at varying levels of decision-making in a distributive leadership approach to building integrated youth services.

A Complete Study of Remote Sensing- Sentinel-2 Satellite Data for Land Use / Land Cover (LULC) Analysis

Remote sensing based Satellite collected Images is a complete advanced process of both the automatic detecting observation and overall earth monitoring as well as observing the mainly physical level of characteristics of an area by mainly measuring its reflected and emitted radiation at a distance mainly used from both satellite and aircraft. Special quality cameras collect remotely based sensed images, which help all researchers "sense" things about overall Earth observation. The European Research Space Agency (ESA) and the European (member countries) Union-EU both have equally provided together to specially a very powerful observation of the whole planet surface by simply making the remote sensing based Sentinel type-2 numerous spectral based results. The Sentinel category 2 satellite was successfully launched on June, 23rd 2015 to deliver satellite collection of data and imagery. The sentinel category-1 is the mission of the introductory satellite of Copernicus oriented research project for the artificial satellite based asterism organized by all Europe countries Space Research Agency. The satellite series of Sentinel 1A was successfully produced in the month of April, 03 2014, the same as Sentinel-1B satellite was successfully launched in the month of April, 25 2016. The Sentinel category-2 is the second category of satellite constellation of the All European countries Space Research Program designed mainly for a series of Sentinel missions. The main goal of the Sentinel category-2 satellite series mission is primary provide a very good quality of best pixel-resolution space satellite data / information used mainly for Land surface Cover (LC)/ Land Usage (LU) observation, coastal surface observation, different climate (Cloud) change and natural disaster damage mapping, Natural disaster, Earth Observatory, Forestry, Wildfire Assessment, Humanitarian operations with a best complementing to the other remote sensing satellite series missions mainly as-”LANDSAT”. Therefore, the review of advanced Sentinel type -2 is primary highlighted on these two important parameters: First, The main advantage of the ESA-Sentinel type 2 to different Land surface cover / Land usage classification or monitoring, second, Discovering total overall results of the ESA- Sentinel type-2 satellite data / imagery in various remote sensing based applications (such as the green-forest monitoring, urban area locality, natural level incident hazard analyze monitoring, coastal monitoring). The complete overall accuracy of data analysis can be improved when the possibility to simply integrate Sentinel type-2 satellite data with additional various types of advanced remotely based satellite observed details. The overall literature review of the Sentinel type-2 also describes a use of advanced remote sensing Sentinel type-2 details which are produced a very high level accuracies (&gt;81 to 85% by using various advanced machine-learning classification algorithms mainly as: Logistic standard Regression, Support based-Vector (classification) Machine-SVM, Decision level of Trees and Random standard forest-RF algorithm. Remaining, other classifier techniques commonly known as Maximum-Likelihood Analysis (MLA) are most commonly used. Although the sentinel type-2 also provides good level of opportunities mainly for Land surface cover-LC / Land usage-LU based on various classification, as well as they have many complex difficulties which added different mismatching with data of Landsat satellite based on OLI-category 8 additionally a gap of various thermal level bands, and different spatial based resolution (pixel) among the various bands of Sentinel type-2 satellite. The Sentinel-2 data has the good quality of probable to significantly present in the direction of land surface coverage with land usage monitoring as well as classification.

Dynamic evidence fusion neural networks with uncertainty theory and its application in brain network analysis

Deep learning has demonstrated significant potential and advantages, achieving notable success in the medical field, particularly in the application of brain network analysis. However, most models ignore the uncertainty caused by inconsistent view quality and fail to effectively leverage the potential correlations and temporal sequences present in multi-view data, preventing neural networks from fully showcasing their strengths. To this end, this paper proposes dynamic evidence fusion neural networks (DEF-NNs) with uncertainty theory, and applies it to brain network analysis. Our model is established within a multi-view learning framework that considers the functional connections under each window as a view. We employ a dynamic evidence learning module to capture the evidence for each time window of the dynamic brain network, utilizing three distinct convolutional filters to extract feature maps. Then, a dynamic evidence fusion mechanism is designed and a dynamic trust function is constructed according to the temporal nature of dFC data. The evidence generated by multiple windows is fused at the decision level of classification, dealing with the uncertainty caused by inconsistent view quality and improving the classification performance. We verified the effectiveness of DEF-NNs through comparison with advanced algorithms on three schizophrenia datasets, and the results show that DEF-NNs significantly improved the classification performance of brain disease diagnosis tasks.

