Decision-making Process Research Articles

Participatory management and its impact on local development in the canton of La Libertad, Santa Elena province

The objective of this article is to evaluate the impact of participatory management on local development in the canton of La Libertad, Santa Elena province. Several techniques and tools were used to address the challenges identified and ensure the active and equitable participation of all sectors of the community in the decision-making process and project implementation. The study employed descriptive research based on qualitative data collection and analysis, including interviews with key local stakeholders, document review and participant observation. The results reveal that participatory management has had a positive impact on several aspects of local development. First, there has been a strengthening of the social fabric, with greater collaboration and solidarity among local inhabitants. This active participation has generated a sense of belonging and shared identity, fundamental for addressing common challenges and promoting more integrated and sustainable development. The need was identified to strengthen the technical and organizational capacities of local stakeholders, as well as to ensure adequate financial resources and maintain an ongoing commitment to citizen participation.

Human Papillomaviruses 16 and 18 E6 Oncoprotein Detection Test in Primary Oropharyngeal Carcinomas and Metastatic Lymph Nodes: A Cross-Sectional Study.

Accuracy in the diagnosis of HPV-associated oropharyngeal carcinoma (OPSCC) of a rapid, low-cost lateral flow immunochromatographic assay for detecting E6 oncoprotein of HPV-16 and HPV-18 was previously evaluated in a small pilot study. This cross-sectional study aimed to assess on a large case series the sensitivity and specificity of E6 oncoprotein as a diagnostic marker for HPV-associated carcinogenesis in OPSCC. 137 consecutive patients with histologically confirmed OPSCC were enrolled in two hospitals in Northeast Italy. HPV status was determined by PCR for HPV DNA and p16INK4a immunohistochemistry on primary tumor biopsies. An OPSCC was defined as HPV-associated when double positive for high-risk HPV-DNA and p16INK4a overexpression in primary lesion. Cytological samples from primary tumors and metastatic lymph nodes were obtained and tested for HPV16/18 E6 oncoproteins using the lateral flow immunochromatographic assay, which requires between 90 and 120 min to provide a result. Diagnostic performances were calculated as percentage with confidence intervals (CI). Of the 137 OPSCC cases, 68 (49.6%) were HPV-associated, testing positive for both high-risk HPV-DNA and p16INK4a, with HPV16 predominating (82.4%). An average waiting time of 22 days was observed to obtain the results of p16INK4a and HPV-DNA after primary lesions biopsy. In patients with HPV16/18-associated OPSCC, the HPV16/18 E6 oncoprotein was detected in 59 out of 60 cytological samples from the primary lesion (sensitivity: 98.3%; 95% CI: 91.1-100%) and in 45 out of 51 cytological samples from lymph node metastases (sensitivity: 88.2%; 95% CI: 76.1-95.6%). The E6 oncoprotein assay showed a specificity of 100% in both primary tumors and lymph node metastases. The low-cost lateral flow immunochromatographic assay for detecting HPV16/18 E6 oncoproteins confirmed high accuracy for identifying HPV-associated OPSCC, particularly in primary tumors, suggesting its potential as a valuable diagnostic tool in clinical practice. Its rapid diagnostic capability could significantly accelerate the process of treatment decision-making, enhancing the timely management of patients.

Transformative Local Governments: Addressing Social Urban Challenges by Bringing People and Politics Together

As the tier of government closest to people, local government plays a key role in answering local needs and aspirations. However, local governments are often under-resourced, leaving them inadequately responsive to many of these critical local demands. Moreover, accountability to their citizens, which is the core of a well-functioning local democracy, is often lacking because the necessary formal laws and informal norms do not enable citizens to influence the decision-making process in a collaborative, creative, and evidence-based process that promotes collective learning. Through innovative case study analyses from around the world, the 12 contributions in this thematic issue delve into the multifaceted ways in which local governments can bridge the gap between people and politics, offering innovative perspectives on participatory governance, co-creation, and collaborative decision-making for inclusive, safe, resilient, and sustainable cities. The cases showcase innovations and challenges in the urban environment, capturing different ways to bring people and politics together in an attempt to co-create solutions for a sustainable and resilient urban future. The insights provided by this plethora of cases provides lessons that can help revolutionise international, national, and local urban policy to empower local authorities and their local communities to address the increasingly urgent challenges faced by municipalities around the world, ensuring an inclusive, safe, resilient, and sustainable future for the generations to come.

