Decision Situations Research Articles

A Study on the Accuracy of Micro Expression Based Deception Detection with Hybrid Deep Neural Network Models

This article details a study on enhancing deception detection accuracy by using Hybrid Deep Neural Network (HDNN) models. The research, focusing on fear-related micro-expressions, utilizes a diverse dataset of responses to high-stakes questions. It analyzes facial action units (AUs) and pupil size variations through data preprocessing and feature extraction. The HDNN model outperforms the traditional Convolutional Neural Network (CNN) with a 91% accuracy rate. The findings’ implications for security, law enforcement, psychology, and behavioral treatments are discussed. Ethical considerations of deception detection technology deployment and future research directions, including cross-cultural studies, real-world assessments, ethical guidelines, studies on emotional expression dynamics, “explainable AI” development, and multimodal data integration, are also explored. The study contributes to deception detection knowledge and highlights the potential of machine learning techniques, especially HDNN, in improving decision-making and security in high-stakes situations.

Individual differences in the discounting of combination outcomes in which immediate gains are followed by delayed losses.

The vast majority of studies on discounting have focused on simple delayed outcomes, but most everyday decisions are more complicated. The present experiment focused on one such scenario, an iconic self-control situation in which immediate gains are followed by delayed losses. The same participants were studied in all conditions to permit examination of individual differences in choice behavior using intercorrelations and factor analysis. Consistent with previous research, the hyperboloid model accurately described the form of the discounting function and discounting was not affected by the amount of the delayed loss when it was presented alone. However, replicating other studies, smaller delayed losses were discounted more steeply than larger ones when presented in combination with immediate gains. Exploratory factor analysis revealed two factors, one loading primarily on loss-only conditions and the other loading primarily on conditions involving outcomes that combined gains and losses. These results imply that there are individual differences in how one combines gains and losses and that this characteristic of individual decision making might be an important predictor of decisions in the many everyday choice situations that involve complex outcomes.

The top tier of the Big Five does not predict police decisions in ambiguous and high-pressure situations

The top tier of the Big Five does not predict police decisions in ambiguous and high-pressure situations

Use of Real-World FHIR Data Combined with Context-Sensitive Decision Modeling to Guide Sentinel Biopsy in Melanoma.

Background: To support clinical decision-making at the point of care, the "best next step" based on Standard Operating Procedures (SOPs) and actual accurate patient data must be provided. To do this, textual SOPs have to be transformed into operable clinical algorithms and linked to the data of the patient being treated. For this linkage, we need to know exactly which data are needed by clinicians at a certain decision point and whether these data are available. These data might be identical to the data used within the SOP or might integrate a broader view. To address these concerns, we examined if the data used by the SOP is also complete from the point of view of physicians for contextual decision-making. Methods: We selected a cohort of 67 patients with stage III melanoma who had undergone adjuvant treatment and mainly had an indication for a sentinel biopsy. First, we performed a step-by-step simulation of the patient treatment along our clinical algorithm, which is based on a hospital-specific SOP, to validate the algorithm with the given Fast Healthcare Interoperability Resources (FHIR)-based data of our cohort. Second, we presented three different decision situations within our algorithm to 10 dermatooncologists, focusing on the concrete patient data used at this decision point. The results were conducted, analyzed, and compared with those of the pure algorithmic simulation. Results: The treatment paths of patients with melanoma could be retrospectively simulated along the clinical algorithm using data from the patients' electronic health records. The subsequent evaluation by dermatooncologists showed that the data used at the three decision points had a completeness between 84.6% and 100.0% compared with the data used by the SOP. At one decision point, data on "patient age (at primary diagnosis)" and "date of first diagnosis" were missing. Conclusions: The data needed for our decision points are available in the FHIR-based dataset. Furthermore, the data used at decision points by the SOP and hence the clinical algorithm are nearly complete compared with the data required by physicians in clinical practice. This is an important precondition for further research focusing on presenting decision points within a treatment process integrated with the patient data needed.

On the Construct of Subjective Risk Intelligence and Its Relationships with Personality, Emotional Intelligence and Coping Strategies: A Comparison between Adolescents and Adults.

