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Optimization of Curing Temperature for Epoxy/SiO2/Glass Fiber Composite: Dual Enhancement of Mechanical Strength and Thermal Stability for Thin-Walled Pressure Vessels
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The data that support the findings of this study are available from the corresponding author upon reasonable request.The development of advanced composite materials for thin-walled pressure vessels demands a balance between mechanical strength and thermal insulation. In this study, a novel three-phase composite system comprising epoxy resin, silica (SiO2) micro-particles, and glass fiber reinforcement was fabricated and characterized for potential application in high-performance thin vessel structures. Specimens were cured at varying temperatures (60°C to 160°C) to systematically investigate the influence of curing conditions on the structural and thermal properties. Comprehensive material characterization, including Fourier transform infrared (FTIR) spectroscopy and x-ray diffraction (XRD) analysis, confirmed the successful integration of silica and glass fiber within the amorphous epoxy matrix. Thermogravimetric analysis (TGA) revealed a two-stage degradation process, with maximum thermal stability observed at 120°C curing temperature. Specific heat capacity (Cp) and measurements indicated decreasing trends with increasing curing temperature, enhancing thermal insulation. Mechanical testing demonstrated that hoop strength (SH) and burst pressure (Pb) improved significantly with curing temperatures up to 140°C, following third-degree polynomial relationships. Notably, the composite cured at 120°C exhibited the highest combination of hoop strength (341.3 ± 6.5 MPa), burst pressure (16.66 ± 0.3 MPa), Cp (2.33 J/g·K), thermal conductivity (0.198 W/m·K) and Factor of Safety (1.39 ± 0.024), while maintaining superior thermal resistance. Theoretical predictions showed strong agreement with experimental results across all evaluations. Overall, the optimized epoxy/SiO2/glass fiber composites offer a lightweight, thermally stable, and mechanically robust alternative to traditional metallic vessels, highlighting their potential for use in chemical, oil, and pharmaceutical industries requiring durable thin-walled pressure containment solutions.This work was supported by the European Commission, CZ.10.03.01/00/22_003/0000048
Feasibility of a real-world digital hybrid pulmonary rehabilitation model using a smartphone app
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Hybrid PR, using a digital app to support standard pulmonary rehabilitation, does not reduce scheduled pulmonary rehabilitation sessions. Poor digital literacy is an important barrier to implementation of hybrid PR in practice. https://bit.ly/4dMdNVBSupplementary material:
Please note: supplementary material is not edited by the Editorial Office, and is uploaded as it has been supplied by the author.
Supplementary material is available online at: https://publications.ersnet.org/content/erjor/11/6/00139-2025/DC1/embed/inline-supplementary-material-1.pdf .Background:
There is interest in digital technology-enabled models of pulmonary rehabilitation (Digital-PR) as a means of increasing capacity, uptake and accessibility. However, there are little data on real-world implementation or how Digital-PR could support other models of pulmonary rehabilitation delivery.
Methods:
We conducted a mixed-methods, feasibility study to evaluate the acceptability of a hybrid model of pulmonary rehabilitation (Hybrid-PR) blending Digital-PR with traditional, supervised pulmonary rehabilitation (PR). To determine acceptability, we measured engagement and use of the app and conducted patient interviews. We assessed differences in PR completion, number of scheduled sessions and staff time between Hybrid-PR and a propensity-matched control group attending PR without Digital-PR (Control-PR).
Results:
Of 69 people undergoing Hybrid-PR, 87% opted for in-person, centre-based care and 13% for home-based care (10% supported by video-teleconferencing, 3% supported by telephone). 86% activated Digital-PR at least once, but only 35% activated regularly (at least weekly for 8 weeks). 88% never accessed the exercise components of Digital-PR. There were no significant differences in PR completion rates, number of supervised PR sessions, nor staff time in Hybrid-PR when compared to Control-PR. Both patients and staff identified digital literacy, limited flexibility to adapt/tailor Digital-PR and increased time-commitment as potential barriers.
