Decision Level Research Articles

A Space Infrared Dim Target Recognition Algorithm Based on Improved DS Theory and Multi-Dimensional Feature Decision Level Fusion Ensemble Classifier

Space infrared dim target recognition is an important applications of space situational awareness (SSA). Due to the weak observability and lack of geometric texture of the target, it may be unreliable to rely only on grayscale features for recognition. In this paper, an intelligent information decision-level fusion method for target recognition which takes full advantage of the ensemble classifier and Dempster–Shafer (DS) theory is proposed. To deal with the problem that DS produces counterintuitive results when evidence conflicts, a contraction–expansion function is introduced to modify the body of evidence to mitigate conflicts between pieces of evidence. In this method, preprocessing and feature extraction are first performed on the multi-frame dual-band infrared images to obtain the features of the target, which include long-wave radiant intensity, medium–long-wave radiant intensity, temperature, emissivity–area product, micromotion period, and velocity. Then, the radiation intensities are fed to the random convolutional kernel transform (ROCKET) architecture for recognition. For the micromotion period feature, a support vector machine (SVM) classifier is used, and the remaining categories of the features are input into the long short-term memory network (LSTM) for recognition, respectively. The posterior probabilities corresponding to each category, which are the result outputs of each classifier, are constructed using the basic probability assignment (BPA) function of the DS. Finally, the discrimination of the space target category is implemented according to improved DS fusion rules and decision rules. Continuous multi-frame infrared images of six flight scenes are used to evaluate the effectiveness of the proposed method. The experimental results indicate that the recognition accuracy of the proposed method in this paper can reach 93% under the strong noise level (signal-to-noise ratio is 5). Its performance outperforms single-feature recognition and other benchmark algorithms based on DS theory, which demonstrates that the proposed method can effectively enhance the recognition accuracy of space infrared dim targets.

Processing method for multi-source data fabric system based on intelligent system architecture

Currently, the data management of power enterprises faces the need to analyze data sources from multiple places. However, traditional multi-source data fabric systems have problems such as low analysis efficiency and high error rates, which brings great inconvenience to the data analysis of power enterprises. In order to improve the accuracy and efficiency of data analysis in data structure systems, the intelligent system architecture is applied to the construction of source data structure systems. The main modules are data collection, data matching, data integration, and data analysis. This article uses simulated annealing genetic algorithm to perform high-performance calculations on system timing data, thus achieving data matching. This article conducted data level data integration, feature level data integration, and decision level data integration. The access survey method was used to analyze the current data management problems faced by power companies. The evaluation and analysis of general multi-source data fabric systems and multi-source data fabric systems based on intelligent system architecture were conducted using the evaluation panel evaluation method. The analysis results showed that the operational convenience of the multi-source data fabric system based on intelligent system architecture could reach 60%–80%, which greatly improved compared to general multi-source data fabric systems; the information sharing of multi-source data fabric systems based on intelligent system architecture was greatly improved; the data processing efficiency of general multi-source data fabric systems was much lower than that of multi-source data fabric systems based on intelligent system architecture; however, the symmetry of data collection and matching in the multi-source data fabric system based on intelligent system architecture was slightly insufficient, and further improvement was still needed. In order to benefit more power companies through the intelligent system architecture based multi-source data fabric system, it was necessary to strengthen the management of data collection and matching symmetry.

Microfoundations of dominance of fundamentalism in economic policy: Is there an antidote?