Сенсорный маркетинг в условиях диджитал-экономики

In the context of the increasing complexity of marketing relations associated with the transformation of understanding the role of the consumer, the issues of a deeper study of consumer behavior and its features in the “black box” model are of particular importance, since understanding consumer motives and reactions to marketing incentives will contribute not only to sustainable production and sale of goods, increasing the effectiveness of marketing solutions and maximizing profits in the long run, but also to the fast search and selection of goods by consumers that really meet their needs. The development of sensory marketing, taking into account the emergence of new neuromarketing technologies, meets the challenges of modern society. Previous studies of the influence of sensory stimuli on consumer behavior, as a rule, focused on the use of traditional marketing tools (observation, survey, focus groups, etc.), as a result of which only one-sided results of visible consumer reactions were obtained, which do not always reflect their true intentions to purchase a product. Marketing concepts in the context of changing market relations during the transition to a post-industrial society are examined; identifies the place of sensory marketing in the digital economy and its importance in the transformation of society into a humane community, shows the relationship between sensory marketing and neuromarketing, the use of technological tools which will allow to obtain hidden neurophysiological reactions of consumers to marketing incentives, which will provide a comprehensive an approach to evaluating incentive triggers in the purchase decision-making process. The role of neuroethics in conducting these studies is indicat.

Where is democracy nestled? Reflections on organizational degeneration based on an artists’ cooperative

PurposeThis article presents a compelling case study of a workers’ cooperative in the cultural sector. It offers a unique opportunity to delve into the tensions between managerial and democratic control, particularly in the context of heightened artistic claims. The pivotal role of an accountant in management for nearly two decades sparks a thought-provoking question about the potential for cooperative degeneration.Design/methodology/approachOur research employs a rigorous qualitative method, utilizing semi-structured interviews of six key members and a comprehensive analysis of legal, accounting and other media documents.FindingsOur findings offer a significant perspective, refuting any indications of organizational degeneration. The decision-making processes continue to uphold democratic principles. While the manager and his administrative staff wield substantial authority, this is justified by their duty to preserve the collective. This duty is executed under democratic control, facilitated by information transparency. The low level of democratic participation poses a challenge, but the manager’s initiatives are aimed at addressing this. The effectiveness of this control, however, relies on the active participation of the members, which acts as a strong deterrent against organizational degeneration.Originality/valueThe originality of our contribution lies in our reference to Chester Barnard whose reflections on industrial democracy have been forgotten, reflections linked to his conception of managerial authority. We also highlight the importance of empowering individual members, which leads them to consider the consequences of their actions. As a result, the manager is not placed in a situation where he has to decide alone, as the scope of his unilateral powers is de facto delimited.

Immersive Decision-Making : A Quantitative Analysis of Metaverse Applications in Tourism Marketing

This article investigates the transformative impact of metaverse-based virtualization on tourism marketing and consumer experiences. Through quantitative analysis of data collected via standardized questionnaires, we examine how virtual reality tours, 3D representations, and simulated experiences in the metaverse influence tourist decision-making processes and pre-travel engagement. Our findings reveal that metaverse applications significantly enhance tourists' ability to make informed decisions, reduce travel anxiety, and engage with personalized marketing content. The article demonstrates a positive correlation between virtual destination previews and increased consumer confidence, with 78% of respondents reporting higher satisfaction in their travel choices. Moreover, the study identifies a 35% reduction in pre-travel uncertainty among first-time visitors to unfamiliar destinations who utilized metaverse-based virtual tours. These results underscore the potential of metaverse technology to revolutionize tourism marketing strategies, offering implications for industry practitioners and paving the way for future research in digital tourism experiences.

