Search

Copyright of Revue Francaise d'Allergologie is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Physics-Informed Neural Networks (PINNs) have gained popularity in solving nonlinear partial differential equations (PDEs) via integrating physical laws into the training of neural networks, making them superior in many scientific and engineering applications. However, conventional PINNs still fall short in accurately approximating the solution of complex systems with strong nonlinearity, especially in long temporal domains. Besides, since PINNs are designed to approximate a specific realization of a given PDE system, they lack the necessary generalizability to efficiently adapt to new system configurations. This entails computationally expensive re-training from scratch for any new change in the system. To address these shortfalls, in this work a sequential meta-transfer (SMT) learning framework is proposed, offering a unified solution for both fast training and efficient adaptation of PINNs in highly nonlinear systems with long temporal domains. Specifically, the framework decomposes PDE's time domain into smaller time segments to create "easier" PDE problems for PINNs training. Then for each time interval, a meta-learner is assigned and trained to achieve an optimal initial state for rapid adaptation to a range of related tasks. Transfer learning principles are then leveraged across time intervals to further reduce the computational cost. Through a composites autoclave processing case study, it is shown that SMT is clearly able to enhance the adaptability of PINNs while significantly reducing computational cost, by a factor of 100. [ABSTRACT FROM AUTHOR]

This work presents a prototype of an intelligent quality inspection tool for application to fibers distortion monitoring in the fabric composites forming processes. To this end, a series of hemisphere draping tests on a typical commingled fiberglass/polypropylene twill weave were conducted at the dry form, and the defects (in-plane and out-of-plane) for each formed part were inspected via camera vision. By gathering data from around 30 samples and over 1200 images from different forming regions, different machine-learning algorithms were trained and validated. As an application scenario in a smart factory, the developed simple AI tool would be used by an operator, or a robot, to scan different areas of the formed parts and identify 'defected' (fail) versus 'non-defected' (pass) scenarios. It was found that the K-nearest neighbors and Support Vector Machine models detect the defects with an error rate of less than 5% in the present case study, regardless of the background noise in the images such as external objects, marks on samples, or blurriness. [ABSTRACT FROM AUTHOR]

Radiologists and radiographers play a complementary role in providing an optimal image quality with decrease radiation dose and proper diagnosis during chest radiographs. We aim Investigate years of experience among radiologists and radiographers on perception of image quality and its impact on repeat rate when evaluating portable pediatric chest radiographs. IRB approved retrospective study consisted of randomly selected images (n = 131) of pediatric portable chest radiographs. Images were blindly assessed by four radiologists and four radiographers. Readers were asked to assess qualitative and quantitative image quality by rating: image quality, decision to repeat and image technique. All data was compared employing Pearson's Correlation, Visual grading characteristic (VGC) and Cohens' kappa analyses. Image quality: Radiologists (88.4%) rated images as excellent significantly more than radiographers (11.6%), and radiographers (90.1%) as poor significantly more than radiologists (9.9%) (p < 0.05). Repeat: Radiologists (57%) decided not to repeat images significantly more than radiographers (43%) (p < 0.05). Image technique: Radiologists rated images as acceptable (65%) and excellent (97.7%) significantly more than radiographers (35% and 2.3% respectively) (p < 0.05), whereas radiographers (84%) assessed image technique as poor significantly more than radiologists (16%) (p < 0.05). VGC: radiographers had slightly better qualitative evaluation of image quality than radiologists. An association between image quality (p < 0.002) and repeat decision (p < 0.044) with years of experience was established when comparing years of experience with image assessment rubric, while no association was noted with image technique (p < 0.9). Radiologists demonstrated more decisiveness than their fellow radiographers in reducing the repeat rate of portable pediatric chest radiographs. Interestingly, years of experience only seem to affect image technique and image quality assessment among radiologists. Continuous education of radiographers and close collaboration with radiologists is crucial to achieve optimal image quality and low radiation doses. [ABSTRACT FROM AUTHOR]

