Search Results (231)

This study proposes an intelligent decision-support framework for alternative selection in complex construction projects, where evaluation processes are affected by uncertainty, multiple decision-makers, and interdependent criteria. The framework integrates the fuzzy group best–worst method with fuzzy TOPSIS into a unified structure that explicitly captures cross-criterion influence effects. First, triangular fuzzy judgments from multiple experts are used to derive criterion weights, while interdependencies among criteria are represented through a fuzzy influence-intensity matrix and incorporated into fuzzy nonlinear optimization models. This process enables the systematic estimation of both independent and interdependency-adjusted criterion weights. Second, the resulting weights are used in a fuzzy ranking procedure to evaluate alternatives according to their relative closeness to fuzzy ideal solutions. To enhance transparency, reproducibility, and practical usability, the proposed method is implemented in Python as an automated computational workflow for decision analysis. Its applicability is demonstrated through a real-world case study on access platform system selection for mechanical, electrical, and plumbing installation in an airport terminal subject to safety, productivity, workspace, and elevation-related constraints. The results show that explicitly modeling criterion interdependencies provides a more realistic evaluation structure and enhances the robustness and reliability of alternative selection in complex construction management contexts. Full article

Background/Objectives: High sodium intake is a leading dietary risk factor for stomach cancer, particularly in East Asia. In Japan, traditional dietary patterns contribute to elevated sodium consumption and a high burden of stomach cancer. This study aims to forecast disability-adjusted life years (DALYs) for stomach cancer attributable to high sodium intake in Japan from 2022 to 2050, and to assess the impact of multiple sodium reduction policy scenarios. Methods: We conducted a longitudinal forecasting study using autoregressive integrated moving average with exogenous variables (ARIMAX) models based on Global Burden of Disease 2021 data (1990–2021). The Japanese population was stratified by sex and age groups (15–49, 50–69, and ≥70). Five future exposure scenarios were modelled: (1) reference (current trends), (2) best-case (50% reduction in sodium exposure by 2050), (3) optimal (30% reduction by 2032), (4) moderate (30% reduction by 2050), and (5) worst-case (highest exposure levels from recent years maintained). These scenarios were aligned with national and international sodium reduction targets, including the revised “Health Japan 21” (third term; 7 g/day by 2032) and the World Health Organisation (WHO) 5 g/day/30% reduction goals. Results: Under the reference scenario, age-standardised DALY rates are projected to decline by 31.4% (to 15.4 per 100,000) by 2050. The best-case scenario projects a 54.7% decline (to 10.1 per 100,000). Substantial demographic disparities persist: males and those aged ≥70 consistently show higher burdens. Notably, the 50–69 age group shows the greatest variation in 2050 projections across scenarios (17.1 to 73.5 per 100,000), indicating high policy sensitivity. Meanwhile, in the ≥70 group, DALY rates remain high regardless of scenario, especially among males (199.4 vs. 57.8 per 100,000 for females), reflecting cumulative lifetime exposure. Conclusions: Under modelled assumptions, sustained achievement of national sodium reduction targets could meaningfully reduce future stomach cancer DALYs in Japan, with the largest absolute gains in older adults but the largest relative gains in younger and middle-aged groups. Because stomach cancer aetiology is multifactorial and the projections rest on modelled associations and a continuity-of-trend assumption, these findings support strengthened, demographically targeted sodium reduction interventions as one complementary component of a broader, multi-risk factor approach to stomach cancer prevention. Full article

Food Loss and Waste (FLW) remain major challenges for global food security, environmental sustainability, and economic stability, with nearly one-third of food produced each year being lost or wasted. Although many technologies exist to mitigate FLW, they are often assessed separately, making it difficult for decision-makers to compare options and select solutions suited to specific contexts. This research introduces an explainable decision support system (XDSS) that helps prioritise FLW mitigation strategies while accounting for uncertainty in stakeholder preferences. The proposed framework combines the Best–Worst Method (BWM) with Stochastic Multi-criteria Acceptability Analysis for Group Decision-Making (SMAA-2) to produce transparent and uncertainty-aware rankings. It evaluates one hundred FLW mitigation strategies across five contextual criteria: geographic fit, product category, food supply-chain stage, stakeholder role, and technology type. Rather than producing a single fixed ranking, the system generates probabilistic rank-acceptability profiles that indicate the likelihood of each strategy performing well under different preference conditions. Illustrative scenarios demonstrate that the framework can translate qualitative user preferences into robust prioritisation outcomes, with leading alternatives achieving first-rank-acceptability levels between 62% and 74%. These results indicate that the system can support clearer and more flexible decision-making when preferences are incomplete, inconsistent, or uncertain. Although the current results are based on simulated structured cases, the proposed XDSS provides a transparent methodological foundation for future real-world validation and operational deployment. The framework offers practical value for selecting FLW technologies and for policy planning, contributing to more sustainable food systems and supporting progress toward SDG 12.3. Full article

