Search Results (1,475)

Interdependent criteria play a crucial role in complex decision-making across various domains. Traditional methods often struggle to evaluate and prioritize criteria with intricate dependencies. This paper introduces a generalized method integrating the analytic network process (ANP), the decision-making trial and evaluation laboratory (DEMATEL), and the consistent fuzzy analytic hierarchy process (CFAHP) in a fuzzy environment. The Drazin inverse technique is applied to derive a fuzzy total priority matrix, and we normalize the row sum to achieve the steady-state fuzzy priorities. A numerical example in the information systems (IS) industry demonstrates the approach’s real-world applications. The proposed method derives narrower fuzzy spreads compared to the past fuzzy analytic network process (FANP) approaches, minimizing objective uncertainty. Total priority interdependent maps provide insights into complex technical and usability criteria relationships. Comparative analysis highlights innovations, including non-iterative convergence of the total priority matrix and the ability to understand interdependencies between criteria. The integration of the FANP’s network structure with the fuzzy DEMATEL’s influence analysis transcends the capabilities of either method in isolation, marking a significant methodological advancement. By addressing challenges such as parameter selection and mathematical complexity, this research offers new perspectives for future research and application in multi-attribute decision-making (MADM). Full article

The multi-functional integrated RF system (MIRFS) is a crucial component of aircraft onboard systems. In the testability design process, traditional methods cannot effectively deal with the inevitable differences between system designs and usage requirements. By considering the MIRFS’s full lifecycle characteristics, a new testability allocation method based on multi-criteria decision-making (MCDM) is proposed in this paper. Firstly, the testability framework was constructed and more than 100 indicators were given, which included both different system-level and inter-system indicators. Secondly, to manage parameter diversity and calculate complexity, the basic 12 testability indicators were optimized through the Analytic Hierarchy Process and Technique for Order Preference by Similarity to Ideal Solution (AHP-TOPSIS) method. Thirdly, the detailed testability parameters were obtained by using the Decision-Making Trial and Evaluation Laboratory and Analytic Network Process (DEMATEL-ANP) to reduce the subjectivity and uncertainty. Finally, an example was utilized, and the results show that the MCDM method is significantly better than traditional methods in terms of accuracy and effectiveness, which will provide a more scientific basis for the MIRFS testability design process. Full article

This paper explores the adaptation of pseudo-rigid-body models (PRBMs) for simulating large geometric nonlinear deflections in passive exoskeletons, expanding upon their traditional application in small compliant systems. Utilizing the AnyBody modeling system, this study employs force-dependent kinematics to reverse the conventional simulation process, enabling the calculation of forces from the deformation of PRBMs. A novel approach, termed “Constraint Force”, is introduced to facilitate this computation. The approach is thoroughly validated through comparative analysis with laboratory trials involving a beam under bending loads. To demonstrate the functionality, the final segment of this study conducts a biomechanical simulation incorporating motion capture data from a lifting test, employing a novel passive exoskeleton equipped with flexible spring elements. The approach is meticulously described to enable easy adaptation, with an example code for practical application. The findings present a user-friendly and visually appealing simulation solution capable of effectively modeling complex mechanical load cases. However, the validation process highlights significant systematic errors in the direction and amplitude of the calculated forces (20% and 35%, respectively, in the worst loading case) compared to the laboratory results. These discrepancies emphasize the inherent accuracy challenges of the “Constraint Force” approach, pointing to areas for ongoing research and enhancement of PRBM methods. Full article

In the aftermath of the COVID-19 pandemic, people are gradually realizing that urban community resilience is pivotal for effectively managing public health emergencies. This study employed grounded theory to establish a theoretical framework for epidemic resilience of urban communities (ERUC) in the post-pandemic era. Subsequently, the decision-making trial and evaluation laboratory (DEMATEL)-interpretive structural modeling (ISM) method is utilized to discern the significance and hierarchical interrelations among influencing factors. The findings delineate that 14 determinants shaping ERUC are organized into five distinct tiers. Notably, nine determinants emerge as principal: vulnerable group; educational attainment; risk perception; medical insurance coverage; communal norms; community emergency response; community services; resident participation; and government efficacy. Among these, the vulnerable group and government efficiency are identified as foundational factors, while medical insurance coverage, resident participation, and community infrastructure are identified as direct influences. Full article

Blood analysis plays a pivotal role in assessing the health of laboratory animals, including pigs. This study investigated the hematological profiles of transgenic pigs of the MGH breed for xenotransplantation, focusing on the effect of housing conditions on blood parameters. A cohort of pigs was longitudinally monitored from 6 to 18 months of age in both conventional and specific pathogen-free (SPF) environments. Red blood cells (RBCs), hemoglobin (HGB), and white blood cells (WBCs) were analyzed using standardized hematology analyzers. The results revealed that RBC and HGB levels were consistently higher in SPF-housed pigs. Notably, WBC counts were significantly lower in SPF-housed pigs, suggesting that reduced pathogen exposure under SPF conditions effectively diminished immune system activation. These findings raise a novel question as to whether distinct hematological parameters of specific and/or designated PF pigs would be advantages for the success of clinical xenotransplantation trials. Full article