The complexity of today's scenario has made it necessary to investigate the need for individuals to make choices that entail increasing exposure to risk and uncertainty. Among the individual resources that could help people to cope with situations of uncertainty, the new construct of subjective risk intelligence (SRI), known as a person's ability to effectively weigh the pros and cons of a decision in situations where not all the outcomes are foreseen, would seem to play a prominent role. Considering that personality and coping strategies have been shown to be significantly related in previous research, the present study investigates the relationships between subjective risk intelligence, emotional intelligence, personality traits and coping strategies in both adults and adolescents. This cross-sectional study was conducted on 1390 Italian people, divided into two subsamples of 641 adolescents and 749 adults. The results showed that SRI mediated the roles that personality traits and emotional intelligence have in coping strategies differently in the adult sample, in which the mediating role of SRI was found for avoidance coping, and in the adolescent sample, in which SRI influenced all of the antecedents analysed in the study for almost all of the identified coping strategies. In light of these findings, subjective risk intelligence could be activated to deal with uncertain and risky situations, influencing the choice of effective or ineffective strategies in both adults and adolescents.

Stochastic calculus-guided reinforcement learning: A probabilistic framework for optimal decision-making

Stochastic Calculus-guided Reinforcement learning (SCRL) is a new way to make decisions in situations where things are uncertain. It uses mathematical principles to make better choices and improve decision-making in complex situations. SCRL works better than traditional Stochastic Reinforcement Learning (SRL) methods. In tests, SCRL showed that it can adapt and perform well. It was better than the SRL methods. SCRL had a lower dispersion value of 63.49 compared to SRL's 65.96. This means SCRL had less variation in its results. SCRL also had lower risks than SRL in the short- and long-term. SCRL's short-term risk value was 0.64, and its long-term risk value was 0.78. SRL's short-term risk value was much higher at 18.64, and its long-term risk value was 10.41. Lower risk values are better because they mean less chance of something going wrong. Overall, SCRL is a better way to make decisions when things are uncertain. It uses math to make smarter choices and has less risk than other methods. Also, different metrics, viz training rewards, learning progress, and rolling averages between SRL and SCRL, were assessed, and the study found that SCRL outperforms well compared to SRL. This makes SCRL very useful for real-world situations where decisions must be made carefully.•By leveraging mathematical principles derived from stochastic calculus, SCRL offers a robust framework for making informed choices and enhancing performance in complex scenarios.•In comparison to traditional SRL methods, SCRL demonstrates superior adaptability and efficacy, as evidenced by empirical tests.

DEVELOPMENT OF ECOLOGICAL EDUCATIONAL MECHANISMS IN HIGHER EDUCATION AS A SOCIAL NEED AND NECESSITY

In the article, the importance to improve the environmental education and culture of future specialists studying in higher educational institutions, to ensure their environmental competence, and to develop the skills of making rational decisions in environmental life situations was justified. In this regard, education, environmental education, concepts of mechanisms, identification of mechanisms of environmental education as components, perceptionof the environment, conscious attitude towards nature, sublimation mechanisms were analyzed in detail.

Медицинская реабилитация: исследование проблем этического и правового характера

Medical rehabilitation is currently in an active phase of its development. This relevant area of domestic medicine is essential for human health. It helps patients recover from long-term illnesses, effects of injuries and diseases of the musculoskeletal system, peripheral nervous system and has a huge impact on the prognosis and outcome of treatment in general. This publication provides a comprehensive analysis of the ethical aspects of medical rehabilitation with an emphasis on legal definitions in medicine, which will improve understanding and regulation of relationship between rehabilitation, prevention and treatment. The research includes the study of domestic and international regulatory legal acts concerning medical rehabilitation, history of the specialty, the formulations that laid the foundation for the concept further development, consolidating an integrated approach to the issue of medical rehabilitation, as well as review of the problem-associated scientific papers. Explaining the principles of medical rehabilitation will help doctors avoid legal risks associated with their professional activity and serve as a guideline for taking ethically sound decisions in difficult clinical situations, whereas patients will get a full picture of their rights within the framework of medical rehabilitation.