Plasma proteomic and polygenic profiling improve risk stratification and personalized screening for colorectal cancer

This study aims to identify colorectal cancer (CRC)-related proteomic profiles and develop a prediction model for CRC onset by integrating proteomic profiles with genetic and non-genetic factors (QCancer-15) to improve the risk stratification and estimate of personalized initial screening age. Here, using a two-stage strategy, we prioritize 15 protein biomarkers as predictors to construct a protein risk score (ProS). The risk prediction model integrating proteomic profiles with polygenic risk score (PRS) and QCancer-15 risk score (QCancer-S) shows improved performance (C-statistic: 0.79 vs. 0.71, P = 4.94E–03 in training cohort; 0.75 vs 0.69, P = 5.49E–04 in validation cohort) and net benefit than QCancer-S alone. The combined model markedly stratifies the risk of CRC onset. Participants with high ProS, PRS, or combined risk score are proposed to start screening at age 46, 41, or before 40 years old. In this work, the integration of blood proteomics with PRS and QCancer-15 demonstrates improved performance for risk stratification and clinical implication for the derivation of risk-adapted starting ages of CRC screening, which may contribute to the decision-making process for CRC screening.

Acquiring better load estimates by combining anomaly and change point detection in power grid time-series measurements

In this paper we present novel methodology for automatic anomaly and switch event filtering to improve load estimation in power grid systems. By leveraging unsupervised methods with supervised optimization, our approach prioritizes interpretability while ensuring robust and generalizable performance on unseen data. Through experimentation, a combination of binary segmentation for change point detection and statistical process control for anomaly detection emerges as the most effective strategy, specifically when ensembled in a novel sequential manner. Results indicate the clear wasted potential when filtering is not applied. The automatic load estimation is also fairly accurate, with approximately 90% of estimates falling within a 10% error margin, with only a single significant failure in both the minimum and maximum load estimates across 60 measurements in the test set. Our methodology’s interpretability makes it particularly suitable for critical infrastructure planning, thereby enhancing decision-making processes.

Interaction of Cognitive and Motivational Processes in Asymmetric Preferences for Gains and Losses

An experiment on repetitive decision-making was conducted to explore the interaction between cognitive and motivational processes in decision-making. The gain-loss framing of risky choice options was manipulated to investigate cognition, and participants’ chronic regulatory focus was measured to assess motivation. Risky choice demonstrated interactive effects between gain-loss framing and regulatory focus. Participants made less risky or riskier choices in the gain or loss condition, respectively. Furthermore, promotion or prevention-focused individuals chose the risky option more frequently in the gain or loss condition, respectively. The results for response times showed the main effects for the two factors, with no interaction. These results, along with supplementary post-hoc analyses, provide evidence that cognitive and motivational processes are simultaneously involved in risky decision-making.

Comparison of Criteria Weight Determination Using MEREC and CRITIC Methods in Choosing The Best Student Accommodation with the MOORA Method Case Study: Coventry University

One of the challenges faced by IISMA Awardees and students in general in Coventry University is choosing a comfortable place to live. Although various student accommodations are provided, differences in facilities and considerations from other parties such as parents and friends make the selection process complicated. This study develops a decision support system to help students choose student accommodation objectively without any intervention from others and provides a comparison of the use of different combinations of methods as additional guidance in the decision-making process. Two methods, Method Based on the Removal Effects of Criteria (MEREC) and Criteria Importance Through Intercriteria Correlation (CRITIC), are used to weight the criteria. The Multi-Objective Optimization (MOORA) method is used to determine the best alternative after the weight calculation is known. The results using a combination of the MEREC-MOORA method and a combination of the CRITIC-MOORA method place Alternative 5 (A5) in first place, while the remaining alternatives show a similar ranking order. In this study, scenario testing was also carried out by deleting and adding criteria and alternatives which then provided ranking results with a positive correlation even though different combinations of methods were used in determining the ranking.

Estimating best nanomaterial for energy harvesting through reinforcement learning DQN coupled with fuzzy PROMETHEE under road-based conditions

Energy harvesters based on nanomaterials are getting more and more popular, but on their way to commercial availability, some crucial issues still need to be solved. The objective of the study is to select an appropriate nanomaterial. Using features of the Reinforcement Deep Q-Network (DQN) in conjunction with Fuzzy PROMETHEE, the proposed model, we present in this work a hybrid fuzzy approach to selecting appropriate materials for a vehicle-environmental-hazardous substance (EHS) combination that operates in roadways and under traffic conditions. The DQN is able to accumulate useful experience of operating in a dynamic traffic environment, accordingly selecting materials that deliver the highest energy output but at the same time bring consideration to factors such as durability, cost, and environmental impact. Fuzzy PROMETHEE allows the participation of human experts during the decision-making process, going beyond the quantitative data typically learned by DQN through the inclusion of qualitative preferences. Instead, this hybrid method unites the strength of individual approaches, as a result providing highly resistant and adjustable material selection to real EHS. The result of the study pointed out materials that can give high energy efficiency with reference to years of service, price, and environmental effects. The proposed model provides 95% accuracy with a computational efficiency of 300 s, and the application of hypothesis and practical testing on the chosen materials showed the high efficiency of the selected materials to harvest energy under fluctuating traffic conditions and proved the concept of a hybrid approach in True Vehicle Environmental High-risk Substance scenarios.

