Decision-making Process Research Articles

Advances in Exosome Plasmonic Sensxing: Device Integration Strategies and AI-Aided Diagnosis

Exosomes, as next-generation biomarkers, has great potential in tracking cancer progression. They face many detection limitations in cancer diagnosis. Plasmonic biosensors have attracted considerable attention at the forefront of exosome detection, due to their label-free, real-time, and high-sensitivity features. Their advantages in multiplex immunoassays of minimal liquid samples establish the leading position in various diagnostic studies. This review delineates the application principles of plasmonic sensing technologies, highlighting the importance of exosomes-based spectrum and image signals in disease diagnostics. It also introduces advancements in miniaturizing plasmonic biosensing platforms of exosomes, which can facilitate point-of-care testing for future healthcare. Nowadays, inspired by the surge of artificial intelligence (AI) for science and technology, more and more AI algorithms are being adopted to process the exosome spectrum and image data from plasmonic detection. Using representative algorithms of machine learning has become a mainstream trend in plasmonic biosensing research for exosome liquid biopsy. Typically, these algorithms process complex exosome datasets efficiently and establish powerful predictive models for precise diagnosis. This review further discusses critical strategies of AI algorithm selection in exosome-based diagnosis. Particularly, we categorize the AI algorithms into the interpretable and uninterpretable groups for exosome plasmonic detection applications. The interpretable AI enhances the transparency and reliability of diagnosis by elucidating the decision-making process, while the uninterpretable AI provides high diagnostic accuracy with robust data processing by a “black-box” working mode. We believe that AI will continue to promote significant progress of exosome plasmonic detection and mobile healthcare in the near future.

Exploring the impact of landscape design on user preferences in shopping centers post the COVID-19 Pandemic

This study explores the impact of landscape design, which has gained importance alongside architectural changes in shopping centers, and investigates preferences in the post-COVID-19 pandemic era. Although e-commerce has increased during the pandemic, research and societal behaviors suggest that shopping centers will continue to serve as contemporary agoras due to their socialization and leisure functions. However, in response to the pandemic, it is anticipated that newly designed shopping centers will be architecturally conceived as semi-outdoor or outdoor spaces, regardless of climate conditions. This transformation will involve the softening of rigid appearances through landscape elements, making them both ecologically and psychologically sustainable. The number of studies on this subject is limited. To address this knowledge gap, an online survey was conducted with 586 randomly selected users across nine shopping centers located in three major cities of Turkey: Istanbul, Ankara, and Izmir, with three shopping centers in each city. The survey investigated users' preferences for architectural styles (indoor, semi-outdoor, or outdoor) and landscape design in shopping centers. The findings of the study indicate that post-pandemic, outdoor and semi-outdoor shopping centers are likely to be preferred more frequently. An emphasis is likely to be placed on natural landscape elements such as plants and water features in landscape design. Furthermore, shopping center designs are likely to become simpler, offering comfortable and spacious navigation areas while optimizing parking and transportation conditions. This study is expected to shed light on decision-making processes for future shopping centers. By incorporating consumer preferences, architects can enhance the sustainability of shopping center investments through next-generation design.

Neural mechanisms of altruistic decision-making: EEG functional connectivity network analysis.

Altruism is an enigmatic form of prosocial behavior, characterized by diverse motivations and significantinterindividual differences. Studying neural mechanisms of altruism is crucial to identify objective markers of pro- and antisocial tendencies in behavior. This study was designed to delve into the mechanisms of altruism by analyzing EEG-based functional connectivity patterns within the framework of the network approach. To experimentally induce a situation of altruistic decision-making, we employed the Pain versus Gain (PvsG) task, which implies making choices concerning financial self-benefit and pain of the other. Our results reveal that the behavioral measure of altruism in the experiment correlated with emotional empathy, which is in line with the "empathy-altruism" hypothesis. Applying the network approach to EEG functional connectivity analysis, we discovered that the very process of decision-making in the PvsG is characterized by the synchronous activity of structures in the right hemisphere, which are involved in empathy for pain. The prosociality of decisions was reflected in functional connectivity between the rostral ACC and orbital IFG in the left hemisphere and the overall network centrality of the caudal ACC. This finding additionally points to the distinct functional roles of the ACC subregions in altruistic decision-making. The proposed neural mechanisms of altruism can furtherbe used to identify neurophysiological markers of prosociality in behavior.

Identifying students with dyslexia: exploration of current assessment methods.