Precision Targeting Companion: Empowering Marksmen with Smart Rifle Scopes

In the realm of modern warfare, the ability to swiftly identify and engage adversaries with precision is indispensable. This paper outlines our objective to contribute to this paradigm shift by undertaking a comprehensive exploration, and design, of accessories crucial for rifles, including smart scopes, auto- matic triggers, and intelligent magazines. Moreover, this paper introduces a groundbreaking innovation - the Precision Targeting Companion, an intuitive system integrated into rifle scopes, aimed at elevating the effectiveness and accuracy of marksmanship in dynamic combat scenarios. At its core, this system is a testament to the fusion of advanced technology with human ingenuity. By harnessing the power of cutting-edge image processing and predictive analytic, the Precision Targeting Companion offers marksmen a seamless and intuitive interface for detecting potential threats and predicting bullet trajectories in real-time. Yet, beyond its computational prowess, this system embodies a deeper understanding of the challenges faced by soldiers and law enforcement officers on the front-lines. It is a trusted companion, empowering users with the confidence to make split-second decisions in high-pressure situations. Key features of the Precision Targeting Companion include: 1. Swift identification of threats: Leveraging sophisticated image processing algorithms, the system swiftly identifies adver- saries within the user’s line of sight, providing crucial situational awareness. 2. Predictive guidance: Through intricate calculations and analysis, the system predicts bullet trajectories, enabling marksmen to make informed decisions and engage targets with precision. 3. Intuitive assistance: The system’s user-friendly interface overlays visual cues on detected targets and cross-hair alignment, offering intuitive guidance and facilitating seamless target tracking. In essence, the Precision Targeting Companion represents more than just a technological advancement; it is a testament to the enduring partnership between man and machine, working in harmony to safeguard lives and achieve mission success on the battlefield. Keywords: Precision Targeting Companion, Smart Rifle Scopes, Human-Centric Design, Tactical Engagement, Threat Detection, Predictive Guidance.

TraumaFlow—development of a workflow-based clinical decision support system for the management of severe trauma cases

PurposeThe treatment of severely injured patients in the resuscitation room of an emergency department requires numerous critical decisions, often under immense time pressure, which places very high demands on the facility and the interdisciplinary team. Computer-based cognitive aids are a valuable tool, especially in education and training of medical professionals. For the management of polytrauma cases, TraumaFlow, a workflow management-based clinical decision support system, was developed. The system supports the registration and coordination of activities in the resuscitation room and actively recommends diagnosis and treatment actions.MethodsBased on medical guidelines, a resuscitation room algorithm was developed according to the cABCDE scheme. The algorithm was then modeled using the process description language BPMN 2.0 and implemented in a workflow management system. In addition, a web-based user interface that provides assistance functions was developed. An evaluation study was conducted with 11 final-year medical students and three residents to assess the applicability of TraumaFlow in a case-based training scenario.ResultsTraumaFlow significantly improved guideline-based decision-making, provided more complete therapy, and reduced treatment errors. The system was shown to be beneficial not only for the education of low- and medium-experienced users but also for the training of highly experienced physicians. 92% of the participants felt more confident with computer-aided decision support and considered TraumaFlow useful for the training of polytrauma treatment. In addition, 62% acknowledged a higher training effect.ConclusionTraumaFlow enables real-time decision support for the treatment of polytrauma patients. It improves guideline-based decision-making in complex and critical situations and reduces treatment errors. Supporting functions, such as the automatic treatment documentation and the calculation of medical scores, enable the trauma team to focus on the primary task. TraumaFlow was developed to support the training of medical students and experienced professionals. Each training session is documented and can be objectively and qualitatively evaluated.

UAV Multi-Dynamic Target Interception: A Hybrid Intelligent Method Using Deep Reinforcement Learning and Fuzzy Logic

With the rapid development of Artificial Intelligence, AI-enabled Uncrewed Aerial Vehicles have garnered extensive attention since they offer an accessible and cost-effective solution for executing tasks in unknown or complex environments. However, developing secure and effective AI-based algorithms that empower agents to learn, adapt, and make precise decisions in dynamic situations continues to be an intriguing area of study. This paper proposes a hybrid intelligent control framework that integrates an enhanced Soft Actor–Critic method with a fuzzy inference system, incorporating pre-defined expert experience to streamline the learning process. Additionally, several practical algorithms and approaches within this control system are developed. With the synergy of these innovations, the proposed method achieves effective real-time path planning in unpredictable environments under a model-free setting. Crucially, it addresses two significant challenges in RL: dynamic-environment problems and multi-target problems. Diverse scenarios incorporating actual UAV dynamics were designed and simulated to validate the performance in tracking multiple mobile intruder aircraft. A comprehensive analysis and comparison of methods relying solely on RL and other influencing factors, as well as a controller feasibility assessment for real-world flight tests, are conducted, highlighting the advantages of the proposed hybrid architecture. Overall, this research advances the development of AI-driven approaches for UAV safe autonomous navigation under demanding airspace conditions and provides a viable learning-based control solution for different types of robots.

