Complex Decision Research Articles

Assessing ChatGPT's theoretical knowledge and prescriptive accuracy in bacterial infections: a comparative study with infectious diseases residents and specialists.

Advancements in Artificial Intelligence(AI) have made platforms like ChatGPT increasingly relevant in medicine. This study assesses ChatGPT's utility in addressing bacterial infection-related questions and antibiogram-based clinical cases. This study involved a collaborative effort involving infectious disease (ID) specialists and residents. A group of experts formulated six true/false, six open-ended questions, and six clinical cases with antibiograms for four types of infections (endocarditis, pneumonia, intra-abdominal infections, and bloodstream infection) for a total of 96 questions. The questions were submitted to four senior residents and four specialists in ID and inputted into ChatGPT-4 and a trained version of ChatGPT-4. A total of 720 responses were obtained and reviewed by a blinded panel of experts in antibiotic treatments. They evaluated the responses for accuracy and completeness, the ability to identify correct resistance mechanisms from antibiograms, and the appropriateness of antibiotics prescriptions. No significant difference was noted among the four groups for true/false questions, with approximately 70% correct answers. The trained ChatGPT-4 and ChatGPT-4 offered more accurate and complete answers to the open-ended questions than both the residents and specialists. Regarding the clinical case, we observed a lower accuracy from ChatGPT-4 to recognize the correct resistance mechanism. ChatGPT-4 tended not to prescribe newer antibiotics like cefiderocol or imipenem/cilastatin/relebactam, favoring less recommended options like colistin. Both trained- ChatGPT-4 and ChatGPT-4 recommended longer than necessary treatment periods (p-value = 0.022). This study highlights ChatGPT's capabilities and limitations in medical decision-making, specifically regarding bacterial infections and antibiogram analysis. While ChatGPT demonstrated proficiency in answering theoretical questions, it did not consistently align with expert decisions in clinical case management. Despite these limitations, the potential of ChatGPT as a supportive tool in ID education and preliminary analysis is evident. However, it should not replace expert consultation, especially in complex clinical decision-making.

D2 dopamine receptor expression, reactivity to rewards, and reinforcement learning in a complex value-based decision-making task.

Different dopamine (DA) subtypes have opposing dynamics at postsynaptic receptors, with the ratio of D1 to D2 receptors determining the relative sensitivity to gains and losses, respectively, during value-based learning. This effective sensitivity to different reward feedback interacts with phasic DA levels to determine the effectiveness of learning, particularly in dynamic feedback situations where the frequency and magnitude of rewards need to be integrated over time to make optimal decisions. We modeled this effect in simulations of the underlying basal ganglia pathways and then tested the predictions in individuals with a variant of the human dopamine receptor D2 (DRD2; -141C Ins/Del and Del/Del) gene that associates with lower levels of D2 receptor expression (N = 119) and compared their performance in the Iowa Gambling Task to noncarrier controls (N = 319). Ventral striatal (VS) reactivity to rewards was measured in the Cards task with fMRI. DRD2 variant carriers made less effective decisions than noncarriers, but this effect was not moderated by VS reward reactivity as is hypothesized by our model. These results suggest that the interaction between DA receptor subtypes and reactivity to rewards during learning may be more complex than originally thought.

Logic-driven, simulation-based risk engineering to ensure the sustainability of productive processes even with data scarcity

