Decision-making Process Research Articles

Cost-effective and optimal control analysis for mitigation strategy to chocolate spot disease of faba bean

Faba bean is one of the most important grown plants worldwide for human and animal. Despite its various importance, the productivity of faba bean has been constrained by several biotic and abiotic factors. Many faba bean pathogens have been reported so far, of which the most important yield limiting disease is Chocolate Spot Disease (Botrytis fabae). The dynamics of disease transmission and decision-making processes for intervention programs for disease control are now better understood through the use of mathematical modeling. In this paper a deterministic mathematical model for Chocolate Spot disease (CSD) on faba bean plant with an optimal control model was developed and analyzed to examine the best strategy in controlling CSD. The optimal control model is developed with three control interventions, namely prevention (u1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$u_{1}$$\\end{document}), quarantine (u2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$u_{2}$$\\end{document}) and chemical control (u3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$u_{3}$$\\end{document}). The Pontryagin’€™s maximum principle isused to derive the Hamiltonian, the adjoint variables, the characterization of the controls and the optimality system. A cost-effective approach is chosen from a set of possible integrated strategies using the incremental cost-effectiveness ratio (ICER). The forward-backward sweep iterative approach is used to run numerical simulations. We obtained the Hamiltonian, the adjoint variables, the characterization of the controls and the optimality system. The numerical results demonstrate that each integrated strategy can reduce the diseases within the specified period. However due to limited resources, an integrated strategy prevention and uprooting was found to be a best cost-effective strategy to combat CSD. Therefore, attention should be given for the integrated cost-effective and environmentally eco-friendly strategy by stake holders and policy makers to control CSD and disseminate the integrated intervention to the farmers in order to fight the spread of CSD in the Faba bean population and produce the expected yield from the field.

Channel structures of third-party platforms

The growing prominence of third-party (3P) platforms in the online retail sector has made the selection of an appropriate channel structure strategy a critical concern for manufacturers, 3P platforms, and retailers operating within this environment. We construct a Stackelberg game model with the manufacturer as the leader, evaluating four channel structures: (A) reselling and retailer agency selling, (B) agency selling and retailer agency selling, (C) reselling and agency selling, and (D) reselling, agency selling, and retailer agency selling. The supply chain can adopt two pricing strategies: uniform pricing (UP) and differential pricing (DP). Research indicates that expanding the number of channels may not always enhance profitability for suppliers and supply chain participants. Under strategy UP, the 3P platform opts for structure B, whereas the retailer favors structure C. Incorporating agency selling is advantageous for the manufacturer; however, structure A represents the least beneficial option for the supply chain. Under strategy DP, a substantial portion of the pareto optimal region is present. Intense competition and reduced agency fees lead both the supply chain as a whole and individual member to prefer structure A. The addition of a reselling or retailer agency channel boosts the manufacturer's profits, and benefits the 3P platform, while structure D emerges as the least favorable option for the retailer. Furthermore, under strategy DP, structure C is rendered obsolete, signifying the retailer's essential role in the supply chain to the advantage of all involved parties. Our study advances the scholarly understanding of sales models and platform economies by offering valuable insights into the decision-making processes of manufacturers, third-party (3P) platforms, and retailers regarding channel structure choices in a 3P platform environment.

Resilience enhancement for interdependent power systems by AI-assisted disaster response

The failure of urban Electrical Distribution Systems (EDSs) can lead to direct or indirect consequences for their interdependent critical infrastructures, for example, the hospitals, the fire department, etc., negatively affecting the well-being of citizens in the city. To improve the resilience of the coupled power network, the Operational Interdependence Simulator (OIS) is proposed to help prioritize the queue of failure recovery and Minimum Spanning Tree and Shortest Path Tree topology reconfiguration to minimize critical load downtime. Additionally, AI agents are introduced to assist decision-making processes. By utilizing machine learning and training in thousands of offline scenarios, the system can be used for real-time calculation of the optimal recovery paths when given the number and location of electrical faults. The IEEE 70-node distribution system case validated the high prediction accuracy and post-disaster topology reconfiguration while considering the interdependencies.

A low-carbon supply chain pricing mechanism considering CSR under carbon cap-and-trade policy.