The article examines the possible reasons for the dominance of fundamentalism( in the form of Pigouvianism or market fundamentalism) over functionalism (Coasianism) in the practice of decision-making in economic policy. Continuing the study of the relationship between Coasianism, Pigouvianism and market fundamentalism, presented in previous works, the article focuses on finding the basis for the dominance of fundamentalism at the level of individual decisions and actions — in particular, in the field of cognitive bias and behavioral effects. Decision-making using an automatic cognitive system, subject to cognitive bias, as opposed to a reflective system, can lead to the choice of a fundamentalist solution to a problem where it is not the most efficient, and also reduces the efficiency of functionalism, since it prevents the correct identification of the problem itself, of the structural alternatives for its solution, as well as identifying and comparing the effects associatedwith each of the alternatives. The microfoundations of insufficient supply and demand of the Coasian approach are explored. Based on the identified problems of supply and demand for Coasianism, as one of the possible ways to promote it, it is proposed to consider the primary socialization of the individual in the learning process, the formation of substantive critical thinking. It is emphasized that adjustments to the socialization process are a matter of long-term strategy

"Extended Descriptive Risk-Averse Bayesian Model" a More Comprehensive Approach in Simulating Complex Biological Motion Perception.

The ability to perceive biological motion is crucial for human survival, social interactions, and communication. Over the years, researchers have studied the mechanisms and neurobiological substrates that enable this ability. In a previous study, we proposed a descriptive Bayesian simulation model to represent the dorsal pathway of the visual system, which processes motion information. The model was inspired by recent studies that questioned the impact of dynamic form cues in biological motion perception and was trained to distinguish the direction of a soccer ball from a set of complex biological motion soccer-kick stimuli. However, the model was unable to simulate the reaction times of the athletes in a credible manner, and a few subjects could not be simulated. In this current work, we implemented a novel disremembering strategy to incorporate neural adaptation at the decision-making level, which improved the model's ability to simulate the athletes' reaction times. We also introduced receptive fields to detect rotational optic flow patterns not considered in the previous model to simulate a new subject and improve the correlation between the simulation and experimental data. The findings suggest that rotational optic flow plays a critical role in the decision-making process and sheds light on how different individuals perform at different levels. The correlation analysis of human versus simulation data shows a significant, almost perfect correlation between experimental and simulated angular thresholds and slopes, respectively. The analysis also reveals a strong relation between the average reaction times of the athletes and the simulations.

‘Athletes’ participation in the National Anti-Doping Organisations of Germany and Poland: democratic governance?’

ABSTRACT Athletes’ participation in sports governance gains momentum at multiple levels and challenges the long-prevailing power relations in organised sport. At the same time, the sport-specific discourse on good governance extends to the field of anti-doping, following low levels of testing effectiveness, untransparent decision-making and ethical misconduct in leading anti-doping authorities. Adopting a case-oriented comparative approach between the National Anti-Doping Organisations (NADOs) of Germany (NADA) and Poland (POLADA), two consecutive data collection steps were applied in mixed-methods design to assess and compare the status quo, and to discuss the future development of athletes’ participation in anti-doping through the lens of democratic processes in good governance research. First, document analysis showed important similarities and differences between the organisations’ approaches to athletes’ participation. Overall, NADA implements a more democratic and transparent approach than POLADA. Second, expert interviews revealed three key issues in relation to democratic forms of athletes’ participation in the two NADOs: athletes’ and their representatives’ (limited) personal resources and engagement (individual); an adequate degree of codification and institutionalisation of athletes’ representation on NADOs’ internal bodies (organisational); and NADOs’ operational (in-)dependence (political/systemic). Researchers and practitioners are recommended to further examine how NADOs’ control functions over athletes and athletes’ participation in their decision-making can be adequately balanced as part of aspirations to foster democratic governance in these organisations.

Effect of Dental Practicality Index training using an online video on decision-making and confidence level in treatment planning by dental undergraduates.