The Logarithmic Mean Divisia Index (LMDI) method is widely applied in research on carbon emissions, urban energy consumption, and the building sector, and is useful for theoretical research and evaluation. The approach is especially beneficial for combating climate change and encouraging energy transitions. During the method’s development, there are opportunities to develop advanced formulas to improve the accuracy of studies, as indicated by past research, that have yet to be fully explored through experimentation. This study reviews previous research on the LMDI method in the context of building carbon emissions, offering a comprehensive overview of its application. It summarizes the technical foundations, applications, and evaluations of the LMDI method and analyzes the major research trends and common calculation methods used in the past 25 years in the LMDI-related field. Moreover, it reviews the use of the LMDI in the building sector, urban energy, and carbon emissions and discusses other methods, such as the Generalized Divisia Index Method (GDIM), Decision Making Trial and Evaluation Laboratory (DEMATEL), and Interpretive Structural Modeling (ISM) techniques. This study explores and compares the advantages and disadvantages of these methods and their use in the building sector to the LMDI. Finally, this paper concludes by highlighting future possibilities of the LMDI, suggesting how the LMDI can be integrated with other models for more comprehensive analysis. However, in current research, there is still a lack of an extensive study of the driving factors in low-carbon city development. The previous related studies often focused on single factors or specific domains without an interdisciplinary understanding of the interactions between factors. Moreover, traditional decomposition methods, such as the LMDI, face challenges in handling large-scale data and highly depend on data quality. Together with the estimation of kernel density and spatial correlation analysis, the enhanced LMDI method overcomes these drawbacks by offering a more comprehensive review of the drivers of energy usage and carbon emissions. Integrating machine learning and big data technologies can enhance data-processing capabilities and analytical accuracy, offering scientific policy recommendations and practical tools for low-carbon city development. Through particular case studies, this paper indicates the effectiveness of these approaches and proposes measures that include optimizing building design, enhancing energy efficiency, and refining energy-management procedures. These efforts aim to promote smart cities and achieve sustainable development goals. Full article

To address the common issues of lacking indicator system identification, causal relationship quantification, and path simulation analysis in the current research on safety performance in prefabricated construction, a method for improving safety performance in prefabricated construction based on the decision-making trial and evaluation laboratory (DEMATEL) and NK model is proposed. Firstly, through theoretical analysis and literature review, the indicator system for safety performance in prefabricated construction is identified using the grounded theory. Secondly, expert research and quantitative analysis are combined to analyze the causal relationship of the indicators using the DEMATEL method. Then, the DEMATEL method is integrated with the NK model to carry out a key indicator adaptability modeling analysis and three-dimensional simulation. Finally, a case study is conducted to validate the usability and effectiveness of the proposed model and method. The results show that X₆ (construction and implementation of safety management system) had the highest impact on the other indicators, and X₁₄ (quality and safety status of prefabricated components) was most influenced by other indicators. X₆ (construction and implementation of safety management system), X₁ (personnel safety awareness and attitude), X₁₄ (quality and safety status of prefabricated components), and X₁₂ (construction site working environment) were identified as key performance indicators. “X₆ (construction and implementation of safety management system) → X₁ (personnel safety awareness and attitude) → X₁₄ (quality and safety status of prefabricated components) → X₁₂ (construction site working environment)” was considered the optimal path to improve construction safety performance. Full article

This study investigates novel methods for estimating metering errors in water meters, crucial for effectively managing apparent losses in Water Distribution Networks. Laboratory experiments were conducted to analyze the impact of various factors on metering errors. The Gene Expression Programming (GEP) algorithm was used to create a metering error assessment model, which was then validated through field trials in a DMA. Results indicate that metering errors are influenced by factors such as installation position, tilt angle, flow rates, and operational time. The GEP-based model showed high accuracy, with a mean squared error as low as 0.08, and a mere +0.5% difference from actual field measurements. This model offers water utility companies a cost-effective tool to assess metering errors without disassembly, offering a cost-effective tool for enhancing water supply management. Full article