Designing and operating production systems and keeping them up to date at the speed of innovation to meet competition, consumer trends and sustainability requirements is a challenging task. The problem becomes even more challenging when the production system is highly uncertain and data-poor. This complexity is increasingly recognised, as is the value that risk engineering can bring to the design, operation, maintenance and upgrading of production systems to avoid compromising their viability and ensure their continued efficiency.The manuscript is guided by this challenge and has a twofold objective. First, it analyses and explains the importance of adopting a logic-driven and simulation-based risk engineering approach to support decision-making. This objective is achieved by going to the root of the methodological constructs that characterise the methodologies available in the literature and by highlighting the main limitations. Second, it applies the HoRAM method to the use case of food banks to verify its suitability. The use case of food banks was chosen because it is characterised by more and unusual uncertainties compared to conventional production systems (such as uneven labour and raw material availability).The main contributions of this research are as follows. First, it provides a critical analysis of currently available risk assessment methodologies. It highlights the weaknesses and explains the implications of these weaknesses for decision support. Second, it concerns the development of a complete risk engineering process, offering an approach that goes beyond conventional risk assessment approaches that stop at the identification of the critical elements associated with the problem at hand. It explains the importance of closing the risk analysis loop by quantitatively verifying the efficiency of the identified mitigation solutions, thus moving from risk assessment to risk engineering.The results suggest that the proposed approach is suitable for supporting complex decision-making processes characterised by high uncertainty and data scarcity. Furthermore, due to the logic-driven nature of the proposed approach, non-experts can be involved and contribute to the analysis and engineering of risks. This allows to increase the situational awareness of decision makers and consequently their efficiency in making complex decisions.

Determining sample size in a personalized randomized controlled (PRACTical) trial.

In clinical settings with no commonly accepted standard-of-care, multiple treatment regimens are potentially useful, but some treatments may not be appropriate for some patients. A personalized randomized controlled trial (PRACTical) design has been proposed for this setting. For a network of treatments, each patient is randomized only among treatments which are appropriate for them. The aim is to produce treatment rankings that can inform clinical decisions about treatment choices for individual patients. Here we propose methods for determining sample size in a PRACTical design, since standard power-based methods are not applicable. We derive a sample size by evaluating information gained from trials of varying sizes. For a binary outcome, we quantify how many adverse outcomes would be prevented by choosing the top-ranked treatment for each patient based on trial results rather than choosing a random treatment from the appropriate personalized randomization list. In simulations, we evaluate three performance measures: mean reduction in adverse outcomes using sample information, proportion of simulated patients for whom the top-ranked treatment performed as well or almost as well as the best appropriate treatment, and proportion of simulated trials in which the top-ranked treatment performed better than a randomly chosen treatment. We apply the methods to a trial evaluating eight different combination antibiotic regimens for neonatal sepsis (NeoSep1), in which a PRACTical design addresses varying patterns of antibiotic choice based on disease characteristics and resistance. Our proposed approach produces results that are more relevant to complex decision making by clinicians and policy makers.

Survey on Explainable AI: Techniques, challenges and open issues

Artificial Intelligence (AI) has become an important component of many software applications. It has reached a point where it can provide complex and critical decisions in our life. However, the success of most AI-powered applications is based on black-box approaches (e.g., deep neural networks), which can create learned models that are able to predict and make decisions. While these advanced models could achieve high accuracy, they are generally unable to explain their decisions (e.g., predictions) to users. As a result, there is a pressing need for explainable machine learning systems in order to be trustworthy by governments, organizations, industries, and users. This paper classifies and compares the main findings in the domain of explainable machine learning and deep learning. We also discuss the application of Explainable AI (XAI) in sensitive domains such as cybersecurity. In addition, we characterize each reviewed article on the basis of the methods and techniques used to achieve XAI. This, in turn, allows us to discern the strengths and limitations of the existing XAI techniques. We finally discuss some substantial challenges and future research directions related to XAI.

RECORD, a high-throughput, customizable system that unveils behavioral strategies leveraged by rodents during foraging-like decision-making

Translational studies benefit from experimental designs where laboratory organisms use human-relevant behaviors. One such behavior is decision-making, however studying complex decision-making in rodents is labor-intensive and typically restricted to two levels of cost/reward. We design a fully automated, inexpensive, high-throughput framework to study decision-making across multiple levels of rewards and costs: the REward-COst in Rodent Decision-making (RECORD) system. RECORD integrates three components: 1) 3D-printed arenas, 2) custom electronic hardware, and 3) software. We validated four behavioral protocols without employing any food or water restriction, highlighting the versatility of our system. RECORD data exposes heterogeneity in decision-making both within and across individuals that is quantifiably constrained. Using oxycodone self-administration and alcohol-consumption as test cases, we reveal how analytic approaches that incorporate behavioral heterogeneity are sensitive to detecting perturbations in decision-making. RECORD is a powerful approach to studying decision-making in rodents, with features that facilitate translational studies of decision-making in psychiatric disorders.