Conclusion:
Hybrid-PR was not considered acceptable due to intervention fidelity and limited patient engagement with Digital-PR. Hybrid-PR was not associated with reduction in scheduled supervised sessions. Poor digital literacy is an important barrier to implementation of Digital-PR in the real-world setting.The study was funded by the National Health Service Accelerated Access Collaborative through a Small Business Research Initiative healthcare award. Neither the funder nor the digital health platform manufacturer (Aseptika Ltd) played a role in the design, analyses, data interpretation or decision to submit results. T.O. Jenkins is funded by a Health Education England (HEE) and National Institute for Health Research (NIHR) ICA Predoctoral Clinical and Practitioner Academic Fellowship. G.D. Edwards and W.D-C. Man are funded by a NIHR Artificial Intelligence Award. S. Patel is funded by a HEE/NIHR ICA Programme Clinical Doctoral Research Fellowship. Funding information for this article has been deposited with the Open Funder Registry
Sequential optimality conditions and solution existence for nonsmooth multiobjective optimization problems
No full textIn this paper, we consider a multiobjective optimization problem, where its feasibility set is unbounded and the related functions are nonsmooth and nonconvex. We first present new sequential necessary/sufficient optimality conditions that are established in terms of asymptotic value sets for weak Pareto solutions and weak Pareto values of the considered problem. We then examine how asymptotic values and Karush–Kuhn–Tucker values guarantee the existence of different types of Pareto solutions of the underlying problem. To achieve these goals, we utilize the tool of variational analysis to define sets of asymptotic values and Karush–Kuhn–Tucker values of the multiobjective optimization problem.This research is funded by Vietnam National University, Ho Chi Minh City (VNUHCM) under grant number T2025-18-01
AI-driven semantic similarity-based job matching framework for recruitment systems
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I have shared the data links in the references.Supplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S0020025525008643?via%3Dihub#s0155 .This paper presents a real-time online recruitment application that integrates semantic similarity and artificial intelligence (AI) to improve job-candidate matching. It addresses the growing volume of job applications and the limitations of traditional keyword-based systems, which often fail to capture contextual meaning and complex semantic relationships in job-candidate alignment. The proposed system leverages natural language processing (NLP) techniques, specifically TF-IDF vectorization, cosine similarity scoring, and domain-specific keyword weighting, to interpret conceptual relevance between resumes and job descriptions, enabling more accurate and inclusive recruitment outcomes.
This research developed the system in Python and evaluated it using simulated and real-world recruitment datasets. Experimental results show that the semantic model consistently outperforms keyword-based matching across diverse job domains. For instance, in simulated tests, similarity scores reached 0.74 in the Software Engineer domain, compared to just 0.35 using keyword-based methods. Real-world evaluations further confirmed the model’s effectiveness, with semantic scores of 0.83, 0.76, and 0.74 for the Hadoop, Data Science, and PMP domains, respectively. In contrast, the corresponding keyword-based scores remained below 0.17.
Additionally, the system performs well in aligning generalist and specialist profiles, achieving a score of 0.88 for Data analysis roles. These findings validate the system’s robustness, scalability, and ability to interpret varied terminology across job sectors. The research presents a scalable, AI-driven framework that supports context-aware, fair, and accurate job matching, significantly advancing intelligent recruitment technology
On the Performance Analysis of Pinching-Antenna-Enabled SWIPT Systems
Get PDFIn this paper, we study the performance of a novel simultaneous wireless information and power transfer (SWIPT) system assisted by a flexible pinching antenna. To enable practical deployment and enhance the energy-rate trade-off performance, three pinching antenna placement schemes are proposed, namely the edge deployment scheme (EDS), the center deployment scheme (CDS), and the diagonal deployment scheme (DDS). In addition, a hybrid time-switching (TS) and power-splitting (PS) protocol is introduced to dynamically balance energy harvesting and information decoding. For each deployment strategy, closed-form expressions for the average harvested energy and achievable rate of a randomly located user equipment (UE) are derived based on the optimal antenna position along the waveguide. Numerical results validate the analytical expressions and demonstrate the inherent trade-off between information rate and harvested energy under different deployment schemes.This paper was partly funded by Jiangsu Major Project on Fundamental Researches (Grant No.:
BK20243059), Gusu Innovation Project for People (Grant No.: ZXL2024360), Natural Science Foundation
of China (Grant No. 62531008, 62132004) and Nanjing University-China Mobile Communications
Group Co.,Ltd. Joint Institute
Enhanced Velocity-Adaptive Scheme: Joint Fair Access and Age of Information Optimization in Vehicular Networks
Get PDFPart of this paper has been accepted by IEEE RFAT 2025 conference.In this paper, we consider the fair access problem and the Age of Information (AoI) under 5G New Radio (NR) Vehicle-to-Infrastructure (V2I) Mode 2 in vehicular networks. Specifically, vehicles follow Mode 2 to communicate with Roadside Units (RSUs) to obtain accurate data for driving assistance. Nevertheless, vehicles often have different velocity when they are moving in adjacent lanes, leading to difference in RSU dwell time and communication duration. This results in unfair access to network resources, potentially influencing driving safety. To ensure the freshness of received data, the AoI should be analyzed. Mode 2 introduces a novel preemption mechanism, necessitating simultaneous optimization of fair access and AoI to guarantee timely and relevant data delivery. We propose a joint optimization framework for vehicular network, defining a fairness index and employing Stochastic Hybrid Systems (SHS) to model AoI under preemption mechanism. By adaptively adjusting the selection window of Semi-Persistent Scheduling (SPS) in Mode 2, we address the optimization of fairness and AoI. We apply a large language model (LLM)-Based Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D) to solve this problem. Simulation results demonstrate the effectiveness of our scheme in balancing fair access and minimizing AoI.This work was supported in part by Jiangxi Province Science and Technology Development Programme under Grant No. 20242BCC32016, in part by the National Natural Science Foundation of China under Grant No. 61701197, in part by the National Key Research and Development Program of China under Grant No. 2021YFA1000500(4), in part by the Shanghai Kewei under Grant 22JC1404000and Grant 24DP1500500, in part by the Research Grants Council under the Areas of Excellence Scheme under Grant AoE/E-601/22-R and in part by the 111 Project under Grant No. B23008
Measuring the Effects of Interventions on Participation in Children with Developmental Coordination Disorder (DCD)
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No datasets were generated or analysed during the current study.Purpose of Review:
The 2019 International clinical practice recommendations for developmental coordination disorder” recommended setting goals, and targeting interventions, at the activity and participation level. This review will explore how clinicians can make a positive move towards participation focused practice by measuring the impact of interventions at a participation level.
