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Investigation of the Time-Dependent Deformation of Recycled Aggregate Concrete in a Water Environment
Get PDFData Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request.The water environment greatly affects the creep deformation of recycled aggregate concrete (RAC). Hence, a humidity–stress–damage coupling numerical model was used for investigating the time-dependent deformation of RAC in the water environment in this study. Firstly, uniaxial compression and water absorption tests were performed to determine the calculation parameters of the creep numerical simulation of RAC in a water environment. Experimental results indicate that the elastic modulus and compressive strength drop as the water content increases. Then, the time-dependent deformation of RAC in a water environment was studied using a numerical simulation test of compressive creep when multiple stress levels were applied, and the critical stress for accelerated creep and the long-term strength of RAC were obtained. Finally, the influence of confining pressures on the long-term deformation of RAC in a water environment was discussed. When there is no confining pressure, the long-term strength of RAC is 23.53 MPa. However, when a confining pressure of 3.921 MPa is loaded onto RAC, the long-term strength of RAC is 47.052 MPa, which increases by 100%. Increasing confining pressures has an obvious effect on ensuring the long-term stable application of RAC in a water environment. Compared with the creep test, the method adopted in this study saves time and money and provides the theoretical basis for evaluating the time-dependent deformation of RAC in a water environment.Natural Science Foundation of Shandong Province, China (Grant No. ZR2020QE110); National Natural Science Foundation of China, China (Grant No. 52104089)
Do Vice Chancellors’ Career Horizon Matter for University Sustainability Performance? The Moderating Role of Soft Information
Get PDFData Availability Statement:
The data that support the findings of this study are available from the corresponding author upon reasonable request.In the evolving landscape of higher education, leadership plays a pivotal role in directing institutional strategies towards sustainability. This study examines how the career horizons of Vice Chancellors (VCs)—often akin to CEOs in the corporate sector—influences the sustainability performance of UK universities. Using a unique hand-collected dataset covering the years 2018–2022, our results show that shorter VC career horizons negatively impact universities' sustainability performance, indicating that VCs closer to retirement are more ethically and sustainability-focused. Moreover, we explore how the disclosure of soft information—characterised as boilerplate and forward-looking language in sustainability reports—affects this relationship. Our analysis indicates that this soft information significantly moderates the effects of VCs' career horizons on sustainability outcomes. Specifically, we discover that extensive use of forward-looking and boilerplate language tends to exacerbate the negative impacts of shorter VC tenures on sustainability performance. This research contributes to the academic discourse by documenting how leadership tenure and the strategic use of narrative in public disclosures interact to shape institutional sustainability. The findings advocate for a strategic approach in leadership appointments and reporting practices, enhancing the alignment between leadership characteristics and the long-term sustainability goals of higher education institutions.The authors received funding from Principles for Responsible Management Education UK & Ireland Chapter
Metaverse Meets Intelligent Transportation System: An Efficient and Instructional Visual Perception Framework
Get PDFThe combination of the Metaverse and intelligent transportation systems (ITS) holds significant developmental promise, especially for visual perception tasks. However, the acquisition of high-quality scene data poses a challenging and expensive endeavor. Meanwhile, the visual disparity between the Metaverse and the physical world poses an impact on the practical applicability of the visual perception tasks. In this paper, a Metaverse Intelligent Traffic Visual Framework, MITVF, is developed to guide the implementation of visual perception tasks in the physical world. Firstly, a two-stage metadata optimization strategy is proposed that can efficiently provide diverse and high-quality scene data for traffic perception models. Specifically, an element reconfigurability strategy is proposed to flexibly combine dynamic and static traffic elements to enrich the data with a low cost. A diffusion model-based metadata optimization acceleration strategy is proposed to achieve efficient improvement of image resolution. Secondly, a Meta-Physical adaptive learning method is proposed, and further applied to visual perception tasks to compensate for the visual disparity between the Metaverse and the physical world. Experimental results show that MITVF achieves a 10 × acceleration in optimization speed, ensuring the image quality and reconstructing diverse. Further, MITVF is applied to the traffic object detection task to verify the effectiveness and validity. The performance of the model trained with 5k real data exceeded that of the model trained with 200k real data, with AP 50 reaching 67.7%.10.13039/501100013142-Key Research and Development Project of Hangzhou (Grant Number: 2022AIZD0009 and 2022AIZD0022);
10.13039/501100013064-Key Research and Development Program of Zhejiang Province (Grant Number: 2022C01062)
Game-Theory-Based Design and Analysis of a Peer-to-Peer Energy Exchange System between Multi-Solar-Hydrogen-Battery Storage Electric Vehicle Charging Stations
Get PDFData Availability Statement: Data are contained within the article.As subsidies for renewable energy are progressively reduced worldwide, electric vehicle charging stations (EVCSs) powered by renewable energy must adopt market-driven approaches to stay competitive. The unpredictable nature of renewable energy production poses major challenges for strategic planning. To tackle the uncertainties stemming from forecast inaccuracies of renewable energy, this study introduces a peer-to-peer (P2P) energy trading strategy based on game theory for solar-hydrogen-battery storage electric vehicle charging stations (SHS-EVCSs). Firstly, the incorporation of prediction errors in renewable energy forecasts within four SHS-EVCSs enhances the resilience and efficiency of energy management. Secondly, employing game theory’s optimization principles, this work presents a day-ahead P2P interactive energy trading model specifically designed for mitigating the variability issues associated with renewable energy sources. Thirdly, the model is converted into a mixed integer linear programming (MILP) problem through dual theory, allowing for resolution via CPLEX optimization techniques. Case study results demonstrate that the method not only increases SHS-EVCS revenue by up to 24.6% through P2P transactions but also helps manage operational and maintenance expenses, contributing to the growth of the renewable energy sector.This research received no external funding
Design and development of composite enclosure with variable thermal conductivity for Li-ion battery module
Get PDFThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThermal management of batteries can be accomplished through active or passive built in cooling sources. Air, liquid (mainly water) and phase-changing materials or a combination of these three methods are used in existing thermal management systems.