• The VIA Water programme created a level playing field for both African and non-African innovators. • Over 50 % of the innovations reached market introduction or dissemination. • Key success factors were the country focus, flexibility, partnership building and entrepreneurial spirit. • Building a trustworthy environment stimulated learning and exchange. VIA Water was an innovation programme of Aqua for All and IHE-Delft funded by the Netherlands Ministry of Foreign Affairs. VIA Water stands for Valorisation and Innovation in Africa in the Water sector. Between 2015 and 2019 the programme supported 62 urban water innovations across eight countries in Africa with a total grant of € 8.3 million. The programme offered grants to pilot new ideas and innovations in combination with technical, learning and business development support. Of the 525 original concept notes that were received, 62 secured a contract with VIA Water (12 %). Of the supported innovations, 53 % reached a stage of market introduction and dissemination, while less than 10 % of pilots failed. VIA Water has demonstrated a high inclusivity of African-led innovators with a similar level of effectiveness. Its portfolio was almost evenly distributed between innovations led by African and non-African lead partners. The programme was able to create a level playing field by providing special support to African innovators. This article explores the conditions which are needed to create this level playing field, including building a trustworthy environment in which learning and exchange are stimulated. Further, the article analyses some possible success factors, such as the country in which the innovation is applied, the theme ("pressing needs") addressed by the innovation, the type of contract organisation and the approach chosen by the programme. Compared to three selected reference programmes, VIA Water has shown comparable performance despite being less risk averse. [ABSTRACT FROM AUTHOR]

The Shared Trauma Professional Post Traumatic Growth Inventory (STPPG) was developed by Tosone et al. (2014) to help understand shared trauma (ST) in social workers. ST occurs when the healthcare professional and client both experience the same collective traumatic event. This inventory has been adapted for use with mental health nurses. A cross-sectional study of N = 552 mental health nurses was completed in the spring of 2023 to assess the feasibility of using the STPPG to explore shared trauma in mental health nurses. An exploratory factor analysis was run for the STPPG using squared multiple correlations with the maximum likelihood method. The alpha coefficient ranged from 0.82 to 0.89 for 2-factors and 0.73 to 0.89 for 3-factors. The results indicated that all correlations were significant among the total scales and subscales. All correlations were positive, ranging from 0.81 to 0.95 for two factors and 0.58 to 0.89 for three factors. The STPPG has confirmed a two-factor analysis for mental health nurses. The STPPG is a valid inventory to measure ST in mental health nurses and will allow the concept to be further studied. • Exploratory Factor Analysis using the Shared Trauma Professional Posttraumatic Growth Inventory (STPPG) in psychiatric-mental health nurses. • The sample size of this study was N=552 nurses. • The STPPG was found to be a reliable inventory to measure shared trauma and professional posttraumatic growth [ABSTRACT FROM AUTHOR]

Tubular flow diverters (FDs) represent an important subset of the endovascular treatment of cerebral aneurysms (CAs), acting to reduce aneurysm inflow, eventually resulting in aneurysm thrombosis and occlusion. eCLIPs (product of Evasc Neurovascular Enterprises, Vancouver, Canada), an innovative non-tubular implant causes flow diversion by bridging the neck of bifurcation CAs. However, in a small subset of challenging bifurcation aneurysms with fusiform pathology, the currently available eCLIPs models do not provide sufficient neck bridging resulting in a gap created between the device structure and the aneurysm/artery wall. To overcome this challenge, a new design of the eCLIPs (VR-eCLIPs) was developed by varying the rib length to cover such an inflow gap. To optimize the new product development process, and avoiding expensive and time-consuming iterative manufacture of prototype devices, we have developed a new finite element model to simulate the crimping and expansion processes of the VR-eCLIPs implant, and assess the possibility of plastic deformation. Results indicated that neither eCLIPs nor VR-eCLIPs experience plastic deformation during the crimping process. Upon full expansion, the ribs of VR-eCLIPs interact with the aneurysm and artery wall to cover the inflow gap that exists in certain challenging anatomies. This process serves as a basis to expedite design development prior to prototype manufacturing. [ABSTRACT FROM AUTHOR]

Increasing demand of multifunctional devices has attracted researchers and scientists toward materials having magnetoelectric coupling as they can provide an extra degree of freedom to device fabrication. In this regard, composites of antiferromagnetic perovskites and ferromagnetic spinels comprise a prospective recipe. Here, we report (1-x)BiFe 0.9 Mn 0.1 O 3 -xNiFe 2 O 4 (x = 0.00, 0.10, 0.20, 0.30, 0.40, and 0.50) nanocomposites prepared by a two-step route. Analysis of the data obtained from diffractometer confirmed the corresponding crystalline phases of Mn-doped BiFeO 3 and NiFe 2 O 4. Formation of nanoparticles was confirmed by field emission scanning electron microscope. Presence of stoichiometric amounts of all elements in composites was confirmed by energy dispersive X-ray spectroscopy. To understand the dielectric relaxation phenomenon, we examined dielectric spectra in a wide frequency range, something that helped us probe the nature of charge transition inside the samples. Increasing NiFe 2 O 4 contents in composites improved the ferroelectric and magnetic response of the material with noticeable magneto-electric coupling, showing the potential of these composites for multifunctional device fabrication. [ABSTRACT FROM AUTHOR]