Short term clinical outcomes of Complex Percutaneous Coronary Intervention in patients with intermediate to high SYNTAX score: Ain Shams complex PCI Registry

Abstract Background Risk stratification is an important and essential component in proper patient counseling when dealing with complex coronary anatomy and decision making between either coronary artery bypass graft (CABG) or percutaneous coronary intervention (PCI). The SYNTAX score was pioneered as an anatomical-based risk score that aided in this decision-making process. Objective To develop an electronic registry for patients with complex coronary anatomy with intermediate to high SYNTAX score undergoing non-emergent PCI who were indicated for CABG but either they were rejected by the surgeons or they refused it. Patients and Methods Our registry was conducted on 193 patients presented during eight months from the first of July 2021 till the end of February 2022 to Ain Shams University Hospitals and underwent PCI. Results comparing the intermediate to high SYNTAX score groups, there is a statistically significant increase in the in hospital and short term MACE and mortality with high SYNTAX score patients, while there was no difference regarding in hospital CIN. Regarding MACE it significantly increases in patients with anemia, CKD, LV dysfunction. For multivariate analysis it is shown that both incomplete revascularization and clinical SYNTAX II are independent predictors for the occurrence of short term MACE. Conclusion Both incomplete revascularization and clinical SYNTAX II score are independent predictors of short term (6 months) occurrence of MACE.

P-060 When is it the sperm’s fault? The incidence of sperm-related poor fertilization after ICSI in 13,859 matched oocyte donation cycles

Abstract Study question What is the true incidence and impact of male factor-related poor fertilization in ICSI cycles when analyzed using matched pairs of high quality donor oocytes? Summary answer In ICSI cycles with donor oocytes, male factor-related poor fertilization occurs in 3.3% of cases and contributes to 86.7% of all poor (<30%) fertilization outcomes. What is known already Opting for oocyte donation is a complex decision for couples, involving significant financial and long-term implications. This decision is typically prompted by presumed female infertility as male fertility diagnostics remain limited. Although ICSI is a highly effective treatment, cases of poor or complete fertilization failure can still occur, even when sperm parameters appear normal. This suggests the presence of unidentified male-related factors. However, the precise incidence of such factors remains poorly understood. This study aims to address this gap, offering a unique perspective, by employing a matched oocyte donation model to investigate the influence of sperm-related factors on fertilization outcomes. Study design, size, duration This retrospective cohort study analyzed 14,928 oocyte donation ICSI cycles, derived from 7,519 ovarian stimulations (OS) of 2,963 unique oocyte donors. The data were from a single center from January 2015 to December 2022. Sibling oocytes from the same OS were utilized for at least two different recipients (n = 13,859), facilitating comparisons under varying paternal conditions. Participants/materials, setting, methods Cases from the same oocyte lot with poor (<30%) and good (>65%) fertilization rates were matched to isolate sperm-related factors influencing fertilization success. Paired t-tests were used to compare outcomes. Ordinary least squares regression was used to isolate variables associated with poor fertilization, such as oocyte status (fresh versus vitrified), sperm origin (partner versus donor) and the presence of severe male factor (defined as a concentration of < 1 million/mL and/or <1% progressive motility). Main results and the role of chance The mean fertilization rate was 73.0% across all cycles analyzed. The incidence of poor fertilization (≤30%) was found to be 3.8% across the entire cohort (532 out of 13,859 cycles). Of these, 86.7% (461/532) cases using the same oocyte lot could be matched to cases with high fertilization rates (>65%) in other recipient cycles (n = 1,441), indicating that poor fertilization was male-related. We observed comparable numbers of inseminated oocytes between matched cases with high and low fertilization (7.23 ± 1.32 and 7.42 ± 1.42, respectively, p = 0.011), while the mean number of fertilized oocytes (1.44 ± 0.77 versus 6.12 ± 1.36, p = <0.001) and mean fertilization rates (20% versus 83%, p < 0.001) differed significantly. Notably, oocyte vitrification and the presence of a severe male factor negatively affected fertilization outcomes (coeff. -0.0898, p < 0.001 and coeff. -0.1500, p < 0.001, respectively). Nevertheless, the majority of poor fertilization cases (92.7%) occurred in the absence of these factors, underscoring the limitations of conventional semen diagnosis tests as predictors of poor fertilization. Across the entire cohort male factor alone accounted for 3.3% (461/13,859) of cycles with <30% fertilization rates. Furthermore, sperm-related factors were the primary cause of poor fertilization outcomes in oocyte donation cycles, accounting for 86.7% of cases. Limitations, reasons for caution Certain variables were not analyzed due to the retrospective nature and extended timeframe of the study. The matched oocyte model may overlook certain non-male factor-related instances of poor fertilization, such as procedural or technical issues. Wider implications of the findings This extensive analysis emphasizes the clinical relevance of male factor in fertilization outcomes, highlighting the need for improved semen diagnosis. It also indicates the importance of considering male-related factors in treatment decisions and shifting the focus from female-centric to more balanced fertility evaluations. Trial registration number Not applicable