How voting rules impact legitimacy

Collective action is essential for addressing the grand challenges of our time. However, for such action to be successful, decision-making processes must be perceived as legitimate. In this study, we investigate the legitimacy of different voting methods. Using a pre-registered human subject experiment, 120 participants cast their votes using four voting methods: majority voting, combined approval voting, range voting, and the modified Borda count. These methods represent a range of preference elicitation designs, from low to high complexity and flexibility. Furthermore, we developed a legitimacy scale upon which the participants rate the voting methods. The experiment was conducted in a non-political setting (voting on color preferences) and a political context (voting on COVID-19-related questions). Our findings suggest that the perceived legitimacy of a voting method is context-dependent. Specifically, preferential voting methods are seen as more legitimate than majority voting in a political decision-making situation, but only for individuals with well-defined preferences. Furthermore, preferential voting methods are more legitimate than majority voting in a highly polarized situation.

Research on Green Supply Chain Decision-Making Considering Consumers' Low Carbon Awareness and Cost Sharing

With the global vigorous promotion of the development of green economy, advocating the practice of green concept, sustainable development has become a new problem that the world attaches great importance to. This paper considers the factors of consumers ' low-carbon awareness, discusses the pricing decisions in different situations, and looks for the optimal decisions of manufacturers and retailers. It is found that the enhancement of consumers ' low-carbon awareness is conducive to the increase of supply chain profit, and the retailer 's cost sharing ratio can improve the supply chain profit within a certain range.

Norm violations and behavioral spillovers—Evidence from the lab and the field

This paper explores the contagion effects of norm-violating behavior across decision situations. Through a series of laboratory and field experiments, we empirically establish the conditions under which norm-breaking behavior in one decision situation leads individuals who observe this to violate norms in other, distinct decision situations. Our laboratory findings show that these spillover effects are more pronounced when the norms underlying the decision situations are perceived to be similar. However, spillovers can also affect decision situations governed by relatively dissimilar norms if the observers of norm violations have had the opportunity to first violate the same norm as the observed violator themselves. In an accompanying field experiment, we underscore the economic importance of norm similarity for spillover effects. When workers are exposed to information about celebrities evading taxes, they exhibit significantly higher rates of workplace theft than those in the control group, yet this exposure does not negatively affect work morale.

Social learning with partial and aggregate information: Experimental evidence

In our information cascade experiments, we study social learning in decision-making situations in which decisions “not to do” are unobservable. Subjects, in sequence, choose whether to invest or not, without knowing their position. They observe a private signal and the number of investments made by their predecessors, but not how many predecessors have chosen not to invest. We find that down cascades, in which agents neglect the signal and do not invest, occur, in contrast with the equilibrium predictions. Up cascades, in which agents invest independently of the signal, occur, but less than in equilibrium.

A trust relationship network-based consensus model in large-scale TODIM group decision-making

PurposeIn large-scale group decision-making (LSGDM) situations, existing TODIM group decision-making methods often fail to account for the influence of social network relationships and the bounded rationality of decision-makers (DMs). To address this issue, a new TODIM-based group decision-making method is proposed that considers the current trust relationships among DMs in a large-scale trust relationship network.Design/methodology/approachThis method consists of two main stages. In the first stage, the large-scale group is partitioned into several sub-clusters based on trust relationships among DMs. The dominance degree matrix of each sub-cluster is then aggregated into the large-scale group dominance degree. In the second stage, after aggregating the large-scale group dominance degree, the consensus index is calculated to identify any inconsistent sub-clusters. Feedback adjustments are made based on trust relationships until a consensus is reached. The TODIM method is then applied to calculate the corresponding ranking results. Finally, an illustrative example is applied to show the feasibility of the proposed model.FindingsThe proposed method is practical and effective which is verified by the real case study. By taking into account the trust relationships among DMs in the core process of LSGDM, it indeed has an impact on the decision outcomes. We also specifically address this issue in Chapter Five. The proposed method fully incorporates the bounded rationality of DMs, namely their tendency to accept the opinions of trusted experts, which aligns more with their psychology. The two-stage consensus model proposed in this paper effectively addresses the limitations of traditional assessment-based methods.Originality/valueThis study establishes a two-stage consensus model based on trust relationships among DMs, which can assist DMs in better understanding trust issues in complex decision-making, enhancing the accuracy and efficiency of decisions, and providing more scientific decision support for organizations such as businesses and governments.

Positional Differences in Decision-Making Situations during Professional Rugby League Match-Play.