The growth of interest in alternative forms of animal-assisted therapies (AAT) with chickens creates a need to identify breeds with appropriate behavioral predispositions. This pilot study evaluated the suitability of four breeds of ornamental chickens (Gallus gallus domesticus), Silkie bantam, Pekin bantam, Ko-Shamo, and Chabo daruma, for work in chicken-assisted therapy (CAT). Tests simulated an AAT session, assessing human approach and staying in their vicinity, reactions to sudden stimuli, and touch tolerance. A standard tonic immobility test was also performed. Significant interbreed differences were demonstrated (p < 0.05 for most tests). Pekin bantam showed the greatest therapeutic potential. Contrary to expectations, Silkie bantam obtained the weakest results, manifesting the highest fear levels and avoidance of contact. Ko-Shamo and Chabo daruma showed moderate predispositions. The tonic immobility test did not significantly differentiate the groups (p = 0.28), indicating it is not a sufficient alternative to the proposed behavioral tests. According to a seven-point predisposition scale for AAT, Pekin bantam was the breed best predisposed for AAT, while Silkie bantam achieved the worst results (p = 0.04). The results confirm that the breed is significant when choosing chickens for therapy and indicate the need for testing other chicken breeds for therapeutic work. Full article

This paper presents a mobile application to encourage sustainable travel in urban areas as a proof-of-concept for user-centred sustainable urban transport. The app provides real-time route evaluation based on the environmental impact of different transport modes and local sensor monitoring feedback. Its core feature is an ecological route assessment using life cycle assessment calculations. Users receive quantitative feedback on their carbon footprint and a mobility score ranging from one (worst, red) to five (best, green). Providing both ecological and time-based navigation assessments, the app generates a comprehensive ecological footprint based on individual behaviour, raising awareness of United Nations climate targets. To increase its appeal, the app integrates a quest model offering vouchers from local partners (e.g., half-priced coffee) and competitions (e.g., complete the most journeys under 5 km by bike or on foot per week). A user-centred development process involving multiple test groups and a physical mock-up has been used to optimize the user interface, concept, and gamification elements. The app will be extended to include location-based quests and interactive chat quizzes. The project addresses key aspects of sustainable individual mobility and could be adapted for other cities, universities, or regions. Full article

Many initiatives aimed at improving population-wide health or providing food sources that are sustainable and environmentally friendly are focused on a switch from primarily meat-based diets to diets that are more vegetable-based. Building rational approaches to promoting such changes requires an understanding of consumers’ motives for their dietary choices. Aiming to extend prior research, the present study examines eleven food choice motives across nine dietary groups varying in their adoption of diets that are plant-based, from omnivores through meat-reducing flexitarian groups to vegetarian sub-groups and vegans. Using a large population sample and Best–Worst scaling, a novel approach to assessing the relative importance of these motives, we show that the dietary groups are distinguished from one another by a relatively small number of food choice motives. The most substantive of these are Sensory Appeal and Animal Welfare concerns, the former being most characteristic of those consuming meat as part of their diet, and the latter being rated more important by the different vegetarian and vegan groups. Various forms of flexitarian diets are driven by differences in the relative importance of several food choice motives. Generally notable is the finding that, in contrast to previous studies, the importance attached to Health, Weight Control, and Natural Content is not particularly characteristic of any specific dietary approach. The research contributes new fine-grained knowledge about motives for different dietary choices, which can be harnessed for intervention and policy actions. Full article