Early identification plays a crucial role in providing timely support to students with learning disabilities, such as dyslexia, in order to overcome their reading difficulties. However, there is significant variability in the methods used for identifying dyslexia. This study aimed to explore and understand the practices of dyslexia identification in the UK. A survey was conducted among 274 dyslexia professionals, including educational psychologists and dyslexia specialists, to investigate the types of assessments they employ, their approach to utilizing assessment data, their decision-making processes, and their conceptualization of dyslexia. Additionally, the study examined whether these professionals held any misconceptions or myths associated with dyslexia. Analysis of the survey data revealed substantial variability in how professionals conceptualize dyslexia, as well as variations in assessment methods. Furthermore, a significant proportion of the survey respondents subscribed to one or more misconceptions regarding dyslexia; the most common misconception identified among professionals was the belief that children with dyslexia read letters in reverse order. The findings highlight the need for standardized approaches to dyslexia identification and debunking prevailing misconceptions. The implications of these findings are discussed, emphasizing the importance of informed policy and practice in supporting students with dyslexia. Recommendations are provided to enhance consistency and accuracy in dyslexia identification, with the aim of facilitating early intervention and support for affected students.

Financial risk analysis of Public-Private Partnership (PPP) projects case study on Pandaan - Malang Toll Road

This study aims to analyze the financial risks faced by the Pandaan-Malang Toll Road PPP (Public-Private Partnership) project in Indonesia. Data collection techniques include semi-structured in-depth interviews with parties related to the PPP project, including the Directorate General of Infrastructure Financing, the Tolling Road Management Agency (government), and Business Entities (PT Jasamarga Pandaan Malang). The collected data will be analyzed to determine the financial risk factors faced by PPP projects and the financial controls carried out to overcome financial risks in PPP. The results show that the risk of errors in forecasting in the calculation of demand, cost, and revenue projections is a risk that almost often occurs. The Government emphasizes the importance of optimizing the role of PII (Persero) or Indonesia Infrastructure Guarantee Fund (IIGF) as an Infrastructure Guarantee Business Entity (BUPI) to ensure financial sustainability and minimize the impact of sudden shocks on the State Budget due to the provision of Government Guarantees for cooperation projects. This study provides insights or frameworks that can be applied to similar projects, helping to improve risk assessment and decision-making processes in future infrastructure developments.

Team Factors in Ethical Decision Making: A Content Analysis of Interviews with Scientists and Engineers.

The ethical decision making of researchers has historically been studied from an individualistic perspective. However, researchers rarely work alone, and they typically experience ethical dilemmas in a team context. In this mixed-methods study, 67 scientists and engineers working at a public R1 (very high research activity) university in the United States responded to a survey that asked whether they had experienced or observed an ethical dilemma while working in a research team. Among these, 30 respondents agreed to be interviewed about their experiences using a think-aloud protocol. A total of 40 unique ethical incidents were collected across these interviews. Qualitative data from interview transcripts were then systematically content-analyzed by multiple independent judges to quantify the overall ethicality of team decisions as well as several team characteristics, decision processes, and situational factors. The results demonstrated that team formalistic orientation, ethical championing, and the use of ethical decision strategies were all positively related to the overall ethicality of team decisions. Additionally, the relationship between ethical championing and overall team decision ethicality was moderated by psychological safety and moral intensity. Implications for future research and practice are discussed.

Closing the value-based circle in shared decision-making: a digital framework for informing the shared decision-making process through patient reported outcome and experience measures

Closing the value-based circle in shared decision-making: a digital framework for informing the shared decision-making process through patient reported outcome and experience measures

Transplantation, bridging, and support technologies in pulmonary hypertension.

Despite the progress made in medical therapies for treating pulmonary hypertension (PH), a subset of patients remain susceptible to developing a maladaptive right ventricular phenotype. The effective management of end-stage PH presents substantial challenges, necessitating a multidisciplinary approach and early identification of patients prone to acute decompensation. Identifying potential transplant candidates and assessing the feasibility of such a procedure are pivotal tasks that should be undertaken early in the treatment algorithm. Inclusion on the transplant list is contingent upon a comprehensive risk assessment, also considering the specific type of PH and various factors affecting waiting times, all of which should inform the decision-making process. While bilateral lung transplantation is the preferred option, it demands expert intra- and post-operative management to mitigate the heightened risks of pulmonary oedema and primary graft dysfunction in PH patients.Despite the availability of risk assessment tools, the occurrence of acute PH decompensation episodes can be unpredictable, potentially leading to refractory right ventricular failure even with optimal medical intervention, necessitating the use of rescue therapies. Advancements in right ventricular assist techniques and adjustments to graft allocation protocols for the most critically ill patients have significantly enhanced the survival in intensive care, affording the opportunity to endure while awaiting an urgent transplant. Given the breadth of therapeutic options available, specialised centres capable of delivering comprehensive care have become indispensable for optimising patient outcomes. These centres are instrumental in providing holistic support and management tailored to the complex needs of PH patients, ultimately enhancing their chances of a successful transplant and improved long-term prognosis.