The aim of this study was to explore the types and frequency of decision-making situations of rugby league players during defensive situations and examine whether they were predictive of key performance indicators (KPI). Fifteen elite rugby league matches were coded using notational analysis methods. Specific defensive situations were analysed, including the number of: one-on-one situations with an opposing attacker (1-on-1), two-on-one situations (2-on-1), and combined 1-on-1 and 2-on-1 situations (i.e., total decisions; TDs). There was no relationship between the game outcome and game KPIs for TDs or 1-on-1 decision-making situations. However, successful tackles and missed tackles were predictive of 2-on-1 decision-making situations. Positional differences revealed that back rowers were exposed to the greatest number of decision-making situations, while wingers had the lowest exposure. The total number of decisions and the number of 1-on-1 decisions made by the centres and wingers were significant predictors of line breaks. Additionally, 2-on-1 decisions were significant predictors of line breaks for backrowers. The findings of this study suggest that the type and frequency of decision-making situations in Rugby League are position specific. Practical applications for coaches are discussed to ensure that practice approaches are representative of the various defensive decision-making demands players experience during a game, based on their position.

“I do not know the advantages of having a general practitioner” - a qualitative study exploring the views of low-acuity emergency patients without a regular general practitioner toward primary care

BackgroundEmergency departments (ED) worldwide have to cope with rising patient numbers. Low-acuity consulters who could receive a more suitable treatment in primary care (PC) increase caseloads, and lack of PC attachment has been discussed as a determinant. This qualitative study explores factors that contribute to non-utilization of general practitioner (GP) care among patients with no current attachment to a GP.MethodQualitative semi-structured telephone interviews were conducted with 32 low-acuity ED consulters with no self-reported attachment to a GP. Participants were recruited from three EDs in the city center of Berlin, Germany. Data were analyzed by qualitative content analysis.ResultsInterviewed patients reported heterogeneous factors contributing to their PC utilization behavior and underlying views and experiences. Participants most prominently voiced a rare need for medical services, a distinct mobility behavior, and a lack of knowledge about the role of a GP and health care options. Views about and experiences with GP care that contribute to non-utilization were predominantly related to little confidence in GP care, preference for directly consulting medical specialists, and negative experiences with GP care in the past. Contrasting their reported utilization behavior, many interviewees still recognized the advantages of GP care continuity.ConclusionUnderstanding reasons of low-acuity ED patients for GP non-utilization can play an important role in the design and implementation of patient-centered care interventions for PC integration. Increasing GP utilization, continuity of care and health literacy might have positive effects on patient decision-making in acute situations and in turn decrease ED burden.Trial registrationGerman Clinical Trials Register: DRKS00023480; date: 2020/11/27.

Research and Development of an Embedded Multi-Channel Sensor Data Acquisition Device for Reservoir Dams

The embedded system of intelligent reservoir dam achieves the integration and efficient utilization of water conservancy dam system data through multi-channel data collection and analysis calculated by computer technology, CNC system, and neural network. Compared with traditional data collection and processing methods, both timeliness and accuracy have been greatly improved. This study aims to develop a multi-channel sensor data acquisition device for reservoir dams based on embedded system technology. This device can collect real-time and efficient data from sensors in various parts of the dam, ensuring the safe operation of the reservoir dam. By using advanced embedded system technology, this device has advantages such as low power consumption, high stability, and real-time data transmission. The Analytic Hierarchy Process (AHP) was used to study the embedded multi-channel sensor data acquisition device for reservoir dams in multiple directions and factors. The AHP method provides an effective means for problem decision-making in complex situations. Referring to the AHP method, the factors that affect reservoir dams can be divided into different levels. Compare the importance of two random factors in each level to obtain a specific quantitative expression of the relative important factors on a scale. Then repeat this step to obtain the weight ranking for different levels. At the same time, the device monitors key parameters such as temperature, humidity, displacement, and pressure in various parts of the dam through multiple sensors, providing strong support for early warning and decision-making of reservoir dams. The results of this study have important practical significance and application value for improving the safety and stability of reservoir dams.

A Prompt for Generating Script Concordance Test Using ChatGPT, Claude, and Llama Large Language Model Chatbots

Medical education always evolves to incorporate more tools for specific needs in assessing clinical reasoning skills. Among these tools, Script Concordance Test (SCT) has a particular importance due to its focus on assessing decision-making in uncertain clinical situations. However, development of SCT items is effortful. Artificial intelligence tools, such as large language models, offer significant benefits. These models are already used for generating multiple-choice questions, and their use in generating SCTs offers great promise. However, this requires well-designed prompts to generate SCTs. This article proposes a generic prompt for the ChatGPT-4, Claude 3, Llama 3, and ChatGPT-4o large language model chatbots to generate SCTs, which can be tailored to various fields of medicine and different stages of medical education. It can help to streamline the development process of SCTs. Initial findings are promising, and there is a need for generating SCTs using large language models and conducting research to assess the quality of SCTs.