ChatGPT on ChatGPT: An Exploratory Analysis of its Performance in the Public Sector Workplace

This study explores the impact of Generative Artificial Intelligence (GenAI), in particular, ChatGPT, on the public sector workforce in the United States, focusing on task replacement, assistance potential, and the evolving landscape of skills. Utilizing GPT-4 to evaluate 1,022 core tasks across 51 public sector occupations, we provide an exploratory analysis of the roles susceptible to ChatGPT automation and those in which ChatGPT can augment human efforts. Our findings reveal that while 63% of tasks are resistant to ChatGPT replacement, primarily due to their requirement for physical presence, emotional intelligence, and complex decision-making, tasks that are routine, rule-based, and involving basic content generation show a high potential for automation. The study also identifies key skills that will remain vital, those likely to become obsolete, and new skills that will emerge as essential, highlighting the need for a strategic approach to workforce development in the face of AI advancements. In particular, our findings underscore the growing importance of skills in applying AI technologies and the ability to validate and interpret AI-generated content for humans to remain competitive. We offer insights into public-sector-specific impacts and propose a methodological framework for future research, emphasizing the importance of adapting educational curricula and policies to prepare for an AI-integrated future.

Exploring the clinical reasoning of an advanced clinical practitioner through a respiratory case encountered in primary care

Advanced clinical practitioners work autonomously, utilising a high degree of complex decision making within situations of clinical uncertainty. This case report examines the clinical reasoning surrounding the decision to refer a patient for a chest X-ray to confirm the presence of lung cancer. Although advocated for within clinical guidelines, the sensitivity of X-ray in the detection of early lung cancer has been questioned, and the value of alternative imaging in the form of low-dose computed tomography proposed. Conditions within clinical practice, including time constraints and fatigue, may negatively impact clinical reasoning. A national move towards 15-minute appointments in primary care would aid this.

Evaluating ChatGPT's moral competence in health care-related ethical problems.