‘I’ve just heard that there are people who feel like they need to exercise’: a photo-elicitation study of values and priorities influencing physical activity in a socioeconomically disadvantaged neighbourhood in Uppsala, Sweden

ObjectivePeople living in socioeconomically disadvantaged neighbourhoods in Sweden engage less in physical activity compared with the general population, contributing to an elevated risk of cardiometabolic diseases. To inform targeted and...

Vaccine decision-making among pregnant women: a protocol for a cross-sectional mixed-method study in Brazil, Ghana, Kenya and Pakistan

Maternal immunization is a critical strategy to prevent both maternal and infant morbidity and mortality from several infectious diseases. When the first COVID-19 vaccines became available during the pandemic, there was mixed messaging and confusion amongst the broader public and among those associated with health care systems about the recommendations for COVID-19 vaccinations in pregnancy in many countries. A multi-country, mixed-methods study is being undertaken to describe how vaccine decision-making occurs amongst pregnant and postpartum women, with a focus on COVID-19 vaccines. The study is being conducted in Brazil, Ghana, Kenya, and Pakistan. In each country, participants are being recruited from either 2 or 3 maternity hospitals and/or clinics that represent a diverse population in terms of socio-economic and urban/rural status. Data collection includes cross-sectional surveys in pregnant women and semi-structured in-depth interviews with both pregnant and postpartum women. The instruments were designed to identify attitudinal, behavioral, and social correlates of vaccine uptake during and after pregnancy, including the decision-making process related to COVID-19 vaccines, and constructs such as risk perception, self-efficacy, vaccine intentions, and social norms. The aim is to recruit 400 participants for the survey and 50 for the interviews in each country. Qualitative data will be analyzed using a grounded theory approach. Quantitative data will be analyzed using descriptive statistics, latent variable analysis, and prediction modelling. Both the quantitative and qualitative data will be used to explore differences in attitudes and behaviors around maternal immunization across pregnancy trimesters and the postpartum period among and within countries. Each country has planned dissemination activities to share the study findings with relevant stakeholders in the communities from which the data is collected and to conduct country-specific secondary analyses.

Heatmap Design for Probabilistic Driver Repositioning in Crowdsourced Delivery

This paper proposes the use of heatmaps as a control lever to manage the probabilistic repositioning of independent drivers in crowdsourced delivery platforms. The platform aims to maximize order fulfillment by dynamically matching drivers and orders and selecting heatmaps that trigger the probabilistic flow of unmatched drivers to balance driver supply and delivery requests across the service region. We develop a Markov decision process (MDP) model to sequentially select matching and heatmap decisions in which the repositioning behavior of drivers is captured by a multinomial logit discrete choice model. Because of the curse of dimensionality and the endogenous uncertainty of driver repositioning, the MDP model is solved using a rolling-horizon stochastic lookahead policy. This policy decomposes matching and heatmap decisions into two optimization problems: a two-stage stochastic programming upper bounding problem for matching decisions and a mixed-integer programming problem for heatmap decisions. We also propose a simple policy for efficiently solving large-scale problems. An extensive computational study on instances derived from the Chicago ride-hailing data set is conducted. Computational experiments demonstrate the value of heatmaps in improving order fulfillment beyond the level achieved by matching alone (up to 25%) and identify conditions that affect the benefit of using heatmaps to guide driver repositioning. Funding: The authors gratefully acknowledge the support of the Natural Sciences and Engineering Research Council of Canada through Discovery Grants [Grants RGPIN-2024-04881, RGPIN-2020-04498, and RGPIN-2019-06207] awarded to the first, second, and third authors, respectively. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0418 .