Abstract Experimental investigations were carried out to explore the performance and emission characteristics of three cylinder diesel engine with its piston coated with three different ceramic coating materials namely zirconia, zirconia + aluminum oxide (ZrO 2 +Al 2 O 3) and fused zirconia (40FZA). The coating process were done through plasma spray coating. Further the experimental results of these three candidate materials were compared and analyzed to draw the best ceramic coating material based on their engine performance and emission characteristics with the coating thickness of 0.5 mm. Finally, the results revealed that coating with fused zirconia provides better results on engine performance and emission characteristics including smoke density, rate of emission of carbon monoxide, carbon di-oxide, hydro carbon and nitric oxide. This study sought to explore the scientific relevance through analyzing the performance of the engine and encourage the cleaner production through considering carbon emissions. Conclusion of this study promoted the performance of coating material in addition with shed light on the future research opportunities exists within this area. [ABSTRACT FROM AUTHOR]

Construction of residential buildings using off-site methods, in particular modular construction, is receiving considerable attention. However, the sustainability performance of modular buildings has rarely been investigated through a life cycle perspective. In this paper, a life cycle sustainability performance assessment framework is developed for modular buildings and its application is examined. In the first part of the paper, suitable life cycle sustainability performance criteria (SPCs) for modular buildings were developed and ranked. In this regard, potential SPCs were identified through a comprehensive literature review and expert interviews. These SPCs were then evaluated by construction experts through two questionnaire surveys against three evaluation criteria: applicability, data availability, and data accuracy. The evaluation criteria's weights were determined through a group decision making process using the Analytic Hierarchy Process (AHP) multi-criteria decision analysis (MCDA) method. Consequently, the experts' feedback was analyzed with the help of the Elimination and Choice Translating Reality (ELECTRE) outranking MCDA method and all the SPCs were ranked within their associated sustainability categories (i.e., environmental, economic, and social). In the second part of the paper, application of the proposed framework has been discussed and validated through a case study of a modular building in British Columbia, Canada. Sustainability performance of modular buildings in the proposed framework were assessed by developing aggregated sustainability indices for the selected SPCs and comparing them with corresponding benchmarks. In this regard, appropriate sustainability performance indicators (SPIs) under each selected SPC have been developed and calculated. Consequently, through an aggregation process, sustainability indices are developed using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) MCDA method. In this paper, the environmental life cycle performance of the case study building has been benchmarked and recommendations have been made for performance improvements. This research is deemed useful for the construction practitioners since it provides a methodical framework for life cycle sustainability performance assessment of modular buildings and assists with the selection of sustainable methods of construction. [ABSTRACT FROM AUTHOR]

To date, no specific framework has been developed to guide composite structure designers to select the optimum fiber types and fabric weave patterns for a given application. This article aims to, first, investigate the effect of weighting methods in multiple criteria decision making (MCDM) and then arrive at a systematic framework for optimum weave pattern selection in fiber reinforced polymer (FRP) composites. Namely, via measured data from an industrial case study, the TOPSIS MCDM technique has been applied to choose the best candidate among different polypropylene/glass laminates. As an input to TOPSIS, different types of subjective and objective weighting methods were initially compared to assess the role of relative importance values (weights) of design criteria. These included the Entropy method, the modified digital logic (MDL) method, and the criteria importance through inter-criteria correlation (CRITIC) method. Next, two new subjective weighting methods, named ‘Numeric Logic (NL)’ and ‘Adjustable Mean Bars (AMB)’ methods, were introduced to give more practical and effective means to the decision makers during the weighting of criteria. In particular, compared to the MDL, the NL method increased the accuracy of assigned weights for an expert DM. On the other hand, the AMB provided a more interactive, visual approach through MCDM weighting process for less experienced DMs. Finally, a generalized combinative weighting framework is presented to show how different types of weightings may be combined to find more reliable rankings of alternatives. The combinative weighting could specifically accommodate different scenarios where a group of designers are involved and have different levels of experience, while given a large number of alternatives/criteria in highly nonlinear applications such as impact design of composite materials. [ABSTRACT FROM AUTHOR]