The purpose of this study was to evaluate the effect of Dental Practicality Index (DPI) training using an online video on the treatment planning decisions and confidence level of dental undergraduates (DUs). Ninety-four DUs were shown 15 clinical case scenarios and asked to decide on treatment plans based on 4 treatment options. The most appropriate treatment plan had been decided by a consensus panel of experienced dentists. DUs then underwent DPI training using an online video. In a post-DPI-training test, DUs were shown the same clinical case scenarios and asked to assign the best treatment option. After 6 weeks, DUs were retested to assess their knowledge retention. In all 3 tests, DUs completed the confidence level scale questionnaire. Data were analyzed using the related-samples Wilcoxon signed rank test and the independent-samples Mann-Whitney U test with the level of significance set at p < 0.05. DPI training significantly improved the mean scores of the DUs from 7.53 in the pre-DPI-training test to 9.01 in the post-DPI-training test (p < 0.001). After 6 weeks, the mean scores decreased marginally to 8.87 in the retention test (p = 0.563). DPI training increased their confidence level from 5.68 pre-DPI training to 7.09 post-DPI training. Training DUs using DPI with an online video improved their decision-making and confidence level in treatment planning.

Presentation of the Model for the Impact of Human Capital and Open Innovation on Organizational Productivity Using the Qualitative Grounded Theory Method with Glaser's Approach

Objective: The aim of the present study was to present a model for the impact of human capital and open innovation on organizational productivity. Methodology: This research gathered the necessary information by reviewing and evaluating previous studies in the field, as well as conducting in-depth interviews with experts and specialists in the areas of human capital, open innovation, and organizational productivity, who had practical experience at decision-making levels. Using the grounded theory analysis method, the study coded, categorized, and presented a conceptual model. Findings: After conducting interviews with experts and specialists in the fields of human capital, open innovation, and organizational productivity, and implementing the coding process using the grounded theory method, a total of 3 selective codes, 24 axial codes, and 71 open codes were identified. To validate the extracted codes, a focus group consisting of 10 experts, who were initially interviewed, was formed. The extracted codes were presented to them, and they were asked to provide their opinions on these codes. The result of the focus group's evaluation and discussion on the extracted codes indicated that the suitable model for assessing the impact of human capital and open innovation on organizational productivity includes 3 dimensions: human capital, open innovation, and organizational productivity, and 24 components, including 1) individuals, 2) leadership, 3) knowledge management, 4) strategic decision-making, 5) organizational management and structure, 6) system reorganization, 7) process versus activities, 8) progress measurement, 9) organizational culture, 10) employees, 11) structure and process, 12) innovative performance, 13) outsourcing, 14) financial resources, 15) legal factors, 16) collaboration with competitors, 17) customer, 18) achieving positive productivity outcomes, 19) cultural development, 20) process orientation, 21) employee participation, 22) connection with supply chain actors, 23) learning, creativity, and innovation, and 24) organizational rules and regulations. Conclusion: Organizational managers should define and design clear systems and structures, including appropriate promotion and reward systems, and financial regulations that monitor environmental changes and competitors' actions.

Annotation method of risk data in a certain field based on pattern matching

With the development of information technology and the increasing complexity of industrial technology, there is an urgent need for a certain field to use big data and artificial intelligence to improve the management and decision-making level. In order to classify the field’s risk text data through intelligent algorithms, analysing the risk distribution and the major problems, this paper researches on the annotation methods of training data in this field. The proposed data annotation method is based on pattern matching, addressing the special problems of risk data annotation in this field (such as strong professionalism, small data volume, high accuracy requirement and timeliness requirements). A new matching pattern is generated through the steps of text segmentation, keyword extraction, pattern preliminary generation, pattern relation tree construction, pattern optimization, pattern generalization, pattern verification, classification and annotation, and final classification and annotation are performed after pattern matching. Performance tests in terms of accuracy, recall rate, and annotation time have shown that the overall performance of the proposed method outperforms that of traditional item-by-item manual annotation, and semi-automatic annotation methods through machine learning. The method described in this paper has strong application value for risk data annotation in this field, and also has certain reference significance for high-density, high-accuracy and high-timeliness data annotation in other fields.