Background: Canada’s cervical cancer elimination plan is challenged by suboptimal screening participation and rising incidence of cervical cancer over the past decade. Cytology, the primary cervical screening method in Canada, is being replaced with HPV testing, which offers superior sensitivity for detecting pre-cancerous lesions and supports initiating screening at age 25 or older and extending screening intervals to five years. Research has shown that women’s insufficient knowledge and negative attitudes toward HPV screening represent a significant barrier to screening uptake. Methods: We conducted a web-based national survey using Best–Worst Scaling (trade off utilities) to quantify women’s preferences for screening test modality, age of initiation, and screening intervals. We also assessed preferences for information sources, provider type, and communication methods. Underscreened individuals were oversampled. Results: Among adequately screened (N = 1778) and underscreened (N = 1570) individuals, preferences favoured co-testing (cytology plus HPV testing), initiating screening at age 21, and three-year screening intervals. Underscreened participants showed relatively higher preference for HPV self-sampling, and as opposed to adequately screened participants, preferred screening by a gynecologist rather than a family physician. Across groups, participants preferred receiving screening-related information and communication by email over postal mail. Conclusions: The misalignment between women’s preferences and current HPV test-based screening implementation plans requires immediate education interventions and modernized, user-preferred communication channels for cervical screening-eligible individuals in Canada. Full article

(1) Objective: To compare the marginal and internal fit of 3D-printed and milled three-unit fixed dental prostheses (FDPs) made from tetragonal zirconia polycrystal (3Y-TZP). (2) Methods: Three-unit FDPs were designed for a typodont maxillary model with crown preparation for the second premolar and second molar. Nominal cement gap widths were set to 30 µm at the margins and 80 µm internally. A total of 40 FDPs (n = 10/group) differing in wall thickness (w = 0.6/1.0 mm) and support structures (with/without a stiffening frame) were fabricated from 3Y-TZP by 3D printing. A total of 10 milled FDPs with w = 0.6 mm served as a control group. After adhesive cementation on the respective replicated maxillary models, FDPs were sectioned and the cement gap dimension was assessed with a digital microscope. The marginal and internal fit found for the different test groups were compared using non-parametric tests. (3) Results: The best marginal fit—qualified by median/maximum marginal gap width—was given for milled FDPs (79/127 µm vertical; 85/171 µm tangential), whereas the marginal fit of 3D-printed FDPs with w = 0.6 mm and regular support structures was the worst (144/284 µm vertical; 107/198 µm tangential). Use of an additional support frame improved the marginal fit of 3D-printed FDPs, in particular FDPs with w = 0.6 mm (108/197 µm vertical; 87/161 µm tangential). (4) Conclusions: 3D-printed zirconia FDPs showed conditionally comparable marginal and internal fit as their milled counterparts, but with slightly higher scattering. When fabricating thinner 3D-printed FDPs, additional support structures are mandatory to achieve clinically well-fitting restorations. Full article

Background/Objectives: Sex and diabetes are important determinants of risk in heart failure with mildly reduced and preserved ejection fraction (HFmrEF/HFpEF), yet their combined effects have not been systematically evaluated. This study examined how sex–diabetes phenotypes influence clinical characteristics and the risk of heart failure rehospitalization. Methods: We retrospectively analyzed 1018 HFmrEF/HFpEF patients (2019–2023), classified into four sex–diabetes phenotypes, and performed group comparisons. The primary endpoint was heart failure rehospitalization. Results: Over a mean follow-up of 1463 ± 496 days, 307 patients (30.1%) were rehospitalized for heart failure decompensation. The four phenotypes differed significantly in age, renal function, LV mass, LV dimensions, glycemia, and comorbidity burden (all p < 0.05). Men—particularly those with diabetes—had greater structural remodeling and higher prevalence of smoking, hypercholesterolemia, and atrial fibrillation. In univariate analysis, male sex, diabetes, smoking, NYHA class, lower TAPSE, and lower LVEF were associated with increased risk of rehospitalization. After adjustment for LVEF and NYHA class, male sex (HR 1.28; p = 0.035) and diabetes (HR 1.28; p = 0.036) remained independent predictors. Kaplan–Meier curves demonstrated a clear gradient in event-free survival (log-rank p = 0.015), with women without diabetes showing the best prognosis and diabetic men the worst. Conclusions: Sex and diabetes interact to define distinct risk profiles in HFmrEF/HFpEF. Women without diabetes represent a low-risk phenotype, whereas diabetic men exhibit the highest risk of recurrent heart failure decompensation. These findings support incorporating sex–diabetes phenotyping into routine risk stratification and personalized management. Full article