Research on the Application Framework of Generative AI in Emergency Response Decision Support Systems for Emergencies

With the rapid development of artificial intelligence technology, generative AI has shown broad application prospects and potential in various fields. Frequent emergencies have put forward higher requirements for traditional Emergency Response Decision Support Systems (ERDSS). This paper proposes a theoretical framework of ERDSS based on generative AI (ERDSS-GAI), which deeply integrates generative AI with three stages of emergency response decision-making. The framework aims to leverage the advantages of generative AI in massive data processing, knowledge mining, strategy optimization, and other aspects, thereby enhancing the intelligence level and emergency response capability of ERDSS. The key components and implementation path of ERDSS-GAI are systematically explained from a theoretical perspective, and its application value is analyzed through the case of rainstorm and flood disaster in Shenzhen. This research demonstrates that generative AI can improve the scientific and refined level of emergency response decision-making, providing a theoretical framework and practical insights for its widespread adoption in emergency management.

Infrared and Visible Camera Integration for Detection and Tracking of Small UAVs: Systematic Evaluation

Given the recent proliferation of Unmanned Aerial Systems (UASs) and the consequent importance of counter-UASs, this project aims to perform the detection and tracking of small non-cooperative UASs using Electro-optical (EO) and Infrared (IR) sensors. Two data integration techniques, at the decision and pixel levels, are compared with the use of each sensor independently to evaluate the system robustness in different operational conditions. The data are submitted to a YOLOv7 detector merged with a ByteTrack tracker. For training and validation, additional efforts are made towards creating datasets of spatially and temporally aligned EO and IR annotated Unmanned Aerial Vehicle (UAV) frames and videos. These consist of the acquisition of real data captured from a workstation on the ground, followed by image calibration, image alignment, the application of bias-removal techniques, and data augmentation methods to artificially create images. The performance of the detector across datasets shows an average precision of 88.4%, recall of 85.4%, and mAP@0.5 of 88.5%. Tests conducted on the decision-level fusion architecture demonstrate notable gains in recall and precision, although at the expense of lower frame rates. Precision, recall, and frame rate are not improved by the pixel-level fusion design.

Scalable probabilistic forecasting in retail with gradient boosted trees: A practitioner’s approach

The recent M5 competition has advanced the state-of-the-art in retail forecasting. However, there are important differences between the competition challenge and the challenges we face in a large e-commerce company. The datasets in our scenario are larger (hundreds of thousands of time series), and e-commerce can afford to have a larger stock assortment than brick-and-mortar retailers, leading to more intermittent data. To scale to larger dataset sizes with feasible computational effort, we investigate a two-layer hierarchy, namely the decision level with product unit sales and an aggregated level, e.g., through warehouse-product aggregation, reducing the number of series and degree of intermittency. We propose a top-down approach to forecasting at the aggregated level, and then disaggregate to obtain decision-level forecasts. Probabilistic forecasts are generated under distributional assumptions. The proposed scalable method is evaluated on both a large proprietary dataset, as well as the publicly available Corporación Favorita and M5 datasets. We are able to show the differences in characteristics of the e-commerce and brick-and-mortar retail datasets. Notably, our top-down forecasting framework enters the top 50 of the original M5 competition, even with models trained at a higher level under a much simpler setting.

Integration of weighted majority voting in machine learning algorithms to enhance pine tree crown mapping on UAV imagery

Abstract. The shape and area of the crown of each tree are among the most influential parameters for identifying and controlling the processes of photosynthesis, respiration, transpiration and its management. In such a way that various physiographic functions, such as carbon dioxide absorption, light energy absorption, oxygen release and transpiration, which are vital for the growth and development of the tree, are done in the crown. In this research, the RGB image of the UAV with a spatial resolution of 2 cm was resampled to three pixel sizes of 10, 30 and 50 cm. Then, each image was classified separately by SVM, ANN and MLC algorithms, which are all part of Ensemble. In the next step, each of the obtained crowns was compared with the digitization of the same crown, and based on the area of the crown obtained from each classification and normalization method, the weight was obtained specifically for the same crown. Finally, by using the weighted majority voting method, classifications were fusioned at the decision level. The results showed that the ANN method gives better results in all pixel sizes compared to MLC and SVM. Also, the combination of different classification methods with the weighted majority voting method based on the weight assigned to the same crown based on each classification method has significantly increased the classification accuracy of the tree crown in all the sizes of the analyzed pixels.