Robust Online Selection with Uncertain Offer Acceptance

Online advertising has motivated interest in online selection problems. Displaying ads to the right users benefits both the platform (e.g., via pay-per-click) and the advertisers (by increasing their reach). In practice, not all users click on displayed ads, while the platform’s algorithm may miss the users most disposed to do so. This mismatch decreases the platform’s revenue and the advertiser’s chances to reach the right customers. With this motivation, we propose a secretary problem where a candidate may or may not accept an offer according to a known probability p. Because we do not know the top candidate willing to accept an offer, the goal is to maximize a robust objective defined as the minimum over integers k of the probability of choosing one of the top k candidates, given that one of these candidates will accept an offer. Using Markov decision process theory, we derive a linear program for this max-min objective whose solution encodes an optimal policy. The derivation may be of independent interest, as it is generalizable and can be used to obtain linear programs for many online selection models. We further relax this linear program into an infinite counterpart, which we use to provide bounds for the objective and closed-form policies. For [Formula: see text], an optimal policy is a simple threshold rule that observes the first [Formula: see text] fraction of candidates and subsequently makes offers to the best candidate observed so far. Funding: Financial support from the U.S. National Science Foundation [Grants CCF-2106444, CCF-1910423, and CMMI 1552479] is gratefully acknowledged.

Wind turbine anomaly detection and identification based on graph neural networks with decision interpretability

Abstract Condition monitoring of wind turbines with supervisory control and data acquisition (SCADA) data has attracted increasing interest due to its great benefits in improving operation reliability and reducing unplanned downtimes of wind turbines. However, existing SCADA data-based studies focus primarily on anomaly detection, and few studies have attempted to identify the underlying causes of the anomaly. To this end, this paper proposes a new system-level wind turbine anomaly detection and identification method based on an emerging graph neural network with decision interpretability (DIGNN). A correlation-based GNN is first used to capture complex inter-sensor correlations in SCADA data. More importantly, a decision interpretability module is designed to further analyze anomaly causes and effects through a twp-step global and local anomaly decision process. It can greatly improve the interpretability of the model and provide more accurate and meaningful decision results for maintenance purposes. The effectiveness and robustness of the proposed model were verified by four fault cases with SCADA datasets from a real wind farm. The experimental results demonstrated that the proposed model can provide earlier warning of anomalies with lower false alarm rates and accurate anomaly identification with good interpretability, providing valuable help for field maintenance.maintenance.

Experiences of Indonesian women with breast cancer underwent treatment decision-making: A qualitative study.

Patients with breast cancer face a complex situation upon receiving their diagnosis and considering future treatment options. In Indonesian culture, relatives and others significantly influence decision-making processes. Understanding the perspectives of Indonesian women with breast cancer regarding treatment decision-making can enhance satisfaction with the care provided. This study aimed to explore the experiences of Indonesian women with breast cancer regarding treatment decision-making. A qualitative descriptive study design was utilized. Purposive sampling was employed to select the study participants. In-depth interviews were conducted with 15 women with breast cancer between March and June 2023. The data were analyzed using thematic analysis. Three main themes were developed: 1) Emotional impact and uncertainty in cancer care, 2) Clear communication and support for patients, and 3) Family-centered decision-making in treatment planning. This study highlights the perspectives of Indonesian women with breast cancer on treatment decision-making. Nurses play a crucial role in providing clear information to patients and their families during the decision-making process. Understanding the experiences of women with breast cancer can help support and empower patients through effective communication while they undergo treatment.

Optimizing agricultural data security: harnessing IoT and AI with Latency Aware Accuracy Index (LAAI)

The integration of Internet of Things (IoT) and artificial intelligence (AI) technologies into modern agriculture has profound implications on data collection, management, and decision-making processes. However, ensuring the security of agricultural data has consistently posed a significant challenge. This study presents a novel evaluation metric titled Latency Aware Accuracy Index (LAAI) for the purpose of optimizing data security in the agricultural sector. The LAAI uses the combined capacities of the IoT and AI in addition to the latency aspect. The use of IoT tools for data collection and AI algorithms for analysis makes farming operation more productive. The LAAI metric is a more holistic way to determine data accuracy while considering latency limitations. This ensures that farmers and other end-users are fed trustworthy information in a timely manner. This unified measure not only makes the data more secure but gives farmers the information that helps them to make smart decisions and, thus, drives healthier farming and food security.

