Power transformers are vital components in electric grids;however,methods to optimise their loading to extend lifespan while accounting for insulation degradation remain underdeveloped.This research paper introduces a...Power transformers are vital components in electric grids;however,methods to optimise their loading to extend lifespan while accounting for insulation degradation remain underdeveloped.This research paper introduces a novel integrated data-driven framework that combines particle filtering and model predictive health(PF-MPH)model for the predictive health manage-ment of power transformers.Initially,the particle filter probabilistically estimates power transformers'remaining life(R_(L))using direct winding hotspot temperature(χ_(H))measurements.The obtained R_(L)will then be used to calculate the degree of poly-merisation(DP)level and assess the current insulation condition.After that,a comparative analysis between direct and model-basedχ_(H)measurement methods is performed to highlight the superior accuracy of direct measurements for predictive health management.Then,the MPH optimisation algorithm,which uses the R_(L)and DP forecasts from the PF method,derives an optimal trajectory over the transformer's R_(L)that balances the costs of increased loading against the benefits gained from prolonged insulation longevity.The findings show that the proposed PF-MPH model has successfully reduced the χ_(H)by 2.46%over the predicted 19 years.This approach is expected to enable grid operators to optimise transformer loading schedules to extend the R_(L)of these critical assets in a cost-effective manner.展开更多
While it is well-known that neuronal activity promotes plasticity and connectivity, the success of activity-based neural rehabilitation programs remains extremely limited in human clinical experience because they cann...While it is well-known that neuronal activity promotes plasticity and connectivity, the success of activity-based neural rehabilitation programs remains extremely limited in human clinical experience because they cannot adequately control neuronal excitability and activity within the injured brain in order to induce repair. However, it is possible to non-invasively modulate brain plasticity using brain stimu- lation techniques such as repetitive transcranial （rTMS） and transcranial direct current stimulation （tDCS） techniques, which show promise for repairing injured neural circuits （Henrich-Noack et al., 2013; Lefaucher et al., 2014）. Yet we are far from having full control of these techniques to repair the brain following neurotrauma and need more fundamen- tal research （Ellaway et al., 2014; Lefaucher et al., 2014）. In this perspective we discuss the mechanisms by which rTMS may facilitate neurorehabilitation and propose experimental techniques with which magnetic stimulation may be investi- gated in order to optimise its treatment potential.展开更多
Fractures in sport are a specialised cohort of fracture injuries, occurring in a high functioning population, in which the goals are rapid restoration of function and return to play with the minimal symptom profile po...Fractures in sport are a specialised cohort of fracture injuries, occurring in a high functioning population, in which the goals are rapid restoration of function and return to play with the minimal symptom profile possible. While the general principles of fracture management, namely accurate fracture reduction, appropriate immobilisation and timely rehabilitation, guide the treatment of these injuries, management of fractures in athletic populations can differ significantly from those in the general population, due to the need to facilitate a rapid return to high demand activities. However, despite fractures comprising up to 10% of all of sporting injuries, dedicated research into the management and outcome of sport-related fractures is limited. In order to assess the optimal methods of treating such injuries, and so allow optimisation of their outcome, the evidence for the management of each specific sport-related fracture type requires assessment and analysis. We present and review the current evidence directing management of fractures in athletes with an aim to promote valid innovative methods and optimise the outcome of such injuries. From this, key recommendations are provided for the management of the common fracture types seen in the athlete. Six case reports are also presented to illustrate the management planning and application of sport-focussed fracture management in the clinical setting.展开更多
Pinch analysis, as a technique to optimise the utilisation of resources, traces its beginnings to the 1970s in Switzerland and the UK ETH Zurich and Leeds University to be more precise. Over four decades down the line...Pinch analysis, as a technique to optimise the utilisation of resources, traces its beginnings to the 1970s in Switzerland and the UK ETH Zurich and Leeds University to be more precise. Over four decades down the line, this methodology has entrenched itself in research circles around the world. While the technique was developed, to begin with, for energy (heat) recovery, it has since then expanded to embrace several other fields, and enabled optimisation of resource utilisation in general. The motive behind this article is to perform a focused, selective review of recent case studies from the developing world and transition economies, having ‘pinch analysis’ in their titles and thereby as their ‘core, crux and gist’, during the period 2008-2018. The resources focused on, include heat energy, electrical energy, water, solid waste, money, time, land (surface area), storage space (volume), human resources, mass of resources in general and hydrogen, while a handful of publications have their focus on carbon dioxide (greenhouse gases in general) emissions. Multi-dimensional pinch analysis promises to be an effective tool for sustainability analysis in the years to come;most importantly in the developing world where social well-being and economic development are priorities in the years ahead, and they ought to be attained by a simultaneous truncation of the environmental footprint, in other words, an optimisation of resource utilisation as well as adverse environmental impacts. In other words, the focus ought to be on sustainable production (efficiency) and consumption (sufficiency).展开更多
Over the last decade, the rapid growth in traffic and the number of network devices has implicitly led to an increase in network energy consumption. In this context, a new paradigm has emerged, Software-Defined Networ...Over the last decade, the rapid growth in traffic and the number of network devices has implicitly led to an increase in network energy consumption. In this context, a new paradigm has emerged, Software-Defined Networking (SDN), which is an emerging technique that separates the control plane and the data plane of the deployed network, enabling centralized control of the network, while offering flexibility in data center network management. Some research work is moving in the direction of optimizing the energy consumption of SD-DCN, but still does not guarantee good performance and quality of service for SDN networks. To solve this problem, we propose a new mathematical model based on the principle of combinatorial optimization to dynamically solve the problem of activating and deactivating switches and unused links that consume energy in SDN networks while guaranteeing quality of service (QoS) and ensuring load balancing in the network.展开更多