AI-driven Automated Operational Modal Analysis of Bridges

The growing awareness on the daunting challenge posed by ageing civil infrastructure is fostering the increasingly frequent implementation of dense Structural Health Monitoring (SHM) systems. The Italian context represents a formidable example of this trend, especially after the tragic collapse of the Morandi Bridge in 2018 and the release of the National Guidelines on the Assessment and Management of the Risk Condition of Bridges and Viaducts in 2020. This has motivated the implementation of numerous monitoring systems all throughout the national territory, many of them involving heterogeneous and dense sensor networks. The implementation of such a vast number of sensors presents new challenges at the management and decision-making levels, particularly in the signal processing and feature extraction phases. This is particularly critical for vibration-based SHM techniques, in which classical operational modal analysis (OMA) techniques are proving inefficient for handling such large monitoring databases. As an attempt to alleviate these limitations, this contribution presents the development of an AI-driven OMA technique for rapid identification of bridges. The proposed methodology consists of a multi-task deep feedforward neural network capable of extracting the independent modal components from ambient vibration records. Trained in a supervised manner by a Second Order Blind Source Identification (SOBI) method, the developed AI model can extract both the real and complex independent modal components. The effectiveness of the presented approach is illustrated through of a real-world-bridge, the Méndez-Núñez Bridge in Granada, Spain, demonstrating great potential as a computationally efficient OMA technique for on-site edge modal identification of bridges.

Effects of Lesson Study and Team Teaching on Students’ Achievement in Physics in Selected Secondary Schools in Niger State

The study was carried out to examine the effects of Lesson study and Team teaching on students’ achievement in Physics in selected secondary schools in Niger State. The study employed a quasi-experimental (pre-test post-test non-equivalent group design) and was guided by two research questions and two corresponding hypotheses. The population of the study comprised of 34,694 Senior Secondary School II Physics students (20,320 Male, 16,374 Female). 380 students (213 male, 167 Female) in the six purposively sampled science and technical colleges were used for the study. Data was collected from the sampled students using Physics Achievement Test for Students (PATS). The reliability coefficient for PATS was calculated using Pearson Product Moment Correlation (PPMC) and its value given as 0.77. To answer the research questions, mean and standard deviation were used. The corresponding hypotheses were tested using ANOVA at 0.05 decision level. The Tukey procedure was used to make pairwise comparisons of means where necessary. The results revealed that there was a statistically significant difference among the groups taught Physics using Lesson Study model, Team Teaching and Lecture method (F (2,377) = 8.708, p (0.000) &lt; 0.05). A further analysis shows that significant difference exists between the group of students taught using Lesson Study model and those taught using Lecture method (p = 0.000). Another significant difference was also found between the group of students taught using Team Teaching and those taught using Lecture method. However, there was no statistically significant difference between the students taught using Lesson Study and those taught using Team Teaching The male and female in the experimental groups performed better than male and female in the control group F(5,374)=4.517, p (0.001) &lt; 0.05. From the study, it was recommended that Physics teachers should implement Lesson Study for active participation of students towards enhancing students’ achievement in Physics.

BEVEFNet: A Multiple Object Tracking Model Based on LiDAR-Camera Fusion

As a crucial task in the field of computer vision, object tracking models are widely used in various application domains, such as autonomous driving. However, existing multiple object tracking methods still face challenges in accurately and efficiently tracking moving multi-targets in real time. This paper presents BEVEFNet, a camera-LiDAR multi-target tracking model based on multistage fusion, which effectively utilizes the semantic information from optical images and the spatial and geometric information from LiDAR data to unify multi-modal features in a shared Bird’s Eye View(BEV) representation space. By leveraging LiDAR data to complement optical images, multi-level fusion is achieved at both the feature and decision levels. The proposed efficient sparse 3D feature extraction network significantly enhances the speed of multiple object tracking by incorporating sparse convolution. Experiments conducted on the nuSences dataset demonstrate that BEVEFNet achieves an AMOTA of 69.7, improving the accuracy of multiple object tracking.