In the context of environmental deterioration and people's growing environmental protection awareness, governments or regions have put forward corresponding carbon emission reduction policies. Among them, the carbon trading mechanism, as an effective means to promote enterprises to implement emission reduction measures, plays a crucial role in regulating enterprise behavior and promoting social sustainable development. Since various industries and sectors support each other in social and economic development, it is more reasonable to study the carbon emission reduction optimization decisions of society and enterprises from the perspective of the supply chain. To achieve the carbon reduction target of the supply chain system, manufacturing enterprises usually need to incur additional costs to invest in emission reduction technologies, and retail enterprises also need to conduct low-carbon publicity to increase product market share. On one hand, considering the impact of the government's emission reduction constraints and consumers' low-carbon preferences, manufacturers will take corporate social responsibility (CSR) into consideration to enhance product competitiveness. On the other hand, smaller retailers are more concerned about being treated fairly than about their own profits due to the extra cost of low-carbon advertising. In this paper, considering the background of carbon trading, the manufacturer's CSR and retailer's fairness concern behavior are introduced into the decision-making process of the low-carbon supply chain (LCSC), and the relevant emission reduction decision-making model is constructed by using Stackelberg game theory and backward derivation method. Through comparative analysis of relevant parameters, members' profits and utilities, this paper focuses on the influence of CSR and fairness concerns on system decision-making. The results show that the optimal way for LCSC decision-making is to cooperate with fair-concerned retailers and manufacturers with CSR. When manufacturers consider social responsibility within a certain range and retailers bear part of the cost of social responsibility as followers, it can not only effectively improve the emission reduction level of the supply chain and the profits of each entity, but also help to increase the enthusiasm of each entity for carbon emission reduction and the overall social welfare.

Modeling foot-and-mouth disease dissemination in Rio Grande do Sul, Brazil and evaluating the effectiveness of control measures

IntroductionFoot-and-mouth disease (FMD) affects multiple food-animal species and spreads rapidly among ungulate populations, posing significant challenges for disease control. Understanding the dynamics of FMD transmission and evaluating the effectiveness of control measures are critical for mitigating its impact. This study introduces a multiscale compartmental stochastic model to simulate FMD spread and assess countermeasures.MethodsWe developed a model that integrates population dynamics, including births, deaths, and species-specific transmission dynamics, at both the between-farm and within-farm levels. Four scenarios were created to evaluate different control strategies: the base scenario included vaccinating 20 farms and depopulating four infected farms, while alternative scenarios increased vaccination and depopulation capacities or omitted vaccination altogether.ResultsOur simulations showed that bovines were the most frequently infected species, followed by swine and small ruminants. After 10 days of initial spread, the number of infected farms ranged from 1 to 123, with 90.12% of simulations resulting in fewer than 50 infected farms. Most secondary spread occurred within a 25 km radius. An early response to control actions significantly reduced the time spent managing outbreaks, and increasing daily depopulation and vaccination capacities further enhanced control efforts.DiscussionEmergency vaccination effectively reduced the magnitude and duration of outbreaks, while increasing depopulation without vaccination also eliminated outbreaks. These findings highlight the importance of rapid response and capacity scaling in controlling FMD outbreaks, providing valuable insights for future decision-making processes in disease management.