The main feedstocks for bioethanol are sugarcane （Saccharum offic- inarum） and maize （Zea mays）, both of which are C4 grasses, highly efficient at converting solar energy into chemical energy, and both are food cr...The main feedstocks for bioethanol are sugarcane （Saccharum offic- inarum） and maize （Zea mays）, both of which are C4 grasses, highly efficient at converting solar energy into chemical energy, and both are food crops. As the systems for lignocellulosic bioethanol production become more efficient and cost effective, plant biomass from any source may be used as a feedstock for bioethanol production. Thus, a move away from using food plants to make fuel is possible, and sources of biomass such as wood from forestry and plant waste from cropping may be used. However, the bioethanol industry will need a continuous and reliable supply of biomass that can be produced at a low cost and with minimal use of water, fertilizer and arable land. As many C4 plants have high light, water and nitrogen use efficiency, as compared with C3 species, they are ideal as feedstock crops. We consider the productivity and resource use of a number of candidate plant species, and discuss biomass ＇quality＇, that is, the composition of the plant cell wall.展开更多
A high quality transportation system is necessary in a modem economy, and a road network is a common and significant, component of the system. Road systems have two major objectives： to enable the movement of passeng...A high quality transportation system is necessary in a modem economy, and a road network is a common and significant, component of the system. Road systems have two major objectives： to enable the movement of passenger vehicles and the movement of freight vehicles at reasonable speeds. An important part of the transportation system and an expensive investment, a functional road network must meet both objectives to main- tain an efficient economy. In Australia, the Department of Infrastructure and Regional Development reported that, in 2011/12, the total road length was approximately 900,000 kin, and the total road expenditure was approximately $19 billion. Good policy requires that infrastructure investments provide a return on investment, thus warranting judicious management to ensure that it is maintained in a cost effective manner. Recent studies in Queensland, Australia, have identified differences between financial and engi- neering professionals in their understanding of infrastructure depreciation, condition deterioration, and future funding needs. Furthermore, the Queensland Asset Sustainability Ratio （ASR） requires clearer definitions to ensure that infrastructure remains meaningful to all users. This study proposes a separate sustainability index for road pavements （SIR） unlike the ASR that combines all type of assets. The justification is our ability to assess road condition, the high value of road assets, relative value to other infrastructure, and advanced knowledge of deterioration relative to other infrastructure. The SIR involves community consultation to target an average pavement condition index （PCI）. This study also provides an alternative method to determine the optimal target PCI for a local展开更多
This study proposes a new component of the composite loss function minimised during training of the Super-Resolution(SR)algorithms—the normalised structural similarity index loss LSSIMN,which has the potential to imp...This study proposes a new component of the composite loss function minimised during training of the Super-Resolution(SR)algorithms—the normalised structural similarity index loss LSSIMN,which has the potential to improve the natural appearance of reconstructed images.Deep learning-based super-resolution(SR)algorithms reconstruct high-resolution images from low-resolution inputs,offering a practical means to enhance image quality without requiring superior imaging hardware,which is particularly important in medical applications where diagnostic accuracy is critical.Although recent SR methods employing convolutional and generative adversarial networks achieve high pixel fidelity,visual artefacts may persist,making the design of the loss function during training essential for ensuring reliable and naturalistic image reconstruction.Our research shows on two models—SR and Invertible Rescaling Neural Network(IRN)—trained on multiple benchmark datasets that the function LSSIMN significantly contributes to the visual quality,preserving the structural fidelity on the reference datasets.The quantitative analysis of results while incorporating LSSIMN shows that including this loss function component has a mean 2.88%impact on the improvement of the final structural similarity of the reconstructed images in the validation set,in comparison to leaving it out and 0.218%in comparison when this component is non-normalised.展开更多
The integration of physics-based modelling and data-driven artificial intelligence(AI)has emerged as a transformative paradigm in computational mechanics.This perspective reviews the development and current status of ...The integration of physics-based modelling and data-driven artificial intelligence(AI)has emerged as a transformative paradigm in computational mechanics.This perspective reviews the development and current status of AI-empowered frameworks,including data-driven methods,physics-informed neural networks,and neural operators.While these approaches have demonstrated significant promise,challenges remain in terms of robustness,generalisation,and computational efficiency.We delineate four promising research directions:(1)Modular neural architectures inspired by traditional computational mechanics,(2)physics informed neural operators for resolution-invariant operator learning,(3)intelligent frameworks for multiphysics and multiscale biomechanics problems,and(4)structural optimisation strategies based on physics constraints and reinforcement learning.These directions represent a shift toward foundational frameworks that combine the strengths of physics and data,opening new avenues for the modelling,simulation,and optimisation of complex physical systems.展开更多
Support structure,a critical component in the design for additive manufacturing(DfAM),has been largely overlooked by additive manufacturing(AM)communities.The support structure stabilises overhanging sections,aids in ...Support structure,a critical component in the design for additive manufacturing(DfAM),has been largely overlooked by additive manufacturing(AM)communities.The support structure stabilises overhanging sections,aids in heat dissipation,and reduces the risk of thermal warping,residual stress,and distortion,particularly in the fabrication of complex geometries that challenge traditional manufacturing methods.Despite the importance of support structures in AM,a systematic review covering all aspects of the design,optimisation,and removal of support structures remains lacking.This review provides an overview of various support structure types—contact and non-contact,as well as identical and dissimilar material configurations—and outlines optimisation methods,including geometric,topology,simulation-driven,data-driven,and multi-objective approaches.Additionally,the mechanisms of support removal,such as mechanical milling and chemical dissolution,and innovations like dissolvable supports and sensitised interfaces,are discussed.Future research directions are outlined,emphasising artificial intelligence(AI)-driven intelligent design,multi-material supports,sustainable support materials,support-free AM techniques,and innovative support removal methods,all of which are essential for advancing AM technology.Overall,this review aims to serve as a foundational reference for the design and optimisation of the support structure in AM.展开更多