Cross-impact analysis is frequently used in scenario-analogous studies to identify critical factors influencing ecological change, strategic planning, technology foresight, resource allocation, risk mitigation, cost optimization, and decision support. Scenarios enable different organizations to comprehend prevailing situations, prepare for probable futures, and mitigate conceivable risks. Unfortunately, cross-impact analysis methods are often criticized for their difficulty in handling complex interactions, cognitive bias, time-intensiveness, heavy reliance on a limited pool of experts, and inconsistency in assigning judgment, which can affect the expected outcomes. This paper introduces a novel method for constructing consistent scenarios that addresses these criticisms and those associated with scenario methods. The method is based on cross-impact analysis and crowdsourcing for constructing consistent scenarios. The cross-impact analysis component of the method is based on advanced impact analysis and cross-impact balance analysis to, respectively, provide a time-efficient reduction in complex interdependent factors and construct consistent scenarios from a set of reduced factors. The crowdsourcing element leverages the cumulative intelligence of a group of experts to help mitigate cognitive bias and transparently give a more inclusive analysis. The method was implemented and validated with a practical case of renewable energy adoption, a vital challenge for socioeconomic progress and climate change resilience. While the method provides a sturdy foundation for writing scenario narratives, the result confirms its robustness for constructing consistent scenarios and suggests that the future of renewable energy adoption can be enhanced through careful cogitation of best-case, base-case, and worst-case scenarios, which include varying states of perceived value, awareness, and perceived support. These findings contribute to a more nuanced understanding of how socio-cognitive and institutional factors interact to influence the pace and direction of sustainable energy transitions. Full article

Background/Objectives: Pancreatic cancer remains highly fatal, often diagnosed late with poor prognoses and worse psychological quality of life compared to other cancers. Globally, it is the twelfth most common cancer but the sixth leading cause of cancer-related deaths, with actual 5-year survival rates below 5%. Northern Ireland’s outcomes are among the worst, yet research on people’s experiences across the illness trajectory is scarce. Consequently, the unique needs of people with pancreatic cancer are poorly understood. It is crucial we develop deeper understanding of the entire pancreatic cancer journey to address this. This study aims to explore the lived experiences of people diagnosed with pancreatic cancer in Northern Ireland and generate a theory that explains their journeys, from pre-diagnosis through to survivorship or end of life. Methods: This study will adopt a grounded theory approach, incorporating multiple qualitative data generation methods: semi-structured interviews with patients and care partners, and focus groups with professionals. An optional photovoice (participatory photography) method will be offered to participants. Theoretical sampling principles and constant comparative analysis will guide recruitment, data collection, and analysis to ensure the explanatory theory is rooted in participants’ lived experiences. Conclusions: Establishing a holistic, in-depth understanding of people’s pancreatic cancer journeys will enable us to better comprehend, anticipate, and meet their needs. A theory grounded in empirical data about lived experiences can inform priorities for future care, support services, policy, and research, and contribute to the development of support interventions that help people to maintain the best possible quality of life, whether during a short-term, terminal illness; treatment journey; long-term symptom management; or survivorship. Full article

High uncertainty in new product development is primarily driven by multidimensional risks arising from dynamic interactions among factors including customer requirements (CRs), design characteristics (DCs), and solution decisions. To effectively address decision-making risks in uncertain environments, an integrative framework is proposed incorporating the Best–Worst Method (BWM), Interval-Valued Intuitionistic Fuzzy Quality Function Deployment (IVIF-QFD), and the IVIF-VlseKriterijumska Optimizacija I Kompromisno Resenje (IVIF-VIKOR) approach. Initially, CRs are identified through market research and focus group interviews, with weights determined by the BWM to enhance consensus and efficiency in judgment. Subsequently, an IVIF-QFD model is constructed. This model effectively addresses the fuzziness in expert judgments during the translation of CRs into DCs, strengthening its expressive capability in uncertain environments. Finally, candidate solutions are generated for critical DCs, and the IVIF-VIKOR method is employed to rank these solutions, identifying the Pareto-optimal solution. The framework’s effectiveness is validated by a steering wheel design, in addition, sensitivity analysis and comparative experiments are employed to quantify the robustness of the framework against parameter variations. This paper not only theoretically establishes a collaborative decision-making paradigm for uncertain environments but also provides an operational end-to-end decision support toolchain. Full article