DETECTING CHANGES IN COMFORT, PAIN, AND MOBILITY OVER CLINICAL MILESTONES FOR INDIVIDUALS WITH LOWER LIMB LOSS

BACKGROUND: Functional mobility, comfort and the absence of pain are key goals of prosthetic treatment. Outcome measures (OMs) evaluate the impact of treatment and normative and minimal detectable change (MDC) values are key to interpreting these scores and measuring treatment outcomes. OBJECTIVES: This study seeks to 1) present practice-based normative values of four commonly used OMs at four prosthetic milestones and 2) explore the MDC of the measures over the treatment period. METHODOLOGY: A chart review was conducted of OMs collected with individuals with lower limb loss between January 1, 2015, and December 31, 2023. This included data for individuals with unilateral transtibial (TT), transfemoral (TF) and rotationplasty (RP) amputations and bilateral transtibial amputation (BTT). OMs included the Socket Comfort Score (SCS), Pain Scale (PS), 2 Minute Walk Test (2MWT), and Prosthetic Limb Users Survey of Mobility (PLUS-M). Data were collected at four milestone time points: (1) Baseline and (2) Discharge from Rehabilitation for those in initial prosthetic rehabilitation, and (3) Initial Evaluation and (4) Definitive Delivery for those receiving a replacement socket. Normative values and MDC values were calculated. FINDINGS: Data from 30 individuals undergoing in-patient rehabilitation and 74 individuals receiving a replacement socket were included. Practice-based normative data were different for each level of amputation and milestone and had the following ranges: SCS: 5.7 – 9.1, PS: 0.8 – 3.7, 2MWT: 68.4 – 146.3 m and PLUS-M: 38.9 – 57.3. MDC values also varied based on time in treatment (Rehabilitation: SCS = 2.5, PS = 1.6, 2MWT = 32.6, PLUS-M = 8.8; Replacement Socket: SCS = 3.1, PS = 2.6, 2MWT = 38.9, PLUS-M = 4.0). All measures had a statistically significant change over the intervention, however, no average scores changed by greater than the MDC. CONCLUSIONS: The normative data and MDC scores demonstrate the PS & PLUS-M are useful measures of pain and mobility at all points within treatment. The 2MWT is indicated for individuals in rehabilitation, while the SCS is indicated for those receiving a replacement socket, as both effectively measure treatment goals that are particularly important for each phase of rehabilitation. This provides clinicians with practice-based evidence that enables them to interpret OM scores, a critical part of the decision-making process along the treatment journey. Layman's Abstract When someone receives a prosthesis, the key goals are to promote mobility and comfort and to reduce pain. Outcome measures are questionnaires or performance-based tests that measure the impact of prosthetic treatment. The scores are interpreted by comparing them to normal values and assessing changes over time against a predetermined threshold for what constitutes a significant amount of change. This study analyzed outcome measure scores collected in clinical practice over a 9-year period, from 2015 - 2023. We included individuals with lower limb loss and presented normal values along with threshold change values taken from a clinical setting for four commonly used measures that assess comfort, pain, and mobility. The data included 30 individuals currently attending initial rehabilitation and 74 individuals who had prior experience using a prosthesis. All the normal values were different for each level of amputation and each milestone within rehabilitation. Suggestions are provided regarding which outcome measures are most useful for assessing changes at different points in time. By providing clinicians with evidence based on clinical practice, we can help them use outcome measures to inform decision-making throughout the rehabilitation journey. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/43890/33243 How To Cite: Pousett B.M, Harasym C.C, Rapaport M.S, Richardson T, Spellen J, Moe D.W, et al. Detecting changes in comfort, pain, and mobility over clinical milestones for individuals with lower limb loss. Canadian Prosthetics & Orthotics Journal. 2024; Volume 7, Issue 1, No.7. https://doi.org/10.33137/cpoj.v7i1.43890 Corresponding Author: Brittany Mae Pousett, CP(C), MSc.540 SE Marine Dr, Vancouver, BC, Canada, V5X 2T4Tel: 604.321.1115E-Mail: brittany@pousett.caORCID ID: https://orcid.org/0000-0002-2272-7847

A novel scoring system proposal to guide surgical treatment indications for high grade gliomas in elderly patients: DAK-75.

High-grade gliomas are the most prevalent neurooncological desease in adults, their incidence increases with age, peaking in the seventh decade. This paper aims to address how to select patients for surgical resection by identifying pre-surgical predictors of 12-month mortality in newly diagnosed HGG patients aged ≥ 75years. A prognostic score will be proposed to guide surgical decisions based on expected survival. Retrospective observational single-center cohort study was carried out at the "Città della Salute e della Scienza-Molinette" University Hospital in Turin, Italy. All consecutive patients aged ≥ 75years newly diagnosed with HGG were included, regardless of whether they underwent surgical resection. Clinical, radiological, histological and molecular data were collected.Variables potentially available at the time of diagnosis were considered to develop a multivariable logistic regression predictive model, with 12-months overall survival as the dependent variable. 102 patients aged 75years or older received a new diagnosis of high-grade glioma, of whom 68 underwent surgical resection. Patients undergoing surgery were slightly younger (76.9 vs 79.0years, p = 0.007) and had better performance status (median KPS 80 vs 70). Most tumors undergoing surgery were localized in cortical or subcortical non-motor areas (p < 0.001) and less frequently deep-seated (p = 0.023) or multifocal (p < 0.001). A predictive model, the DAK-75 score, was developed: the AUROC of the final model was 0.822 (95% CI 0.741-0.902). The score includes clinical presentation, tumor location, and KPS, ranging from 0 to 20, categorizing risk scores into low-risk and high-risk groups (< or > 8). Higher scores corresponded to fewer surgical patients and higher one-year mortality rates (92.2% vs 47.1%, p < 0.001). DAK-75 score may represent a valuable tool in the decision-making process for neurosurgical intervention in elderly patients diagnosed with HGG. Further studies are needed to externally and prospectively validate the scoring system.