Electronic components assembled in satellites should be able to withstand the vibration, noise, and impact loads generated by space vehicles during launch. To simulate the impact loading in a laboratory environment, a pyroshock test simulates an impact load resulting from explosions during the stage and pairing separation of launch vehicles, which imposes significant stress on the components, potentially leading to failures and damage. To ensure component reliability before the flight model (FM) stage, where components are mounted on the actual launch vehicle and sent into orbit, a pyroshock test is conducted during the qualification model (QM) stage using identical parts and specifications. This process involves measurements of the acceleration induced by pyroshock to calculate the shock response spectrum (SRS) and evaluate the components’ reliability against the required SRS to confirm their ability to endure the shock and operate normally in post-tests. The aerospace developer determines the SRS requirements based on the space launch vehicle and the installation location of the electronic components. Configuring a suitable pyroshock test to meet these requirements typically involves extensive trial and error. This study aims to minimize such trial and error by examination of SRS changes through a numerical approach by table structural vibration analysis. The structure is subjected to in-plane impacts using a steel ball via a pendulum method. Various SRS profiles are calculated by test factors such as the weight of the steel ball, the pendulum angle, and the installation position of the test specimen. Furthermore, a genetic algorithm is utilized to derive the optimal test conditions that satisfy the required SRS. An automated pyroshock test system is developed to enhance repeatability and reduce human errors. Full article

Background: COVID-19 has had a substantial influence on healthcare systems, requiring early prognosis for innovative therapies and optimal results, especially in individuals with comorbidities. AI systems have been used by healthcare practitioners for investigating, anticipating, and predicting diseases, through means including medication development, clinical trial analysis, and pandemic forecasting. This study proposes the use of AI to predict disease severity in terms of hospital mortality among COVID-19 patients. Methods: A cross-sectional study was conducted at King Abdulaziz University, Saudi Arabia. Data were cleaned by encoding categorical variables and replacing missing quantitative values with their mean. The outcome variable, hospital mortality, was labeled as death = 0 or survival = 1, with all baseline investigations, clinical symptoms, and laboratory findings used as predictors. Decision trees, SVM, and random forest algorithms were employed. The training process included splitting the data set into training and testing sets, performing 5-fold cross-validation to tune hyperparameters, and evaluating performance on the test set using accuracy. Results: The study assessed the predictive accuracy of outcomes and mortality for COVID-19 patients based on factors such as CRP, LDH, Ferritin, ALP, Bilirubin, D-Dimers, and hospital stay (p-value ≤ 0.05). The analysis revealed that hospital stay, D-Dimers, ALP, Bilirubin, LDH, CRP, and Ferritin significantly influenced hospital mortality (p ≤ 0.0001). The results demonstrated high predictive accuracy, with decision trees achieving 76%, random forest 80%, and support vector machines (SVMs) 82%. Conclusions: Artificial intelligence is a tool crucial for identifying early coronavirus infections and monitoring patient conditions. It improves treatment consistency and decision-making via the development of algorithms. Full article

Background: The automatic detection of activities of daily living (ADL) is necessary to improve long-term home-based monitoring of Parkinson’s disease (PD) symptoms. While most body-worn sensor algorithms for ADL detection were developed using laboratory research systems covering full-body kinematics, it is now crucial to achieve ADL detection using a single body-worn sensor that remains commercially available and affordable for ecological use. Aim: to detect and segment Walking, Turning, Sitting-down, and Standing-up activities of patients with PD using a Smartwatch positioned at the ankle. Method: Twenty-two patients living with PD performed a Timed Up and Go (TUG) task three times before engaging in cleaning ADL in a simulated free-living environment during a 3 min trial. Accelerations and angular velocities of the right or left ankle were recorded in three dimensions using a Smartwatch. The TUG task was used to develop detection algorithms for Walking, Turning, Sitting-down, and Standing-up, while the 3 min trial in the free-living environment was used to test and validate these algorithms. Sensitivity, specificity, and F-scores were calculated based on a manual segmentation of ADL. Results: Sensitivity, specificity, and F-scores were 96.5%, 94.7%, and 96.0% for Walking; 90.0%, 93.6%, and 91.7% for Turning; 57.5%, 70.5%, and 52.3% for Sitting-down; and 57.5%, 72.9%, and 54.1% for Standing-up. The median of time difference between the manual and automatic segmentation was 1.31 s for Walking, 0.71 s for Turning, 2.75 s for Sitting-down, and 2.35 s for Standing-up. Conclusion: The results of this study demonstrate that segmenting ADL to characterize the mobility of people with PD based on a single Smartwatch can be comparable to manual segmentation while requiring significantly less time. While Walking and Turning were well detected, Sitting-down and Standing-up will require further investigation to develop better algorithms. Nonetheless, these achievements increase the odds of success in implementing wearable technologies for PD monitoring in ecological environments. Full article