Aligners represent a therapeutic option in orthodontics, offering advantages such as aesthetics, comfort, and individualized prescriptions for each patient. However, the predictability of maxillary expansion is subject to variability. The objective of this article is to evaluate the efficacy and predictability of aligners in maxillary expansion. One hundred adult patients were included in this study, divided into four groups, each assigned to a different brand of clear aligners: Angel Aligners, Invisalign, Spark, or HeySmile. Digital models were obtained at three stages: initial STL (T1), prediction (T2), and final (T3) (before the first refinement). The models were measured to obtain linear distances between canines, first bicuspids, second bicuspids, and first molars. Statistical analysis was performed using SPSS 28.0. The best predictability was obtained in the lower arch (68.900%) for second bicuspids, while the worst accuracy was for canines (39.290% in the upper arch using Invisalign). Angel aligner showed the highest percentage of predictability (60.002%) among the evaluated brands, followed by HeySmile (59.895%), Spark (59.275%), and Invisalign (57.153%). The results show that clear aligners are an effective treatment for transverse movements in both arches. However, further research is needed to improve predictability. Full article

The confluence of financial constraints, climate change mitigation efforts, and evolving user expectations has significantly transformed the concept of facility management (FM). Traditional FM has now evolved to enhance sustainability in the built environment. Sustainable facility management (SFM) can add value to companies, organizations, and governments by balancing the financial, environmental, and social outcomes of the FM processes. The systematic literature review revealed a limited number of studies developing a performance measurement framework for SFM in office buildings and/or other building types in the literature. Given that the lack of this theoretical basis inhibits the effective deployment of SFM practices, this study aims to fill this gap by developing a performance measurement framework for SFM in office buildings. Accordingly, an in-depth literature review was initially conducted to synthesize sustainable performance measurement factors. Next, a series of focus group discussion (FGD) sessions were organized to refine and verify the factors and develop a novel performance measurement framework for SFM. Lastly, consistency analysis, the Bayesian best worst method (BBWM), and sensitivity analysis were implemented to determine the priorities of the factors. What the proposed framework introduces is the combined use of two performance measurement mechanisms, such as continuous performance measurement and comprehensive performance measurement. The continuous performance measurement is conducted using high-priority factors. On the other hand, the comprehensive performance measurement is conducted with all the factors proposed in this study. Also, the BBWM results showed that “Energy-efficient material usage”, “Percentage of energy generated from renewable energy resources to total energy consumption”, and “Promoting hybrid or remote work conditions” are the top three factors, with scores of 0.0741, 0.0598, and 0.0555, respectively. Moreover, experts should also pay the utmost attention to factors related to waste management, indoor air quality, thermal comfort, and H&S measures. In addition to its theoretical contributions, the paper makes practical contributions by enabling decision makers to measure the SFM performance of office buildings and test the outcomes of their managerial processes in terms of performance. Full article

Specific computational methods, such as machine learning algorithms, can assist mining professionals in quickly and consistently identifying and addressing classification issues related to mineralized horizons, as well as uncovering key variables that impact predictive outcomes, many of which were previously difficult to observe. The integration of numerical and categorical variables, which are part of a dataset for defining ore grades, is part of the daily routine of professionals who obtain the data and manipulate the various phases of analysis in a mining project. Several supervised and unsupervised machine learning methods and applications integrate a wide variety of algorithms that aim at the efficient recognition of patterns and similarities and the ability to make accurate and assertive decisions. The objective of this study is the classification of gold ore or gangue through supervised machine learning methods using numerical variables represented by grade and categorical variables obtained through drillholes descriptions. Four groups of variables were selected with different variable configurations. The application of classification algorithms to different groups of variables aimed to observe the variables of importance and the impact of each one on the classification, in addition to testing the best algorithm in terms of accuracy and precision. The datasets were subjected to training, validation, and testing using the decision tree, random forest, Adaboost, XGBoost, and logistic regression methods. The evaluation was randomly divided into training (60%) and testing (40%) with 10-fold cross-validation. The results revealed that the XGBoost algorithm obtained the best performance, with an accuracy of 0.96 for scenario C1. In the SHAP analysis, the variable As was prominent in the predictions, mainly in scenarios C1 and C3. The arsenic class (Class_As), present mainly in scenario C4, had a significant positive weight in the classification. In the Receiver Operating Characteristic (ROC) and Area Under the Curve (AUC) curves, the results showed that XGBoost/scenario C1 obtained the highest AUC of 0.985, indicating that the algorithm had the best performance in ore/gangue classification of the sample set. The logistic regression algorithm together with AdaBoost had the worst performance, also varying between scenarios. Full article