Zero-day attacks use unknown vulnerabilities that prevent being identified by cybersecurity detection tools.This study indicates that zero-day attacks have a significant impact on computer security.A conventional sign...Zero-day attacks use unknown vulnerabilities that prevent being identified by cybersecurity detection tools.This study indicates that zero-day attacks have a significant impact on computer security.A conventional signature-based detection algorithm is not efficient at recognizing zero-day attacks,as the signatures of zero-day attacks are usually not previously accessible.A machine learning(ML)-based detection algorithm is proficient in capturing statistical features of attacks and,therefore,optimistic for zero-day attack detection.ML and deep learning(DL)are employed for designing intrusion detection systems.The improvement of absolute varieties of novel cyberattacks poses significant challenges for IDS solutions that are dependent on datasets of prior signatures of the attacks.This manuscript presents the Zero-day attack detection employing an equilibrium optimizer with a deep learning(ZDAD-EODL)method to ensure cybersecurity.The ZDAD-EODL technique employs meta-heuristic feature subset selection using an optimum DL-based classification technique for zero-day attacks.Initially,the min-max scalar is utilized for normalizing the input data.For feature selection(FS),the ZDAD-EODL method utilizes the equilibrium optimizer(EO)model to choose feature sub-sets.In addition,the ZDAD-EODL technique employs the bi-directional gated recurrent unit(BiGRU)technique for the classification and identification of zero-day attacks.Finally,the detection performance of the BiGRU technique is further enhanced through the implementation of the subtraction average-based optimizer(SABO)-based tuning process.The performance of the ZDAD-EODL approach is investigated on the benchmark dataset.The comparison study of the ZDAD-EODL approach portrayed a superior accuracy value of 98.47%over existing techniques.展开更多
The highly efficient electrochemical treatment technology for dye-polluted wastewater is one of hot research topics in industrial wastewater treatment.This study reported a three-dimensional electrochemical treatment ...The highly efficient electrochemical treatment technology for dye-polluted wastewater is one of hot research topics in industrial wastewater treatment.This study reported a three-dimensional electrochemical treatment process integrating graphite intercalation compound(GIC)adsorption,direct anodic oxidation,and·OH oxidation for decolourising Reactive Black 5(RB5)from aqueous solutions.The electrochemical process was optimised using the novel progressive central composite design-response surface methodology(CCD-NPRSM),hybrid artificial neural network-extreme gradient boosting(hybrid ANN-XGBoost),and classification and regression trees(CART).CCD-NPRSM and hybrid ANN-XGBoost were employed to minimise errors in evaluating the electrochemical process involving three manipulated operational parameters:current density,electrolysis(treatment)time,and initial dye concentration.The optimised decolourisation efficiencies were 99.30%,96.63%,and 99.14%for CCD-NPRSM,hybrid ANN-XGBoost,and CART,respectively,compared to the 98.46%RB5 removal rate observed experimentally under optimum conditions:approximately 20 mA/cm^(2) of current density,20 min of electrolysis time,and 65 mg/L of RB5.The optimised mineralisation efficiencies ranged between 89%and 92%for different models based on total organic carbon(TOC).Experimental studies confirmed that the predictive efficiency of optimised models ranked in the descending order of hybrid ANN-XGBoost,CCD-NPRSM,and CART.Model validation using analysis of variance(ANOVA)revealed that hybrid ANN-XGBoost had a mean squared error(MSE)and a coefficient of determination(R^(2))of approximately 0.014 and 0.998,respectively,for the RB5 removal efficiency,outperforming CCD-NPRSM with MSE and R^(2) of 0.518 and 0.998,respectively.Overall,the hybrid ANN-XGBoost approach is the most feasible technique for assessing the electrochemical treatment efficiency in RB5 dye wastewater decolourisation.展开更多
This paper presents an investigation of the tribological performance of AA2024–B_(4)C composites,with a specific focus on the influence of reinforcement and processing parameters.In this study three input parameters ...This paper presents an investigation of the tribological performance of AA2024–B_(4)C composites,with a specific focus on the influence of reinforcement and processing parameters.In this study three input parameters were varied:B_(4)C weight percentage,milling time,and normal load,to evaluate their effects on two output parameters:wear loss and the coefficient of friction.AA2024 alloy was used as the matrix alloy,while B_(4)C particles were used as reinforcement.Due to the high hardness and wear resistance of B_(4)C,the optimized composite shows strong potential for use in aerospace structural elements and automotive brake components.The optimisation of tribological behaviour was conducted using a Taguchi-Grey Relational Analysis(Taguchi-GRA)and the Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS).A total of 27 combinations of input parameters were analysed,varying the B_(4)C content(0,10,and 15 wt.%),milling time(0,15,and 25 h),and normal load(1,5,and 10 N).Wear loss and the coefficient of friction were numerically evaluated and selected as criteria for optimisation.Artificial Neural Networks(ANNs)were also applied for two outputs simultaneously.TOPSIS identified Alternative 1 as the optimal solution,confirming the results obtained using the Taguchi Grey method.The optimal condition obtained(10 wt.%B_(4)C,25 h milling time,10 N load)resulted in a minimum wear loss of 1.7 mg and a coefficient of friction of 0.176,confirming significant enhancement in tribological behaviour.Based on the results,both the B_(4)C content and the applied processing conditions have a significant impact on wear loss and frictional properties.This approach demonstrates high reliability and confidence,enabling the design of future composite materials with optimal properties for specific applications.展开更多
Earth-abundant copper-tin(CuSn)electrocatalysts are potential candidates for cost-effective and sustainable production of CO from electrochemical carbon dioxide reduction(eCO_(2)R).However,the requirement of highoverp...Earth-abundant copper-tin(CuSn)electrocatalysts are potential candidates for cost-effective and sustainable production of CO from electrochemical carbon dioxide reduction(eCO_(2)R).However,the requirement of highoverpotential for obtaining reasonable current,low Faradaic efficiencies(FE)and low intrinsic catalytic activities require the optimisation of the CuSn nanoarchitecture for the further advancement in the field.In the current work,we have optimised Sn loading on Cu gas diffusion electrodes(GDEs)by electrochemical spontaneous precipitation.Samples with various Sn loadings were tested in a three-chamber GDE reactor to evaluate their CO_(2)reduction performances.The best performance of 92%CO Faradaic efficiency at a cathodic current density of 120 mA cm^(-2)was obtained from the 20 min Sn deposited Cu_(2)O sample operated at-1.13 V vs.RHE.The electrocatalyst had~13%surface coverage of Sn on Cu GDE surface,and had Sn in oxide form and copper in metallic form.The catalyst also showed stable performance and was operable for>3 h under chronoamperometric conditions.The surface of the GDE reduces from Cu2O to Cu during eCO_(2)R and goes further reconstruction during the eCO_(2)R.This study demonstrates the potential of Cu-Sn for selective CO production at high current densities.展开更多