Olive oil extraction by two-phase systems generates a by-product called “alperujo” which presents several difficulties for its valorization. The present work evaluated an industrial approach, based on the application of thermal treatments to alperujo followed by solid/liquid separation using standard two-phase olive oil mill equipment. Treatments consisted of the thermo-malaxation of alperujo at 70 °C for 45 or 90 min, with or without acid addition, followed by solid/liquid separation in an industrial decanter. The solid was characterized concerning subsequent use for composting, while total and hydrophilic phenolics were analyzed in liquid for their recovery. Additionally, a laboratory-scale trial to compare phenolic purification by ethylic acetate extraction with chromatographic procedures was also included. The static respiration index showed that solid fractions presented higher susceptibility to biodegradation processes than raw alperujo. The phenolic content of treated liquid fractions was higher than in raw alperujo. Total phenolics were maximum at the longest exposure time without acid addition, while hydrophilic phenolics were highest at the shortest exposure time in acidified samples. The use of non-ionic resins seemed attractive for obtaining highly concentrated phenolic fractions. The proposed thermal treatments can be applied in olive oil industries, allowing in situ pomace valorization and the recovery of phenolic-enriched liquid fractions. Full article

IgE-mediated food allergy is characterized immunologically by a type 1 immune response triggered upon exposure to specific foods and clinically by a broad range of manifestations and variable severity. Our understanding of food allergy within the allergic march of atopic dermatitis (AD) is still incomplete despite the related risk of unpredictable and potentially severe associated reactions such as anaphylactic shock. The aim of this pilot study was to investigate the effects of dupilumab, an IL-4/IL-13 monoclonal antibody approved for AD, on the allergic sensitization profile of patients with AD and type 1 hypersensitivity-related comorbidities, including oral allergy syndrome, anaphylaxis, and gastrointestinal disorders. We conducted an observational pilot study with a longitudinal prospective design, enrolling 20 patients eligible for treatment with dupilumab. Laboratory exams for total serum IgE, specific IgE, and molecular allergen components were performed at baseline and after 16 weeks of therapy. Our results demonstrate a statistically significant decrease in molecular components, specific IgE for trophoallergens, and specific IgE for aeroallergens following treatment with dupilumab. We suggest that modulating type 2 immunity may decrease IgE-mediated responses assessed with laboratory exams and therefore could minimize allergic symptoms in polysensitized patients. Upcoming results of randomized controlled trials investigating dupilumab in food allergy are highly anticipated to confirm its potential effect in the treatment of IgE-mediated food allergies. Full article

As environmental awareness grows, society emphasizes green business. Thus, semiconductor companies encompass energy-saving processes and innovative product development. This study employs the fuzzy decision-making trial and evaluation laboratory (DEMATEL) analysis method to assess the impact of four key dimensions, externalities, market orientation, green technology, and corporate social responsibility, on semiconductor companies’ green production decisions. This study uses the DEMATEL-based analytic network process (DANP) approach to rank the criteria order in green production decision-making. The results from the DEMATEL causality diagram highlight externalities as the most critical dimension influencing other dimensions of green production decisions. This study suggests that regulatory pollution punishment and subsidies emerge as the primary drivers for green production decisions. Companies adopt environmentally friendly production practices to prevent regulatory pollution penalties or reduce carbon trading costs. Additionally, the DANP results reveal that corporate image criteria in the corporate social responsibility dimension hold the utmost priority in semiconductor firms’ green production decision-making. This implies that considerations for improving a company’s image should take precedence as semiconductor companies seek shareholder support and governmental subsidies to ensure sustainable operations. Externalities arise as the secondary priority dimension in green production decision-making, aligning with the impact of externalities identified in DEMATEL findings. Full article

The sudden collapse of a school roof in the UK brought widespread attention to the structural integrity of buildings constructed with reinforced autoclaved aerated concrete (RAAC), a material widely used from the 1950s to the mid-1990s. RAAC, known for its lightweight and insulating properties, has been found to suffer from weak compressive strength, poor reinforcement anchorage, and high susceptibility to environmental degradation. The structural profiles of RAAC panels in the UK are unique, particularly in their reinforcement configurations and failure modes, which limits the applicability of the existing literature from other regions. This paper conducts a state-of-the-art review, identifying a significant gap in current research due to the unique challenges posed by RAAC in the UK, and highlights the need for novel methodologies. In response to this gap, the paper introduces a multi-criteria decision analysis (MCDA) framework utilising the decision-making trial and evaluation laboratory (DEMATEL) method to assess the interdependencies of RAAC defects. This methodology quantifies the influence of observed defects and guides the selection of appropriate remediation strategies, offering a more structured and objective approach to RAAC panel assessment and retrofitting. Practically, this study aligns with ongoing research efforts towards the digitalisation of RAAC management by integrating the MCDA model within digital asset management systems. This integration supports a holistic approach to addressing the RAAC crisis, enhancing current efforts to digitalise the surveying and management processes and ensuring safer long-term solutions. Full article