Third place visits and associations with walking habit formation: an exploratory study extending Cognitive Chrono-Ethnography

ABSTRACT Third places play a crucial role in urban environments, acting as social spaces distinct from the home and workplace, contributing to citizens’ social and physical health. However, it remains unknown how they contribute to the formation of walking habits during different life phases. Therefore, this conceptual study explores the role of third places for students (n = 3), workers (n = 6), and retirees (n = 2). Building upon past studies that utilized Cognitive Chrono-Ethnography, a mixed-method approach examining people’s in-situ behaviour and decision-making processes, semi-structured interviews questioned the role of third place visits in the process of forming walking habits. The study revealed that students, workers, and retirees may display distinct patterns of walking habits. Moreover, three goal structures were identified, where walking was either considered as a primary goal alongside third place visits, as a secondary goal, or encountered as an incidental activity during third place visits. Furthermore, participants residing in areas with higher walkability were more likely to consciously engage in walking during third place visits. These findings lay the foundation for broader investigations employing the CCE methodology to consolidate and develop the findings made in this conceptual study and promote walking habit formation through third place visits.

Investigating the Determinants of Herd Behavior: an Application of the Hwang–Salmon Method to the Turkish Banking Sector

Efficient financial markets are important for pricing assets at fair value. In an efficient market, investors are rational in the face of fast and accurate information flow, evaluate the information correctly, and reflect it in their pricing decisions. However, particularly in times of crisis and uncertainty, it is observed that some market participants hesitate in their decision-making processes, imitate the behavior of other individuals whom they consider reputable because they cannot rely on their own knowledge and experience, and try to follow the trend. This tendency, which is called herd behavior, destroys market efficiency and prevents correct price formation. Therefore, it is important to identify its determinants. The purpose of the study is analyzing the precense and determinants of herding behavior in the Turkish banking sector during the period 17.10.2017–10.11.2023. Herd behavior is identified using the Hwang–Salmon method, and logistic regression analysis and the Kruskal–Wallis test are applied to identify its determinants. The findings reveal that herding behavior is associated with the rise in risks and returns as well as the fall in interest rates and exchange rates.

Atorvastatin vs Rosuvastatin in the Prevention of Cardiovascular Events: A Systematic Review

The main objective of this study is to compare any differences in the clinical response to rosuvastatin and atorvastatin in patients with cardiovascular disease. PubMed, SCOPUS, Web of Science, Science Direct, and Clinical Key were systematically searched for relevant literature. Rayyan QRCI was employed throughout this comprehensive process. We included eleven studies with a total of 6168 patients; 3231 (52.4%) patients received Atorvastatin, and 2937 (47.6%) received Rosuvastatin. Regarding ACS patients, Rosuvastatin outperformed Atorvastatin in improving laboratory indices and inflammatory markers and lowering LDL. In STEMI patients undergoing PCI, Atorvastatin was linked to less dysfunctional coronary circulation, better coronary microcirculation in patients with STEMI having primary PCI, and may enhance microvascular coronary perfusion immediately following PCI more effectively than a high-dose rosuvastatin preloading. The one study included patients undergoing CABG and did not find any differences between Atorvastatin and Rosuvastatin in preventing post-CABG atrial fibrillation (AF). These results suggest that when developing treatment plans for patients with cardiovascular disease, physicians may be able to combine atorvastatin with rosuvastatin. Cost considerations, tolerability, and patient-specific characteristics should all be taken into account during the decision-making process.