Expansive soils are problematic due to the performances of their clay mineral constituent, which makes them exhibit the shrink-swell characteristics. The shrink-swell behaviours make expansive soils inappropriate for ...Expansive soils are problematic due to the performances of their clay mineral constituent, which makes them exhibit the shrink-swell characteristics. The shrink-swell behaviours make expansive soils inappropriate for direct engineering application in their natural form. In an attempt to make them more feasible for construction purposes, numerous materials and techniques have been used to stabilise the soil. In this study, the additives and techniques applied for stabilising expansive soils will be focused on,with respect to their efficiency in improving the engineering properties of the soils. Then we discussed the microstructural interaction, chemical process, economic implication, nanotechnology application, as well as waste reuse and sustainability. Some issues regarding the effective application of the emerging trends in expansive soil stabilisation were presented with three categories, namely geoenvironmental,standardisation and optimisation issues. Techniques like predictive modelling and exploring methods such as reliability-based design optimisation, response surface methodology, dimensional analysis, and artificial intelligence technology were also proposed in order to ensure that expansive soil stabilisation is efficient.展开更多
Self-piercing riveting(SPR)is a cold forming technique used to fasten together two or more sheets of materials with a rivet without the need to predrill a hole.The application of SPR in the automotive sector has becom...Self-piercing riveting(SPR)is a cold forming technique used to fasten together two or more sheets of materials with a rivet without the need to predrill a hole.The application of SPR in the automotive sector has become increasingly popular mainly due to the growing use of lightweight materials in transportation applications.However,SPR joining of these advanced light materials remains a challenge as these materials often lack a good combination of high strength and ductility to resist the large plastic deformation induced by the SPR process.In this paper,SPR joints of advanced materials and their corresponding failure mechanisms are discussed,aiming to provide the foundation for future improvement of SPR joint quality.This paper is divided into three major sections:1)joint failures focusing on joint defects originated from the SPR process and joint failure modes under different mechanical loading conditions,2)joint corrosion issues,and 3)joint optimisation via process parameters and advanced techniques.展开更多
In the present study, we developed a multi-component one-dimensional mathematical model for simulation and optimisation of a commercial catalytic slurry reactor for the direct synthesis of dimethyl ether （DME） from ...In the present study, we developed a multi-component one-dimensional mathematical model for simulation and optimisation of a commercial catalytic slurry reactor for the direct synthesis of dimethyl ether （DME） from syngas and CO2, operating in a churn-turbulent regime. DME productivity and CO conversion were optimised by tuning operating conditions, such as superficial gas velocity, catalyst concentration, catalyst mass over molar gas flow rate （W/F）, syngas composition, pressure and temperature. Reactor modelling was accomplished utilising mass balance, global kinetic models and heterogeneous hydrodynamics. In the heterogeneous flow regime, gas was distributed into two bubble phases： small and large. Simulation results were validated using data obtained from a pilot plant. The developed model is also applicable for the design of large-scale slurry reactors.展开更多
This paper presents the effect of mooring diameters, fairlead slopes and pretensions on the dynamic responses of a truss spar platform in intact and damaged line conditions. The platform is modelled as a rigid body wi...This paper presents the effect of mooring diameters, fairlead slopes and pretensions on the dynamic responses of a truss spar platform in intact and damaged line conditions. The platform is modelled as a rigid body with three degrees-of-freedom and its motions are analysed in time-domain using the implicit Newmark Beta technique. The mooring restoring force-excursion relationship is evaluated using quasi-static approach. MATLAB codes DATSpar and QSAML, are developed to compute the dynamic responses of truss spar platform and to determine the mooring system stiffness. To eliminate the conventional trial and error approach in the mooring system design, a numerical tool is also developed and described in this paper for optimising the mooring configuration. It has a graphical user interface and includes regrouping particle swarm optimisation technique combined with DATSpar and QSAML. A case study of truss spar platform with ten mooring lines is analysed using this numerical tool. The results show that optimum mooring system design benefits the oil and gas industry to economise the project cost in terms of material, weight, structural load onto the platform as well as manpower requirements. This tool is useful especially for the preliminary design of truss spar platforms and its mooring system.展开更多
Standard genetic algorithms （SGAs） are investigated to optimise discrete-time proportional-integral-derivative （PID） con- troller parameters, by three tuning approaches, for a multivariable glass furnace process w...Standard genetic algorithms （SGAs） are investigated to optimise discrete-time proportional-integral-derivative （PID） con- troller parameters, by three tuning approaches, for a multivariable glass furnace process with loop interaction. Initially, standard genetic algorithms （SGAs） are used to identify control oriented models of the plant which are subsequently used for controller optimisa- tion. An individual tuning approach without loop interaction is considered first to categorise the genetic operators, cost functions and improve searching boundaries to attain the desired performance criteria. The second tuning approach considers controller parameters optimisation with loop interaction and individual cost functions. While, the third tuning approach utilises a modified cost function which includes the total effect of both controlled variables, glass temperature and excess oxygen. This modified cost function is shown to exhibit improved control robustness and disturbance rejection under loop interaction.展开更多
A general and new explicit isogeometric topology optimisation approach with moving morphable voids(MMV)is proposed.In this approach,a novel multiresolution scheme with two distinct discretisation levels is developed t...A general and new explicit isogeometric topology optimisation approach with moving morphable voids(MMV)is proposed.In this approach,a novel multiresolution scheme with two distinct discretisation levels is developed to obtain high-resolution designs with a relatively low computational cost.Ersatz material model based on Greville abscissae collocation scheme is utilised to represent both the Young’s modulus of the material and the density field.Two benchmark examples are tested to illustrate the effectiveness of the proposed method.Numerical results show that high-resolution designs can be obtained with relatively low computational cost,and the optimisation can be significantly improved without introducing additional DOFs.展开更多
基金supported by Shandong Provincial Natural Science Foundation(ZR2024ME229,ZR2024ZD29).