Current solutions with active cooling are adding weight and complexity to EV and HEV batteries. The involvement of phase-changing materials (PCMs) as a passive cooling technique is also adding to the complexity, weight, and the additional thermal resistance in the heat dissipation process of batteries.
An innovative idea for a composite casing for car batteries is considered in this project. The composite casing will have variable thermal conductivity, defined by the local volume fraction of carbon fibres and other conductive elements (including copper pins) within the composite. The selectively high thermal conductivity in areas of the casing will create “thermal avenues” close to the hot areas of the battery in order to provide passive heat dissipation in the areas needed the most. The composite casing will provide a low weight, simple thermal management solution that requires minimum maintenance.
A 3D model of a Li-ion battery single cell was developed and used to evaluate several geometries of a battery module currently being used by battery manufacturers. Heat transfer simulations are validated by experimental results from a custom jig that emulates the battery module arrangement. Heating elements of similar size and power/heat output to individual cells have been used for the experiments.
The composite casing was manufactured using the resin infusion method, and copper pins were inserted into the significant locations of the enclosure during the process of resin infusion. The metallic pin arrangement was validated experimentally with the bespoke test rig. Simulation and experimental results are in better agreement with each other for the scenarios of the composite enclosure and the composite enclosure with the copper pin arrangement.
Furthermore, the simulation results, IR thermography results, and experimental results confirmed that the copper pins arrangement is an effective solution to conduct the heat that is accumulated inside the enclosure to the outside without the use of any kind of active cooling methods such as air-cooling or water cooling.Lloyds Register Foundation (LRF
A national survey of current rehabilitation service provisions for people living with chronic kidney disease in the UK: Implications for policy and practice
Get PDFData availability: Data available from corresponding author upon request; [email protected] supplementary material is available online at: https://bmcnephrol.biomedcentral.com/articles/10.1186/s12882-024-03742-4#Sec30 .Background:
National guidance recognises the key role of rehabilitation in improving outcomes for people living with chronic kidney disease. Implementation of this guidance is reliant upon an adequate and skilled rehabilitation workforce. Data relating to this is currently lacking within the UK. This survey aimed to identify variations and good practices in kidney physiotherapy (PT), occupational therapy (OT) and clinical exercise physiologist (CEP) provision; and to understand barriers to implementation.
Methods:
An online survey was sent to all 87 UK kidney units between June 2022 and January 2023. Data was collected on the provision of therapy services, barriers to service provision and responses to the COVID-19 pandemic. The quantitative survey was analysed using descriptive statistics. Free-text responses were explored using reflexive thematic analysis.
Results:
Forty-five units (52%) responded. Seventeen (38%) units reported having a PT and 15 (33%) an OT with a specialist kidney role; one unit (7%) had access to a CEP. Thirty units (67%) offered inpatient therapy services, ten (22%) outpatient therapy clinics, six (13%) intradialytic exercise, six (13%) symptom management and three (7%) outpatient rehabilitation. Qualitative data revealed lack of money/funding and time (both n = 35, 85% and n = 34, 83% respectively) were the main barriers to delivering kidney-specific therapy. Responders saw an increase in the complexity of their caseload, a reduction in staffing levels and consequently, service provision during the COVID-19 pandemic. Exemplars of innovative service delivery, including hybrid digital and remote services, were viewed as positive responses to the COVID-19 pandemic.