Trust, Contextual Impartiality, and Open-Mindedness in Investor-State Dispute Settlement

Abstract This article analyses impartiality in Investor-State Dispute Settlement (ISDS) by identifying the way that the parties’ trust in arbitrators is constructed. Drawing on the findings of a large-scale empirical project, it questions the applicability of an orthodox judicial doctrine of impartiality to ISDS on the grounds that trust in arbitrators is constructed on a fundamentally different basis from that of trust in judges. The primary feature of a judicial doctrine of impartiality is that trust is founded on an absolutist approach to impartiality which is intended to ensure that judges have no predispositions to parties. In contrast, trust in ISDS is founded on the method of party appointment which is based on a very different assumption—that arbitrators’ predispositions can be valuable and appropriate in the decision-making process. Drawing on empirical findings, moral philosophy, and psychology, this article proposes the idea of contextual impartiality. Under this approach, the question is not whether an arbitrator can meet universal standards of impartiality irrespective of the context within which the arbitrator operates. Rather, the critical distinction is between permissible and impermissible partiality which depends on whether the individual is reasonably expected to act partially because of their circumstances. In this respect, the article identifies open-mindedness as a fundamental feature of a contextual approach to impartiality and a bright line between permissible and impermissible partiality.

Smart Vision Transparency: Efficient Ocular Disease Prediction Model Using Explainable Artificial Intelligence

The early prediction of ocular disease is certainly an obligatory concern in the domain of ophthalmic medicine. Although modern scientific discoveries have shown the potential to treat eye diseases by using artificial intelligence (AI) and machine learning, explainable AI remains a crucial challenge confronting this area of research. Although some traditional methods put in significant effort, they cannot accurately predict the proper ocular diseases. However, incorporating AI into diagnosing eye diseases in healthcare complicates the situation as the decision-making process of AI demonstrates complexity, which is a significant concern, especially in major sectors like ocular disease prediction. The lack of transparency in the AI models may hinder the confidence and trust of the doctors and the patients, as well as their perception of the AI and its abilities. Accordingly, explainable AI is significant in ensuring trust in the technology, enhancing clinical decision-making ability, and deploying ocular disease detection. This research proposed an efficient transfer learning model for eye disease prediction to transform smart vision potential in the healthcare sector and meet conventional approaches’ challenges while integrating explainable artificial intelligence (XAI). The integration of XAI in the proposed model ensures the transparency of the decision-making process through the comprehensive provision of rationale. This proposed model provides promising results with 95.74% accuracy and explains the transformative potential of XAI in advancing ocular healthcare. This significant milestone underscores the effectiveness of the proposed model in accurately determining various types of ocular disease. It is clearly shown that the proposed model is performing better than the previously published methods.

Enormous headward and gully erosion in a floodplain area reclaimed for open-cast lignite mining during the July 2021 flood in the Inde River valley (Western Germany)

BackgroundThe July flood 2021 at the mountain front of the Eifel-Ardennes Mountains and their foreland resulted in the flooding of the lignite mining area of Inden in Germany. The mining activities resulted in large-scale anthropogenic changes to the relief and fluvial system, leading to a landscape that is no longer adjusted to the recent process-response system. This paper concentrates on the Inde River, where lignite mining led to the relocation of a 5-km-long river section. The flood event resulted in the temporary avulsion of the Inde River into the former channel and, ultimately, in the flooding of the open-cast lignite mine Inden.ResultsThe flooding of the open-cast lignite mine Inden led to headwall erosion and enormous sediment mobilisation, mobilising more than half a million cubic metres of sediment within a few hours, forming a 700-m-long deeply incised channel cut. Thereby, the underlying bedrock, near-subsurface man-made structures, and former river channels influence the erosional processes to different degrees. Surface erosion is likely to be the decisive process, and subsurface erosion is likely to play a secondary role. In both cases, former channels and mill ditches were likely impacting the course of erosional processes.ConclusionsDuring high flood events open-cast mining sites in floodplains are endangered by enormous erosion and sediment transport within a short period of time (several hours). Understanding such complex erosion and depositional processes in open-cast mining areas could provide a blueprint for geomorphological processes and hazards in these anthropogenically shaped fluvial landscapes. Further, information on historic impact in the area is crucial to estimate potential risks.