文摘Power transformers are vital components in electric grids;however,methods to optimise their loading to extend lifespan while accounting for insulation degradation remain underdeveloped.This research paper introduces a novel integrated data-driven framework that combines particle filtering and model predictive health(PF-MPH)model for the predictive health manage-ment of power transformers.Initially,the particle filter probabilistically estimates power transformers'remaining life(R_(L))using direct winding hotspot temperature(χ_(H))measurements.The obtained R_(L)will then be used to calculate the degree of poly-merisation(DP)level and assess the current insulation condition.After that,a comparative analysis between direct and model-basedχ_(H)measurement methods is performed to highlight the superior accuracy of direct measurements for predictive health management.Then,the MPH optimisation algorithm,which uses the R_(L)and DP forecasts from the PF method,derives an optimal trajectory over the transformer's R_(L)that balances the costs of increased loading against the benefits gained from prolonged insulation longevity.The findings show that the proposed PF-MPH model has successfully reduced the χ_(H)by 2.46%over the predicted 19 years.This approach is expected to enable grid operators to optimise transformer loading schedules to extend the R_(L)of these critical assets in a cost-effective manner.
文摘While it is well-known that neuronal activity promotes plasticity and connectivity, the success of activity-based neural rehabilitation programs remains extremely limited in human clinical experience because they cannot adequately control neuronal excitability and activity within the injured brain in order to induce repair. However, it is possible to non-invasively modulate brain plasticity using brain stimu- lation techniques such as repetitive transcranial （rTMS） and transcranial direct current stimulation （tDCS） techniques, which show promise for repairing injured neural circuits （Henrich-Noack et al., 2013; Lefaucher et al., 2014）. Yet we are far from having full control of these techniques to repair the brain following neurotrauma and need more fundamen- tal research （Ellaway et al., 2014; Lefaucher et al., 2014）. In this perspective we discuss the mechanisms by which rTMS may facilitate neurorehabilitation and propose experimental techniques with which magnetic stimulation may be investi- gated in order to optimise its treatment potential.
文摘Fractures in sport are a specialised cohort of fracture injuries, occurring in a high functioning population, in which the goals are rapid restoration of function and return to play with the minimal symptom profile possible. While the general principles of fracture management, namely accurate fracture reduction, appropriate immobilisation and timely rehabilitation, guide the treatment of these injuries, management of fractures in athletic populations can differ significantly from those in the general population, due to the need to facilitate a rapid return to high demand activities. However, despite fractures comprising up to 10% of all of sporting injuries, dedicated research into the management and outcome of sport-related fractures is limited. In order to assess the optimal methods of treating such injuries, and so allow optimisation of their outcome, the evidence for the management of each specific sport-related fracture type requires assessment and analysis. We present and review the current evidence directing management of fractures in athletes with an aim to promote valid innovative methods and optimise the outcome of such injuries. From this, key recommendations are provided for the management of the common fracture types seen in the athlete. Six case reports are also presented to illustrate the management planning and application of sport-focussed fracture management in the clinical setting.
文摘Pinch analysis, as a technique to optimise the utilisation of resources, traces its beginnings to the 1970s in Switzerland and the UK ETH Zurich and Leeds University to be more precise. Over four decades down the line, this methodology has entrenched itself in research circles around the world. While the technique was developed, to begin with, for energy (heat) recovery, it has since then expanded to embrace several other fields, and enabled optimisation of resource utilisation in general. The motive behind this article is to perform a focused, selective review of recent case studies from the developing world and transition economies, having ‘pinch analysis’ in their titles and thereby as their ‘core, crux and gist’, during the period 2008-2018. The resources focused on, include heat energy, electrical energy, water, solid waste, money, time, land (surface area), storage space (volume), human resources, mass of resources in general and hydrogen, while a handful of publications have their focus on carbon dioxide (greenhouse gases in general) emissions. Multi-dimensional pinch analysis promises to be an effective tool for sustainability analysis in the years to come;most importantly in the developing world where social well-being and economic development are priorities in the years ahead, and they ought to be attained by a simultaneous truncation of the environmental footprint, in other words, an optimisation of resource utilisation as well as adverse environmental impacts. In other words, the focus ought to be on sustainable production (efficiency) and consumption (sufficiency).
文摘Over the last decade, the rapid growth in traffic and the number of network devices has implicitly led to an increase in network energy consumption. In this context, a new paradigm has emerged, Software-Defined Networking (SDN), which is an emerging technique that separates the control plane and the data plane of the deployed network, enabling centralized control of the network, while offering flexibility in data center network management. Some research work is moving in the direction of optimizing the energy consumption of SD-DCN, but still does not guarantee good performance and quality of service for SDN networks. To solve this problem, we propose a new mathematical model based on the principle of combinatorial optimization to dynamically solve the problem of activating and deactivating switches and unused links that consume energy in SDN networks while guaranteeing quality of service (QoS) and ensuring load balancing in the network.