Russia in the politics of South Caucasus countries after 2022

The research question is how national governments and civil societies reflect Russian involvement in the decision-making process of South Caucasus countries in 2022–2024? The structure of the research includes two sections: 1) Strategic interests of the countries of the South Caucasus and Russia in 2022–2024; 2) Changing the role of Russia in the South Caucasus in 2022–2024. In result of the research it was revealed that opting for a policy of normalization Georgia cannot renounce its territory (Abkhazia and South Ossetia). Azerbaijan has been the most consistent among the Caucasian countries in challenging Russian hegemony in the region. This trend intensified after the beginning of Russia's full-scale invasion of Ukraine: Azerbaijan increased humanitarian aid to Ukraine; pro-Ukrainian sympathies in Azerbaijani society were expressed through a number of leading media outlets that openly supported Kyiv. Baku unilaterally reviewed the ceasefire agreement - 2020, mediated by Russia. Amid the international isolation of the Kremlin, Azerbaijan began to play a much larger role for Russia. Baku is important link in the chain of diplomatic and economic ties of Putin's regime. Russia tries to maintain productive relations with Baku, even at the expense of its long-time ally Armenia. Yerevan revises its foreign policy, favouring a pro-Western vector. In Yerevan, the pro-Russian opposition organized rallies against the current government. Moscow and Baku both gained mutual benefits: Russia destabilized the situation in Armenia, and Azerbaijan restored its territorial integrity. However, these actions of Moscow struck at its influence in the region, marginalizing Russia and its "guarantees." The Caucasus provides a clear example of Russia's inability to continue playing the role of a major power: it simply lacks the strength and means.

Key Factors in the Design of Urban Underground Metro Lines

Designing sustainable underground metro lines in dense urban environments is a highly challenging task that requires the collaboration of numerous stakeholders and consultants to make crucial decisions influenced by several factors. While it is impossible to address every issue influencing the decision-making process, identifying key factors and their interdependencies is essential for optimal design. This study focuses on six critical aspects of the reference design of metro systems: (1) track alignment, (2) tunneling strategy, (3) station typology, (4) operations and maintenance, (5) procurement strategy, and (6) environmental aspects. Amongst these aspects, we identify track alignment as the primary driving factor that influences the other factors. We analyze the decision between shallow and deep alignments as an engineering choice that necessitates balancing conflicting factors and constraints. Our contribution lies in mapping these factors and their dependencies, thus offering policymakers, project managers, and designers a framework to navigate the design process. Our discussion also provides guidance to public agencies in tendering for design teams more efficiently. Drawing from lessons learned by experienced design managers, this study aims to fill the gap in the literature by offering a generalist perspective on metro design.