Artificial intelligence tools such as Chat Generative Pre-trained Transformer (ChatGPT) have been used for many health care-related applications; however, there is a lack of research on their capabilities for evaluating morally and/or ethically complex medical decisions. The objective of this study was to assess the moral competence of ChatGPT. This cross-sectional study was performed between May 2023 and July 2023 using scenarios from the Moral Competence Test (MCT). Numerical responses were collected from ChatGPT 3.5 and 4.0 to assess individual and overall stage scores, including C-index and overall moral stage preference. Descriptive analysis and 2-sided Student's t-test were used for all continuous data. A total of 100 iterations of the MCT were performed and moral preference was found to be higher in the latter Kohlberg-derived arguments. ChatGPT 4.0 was found to have a higher overall moral stage preference (2.325 versus 1.755) when compared to ChatGPT 3.5. ChatGPT 4.0 was also found to have a statistically higher C-index score in comparison to ChatGPT 3.5 (29.03 ± 11.10 versus 19.32 ± 10.95, P =.0000275). ChatGPT 3.5 and 4.0 trended towards higher moral preference for the latter stages of Kohlberg's theory for both dilemmas with C-indices suggesting medium moral competence. However, both models showed moderate variation in C-index scores indicating inconsistency and further training is recommended. ChatGPT demonstrates medium moral competence and can evaluate arguments based on Kohlberg's theory of moral development. These findings suggest that future revisions of ChatGPT and other large language models could assist physicians in the decision-making process when encountering complex ethical scenarios.

Rapid genomic testing in critically ill pediatric patients: Genetic counseling lessons from a national program

Genetic counselors (GCs) face unique challenges in the acute care setting. Acute care environments—such as neonatal and pediatric intensive care units—are characterized by urgency, complexity, and rapid decision making. These settings require GCs to navigate a delicate balance between addressing the immediate clinical needs of patients and providing comprehensive genetic information to families, while demanding adaptation of existing skills for practice.Rapid genomic testing (rGT) is increasingly becoming standard of care in acute care. GCs are well placed to support families through the rGT process. Despite this, there is a lack of consistency in the provision of comprehensive acute care genetic counseling globally and a subsequent need for professional guidance in this area.The Acute Care Genomics study piloted a national approach to delivering rGT for infants and children admitted to intensive care units in Australia with suspected genetic conditions between 2018 and 2022. GCs from across Australia were involved in both pre- and post-test counseling for the families of these critically unwell children. Based on our collective experience of delivering this national rGT program, this article provides a discussion of common challenges for health professionals new to delivering rGT in intensive care. We share some practical solutions and make recommendations for supporting families in this area of practice.

Diffusion model of multi-agent collaborative behavior in public crisis governance network based on complex network evolutionary game

In order to explore the phenomenon of diffusion of group decision making formed by the emergence of decision-making behaviors of governance agents in public crisis governance systems, this research uses a complex network evolutionary game approach, considers BA scale-free networks as network vectors of public crisis governance systems, and develops a diffusion model of collaborative governance decision making behaviors. Simulation experiments are also conducted to show the macro-level impact of micro-subjects’ decision-making behavior on group “Emergence-Diffusion”. The results of this study show that the cost of collaborative governance has the most significant effect on the depth and breadth of the spread of collaborative behavior in governance networks. The size of the network determines the speed of network diffusion. The smaller the network size, the more sensitive it is to the spillover benefits of collaborative governance, and the larger the network size, the more sensitive it is to the penalties of non-compliance. The findings of the study have implications for the collaborative behavior of multiple agents in public crisis governance. The main findings are that (1) in order to ensure the stability of the collaborative governance system, decision making options should be selected according to the size of the network. (2) A reasonable penalty mechanism for breach of contract should be set up to avoid the phenomenon of “free-riding” in collaborative governance. (3) Reasonable allocation of collaboration benefits and maintenance of cooperative relationships between nodes in the neighborhood. (4) External regulators should ensure that information in the network is disseminated without barriers and reduce the phenomenon of information asymmetry.

Clinical practice review: outpatient palliative care for the geriatric chronic kidney disease population.

With a growing geriatric population in the United States, there is an increased need for healthcare resources and collaborative care for serious illnesses. Patients with chronic illnesses including chronic kidney disease (CKD) often experience severe symptoms and face complex decisions, many of which develop or occur in the outpatient setting. Though many of these symptoms overlap between different chronic illnesses, the CKD population remains largely untapped in terms of access to said resources; until recently, the focus in palliative care has been largely in the oncologic population. Older patients with CKD may benefit from additional tools and resources provided from collaborative care models specifically involving palliative care, especially as this population is high risk for experiencing lack of support. In this review, we use case vignettes to discuss the key concepts and roles of outpatient palliative care and how they can be integrated into the nephrology care of older patients with advanced kidney disease. These highlighted concepts include shared decision-making, selective deprescribing and symptom management, psychosocial support, and advance care planning. We also review different outpatient models for integrative palliative care, and the roles and resources of the palliative multidisciplinary team within these models and how these models can potentially be implemented in the care of CKD patients.