Conclusion:
Despite clear evidence of the benefits of rehabilitation, across the UK, there remains limited and variable access to kidney-specific therapy services. Equitable access to kidney-specific rehabilitation services is urgently required to support people to ‘live well’ with kidney disease.HMLY is funded by the NIHR [NIHR302926]
Criminalising Migration: The Vicious Cycle of Insecurity and Irregularity
Get PDFData Availability Statement: The data supporting this study are cited in the “References” section.Recent years have witnessed growing emphasis on exceptional measures to address unauthorised arrivals. This article unpacks the relationship between migration policies, irregularity, and insecurity, by examining the consequences of a specific, yet often neglected, measure: the criminalisation of irregular migration (namely, the introduction of the “crime of irregular migration”). Investigating the cases of Italy and France, two of the countries with the most severe sanctions in Europe, it argues that criminalisation led to a two-fold feedback loop. On the one hand, by exceptionalising migration and constructing a continuum between migrants and criminals, criminalisation enhanced a sense of insecurity among the domestic public. On the other hand, by giving foreigners in irregular situations a criminal record, it increased their reliance on underground networks to stay and work in destination countries. Overall, this fostered demand for restrictive, yet counterproductive, policies, creating a vicious cycle of insecurity and irregularity.Fondazione Luigi Einaudi through the Roberto Einaudi Scholarship
New intelligent optimisation systems for job shop scheduling problems in the manufacturing industry
Get PDFThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThis research focuses on scheduling systems in manufacturing and developing new optimisation
techniques which are capable of dealing with scheduling problems. Scheduling
is an important factor in manufacturing systems and aims to optimise the production
system, reducing time and energy consumption. In this regard, numerous researchers
have studied Job shop Scheduling Problems (JSSPs). In JSSPs, there are several jobs
and machines, and depending on the type of job shops, schedules and related penalties,
each job needs to be executed on machines on specific orders. Finding the best schedule
is challenging, and there is still a need to improve and develop advanced and optimised
scheduling models.
This work designs optimisation models based on hybridising Genetic Algorithm (GA)
techniques and Reinforcement Learning (RL) for scheduling a furnace model and simulated
job shops. Hence, several sophisticated algorithms are developed for this proposal,
namely Stochastic GA, Sexual GA, Ageing GA, Parthenogenetic algorithm and Ethnic
GA. These algorithms are employed to establish a new metaheuristic hybrid parthenogenetic
algorithm (NMHPGA) based on the combinations of the different selections to
hybridise the basic GAs; moreover, two types of advanced RL, including off-policy Qlearning
and on-policy RL based on State-Action-Reward-State-Action (SARSA) are developed.
Following that, all algorithms are tested on two categories of scheduling job
shops, including 10 single-machine job shops and 19 multi-machine job shops; all the job
shops are simulated in MATLAB, and the aim is to reduce the makespan of the job shops.
Results which are compared to basic GA, show that the developed models attain superior
results with a faster convergence rate. As a case study, a reheating furnace model is used
to optimise material heating schedules, finding the most efficient schedule to minimise
time and energy consumption. The models improve the efficiency on average to 40 % on
job shops and furnace fuel consumption by up to 3.20 % and operation time by 3.79 %
Microbial loading and self-healing in cementitious materials: A review of immobilisation techniques and materials
Get PDFData availability: Data will be made available on request.Concrete has been a material of choice when it comes to building materials for decades. However, concrete has a number of challenges in which a major challenge being microcracking leading to excess damage and wastes. The development and advancement of self-healing technology throughout the past decade have seen the popular use of immobilization as a way of protecting bacteria from the harsh environments found in cementitious materials. This paper reviews the materials used for immobilization, categorising into organic materials and inorganic materials, and investigates the various immobilization techniques used to immobilize bacteria into polymeric structures and porous materials. The study evaluates the key findings in literature surrounding immobilization materials and methods as well as highlighting possible alternative sustainable materials and methods including waste/by-product resources. It was found that inorganic materials were superior to organic material in terms of self-healing and mechanical properties, with nanomaterials producing the highest crack closure of 1.20 mm. Various immobilization techniques efficiency was tested comparing microencapsulation, vacuum impregnation and adsorption methods. Further studies are needed to understand the relationship between carrier materials and cementitious matrix and explore the possible use of nanomaterials as a way of uniformly distributing bacteria in cementitious matrix.Engineering and Physical Sciences Research Council (EPSRC)
Prioritized sum-tree experience replay TD3 DRL-based online energy management of a residential microgrid
Get PDFData availability: The data that has been used is confidential.Online energy management utilizing the real-time information of a residential microgrid (RM) can make full use of renewable energy and demand-side resources at the residential level. However, existing online energy management methods for RMs have poor robustness against environmental changes, which limits their applicability in highly uncertain scenarios. To address this, a novel online energy management method based on the prioritized sum-tree experience replay strategy with a double delayed deep deterministic policy gradient (PSTER-TD3) is proposed in this paper. First, we formulate the sequential scheduling decision problem as a Markov decision process (MDP) problem with the objective of minimizing residential energy costs while simultaneously ensuring household thermal comfort and minimizing range anxiety for electric vehicle usage. Then, using the proposed method, we determine the optimal online scheduling strategy under this objective. By integrating the prioritized experience replay strategy of the summation tree structure into TD3, the agent is able to learn the optimal scheduling strategy in complex environments, and its optimization performance and policy learning efficiency are significantly improved. In addition, its ability to handle multidimensional continuous action spaces helps achieve finer-grained optimization for RMs. The case study results demonstrate that the proposed method can effectively reduce the energy costs of residential microgrids while satisfying household thermal comfort requirements and reducing range anxiety for electric vehicle usage. Moreover, the optimization performance of the proposed method is robust when the uncertainty factors fluctuate violently in the environment.National Natural Science Foundation of China under Grant 52107108; the Natural Science Foundation of Hubei Province under Grant 2021CFB163
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