基金supported by the Australian Research Council (ARC) though ARC-linkage project LP0883808
文摘The main feedstocks for bioethanol are sugarcane （Saccharum offic- inarum） and maize （Zea mays）, both of which are C4 grasses, highly efficient at converting solar energy into chemical energy, and both are food crops. As the systems for lignocellulosic bioethanol production become more efficient and cost effective, plant biomass from any source may be used as a feedstock for bioethanol production. Thus, a move away from using food plants to make fuel is possible, and sources of biomass such as wood from forestry and plant waste from cropping may be used. However, the bioethanol industry will need a continuous and reliable supply of biomass that can be produced at a low cost and with minimal use of water, fertilizer and arable land. As many C4 plants have high light, water and nitrogen use efficiency, as compared with C3 species, they are ideal as feedstock crops. We consider the productivity and resource use of a number of candidate plant species, and discuss biomass ＇quality＇, that is, the composition of the plant cell wall.
文摘A high quality transportation system is necessary in a modem economy, and a road network is a common and significant, component of the system. Road systems have two major objectives： to enable the movement of passenger vehicles and the movement of freight vehicles at reasonable speeds. An important part of the transportation system and an expensive investment, a functional road network must meet both objectives to main- tain an efficient economy. In Australia, the Department of Infrastructure and Regional Development reported that, in 2011/12, the total road length was approximately 900,000 kin, and the total road expenditure was approximately $19 billion. Good policy requires that infrastructure investments provide a return on investment, thus warranting judicious management to ensure that it is maintained in a cost effective manner. Recent studies in Queensland, Australia, have identified differences between financial and engi- neering professionals in their understanding of infrastructure depreciation, condition deterioration, and future funding needs. Furthermore, the Queensland Asset Sustainability Ratio （ASR） requires clearer definitions to ensure that infrastructure remains meaningful to all users. This study proposes a separate sustainability index for road pavements （SIR） unlike the ASR that combines all type of assets. The justification is our ability to assess road condition, the high value of road assets, relative value to other infrastructure, and advanced knowledge of deterioration relative to other infrastructure. The SIR involves community consultation to target an average pavement condition index （PCI）. This study also provides an alternative method to determine the optimal target PCI for a local
基金support from the following institutional grant.Internal Grant Agency of the Faculty of Economics and Management,Czech University of Life Sciences Prague,grant no.2023A0004(https://iga.pef.czu.cz/,accessed on 6 June 2025).
文摘This study proposes a new component of the composite loss function minimised during training of the Super-Resolution(SR)algorithms—the normalised structural similarity index loss LSSIMN,which has the potential to improve the natural appearance of reconstructed images.Deep learning-based super-resolution(SR)algorithms reconstruct high-resolution images from low-resolution inputs,offering a practical means to enhance image quality without requiring superior imaging hardware,which is particularly important in medical applications where diagnostic accuracy is critical.Although recent SR methods employing convolutional and generative adversarial networks achieve high pixel fidelity,visual artefacts may persist,making the design of the loss function during training essential for ensuring reliable and naturalistic image reconstruction.Our research shows on two models—SR and Invertible Rescaling Neural Network(IRN)—trained on multiple benchmark datasets that the function LSSIMN significantly contributes to the visual quality,preserving the structural fidelity on the reference datasets.The quantitative analysis of results while incorporating LSSIMN shows that including this loss function component has a mean 2.88%impact on the improvement of the final structural similarity of the reconstructed images in the validation set,in comparison to leaving it out and 0.218%in comparison when this component is non-normalised.
基金supported by the Australian Research Council(Grant No.IC190100020)the Australian Research Council Indus〓〓try Fellowship(Grant No.IE230100435)the National Natural Science Foundation of China(Grant Nos.12032014 and T2488101)。
文摘The integration of physics-based modelling and data-driven artificial intelligence(AI)has emerged as a transformative paradigm in computational mechanics.This perspective reviews the development and current status of AI-empowered frameworks,including data-driven methods,physics-informed neural networks,and neural operators.While these approaches have demonstrated significant promise,challenges remain in terms of robustness,generalisation,and computational efficiency.We delineate four promising research directions:(1)Modular neural architectures inspired by traditional computational mechanics,(2)physics informed neural operators for resolution-invariant operator learning,(3)intelligent frameworks for multiphysics and multiscale biomechanics problems,and(4)structural optimisation strategies based on physics constraints and reinforcement learning.These directions represent a shift toward foundational frameworks that combine the strengths of physics and data,opening new avenues for the modelling,simulation,and optimisation of complex physical systems.
基金supported by the Advanced Research and Technology Innovation Centre (ARTIC)the National University of Singapore under Grant (Project Number:ADTRP1)the sponsorship of the China Scholarship Council (No. 202306130143).
文摘Support structure,a critical component in the design for additive manufacturing(DfAM),has been largely overlooked by additive manufacturing(AM)communities.The support structure stabilises overhanging sections,aids in heat dissipation,and reduces the risk of thermal warping,residual stress,and distortion,particularly in the fabrication of complex geometries that challenge traditional manufacturing methods.Despite the importance of support structures in AM,a systematic review covering all aspects of the design,optimisation,and removal of support structures remains lacking.This review provides an overview of various support structure types—contact and non-contact,as well as identical and dissimilar material configurations—and outlines optimisation methods,including geometric,topology,simulation-driven,data-driven,and multi-objective approaches.Additionally,the mechanisms of support removal,such as mechanical milling and chemical dissolution,and innovations like dissolvable supports and sensitised interfaces,are discussed.Future research directions are outlined,emphasising artificial intelligence(AI)-driven intelligent design,multi-material supports,sustainable support materials,support-free AM techniques,and innovative support removal methods,all of which are essential for advancing AM technology.Overall,this review aims to serve as a foundational reference for the design and optimisation of the support structure in AM.