Winter Storm Severity Index in Alaska: Understanding the Usefulness for Impact-Based Winter Weather Severity Forecast Information

Abstract There is growing interest in impact-based decision support services to address complex decision-making, especially for winter storm forecasting. Understanding users’ needs for winter storm forecast information is necessary to make such impact-based winter forecasts relevant and useful to the diverse regions affected. A mixed-method social science research study investigated extending the winter storm severity index (WSSI) [operational for the contiguous United States (CONUS)] to Alaska, with consideration of the distinct needs of Alaskan stakeholders and the Alaskan climate. Data availability differences suggest the need for an Alaska-specific WSSI, calling for user feedback to inform the direction of product modifications. Focus groups and surveys in six regions of Alaska provided information on how the WSSI components, definitions, and categorization of impacts could align with stakeholder expectations and led to recommendations for the Weather Prediction Center to consider in developing the WSSI Alaska product. Overall, wind (strength and direction) and precipitation are key components to include. Air travel is a critical concern requiring wind and visibility information, while road travel is less emphasized (contrasting with CONUS needs). Special Weather Statements and Winter Storm Warnings are highly valued, and storm trajectory and transition (between precipitation types) information are the important contexts for decision-makers. Alaska is accustomed to and prepared for winter impacts but being able to understand how components (wind, snow, and ice) contribute to overall impact enhances the ability to respond and mitigate damage effectively. The WSSI adapted for Alaska can help address regional forecast needs, particularly valuable as the climate changes and typical winter conditions become more variable. Significance Statement Impact-based support services can assist decision-makers in prioritizing preparedness and mitigation actions related to winter storm events. The winter storm severity index adapted for specific considerations in Alaska (such as including wind and visibility components) can extend winter weather impact-based forecasting’s utility. Additionally, lessons learned from the process of adapting a national product to specific regional needs may inform best practices for gathering stakeholder input and feedback.

Digitalization of Play Activities and Games: Artificial Intelligence in Early Childhood Education

This research study discusses the integration of artificial intelligence (AI) in Early Childhood Education (PAUD) units to improve play and game activities for children as well as innovation in implementing AI-based learning. AI technology has very good prospects and potential in changing teaching and learning activities in educational units with its ability to think and learn like humans, understand human feelings, find solutions in complex decision making, and so on. However, this is sometimes not in line with the application of AI in early childhood education units which are still in their early stages, especially in developing countries like Indonesia. The method used in this research is qualitative research, the data collection technique is from various journal articles about the benefits and impact of AI on early childhood play and play activities. With the support of AI, PAUD units can improve the results of their teaching and learning activities by facilitating more diverse play experiences, increasing operational efficiency, monitoring children's play and play results in real time. Although progress in the application of AI has been seen, there appear to be challenges and shortcomings, including limited AI research in fields outside of STEAM (Science, Technology, Engineering, Arts, and Mathematics), the lack of AI theories and models in play and gaming activities, and resistance in adopting AI in a child's play activities. It is important for PAUD units to integrate AI more optimally in the process of playing and playing activities for children, as well as providing training to educators, education staff and PAUD students to optimize the education system in the future

Private Partner Prioritization for Public–Private Partnership Contracts in a Brazilian Water Company Using a Multi-Criteria Decision Aid Method

Public–private partnerships (PPPs) are long-term contracts between government entities and private companies, and are increasingly being adopted in developing countries due to the large need for investments in sectors such as water and sewerage and also in order to benefit from the experience and to have access to the resources and technology of the private sector. Prioritizing the private party of the contract becomes a complex decision due to the characteristics of PPP contracts, and a standard of evaluation has not been adopted yet, the decision usually being made by evaluating the price. Thus, this research aims to propose a set of criteria to be incorporated into the decision problem that involves technical aspects. It then seeks to rank alternatives by using a multi-criteria decision aid method, FITradeoff, which supports the decision-maker (DM) in prioritization and provides transparency and security to the process.