ПОДХОДЫ К ОСУЩЕСТВЛЕНИЮ РИСК-МЕНЕДЖМЕНТА В УСЛОВИЯХ НЕОПРЕДЕЛЕННОЙ И ТУРБУЛЕНТНОЙ ВНЕШНЕЙ СРЕДЫ (НА ПРИМЕРЕ ЗЕРНОВОГО ПРОИЗВОДСТВА)

In the modern Russian socio-economic system, there is an objective increase in uncertainty and turbulence, which requires adequate management responses. In connection with the sanctions pressure, in agriculture the strategy of reducing the risks of import dependence is becoming a stimulating factor for development. The study was conducted on the example of grain production development, which has some specifics from the standpoint of management and, in particular, risk management. The purpose of the study is to generalize approaches to risk management in an uncertain and turbulent external environment and to develop on this basis a risk distribution scheme between management entities in the risk management system adapted to the specifics of grain production. It is shown that the increase in the values of economic indicators of grain production is influenced by natural and climatic conditions, they determine the quality of grain products, costs and the complex of applied agrotechnological measures. In addition to the observed "standard" variability of natural and climatic conditions, the study also identifies extreme risk events that require forecasting and consideration when managing grain production. Also, risks in the considered sphere are caused by diversity and interdependence of such factors as natural and climatic, soil, technological, economic, social, legal, etc. In their mutual interweaving, these factors determine annual fluctuations of economic indicators (yield, gross harvest, sown area) in grain production in Russia. As a result of the study, it was established that risk management procedures in the sphere of grain production should be carried out according to the hierarchical principle with the allocation of at least two levels: the coordinating level - the executive level. Based on this idea, an industry structural and functional model of risk management at various levels of decision-making has been developed. This model has the property of dualism: some levels are simultaneously coordinating, offering strategic solutions to lower levels, and executive - for higher structures. Implementation of the developed model in the practice of risk management in grain production will increase its sustainability and predictability.

ПОДХОДЫ К ОСУЩЕСТВЛЕНИЮ РИСК-МЕНЕДЖМЕНТА В УСЛОВИЯХ НЕОПРЕДЕЛЕННОЙ И ТУРБУЛЕНТНОЙ ВНЕШНЕЙ СРЕДЫ (НА ПРИМЕРЕ ЗЕРНОВОГО ПРОИЗВОДСТВА)

In the modern Russian socio-economic system, there is an objective increase in uncertainty and turbulence, which requires adequate management responses. In connection with the sanctions pressure, in agriculture the strategy of reducing the risks of import dependence is becoming a stimulating factor for development. The study was conducted on the example of grain production development, which has some specifics from the standpoint of management and, in particular, risk management. The purpose of the study is to generalize approaches to risk management in an uncertain and turbulent external environment and to develop on this basis a risk distribution scheme between management entities in the risk management system adapted to the specifics of grain production. It is shown that the increase in the values of economic indicators of grain production is influenced by natural and climatic conditions, they determine the quality of grain products, costs and the complex of applied agrotechnological measures. In addition to the observed "standard" variability of natural and climatic conditions, the study also identifies extreme risk events that require forecasting and consideration when managing grain production. Also, risks in the considered sphere are caused by diversity and interdependence of such factors as natural and climatic, soil, technological, economic, social, legal, etc. In their mutual interweaving, these factors determine annual fluctuations of economic indicators (yield, gross harvest, sown area) in grain production in Russia. As a result of the study, it was established that risk management procedures in the sphere of grain production should be carried out according to the hierarchical principle with the allocation of at least two levels: the coordinating level - the executive level. Based on this idea, an industry structural and functional model of risk management at various levels of decision-making has been developed. This model has the property of dualism: some levels are simultaneously coordinating, offering strategic solutions to lower levels, and executive - for higher structures. Implementation of the developed model in the practice of risk management in grain production will increase its sustainability and predictability.

Automatic Method for Detecting Deformation Cracks in Landslides Based on Multidimensional Information Fusion

As cracks are a precursor landslide deformation feature, they can provide forecasting information that is useful for the early identification of landslides and determining motion instability characteristics. However, it is difficult to solve the size effect and noise-filtering problems associated with the currently available automatic crack detection methods under complex conditions using single remote sensing data sources. This article uses multidimensional target scene images obtained by UAV photogrammetry as the data source. Firstly, under the premise of fully considering the multidimensional image characteristics of different crack types, this article accomplishes the initial identification of landslide cracks by using six algorithm models with indicators including the roughness, slope, eigenvalue rate of the point cloud and pixel gradient, gray value, and RGB value of the images. Secondly, the initial extraction results are processed through a morphological repair task using three filtering algorithms (calculating the crack orientation, length, and frequency) to address background noise. Finally, this article proposes a multi-dimensional information fusion method, the Bayesian probability of minimum risk methods, to fuse the identification results derived from different models at the decision level. The results show that the six tested algorithm models can be used to effectively extract landslide cracks, providing Area Under the Curve (AUC) values between 0.6 and 0.85. After the repairing and filtering steps, the proposed method removes complex noise and minimizes the loss of real cracks, thus increasing the accuracy of each model by 7.5–55.3%. Multidimensional data fusion methods solve issues associated with the spatial scale effect during crack identification, and the F-score of the fusion model is 0.901.