Multi-UAV Escape Target Search: A Multi-Agent Reinforcement Learning Method

The multi-UAV target search problem is crucial in the field of autonomous Unmanned Aerial Vehicle (UAV) decision-making. The algorithm design of Multi-Agent Reinforcement Learning (MARL) methods has become integral to research on multi-UAV target search owing to its adaptability to the rapid online decision-making required by UAVs in complex, uncertain environments. In non-cooperative target search scenarios, targets may have the ability to escape. Target probability maps are used in many studies to characterize the likelihood of a target’s existence, guiding the UAV to efficiently explore the task area and locate the target more quickly. However, the escape behavior of the target causes the target probability map to deviate from the actual target’s position, thereby reducing its effectiveness in measuring the target’s probability of existence and diminishing the efficiency of the UAV search. This paper investigates the multi-UAV target search problem in scenarios involving static obstacles and dynamic escape targets, modeling the problem within the framework of decentralized partially observable Markov decision process. Based on this model, a spatio-temporal efficient exploration network and a global convolutional local ascent mechanism are proposed. Subsequently, we introduce a multi-UAV Escape Target Search algorithm based on MAPPO (ETS–MAPPO) for addressing the escape target search difficulty problem. Simulation results demonstrate that the ETS–MAPPO algorithm outperforms five classic MARL algorithms in terms of the number of target searches, area coverage rate, and other metrics.

Patient trust in the use of machine learning-based clinical decision support systems in psychiatric services: A randomized survey experiment.

Clinical decision support systems (CDSS) based on machine-learning (ML) models are emerging within psychiatry. If patients do not trust this technology, its implementation may disrupt the patient-clinician relationship. Therefore, the aim was to examine whether receiving basic information about ML-based CDSS increased trust in them. We conducted an online randomized survey experiment in the Psychiatric Services of the Central Denmark Region. The participating patients were randomized into one of three arms: Intervention=information on clinical decision-making supported by an ML model; Active control=information on a standard clinical decision process, and Blank control=no information. The participants were unaware of the experiment. Subsequently, participants were asked about different aspects of trust and distrust regarding ML-based CDSS. The effect of the intervention was assessed by comparing scores of trust and distrust between the allocation arms. Out of 5800 invitees, 992 completed the survey experiment. The intervention increased trust in ML-based CDSS when compared to the active control (mean increase in trust: 5% [95% CI: 1%; 9%], p=0.0096) and the blank control arm (mean increase in trust: 4% [1%; 8%], p=0.015). Similarly, the intervention reduced distrust in ML-based CDSS when compared to the active control (mean decrease in distrust: -3%[-1%; -5%], p=0.021) and the blank control arm (mean decrease in distrust: -4% [-1%; -8%], p=0.022).No statistically significant differences were observed between the active and the blank control arms. Receiving basic information on ML-based CDSS in hospital psychiatry may increase patient trust in such systems.

Illuminating Salient Contributions in Neuron Activation with Attribution Equilibrium.

With the remarkable success of deep neural networks, there is a growing interest in research aimed at providing clear interpretations of their decision-making processes. In this paper, we introduce Attribution Equilibrium, a novel method to decompose output predictions into fine-grained attributions, balancing positive and negative relevance for clearer visualization of the evidence behind a network decision. We carefully analyze conventional approaches to decision explanation and present a different perspective on the conservation of evidence. We define the evidence as a gap between positive and negative influences among gradient-derived initial contribution maps. Then, we incorporate antagonistic elements and a user-defined criterion for the degree of positive attribution during propagation. Additionally, we consider the role of inactivated neurons in the propagation rule, thereby enhancing the discernment of less relevant elements such as the background. We conduct various assessments in a verified experimental environment with PASCAL VOC 2007, MS COCO 2014, and ImageNet datasets. The results demonstrate that our method outperforms existing attribution methods both qualitatively and quantitatively in identifying the key input features that influence model decisions.

Searching for Justice: Indigenous Self-Determination over the Landfill Search as a Matter of Justice for MMIWG2S

“Search the Landfill” is an Indigenous-led movement for justice for missing and murdered Indigenous women, girls, and Two-Spirit people (MMIWG2S) which originated through Indigenous resistance to police racism in Winnipeg, Canada. The movement calls upon all levels of government to support and execute the search of the Prairie Green Landfill, just north of the City of Winnipeg, to recover the bodies of at least two of four First Nations women who were murdered in early 2022 at the hands of a white male serial killer. Through an analysis of the news articles, press releases, and reports, this paper explores the way in which Indigenous leaders resisted and rewrote the discourses that surrounded the search. By at once condemning the Winnipeg Police Service’s refusal to search the landfill and rejecting police authority and control over the decision-making process, Indigenous women who became the leaders of this movement crafted the space to articulate for themselves how and why the search must be done as a matter of Indigenous rights. This paper explores how Indigenous leadership offers glimpses of what a decolonizing approach to justice may look like in cases of MMIWG2S and which, in turn, invites further opportunities to problematize, subvert, and move beyond the Western legal norms and traditions.