International market, network, and opportunity selection: A systematic review of empirical research, integrative framework, and comprehensive research agenda

International Market Selection (IMS) is a strategic and complex decision by which firms choose the markets in which to be present. Despite the undisputed academic and managerial relevance of IMS, extant reviews do not include the most recent empirical literature, do not consider different perspectives linked to alternative units of analysis and research domains, and ignore important changes in the international business environment. This research aims to carry out a holistic and systematic assessment of recent IMS empirical research, propose an IMS framework, and provide directions for future research. We contribute to the international business and management literature by updating and upgrading our understanding of IMS, by expanding the IMS conceptualization, proposing an integrative conceptual framework, and developing research propositions, and by suggesting a comprehensive, updated, and radically original research agenda.

A generalized TODIM evaluation approach based on the novel score function and trust network under interval-valued hesitant fuzzy environment

In multi-attribute group decision-making (MAGDM), the social relationships among group members, professional backgrounds and psychological behaviors of decision makers are important factors for achieving high-quality group decisions. However, realizing the consideration of these factors in fuzzy and complex group decision-making scenarios is an important and urgent issue. In MAGDM problems, interval-valued hesitant fuzzy sets (IVHFSs) can better show the hesitant and fuzzy uncertainty information because they are an extension of fuzzy sets. This paper focuses on the score function of IVHFSs and develops a generalized TODIM evaluation method utilizing the novel score functions of IVHFEs and trust networks. The developed method effectively realizes the application of these factors in fuzzy and complex group decision making scenarios. In this contribution, a new deviation function for IVHFSs is created in this paper. It takes into account two types of deviation that are common in IVHFSs: the ambiguity of interval width and the hesitation and uncertainty of multiple intervals. In the sequel, a novel score function for IVHFSs is developed with help of the novel deviation function and the traditional score function. Concerning the perspective of group consistency, a model for determining expert weights is constructed using quality parameters, which combine the novel score function with the trust network. A subjective and objective comprehensive weighting model is made to get attribute weights based on how certain decision information is and the experts’ subjective experience. This is done because the more uncertain something is, the higher the risk. Eventually, we develop a generalized TODIM technique for MAGDM based on the novel score function and trust network in IVHFS environment. The developed approach is demonstrated with a preventive engineering decision-making problem. Sensitivity and comparative analyses are implemented to demonstrate the superiority, stability, and validity of the proposed method in this paper. According to the findings of this study, we conclude that the proposed methodology offers a comprehensive and systematic approach to evaluate MAGDM in the context of IVHFS with trust networks.

Electroencephalographic Classification Reveals Atypical Speech Motor Planning in Stuttering Adults.

This study explores speech motor planning in adults who stutter (AWS) and adults who do not stutter (ANS) by applying machine learning algorithms to electroencephalographic (EEG) signals. In this study, we developed a technique to holistically examine neural activity differences in speaking and silent reading conditions across the entire cortical surface. This approach allows us to test the hypothesis that AWS will exhibit lower separability of the speech motor planning condition. We used the silent reading condition as a control condition to isolate speech motor planning activity. We classified EEG signals from AWS and ANS individuals into speaking and silent reading categories using kernel support vector machines. We used relative complexities of the learned classifiers to compare speech motor planning discernibility for both classes. AWS group classifiers require a more complex decision boundary to separate speech motor planning and silent reading classes. These findings indicate that the EEG signals associated with speech motor planning are less discernible in AWS, which may result from altered neuronal dynamics in AWS. Our results support the hypothesis that AWS exhibit lower inherent separability of the silent reading and speech motor planning conditions. Further investigation may identify and compare the features leveraged for speech motor classification in AWS and ANS. These observations may have clinical value for developing novel speech therapies or assistive devices for AWS.