대학생의 지각된 진로장벽이 진로결정수준에 미치는 영향에서 진로성숙도의 매개효과

Objectives The purpose of this study was to verify the structural relationship between college students' perceived career barriers, career maturity, and career decision level, and the mediating effect of career maturity on the im-pact of perceived career barriers on career decision level. Methods For this purpose, 358 college students nationwide were selected as research subjects. A survey was conducted with the independent variables of perceived career barriers, mediating variables of career maturity, and career decision level selected as dependent variables. And the collected data were subjected to reliability ver-ification, descriptive statistics, correlation analysis, confirmatory factor analysis, path analysis, and bootstrap ver-ification using SPSS 26.0 &amp; AMOS 18.0 programs to derive research results. Results The relationship between perceived career barriers, career decision level, and career maturity was found to be appropriate, and perceived career barriers and career maturity were found to be important variables in career decision level. In addition, career maturity was found to have a direct and indirect effect in the impact of psycho-logical career barriers on the career decision level, and career maturity was found to have an indirect effect in the impact of environmental career barriers on the career decision level. Conclusions The conclusions of this study are as follows. First, it was confirmed that career maturity plays a medi-ating variable in the impact of college students' perceived career barriers on their career decision level. Second, the validity of this study was supported by the need to examine the concepts separately rather than integrating career barriers to explore career decision levels. Third,It is expected that college students experiencing perceived career barriers will be able to use it as basic data to understand the level of career decision by exploring the path of career planning and selection for each task.

A modified multiple-criteria decision-making approach based on a protein-protein interaction network to diagnose latent tuberculosis

BackgroundDNA microarrays provide informative data for transcriptional profiling and identifying gene expression signatures to help prevent progression of latent tuberculosis infection (LTBI) to active disease. However, constructing a prognostic model for distinguishing LTBI from active tuberculosis (ATB) is very challenging due to the noisy nature of data and lack of a generally stable analysis approach.MethodsIn the present study, we proposed an accurate predictive model with the help of data fusion at the decision level. In this regard, results of filter feature selection and wrapper feature selection techniques were combined with multiple-criteria decision-making (MCDM) methods to select 10 genes from six microarray datasets that can be the most discriminative genes for diagnosing tuberculosis cases. As the main contribution of this study, the final ranking function was constructed by combining protein-protein interaction (PPI) network with an MCDM method (called Decision-making Trial and Evaluation Laboratory or DEMATEL) to improve the feature ranking approach.ResultsBy applying data fusion at the decision level on the 10 introduced genes in terms of fusion of classifiers of random forests (RF) and k-nearest neighbors (KNN) regarding Yager’s theory, the proposed algorithm reached a sensitivity of 0.97, specificity of 0.90, and accuracy of 0.95. Finally, with the help of cumulative clustering, the genes involved in the diagnosis of latent and activated tuberculosis have been introduced.ConclusionsThe combination of MCDM methods and PPI networks can significantly improve the diagnosis different states of tuberculosis.Clinical trial numberNot applicable.

Development of a mechanism for managing the quality of customs services at the post-customs control stage

Subject. The article discusses the need for methodological support and technological justification of assessment and improvement of the customs services quality at the post-customs control stage. Objectives. The study aims to develop a comprehensive methodology to evaluate the effectiveness of structural units and the quality of rendered services as a basic element of the management mechanism. Methods. The study rests on the system methodology. We decomposed the mechanism of customs services quality management, and developed its morphological model during the analysis of individual components of the mechanism. Results. We developed a mechanism to manage the quality of services provided at the stage of post-customs control. It is divided into operational and strategic levels of management decision-making. Furthermore, we disclosed individual elements of the quality management mechanism, identified the main activities of the post-control units, within which key performance indicators are evaluated. As a pragmatic example, we developed a matrix for calculating performance indicators for one of the main areas of activity. Conclusions. The further direction of scientific research should be a model of the consumer of customs services, which determines the economic essence of solving the problem of developing the activities of the Federal Customs Service of Russia at the stage of post-customs control.

The Use of Cloud Platform Technology to Help University Music Teachers Teaching Decision-Making Research

With the COVID-19 pandemic, more and more universities are turning to online teaching, and music teaching is no exception. Because music teaching pays attention to the particularness of teachers' demonstration and students' imitation, the change of teaching environment from offline teaching to online teaching has a greater impact on the music discipline, which has new requirements for teachers' decision-making level. Through literature and questionnaires, this study identified that the most significant factor affecting teachers' decision making after the teaching environment was changed to online teaching was teacher-student interaction and designed a model to improve the interactive teaching of teachers through cloud platform technology. It is found that the model can effectively improve the interaction between teachers and students, so that teachers can make good decisions more easily and easily, so as to improve students' music scores and achieve effective teaching.