基金Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/286/46Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R732),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia+2 种基金Ongoing Research Funding program(ORFFT-2025-100-7),King Saud University,Riyadh,Saudi Arabia for financial supportthe Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia,for funding this research work through the project number“NBU-FFR-2025-2913-07”the Deanship of Graduate Studies and Scientific Research at the University of Bisha for supporting this work through the Fast-Track Research Support Program。
文摘Zero-day attacks use unknown vulnerabilities that prevent being identified by cybersecurity detection tools.This study indicates that zero-day attacks have a significant impact on computer security.A conventional signature-based detection algorithm is not efficient at recognizing zero-day attacks,as the signatures of zero-day attacks are usually not previously accessible.A machine learning(ML)-based detection algorithm is proficient in capturing statistical features of attacks and,therefore,optimistic for zero-day attack detection.ML and deep learning(DL)are employed for designing intrusion detection systems.The improvement of absolute varieties of novel cyberattacks poses significant challenges for IDS solutions that are dependent on datasets of prior signatures of the attacks.This manuscript presents the Zero-day attack detection employing an equilibrium optimizer with a deep learning(ZDAD-EODL)method to ensure cybersecurity.The ZDAD-EODL technique employs meta-heuristic feature subset selection using an optimum DL-based classification technique for zero-day attacks.Initially,the min-max scalar is utilized for normalizing the input data.For feature selection(FS),the ZDAD-EODL method utilizes the equilibrium optimizer(EO)model to choose feature sub-sets.In addition,the ZDAD-EODL technique employs the bi-directional gated recurrent unit(BiGRU)technique for the classification and identification of zero-day attacks.Finally,the detection performance of the BiGRU technique is further enhanced through the implementation of the subtraction average-based optimizer(SABO)-based tuning process.The performance of the ZDAD-EODL approach is investigated on the benchmark dataset.The comparison study of the ZDAD-EODL approach portrayed a superior accuracy value of 98.47%over existing techniques.
文摘The highly efficient electrochemical treatment technology for dye-polluted wastewater is one of hot research topics in industrial wastewater treatment.This study reported a three-dimensional electrochemical treatment process integrating graphite intercalation compound(GIC)adsorption,direct anodic oxidation,and·OH oxidation for decolourising Reactive Black 5(RB5)from aqueous solutions.The electrochemical process was optimised using the novel progressive central composite design-response surface methodology(CCD-NPRSM),hybrid artificial neural network-extreme gradient boosting(hybrid ANN-XGBoost),and classification and regression trees(CART).CCD-NPRSM and hybrid ANN-XGBoost were employed to minimise errors in evaluating the electrochemical process involving three manipulated operational parameters:current density,electrolysis(treatment)time,and initial dye concentration.The optimised decolourisation efficiencies were 99.30%,96.63%,and 99.14%for CCD-NPRSM,hybrid ANN-XGBoost,and CART,respectively,compared to the 98.46%RB5 removal rate observed experimentally under optimum conditions:approximately 20 mA/cm^(2) of current density,20 min of electrolysis time,and 65 mg/L of RB5.The optimised mineralisation efficiencies ranged between 89%and 92%for different models based on total organic carbon(TOC).Experimental studies confirmed that the predictive efficiency of optimised models ranked in the descending order of hybrid ANN-XGBoost,CCD-NPRSM,and CART.Model validation using analysis of variance(ANOVA)revealed that hybrid ANN-XGBoost had a mean squared error(MSE)and a coefficient of determination(R^(2))of approximately 0.014 and 0.998,respectively,for the RB5 removal efficiency,outperforming CCD-NPRSM with MSE and R^(2) of 0.518 and 0.998,respectively.Overall,the hybrid ANN-XGBoost approach is the most feasible technique for assessing the electrochemical treatment efficiency in RB5 dye wastewater decolourisation.
文摘This paper presents an investigation of the tribological performance of AA2024–B_(4)C composites,with a specific focus on the influence of reinforcement and processing parameters.In this study three input parameters were varied:B_(4)C weight percentage,milling time,and normal load,to evaluate their effects on two output parameters:wear loss and the coefficient of friction.AA2024 alloy was used as the matrix alloy,while B_(4)C particles were used as reinforcement.Due to the high hardness and wear resistance of B_(4)C,the optimized composite shows strong potential for use in aerospace structural elements and automotive brake components.The optimisation of tribological behaviour was conducted using a Taguchi-Grey Relational Analysis(Taguchi-GRA)and the Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS).A total of 27 combinations of input parameters were analysed,varying the B_(4)C content(0,10,and 15 wt.%),milling time(0,15,and 25 h),and normal load(1,5,and 10 N).Wear loss and the coefficient of friction were numerically evaluated and selected as criteria for optimisation.Artificial Neural Networks(ANNs)were also applied for two outputs simultaneously.TOPSIS identified Alternative 1 as the optimal solution,confirming the results obtained using the Taguchi Grey method.The optimal condition obtained(10 wt.%B_(4)C,25 h milling time,10 N load)resulted in a minimum wear loss of 1.7 mg and a coefficient of friction of 0.176,confirming significant enhancement in tribological behaviour.Based on the results,both the B_(4)C content and the applied processing conditions have a significant impact on wear loss and frictional properties.This approach demonstrates high reliability and confidence,enabling the design of future composite materials with optimal properties for specific applications.
基金The authors would like to acknowledge the support from the UKRI Interdisciplinary Centre for Circular Chemical Economy(EP/V011863/1)EPSRC LifesCO2R project(EP/N009746/1 EP/N009746/2)and EPSRC NECEM Energy Material Centre(EP/R021503/1)Loughborough Materials Characterisation Centre Pump Prime grant which enabled the access to the characterisation facilities is also acknowledged.