Fault tolerant & priority basis task offloading and scheduling model for IoT logistics

The Internet of Things (IoT) has connected millions of devices to the internet for communication and computation purposes. Due to constraints such as limited battery power, processing capabilities, and storage capacity, IoT devices often rely on remote fog nodes for task execution, a practice known as task offloading. The NP-hard nature of offloading and scheduling IoT tasks onto fog nodes poses significant challenges. Current IoT task offloading often relies on single parameters like device MIPS, RAM, or battery power. Similarly, task allocation on fog nodes frequently prioritizes VM MIPS. To address energy consumption and task failures in IoT systems, robust fault tolerance, efficient task offloading, and effective scheduling are crucial. This paper introduces a novel Fault-Tolerant, Priority-Based Task Offloading and Scheduling Model (FP-TOSM) for IoT logistics. Utilizing the Analytic Hierarchy Process (AHP) for multi-criteria decision-making, IoT tasks are prioritized. Tasks below a specified threshold are executed locally, while those within a defined range are offloaded to a fog node. Tasks exceeding this range are delegated to the cloud. The Euclidean formula is used to determine the proximity between the IoT logistics vehicle and fog nodes for offloading decisions. Dynamic re-clustering fault tolerance mechanisms are utilized to manage task failures after offloading. Successful offloaded tasks are allocated to suitable VMs in the cloud and fog nodes. The proposed strategy selects the most suitable VM for offloaded tasks using a multi-criteria decision-making process to reduce energy consumption, SLA violations, and execution costs. The model's performance is evaluated through simulations in iFogSim2. Results demonstrate reductions in energy consumption by up to 5.8 % and 10.4 %, decreases in SLA violations by up to 25.2 %, and a 16.28 % improvement in response time compared to an ACO-based model with a response time of 17.8 %. Additionally, the task failure ratio shows a significant reduction of up to 22.1 %. These findings highlight the effectiveness of FP-TOSM in enhancing efficiency and reliability within fog computing environments.

GeoGail: A Model-based Imitation Learning Framework for Human Trajectory Synthesizing

Synthesized human trajectories are crucial for a large number of applications. Existing solutions are mainly based on the generative adversarial network (GAN), which is limited due to the lack of modeling the human decision-making process. In this paper, we propose a novel imitation learning based method to synthesize human trajectories. This model utilizes a novel semantics-based interaction mechanism between the decision-making strategy and visitations to diverse geographical locations to model them in the semantic domain in a uniform manner. To augment the modeling ability to the real-world human decision-making policy, we propose a feature extraction model to extract the internal latent factors of variation of different individuals, and then propose a novel self-attention based policy net to capture the long-term correlation of mobility and decision-making patterns. Then, to better reward users’ mobility behavior, we propose a novel multi-scale reward net combined with mutual information to model the instant reward, long-term reward, and individual characteristics in a cohesive manner. Extensive experimental results on two real-world trajectory datasets show that our proposed model can synthesize the most high-quality trajectory data compared with six state-of-the-art baselines in terms of a number of key usability metrics, and can well support practical applications based on trajectory data, demonstrating its effectiveness. Furthermore, our proposed method can learn explainable knowledge automatically from data, including explainable statistical features of trajectories and statistical relation between decision-making policy and features.

Mix and Match: Characterizing Heterogeneous Human Behavior in AI-assisted Decision Making

AI-assisted decision-making systems hold immense potential to enhance human judgment, but their effectiveness is often hindered by a lack of understanding of the diverse ways in which humans take AI recommendations. Current research frequently relies on simplified, ``one-size-fits-all'' models to characterize an average human decision-maker, thus failing to capture the heterogeneity of people's decision-making behavior when incorporating AI assistance. To address this, we propose Mix and Match (M&M), a novel computational framework that explicitly models the diversity of human decision-makers and their unique patterns of relying on AI assistance. M&M represents the population of decision-makers as a mixture of distinct decision-making processes, with each process corresponding to a specific type of decision-maker. This approach enables us to infer latent behavioral patterns from limited data of human decisions under AI assistance, offering valuable insights into the cognitive processes underlying human-AI collaboration. Using real-world behavioral data, our empirical evaluation demonstrates that M&M consistently outperforms baseline methods in predicting human decision behavior. Furthermore, through a detailed analysis of the decision-maker types identified in our framework, we provide quantitative insights into nuanced patterns of how different individuals adopt AI recommendations. These findings offer implications for designing personalized and effective AI systems based on the diverse landscape of human behavior patterns in AI-assisted decision-making across various domains.