Mapping Data to Concepts: Enhancing Quantum Neural Network Transparency with Concept-Driven Quantum Neural Networks

We introduce the concept-driven quantum neural network (CD-QNN), an innovative architecture designed to enhance the interpretability of quantum neural networks (QNNs). CD-QNN merges the representational capabilities of QNNs with the transparency of self-explanatory models by mapping input data into a human-understandable concept space and making decisions based on these concepts. The algorithmic design of CD-QNN is comprehensively analyzed, detailing the roles of the concept generator, feature extractor, and feature integrator in improving and balancing model expressivity and interpretability. Experimental results demonstrate that CD-QNN maintains high predictive accuracy while offering clear and meaningful explanations of its decision-making process. This paradigm shift in QNN design underscores the growing importance of interpretability in quantum artificial intelligence, positioning CD-QNN and its derivative technologies as pivotal in advancing reliable and interpretable quantum intelligent systems for future research and applications.

Assessing the Impact of Technology on the Efficiency and Effectiveness of Investment Banking Services

This paper evaluates the effect of innovation on the effectiveness and adequacy of venture managing an account administrations. It reflects on the way digital innovations such as artificial intelligence, blockchain, and data management systems have transformed such missions as securities underwriting, mergers and acquisitions, and client advisory services. By combining these technologies, investment banks optimize their processes, improve precision, and bring in enhanced customer experiences. Deliberating through critical review of literature and specific cases from the industry, this paper explores how artificial intelligence changes the decision-making processes, automates trading strategies, and enhances risk management, while blockchain changes back-office operations by making transactions faster and more secure. Advanced data management tools equally provide improved insight into the enhanced client services and decisions in investments for the banks. Although this brings cost-cutting and operational efficiency, these technologies pose challenges in cybersecurity and regulatory compliance due to the expansion of digital platforms, which provide increased opportunities for cyber-attacks and a need for strict adherence to ever-changing regulations. The paper provides a panoramic view of the current and future role of technology in evolving investment banking.

Scientification or political legitimation? Tracing the concepts of evidence and context in Nordic school reforms

ABSTRACT Presenting education reforms as evidence-based has become a global trend. This article investigates how evidence is selected and utilised, focusing on the role of academic research in recent school reforms in Denmark, Finland, and Norway. By analysing a dataset of fifteen interviews of policymakers and applying Sobe, Noah. W., and Jamie A. Kowalzcyk's (2012. “The Problem of Context in Comparative Education Research.” ECPS Journal 6:55–74), concept of Context, the findings reveal that experts and committee members selected different types of evidence for various reform topics based on their professional backgrounds, roles, positions, and the specific issues at hand. The decision-making processes were complex negotiations involving people from diverse networks and professions who interpret global ideas within local contexts. Consequently, the choice of evidence was shaped by and shaped further the unique Context of each reform, despite the shared commonalities among the three countries.

A multi-criteria decision-making method based on discrete Z-numbers and Aczel-Alsina aggregation operators and its application on early diagnosis of depression

In mental health diagnostics, the questionnaire is an effective and cost-effective method. However, the traditional questionnaire test methods for depression and anxiety have great ambiguity. The discrete Z-numbers (DZs) provide solutions for describing and resolving complex fuzzy issues in the intelligent multi-criteria decision-making (MCDM) process. However, large-scale datasets are not suited for the present MCDM techniques due to their extremely high computational cost. Additionally, these techniques are less stable and flexible. To address the above issues, a novel MCDM method is introduced, which is based on the DZs theory and the Aczel-Alsina (AA) aggregation operator (AO) for large-scale datasets. To begin with, centroid points are calculated for DZs, and a series of novel AOs are introduced. And then a score function with a parameter is introduced to balance the influence between the possibility restriction and the fuzzy restriction of DZs. Thirdly, a new MCDM method under DZs is presented based on the proposed AA AOs and score function. Finally, to support the early diagnosis of depression and anxiety, we apply our method to the real-life online Depression, Anxiety, and Stress Scale (DASS) which can be transformed into DZs by our proposed preprocessing method. According to experimental results, our method is applicable to large-scale datasets and has much lower complexity as well as higher flexibility and stability.