Recent Findings:
Few studies have been published where the primary outcome measure is participation in activities which reflect child and family goals. The challenge of measuring the impact of participation focused interventions is setting goals and finding measures to capture both attendance and engagement from the child’s perspective. The Canadian Occupational Performance Measure (COPM) is frequently used to set goals and measure outcomes but does not consider levels of attendance and engagement.
Summary:
Participation is a difficult construct to measure. There are tools available aimed at measuring participation, but they capture the parent/carer voice rather than the child’s. The recent publication of national survey studies on the impact of DCD provides valuable data in support of the development of participation focused services in multiple countries. Future research should continue to explore ways of capturing the child’s voice in measures of participation and continue to drive a better understanding of how participation focused practice can help mitigate some of secondary consequences reported in recent impact data.None
Climate policies, energy shocks and spillovers between green and brown stock price indices
Get PDFJEL Classification: C33; G12; G18.Data availability:
Data will be made available on request.This paper examines the effects of climate policies and energy shocks on mean and volatility spillovers between green and brown stock price indices in five countries (Canada, India, Japan, the UK and the US). More specifically, bivariate GARCH-BEKK models including dummy variables controlling for these shocks are estimated using weekly series with start dates ranging from March 13, 2009 to August 24, 2012 (depending on data availability for the green index) and an end date of December 29, 2023. Significant dynamic linkages between green and brown indices are found when climate policy and oil shocks are considered jointly. Some common patterns emerge, such as shifts in spillover dynamics between green and brown assets, but also country-specific effects of the climate policy shocks which reflect differences in regulatory frameworks and policies. By contrast, energy shocks tend to have a more uniform impact. Further, the interaction between climate policy and energy shocks weakens cross-market linkages, enhancing portfolio diversification opportunities for green investors. The conditional correlation analysis confirms this finding, suggesting that green stocks can be used as an effective hedge. These results highlight the benefits of incorporating green assets into diversified portfolios, particularly in financial centers where, in recent years, they have offered higher returns and lower volatility
Zonotopic Set-Membership Fusion Estimation for Complex Networks: A Buffer-Aided Strategy
Get PDFThis article is concerned with the zonotopic set-membership fusion estimation (SMFE) problem for a class of complex networks (CNs). The measurements of the CNs are transmitted to a remote fusion center through a shared communication network. Due to the limited network bandwidth, the transmissions of the measurement information occur intermittently, and the nodes’ transmission intervals may exceed their sampling periods. To enhance the utilization of the measurement information, each node of the CN is equipped with a buffer for real-time data storage, so that the fusion center can utilize more measurement information at time instants when the node’s transmission interval is larger than its sampling period. The aim of this article is to design SMFE algorithms based on both the parallel fusion scheme and the data-compression fusion scheme, respectively, using the data received at the fusion center. First, by iterating the state equation of the CN, a batch processing method is proposed to process the input data of the fusion center concurrently. Subsequently, by employing the zonotopic set-membership estimation (SME) technique, the desired SMFE algorithms are designed. Moreover, sufficient criteria are established to ensure that the sizes of the output zonotopes of the SMFE algorithms remain uniformly bounded. Finally, two numerical examples are presented to illustrate the effectiveness of the proposed algorithms.10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62373103, 62403130 and 62573121);
Jiangsu Provincial Scientific Research Center of Applied Mathematics of China (Grant Number: BK20233002);
Natural Science Foundation of Jiangsu Province of China (Grant Number: BK20241286);
Jiangsu Funding Program for Excellent Postdoctoral Talent of China (Grant Number: 2024ZB601);
China Postdoctoral Science Foundation-China Coal Technology and Engineering Group (CCTEG) Joint Support Program (Grant Number: 2025T055ZGMK);
10.13039/501100000288-Royal Society of UK;
Alexander von Humboldt Foundation of Germany
A Conceptual Hybrid Simulation Approach for Advancing Safety in Connected Automated Vehicles
Get PDFEnsuring traffic safety remains a major challenge due to the complexity of traffic environments and the early stage of autonomous vehicle (AV) technology, despite their potential to significantly reduce accidents and enhance road safety. The Artificial Potential Field (APF) approach offers a promising solution by simulating how vehicles adjust their motion, speed, and interactions with surrounding vehicles to maintain safety. This paper aims to introduce a conceptual hybrid simulation using the APF implemented within a multi-agent framework. The objective is to evaluate the suitability of APF model for real-time safety applications across extended time periods and diverse traffic scenarios. This evaluation is conducted through a hybrid simulation approach to identify advantages and limitations compared to existing risk assessment methodologies
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