Artificial Punishment Signals for Guiding the Decision-Making Process of an Autonomous System

Somatic markers have been evidenced as determinant factors in human behavior. In particular, the concepts of somatic reward and punishment have been related to the decision-making process; both reward and somatic punishment represent bodily states with positive or negative sensations, respectively. In this research work, we have designed a mechanism to generate artificial somatic punishments in an autonomous system. An autonomous system is understood as a system capable of performing autonomous behavior and decision making. We incorporated this mechanism within a decision model oriented to support decision making on stock markets. Our model focuses on using artificial somatic punishments as a tool to guide the decisions of an autonomous system. To validate our proposal, we defined an experimental scenario using official data from Standard & Poor’s 500 and the Dow Jones index, in which we evaluated the decisions made by the autonomous system based on artificial somatic punishments in a general investment process using 10,000 independent iterations. In the investment process, the autonomous system applied an active investment strategy combined with an artificial somatic index. The results show that this autonomous system presented a higher level of investment decision effectiveness, understood as the achievement of greater wealth over time, as measured by profitability, utility, and Sharpe Ratio indicators, relative to an industry benchmark.

Targeted mechanical and energy absorption properties of 3D printed aluminium metamaterials

The potential of 3D-printed AlSi10Mg auxetic structures for diverse mechanical and energy-absorbing needs remains untapped. This article reveals a multi-criteria framework for the laser powder bed fused (L-PBF) −υ architecture considering elastic modulus (E), yield strength (σy), specific energy absorption (SEA), peak crush force (PCF) and crush force efficiency (CFE). The framework seamlessly combines trial data, multi-criteria decision-making, and performance indicators. Five auxetic structures were 3D-printed, characterised for mechanical and energy absorption traits within a 0.17–0.26 relative density range. The outcomes revealed a range of values for various parameters, including the Poisson’s ratio (−0.03 to −0.22), porosity (80.87–87.60 %), CFE (33–83 %), elastic modulus (100–632 MPa), yield strength (1.8–10 MPa), and SEA (0.5–6.8 kJ/kg). The reliability of these structures was ensured through a meticulous selection process based on an extensive literature review and empirical validation. To address the limitations of theoretical models, our work goes beyond theoretical predictions by experimentally validating these properties and integrating advanced methodologies such as the ‘analytic hierarchy process’ (AHP) and the ‘technique for order of preference by similarity to ideal solution’ (TOPSIS). This allows us to determine the best-performing auxetic architecture. The decision-making process was informed by five user-defined parameters prioritised in the order of CFE>−υ> E>σy> SEA based on their relative closeness identifying AUX5 as the best performing auxetic architecture. This study introduces an innovative method for crafting scenario-based auxetic architectures with varying performance levels based on their relative importance.

Pharmacological and ethical comparisons of lung cancer medicine accessibility in Australia and New Zealand

Gaps in funded cancer medicines between New Zealand and Australia can have significant implications for patients and their families. Pharmac, the New Zealand pharmaceutical funding agency, has been criticised for...

Clinical cases referring to the 2023 EACTS/STS guidelines for diagnosing and treating acute and chronic syndromes of the aortic organ.

Clinical cases referring to the EACTS/STS Guidelines for diagnosing and treating acute and chronic syndromes of the aortic organ aim to assist physicians in selecting the best management strategies for individual patients with a given condition. These expert opinions consider the impact on patient outcomes as well as the risk-benefit ratio of different diagnostic or therapeutic methods. These cases serve as a vital tool to aid physicians in making decisions in their daily practice. However, in essence, although these recommendations serve as a valuable resource to guide clinical practice, their application should be tailored to the needs of the individual patient. Each patient's case is unique, presenting its own set of variables and circumstances. This editorial is a tool designed to support, but not supersede, the decision-making process of physicians, based on their knowledge, expertise and understanding of their patients' individual situations. Furthermore, these clinical cases are based on the EACTS/STS Guidelines for diagnosing and treating acute and chronic syndromes of the aortic organ but should not be interpreted as legally binding documents. The legal responsibilities of healthcare professionals remain firmly grounded in applicable laws and regulations, and the guidelines and the clinical cases presented in this document do not alter these obligations.

Decision as ritual: How power structure affects collective tourism destination decision-making

The optimal destination decision made by a group is the one that perfectly balances group utility maximization with individual satisfaction. The power structure of the group and personal interactions underpinning group decision-making will directly determine individuals’ evaluations of and emotions generated by these decisions. However, research has not sufficiently revealed this pattern, which limits our understanding of how optimal group decisions can be made. By integrating the power perspective with the theory of interactive ritual chains, this study applies the experiential focus group observation method to track the decision-making process. The analysis produces six models of that process, which show how different power structures and leadership influence the joint focus of attention, shared mood and behavioral co-presence in reaching a decision. In summary, leader democracy and effective equality can create a rewarding situation, influencer dominance creates a paradoxical situation, while, multiple leadership, ineffective equality and leader dominance lead to a ritualistic punishment situation.