Personalized Data Insights: How­­ Machine Learning is Revolutionizing Everyday Business Decisions

The rise of machine learning has fundamentally transformed the way businesses approach decision-making, particularly in the realm of data analytics. In today’s data-driven landscape, organizations are not just gathering information—they’re using advanced algorithms to turn raw data into personalized, actionable insights that drive business strategy. Machine learning enables businesses to go beyond surface-level trends and dig deeper into customer behavior, operational inefficiencies, and market dynamics. This technology is revolutionizing everyday business decisions by delivering real-time, data-driven intelligence that is specifically tailored to each business's unique needs. By leveraging machine learning, businesses can analyze massive datasets, uncover hidden patterns, and make predictions that were previously unimaginable with traditional analytics methods. One of the key benefits of machine learning is its ability to personalize data insights, offering businesses a clearer, more detailed understanding of their customers and operations. It empowers companies to make more accurate decisions in marketing, product development, customer service, and beyond, leading to improved efficiency, customer satisfaction, and overall business performance. This article explores the transformative role of machine learning in generating personalized data insights and its profound impact on everyday business decisions. It highlights how this technology can improve decision-making processes, enhance customer experiences, and drive more effective business strategies through real-time analysis and prediction. Through practical examples, we will demonstrate how businesses across various sectors are already using machine learning to revolutionize their operations and improve outcomes.

Breaking the silence: Do I tell my child about their sexual abuse history? Longitudinal analysis of parental decision-making regarding disclosure of preverbal sexual abuse by parents to their child: A qualitative study

BackgroundThe Amsterdam Sexual Abuse Case (ASAC) presented parents with a dilemma about disclosing preverbal sexual abuse to their child. Advising parents was challenging due to limited knowledge about the consequences of preverbal child sexual abuse (CSA) and the rarity of children having no conscious awareness of their experience. To enhance recommendations and knowledge, we explored parents' experiences over the years. ObjectiveThis unique qualitative study introduced “parental disclosure”, where non-offending parents disclose CSA to their child, and investigated the decision-making process spanning a decade after the ASAC discovery. MethodsLongitudinal qualitative data were collected through yearly interviews. Participants and settingIn total, 41 parents participated for 47 children. An additional in-depth interview was conducted with 20 parents in 2015. ResultsQuantitative data highlighted that over 10 years, parents of more than half of the children disclosed the preverbal CSA. Our analyses revealed that parental disclosure decisions entail dilemmas and unfold as a gradual process. Factors influencing decision-making included fear of stigma, child's emotional readiness, perceived benefits and risks, parental beliefs, values, and CSA circumstances. ConclusionThis study underscores the complex nature of parental disclosure and shows that it is an ongoing process rather than a singular event. Parents' choices evolved based on changing circumstances and new information. Outcomes enrich the understanding of parental disclosure decision-making, highlighting the need for tailored support services to aid parents navigating the aftermath of preverbal CSA. Parental disclosure is highly individualized and calls for evidence-based interventions and policies.

Crossing disciplinary boundaries: An ethnographic exploration of academic publishing invitations.

Background: This autoethnographic study examines email invitations for health researchers to publish in journals outside their expertise, exploring implications for interdisciplinary research and knowledge production.Methods: Over three months, email invitations to publish outside the author's field were documented and analysed thematically and through reflexive journaling.Results: Five main themes in publication invitations were identified: emphasising novelty, promising rapid publication, appealing to research impact, flattering language, and persistent messaging. Reflexive analysis revealed complex factors shaping responses, including publication pressures, desires for prestige, and tensions between disciplinary norms and interdisciplinary collaboration. While invitations may present opportunities for novel collaborations, they often reflect predatory publishing practices.Conclusions: Navigating this landscape requires careful discernment, commitment to academic integrity, and reflexivity about one's positionality. The study underscores the need for researchers to critically interrogate the motivations behind such invitations. Further research could explore decision-making processes across disciplines and implications for academic publishing integrity and equity.