Inventory management and delivery of perishable products with stochastic demands and risks consideration

In the agri-food supply chain, the perishable nature of the commodities opens a series of challenges at all decision levels: strategical, tactical and operational. This paper presents an age-based model for the inventory-routing problem of perishable commodities with stochastic demands. To deal with this source of uncertainty, the conditional value-at-risk (CVaR) has been adopted as a risk measure, and then the model has been applied to the agri-food supply chain under investigation. The model is the core of a dynamic approach for the optimal distribution and inventory planning over a short-medium horizon. To address a problem as realistic as possible, we assume that the demand for products of each age is unknown and we model this uncertainty by means of random variables with a probability distribution that can be estimated from historical data. Computational experiments on test cases based on a real-life agri-food company located in Italy show the effectiveness of the proposed approach for evaluating different risk attitudes.

Modernization of the infrastructure of a river station of the north-south transport corridor using the simulation method

The paper presents the main fragments of the simulation model of a river port on the water section of the international transport corridor passing through the territory of the Russian Federation. The necessity of modernization and improvement of the infrastructure has been substantiated, the factors influencing the carrying capacity of the water corridor have been identified. A study of the river station in the city of Cheboksary in Republic of Chuvashia (Russia) was carried out and its main objects were presented. The rules for the interaction of the objects being the basis of the simulation model, have been established. The resulting model allows generating scenarios for predicting changes in the characteristics of a river station, ship schedules, assessing the quality of the implementation of processes and the level of managerial decision-making.

"I can't make it safe, so I don't do it": Exploring obstetricians' views on barriers and enablers to promoting vaginal birth after caesarean section in Bangladesh.

The rise in the number of facility-based births in Bangladesh has been accompanied by a caesarean section (CS) epidemic. The current CS rate is 45% and while many are performed when medically unnecessary, there is still maternal mortality due to lack of access to CS. A significant contributor to the rising CS rates in Bangladesh is repeat CS. Evidence from high-income settings has shown that vaginal birth after caesarean section (VBAC) is safe and should be recommended for women with one previous CS, however, its practice in Bangladesh is low. VBAC has the potential to help reduce unnecessary CS in Bangladesh. As obstetricians play a significant role in birth decision-making, their opinions, and perspectives on barriers to VBAC need to be explored. This study will address a gap in the literature exploring barriers and enablers to promoting VBAC from the level of the obstetric decision-maker. This qualitative study was conducted in the Dhaka Division of Bangladesh in July 2023. Criterion sampling was used to select obstetricians for in-depth semi-structured interviews. Seven interviews were conducted in a private hospital in Dhaka city and five interviews were conducted in a non-governmental organisation (NGO) hospital outside Dhaka city. Ethical approval was received from the relevant organisations in both Liverpool and Bangladesh. The thematic analysis gave rise to three main themes: "policy awareness and national situation", "reasons for practice decisions" and "ways to improve service delivery". Despite good awareness of VBAC policies and appreciation of its benefits, obstetricians expressed a preference for repeat CS. From the perspective of obstetricians, the main barriers to VBAC practice are related to the structure and function of the health system. To create an environment that will enable safe practice of VBAC, health system improvement and community awareness of the benefits of normal vaginal birth are required.

Assessing Entrepreneurial Performance of G7 Countries Under Context of Social Sustainable Development Goals: A Multicriteria Decision Analysis Approach

Entrepreneurship is a multilevel concept that connects social, environmental, and economic aspects of sustainability for societal developments, entrepreneurial processes, and market transformations. Social sustainability covers various social aspects and concerns that modern societies should achieve to improve their social infrastructures. Entrepreneurship is one of the main pivots that can strongly affect achieving social sustainability goals. Therefore, considering the current efforts of countries to reach their optimum level of social sustainability and strategic decision-making for long-term goals requires evaluating the current entrepreneurial performance in the first step. In this article, we aim to develop a multicriteria evaluation model using Shannon's entropy and measurement of alternatives and ranking according to the compromise solution to evaluate countries with the most influential entrepreneurial activities which contribute to socially sustainable development goals. As a case study, member countries of the Group of Seven (G7) are selected to assess their entrepreneurial performance using seven key indicators. These indicators are employees by business size, enterprises by business size, starting a business, women inventors, running a business, self-employed with employees, and young self-employed and are chosen for the analysis. The results show that Italy and the United States of America have been the pioneers in utilizing entrepreneurial activities to improve their social sustainability.