文摘Earth-abundant copper-tin(CuSn)electrocatalysts are potential candidates for cost-effective and sustainable production of CO from electrochemical carbon dioxide reduction(eCO_(2)R).However,the requirement of highoverpotential for obtaining reasonable current,low Faradaic efficiencies(FE)and low intrinsic catalytic activities require the optimisation of the CuSn nanoarchitecture for the further advancement in the field.In the current work,we have optimised Sn loading on Cu gas diffusion electrodes(GDEs)by electrochemical spontaneous precipitation.Samples with various Sn loadings were tested in a three-chamber GDE reactor to evaluate their CO_(2)reduction performances.The best performance of 92%CO Faradaic efficiency at a cathodic current density of 120 mA cm^(-2)was obtained from the 20 min Sn deposited Cu_(2)O sample operated at-1.13 V vs.RHE.The electrocatalyst had~13%surface coverage of Sn on Cu GDE surface,and had Sn in oxide form and copper in metallic form.The catalyst also showed stable performance and was operable for>3 h under chronoamperometric conditions.The surface of the GDE reduces from Cu2O to Cu during eCO_(2)R and goes further reconstruction during the eCO_(2)R.This study demonstrates the potential of Cu-Sn for selective CO production at high current densities.
文摘Expansive soils are problematic due to the performances of their clay mineral constituent, which makes them exhibit the shrink-swell characteristics. The shrink-swell behaviours make expansive soils inappropriate for direct engineering application in their natural form. In an attempt to make them more feasible for construction purposes, numerous materials and techniques have been used to stabilise the soil. In this study, the additives and techniques applied for stabilising expansive soils will be focused on,with respect to their efficiency in improving the engineering properties of the soils. Then we discussed the microstructural interaction, chemical process, economic implication, nanotechnology application, as well as waste reuse and sustainability. Some issues regarding the effective application of the emerging trends in expansive soil stabilisation were presented with three categories, namely geoenvironmental,standardisation and optimisation issues. Techniques like predictive modelling and exploring methods such as reliability-based design optimisation, response surface methodology, dimensional analysis, and artificial intelligence technology were also proposed in order to ensure that expansive soil stabilisation is efficient.
文摘Self-piercing riveting(SPR)is a cold forming technique used to fasten together two or more sheets of materials with a rivet without the need to predrill a hole.The application of SPR in the automotive sector has become increasingly popular mainly due to the growing use of lightweight materials in transportation applications.However,SPR joining of these advanced light materials remains a challenge as these materials often lack a good combination of high strength and ductility to resist the large plastic deformation induced by the SPR process.In this paper,SPR joints of advanced materials and their corresponding failure mechanisms are discussed,aiming to provide the foundation for future improvement of SPR joint quality.This paper is divided into three major sections:1)joint failures focusing on joint defects originated from the SPR process and joint failure modes under different mechanical loading conditions,2)joint corrosion issues,and 3)joint optimisation via process parameters and advanced techniques.
文摘In the present study, we developed a multi-component one-dimensional mathematical model for simulation and optimisation of a commercial catalytic slurry reactor for the direct synthesis of dimethyl ether （DME） from syngas and CO2, operating in a churn-turbulent regime. DME productivity and CO conversion were optimised by tuning operating conditions, such as superficial gas velocity, catalyst concentration, catalyst mass over molar gas flow rate （W/F）, syngas composition, pressure and temperature. Reactor modelling was accomplished utilising mass balance, global kinetic models and heterogeneous hydrodynamics. In the heterogeneous flow regime, gas was distributed into two bubble phases： small and large. Simulation results were validated using data obtained from a pilot plant. The developed model is also applicable for the design of large-scale slurry reactors.
基金partially supported by YUTP-FRG funded by PETRONAS
文摘This paper presents the effect of mooring diameters, fairlead slopes and pretensions on the dynamic responses of a truss spar platform in intact and damaged line conditions. The platform is modelled as a rigid body with three degrees-of-freedom and its motions are analysed in time-domain using the implicit Newmark Beta technique. The mooring restoring force-excursion relationship is evaluated using quasi-static approach. MATLAB codes DATSpar and QSAML, are developed to compute the dynamic responses of truss spar platform and to determine the mooring system stiffness. To eliminate the conventional trial and error approach in the mooring system design, a numerical tool is also developed and described in this paper for optimising the mooring configuration. It has a graphical user interface and includes regrouping particle swarm optimisation technique combined with DATSpar and QSAML. A case study of truss spar platform with ten mooring lines is analysed using this numerical tool. The results show that optimum mooring system design benefits the oil and gas industry to economise the project cost in terms of material, weight, structural load onto the platform as well as manpower requirements. This tool is useful especially for the preliminary design of truss spar platforms and its mooring system.
文摘Standard genetic algorithms （SGAs） are investigated to optimise discrete-time proportional-integral-derivative （PID） con- troller parameters, by three tuning approaches, for a multivariable glass furnace process with loop interaction. Initially, standard genetic algorithms （SGAs） are used to identify control oriented models of the plant which are subsequently used for controller optimisa- tion. An individual tuning approach without loop interaction is considered first to categorise the genetic operators, cost functions and improve searching boundaries to attain the desired performance criteria. The second tuning approach considers controller parameters optimisation with loop interaction and individual cost functions. While, the third tuning approach utilises a modified cost function which includes the total effect of both controlled variables, glass temperature and excess oxygen. This modified cost function is shown to exhibit improved control robustness and disturbance rejection under loop interaction.
基金National Natural Science Foundation of China under Grant Nos.51675525 and 11725211.
文摘A general and new explicit isogeometric topology optimisation approach with moving morphable voids(MMV)is proposed.In this approach,a novel multiresolution scheme with two distinct discretisation levels is developed to obtain high-resolution designs with a relatively low computational cost.Ersatz material model based on Greville abscissae collocation scheme is utilised to represent both the Young’s modulus of the material and the density field.Two benchmark examples are tested to illustrate the effectiveness of the proposed method.Numerical results show that high-resolution designs can be obtained with relatively low computational cost,and the optimisation can be significantly improved without introducing additional DOFs.