The Effect of the Duty-Oriented Leadership Style of the Coaches on the Level of Commitment and Ethical Decision-Making of the Players of the Iraqi National Athletics Team with the Mediating Role of Team Satisfaction

The Effect of the Duty-Oriented Leadership Style of the Coaches on the Level of Commitment and Ethical Decision-Making of the Players of the Iraqi National Athletics Team with the Mediating Role of Team Satisfaction

Approach for Linking System Architecture and Business Model based on the Example of Circular Value Propositions

Dynamic and volatile market environments influenced by sustainability and circularity are core topics impacting today’s value creation. They impact business on different levels, ranging from strategy and management to part and production design. In the common systems engineering RFLP-understanding they can be seen as additional requirements to be considered during product development and to be balanced with other requirements derived from stakeholders or regulation. Additionally, circular product design can offer new opportunities for design of business models and strategy on product or company level. This paper presents an approach for coupling of two levels of product related decision making utilizing the concepts of model-based systems engineering. The levels are (1) strategy and business-model related decisions, (2) product architecture including requirements. The presented approach can serve as a support for system architects and business model designers.

Career Decision Making Based On Gelatt's Theory Among Secondary School Students In The Hashemite Kingdom Of Jordan

This research aims to assess the career decision making abilities of secondary school students in the Hashemite Kingdom of Jordan (HKJ) during the academic year 2022/2023 AD. The sample comprises 470 high school students selected from various educational schools. Career decision making competence is assessed using the Gelatt model, developed by Alzaben and Abuased (2017). The researcher used a descriptive-analytical method to detect the participants' abilities in making career-related decisions. It was revealed that secondary school students in the Hashemite Kingdom of Jordan possess a notably high level of career decision making abilities. Furthermore, the results indicate that students in scientific education exhibit a superior aptitude for career decision making compared to their counterparts following the literary education stream.

Bank-Specific Decisions and Banks’ Provisioning Behaviour in Nigeria: The Role of IFRSs Adoption and Banks’ Riskiness

Goal: this study examines the relationship between bank loan loss provisioning behaviour and various bank-specific decisions on loan loss reporting. The evidence is derived from several cases of non-performing exposures of Nigerian deposit money banks. Metodology: to investigate the relationship between bank loan loss provisioning behaviour and various bank-specific decisions on loan loss reporting there was created the loan loss bank-specific decisions index (BDI) according to the International Financial Reporting Standards (IFRSs). The hypotheses are tested by using the estimates of panel corrected standard errors regression model. Findings:the results showed that discretionary adjustments to loan loss provisions (LLPV) are typical of Nigerian banks although the earnings manipulations imbedded in discretionary LLPVs (DLPV) are not factored in. However, during IFRS a reversal is evident suggesting bank-specific decisions prompting increased DLPV except for seemingly financially distressed banks. These findings also confirm the relationship between BDI components and provisioning behaviour in individual tests. The study also found out that the provisioning behaviour in Nigeria during global financial crisis was pro-cyclical (nonrational) while during the local economic recession of 2016–2017, it was counter-cyclical (rational). The latter, which happened during the IFRS period, was characterized by income smoothing while the former was addressed at the level of bank-specific decisions. Originality and contribution of the authors: the study contributes to the literature by testing a list of bank-specific factors engendering adjustments to loan loss decisions. It is unique in terms of the empirical reference to numerous bank-specific decisions as a single factor in accounting for loan losses. The paper also establishes the parochial success of institutional changes in the corporate Nigeria and offers useful suggestions for the way forward.

Robust minimum cost consensus modeling with consensus principle and tolerance level for group decision making

Robust minimum cost consensus modeling with consensus principle and tolerance level for group decision making