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
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
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
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
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
Photovoltaic(PV)systems are electrical systems designed to convert solar energy into electrical energy.As a crucial component of PV systems,harsh weather conditions,photovoltaic panel temperature and solar irradiance ...Photovoltaic(PV)systems are electrical systems designed to convert solar energy into electrical energy.As a crucial component of PV systems,harsh weather conditions,photovoltaic panel temperature and solar irradiance influence the power output of photovoltaic cells.Therefore,accurately identifying the parameters of PV models is essential for simulating,controlling and evaluating PV systems.In this study,we propose an enhanced weighted-mean-of-vectors optimisation(EINFO)for efficiently determining the unknown parameters in PV systems.EINFO introduces a Lambert W-based explicit objective function for the PV model,enhancing the computational accuracy of the algorithm's population fitness.This addresses the challenge of improving the metaheuristic algorithms'identification accuracy for unknown parameter identification in PV models.We experimentally apply EINFO to three types of PV models(single-diode,double-diode and PV-module models)to validate its accuracy and stability in parameter identification.The results demonstrate that EINFO achieves root mean square errors(RMSEs)of 7.7301E-04,6.8553E-04 and 2.0608E-03 for the single-diode model,double-diode model and PV-module model,respectively,surpassing those obtained by using INFO algorithm as well as other methods in terms of convergence speed,accuracy and stability.Furthermore,comprehensive experimental findings on three commercial PV modules(ST40,SM55 and KC200GT)indicate that EINFO consistently maintains high accuracy across varying temperatures and irradiation levels.In conclusion,EINFO emerges as a highly competitive and practical approach for parameter identification in diverse types of PV models.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
With an optimised hall layout,progressive design collaborations,inspiring trends and AIdriven innovations,Heimtextil 2026 reacts to the current market situation–and offers the industry a reliable constant in challeng...With an optimised hall layout,progressive design collaborations,inspiring trends and AIdriven innovations,Heimtextil 2026 reacts to the current market situation–and offers the industry a reliable constant in challenging times.Under the motto‘Lead the Change’,the leading trade fair for home and contract textiles and textile design shows how challenges can be turned into opportunities.From 13 to 16 January,more than 3,100 exhibitors from 65 countries will provide a comprehensive market overview with new collections and textile solutions.As a knowledge hub,Heimtextil delivers new strategies and concrete solutions for future business success.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
Estimating probability density functions(PDFs)is critical in data analysis,particularly for complex multimodal distributions.traditional kernel density estimator(KDE)methods often face challenges in accurately capturi...Estimating probability density functions(PDFs)is critical in data analysis,particularly for complex multimodal distributions.traditional kernel density estimator(KDE)methods often face challenges in accurately capturing multimodal structures due to their uniform weighting scheme,leading to mode loss and degraded estimation accuracy.This paper presents the flexible kernel density estimator(F-KDE),a novel nonparametric approach designed to address these limitations.F-KDE introduces the concept of kernel unit inequivalence,assigning adaptive weights to each kernel unit,which better models local density variations in multimodal data.The method optimises an objective function that integrates estimation error and log-likelihood,using a particle swarm optimisation(PSO)algorithm that automatically determines optimal weights and bandwidths.Through extensive experiments on synthetic and real-world datasets,we demonstrated that(1)the weights and bandwidths in F-KDE stabilise as the optimisation algorithm iterates,(2)F-KDE effectively captures the multimodal characteristics and(3)F-KDE outperforms state-of-the-art density estimation methods regarding accuracy and robustness.The results confirm that F-KDE provides a valuable solution for accurately estimating multimodal PDFs.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 challenge of optimising multimodal functions within high-dimensional domains constitutes a notable difficulty in evolutionary computation research.Addressing this issue,this study introduces the Deep Backtracking ...The challenge of optimising multimodal functions within high-dimensional domains constitutes a notable difficulty in evolutionary computation research.Addressing this issue,this study introduces the Deep Backtracking Bare-Bones Particle Swarm Optimisation(DBPSO)algorithm,an innovative approach built upon the integration of the Deep Memory Storage Mechanism(DMSM)and the Dynamic Memory Activation Strategy(DMAS).The DMSM enhances the memory retention for the globally optimal particle,promoting interaction between standard particles and their historically optimal counterparts.In parallel,DMAS assures the updated position of the globally optimal particle is appropriately aligned with the deep memory repository.The efficacy of DBPSO was rigorously assessed through a series of simulations employing the CEC2017 benchmark suite.A comparative analysis juxtaposed DBPSO's performance against five contemporary evolutionary algorithms across two experimental conditions:Dimension-50 and Dimension-100.In the 50D trials,DBPSO attained an average ranking of 2.03,whereas in the 100D scenarios,it improved to an average ranking of 1.9.Further examination utilising the CEC2019 benchmark functions revealed DBPSO's robustness,securing four first-place finishes,three second-place standings,and three third-place positions,culminating in an unmatched average ranking of 1.9 across all algorithms.These empirical results corroborate DBPSO's proficiency in delivering precise solutions for complex,high-dimensional optimisation challenges.展开更多
Impact ground pressure events occur frequently in coal mining processes,significantly affecting the personal safety of construction workers.Real-time microseismic monitoring of coal rock body rupture information can p...Impact ground pressure events occur frequently in coal mining processes,significantly affecting the personal safety of construction workers.Real-time microseismic monitoring of coal rock body rupture information can provide early warnings,and the seismic source location method is an essential indicator for evaluating a microseismic monitoring system.This paper proposes a nonlinear hybrid optimal particle swarm optimisation(PSO)microseismic positioning method based on this technique.The method first improves the PSO algorithm by using the global search performance of this method to quickly find a feasible solution and provide a better initial solution for the subsequent solution of the nonlinear optimal microseismic positioning method.This approach effectively prevents the problem of the microseismic positioning method falling into a local optimum because of an over-reliance on the initial value.In addition,the nonlinear optimal microseismic positioning method further narrows the localisation error based on the PSO algorithm.A simulation test demonstrates that the new method has a good positioning effect,and engineering application examples also show that the proposed method has high accuracy and strong positioning stability.The new method is better than the separate positioning method,both overall and in three directions,making it more suitable for solving the microseismic positioning problem.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 article presents the design of a microfabricated bio-inspired flapping-wing Nnano Aaerial Vvehicle(NAV),driven by an electromagnetic system.Our approach is based on artificial wings composed of rigid bodies conne...This article presents the design of a microfabricated bio-inspired flapping-wing Nnano Aaerial Vvehicle(NAV),driven by an electromagnetic system.Our approach is based on artificial wings composed of rigid bodies connected by compliant links,which optimise aerodynamic forces though replicating the complex wing kinematics of insects.The originality of this article lies in a new design methodology based on a triple equivalence between a 3D model,a multibody model,and a mass/spring model(0D)which reduces the number of parameters in the problem.This approach facilitates NAV optimisation by using only the mass/spring model,thereby simplifying the design process while maintaining high accuracy.Two wing geometries are studied and optimised in this article to produce large-amplitude wing motions(approximately 40^\circ),and enabling flapping and twisting motion in quadrature.The results are validated thanks to experimental measurements for the large amplitude and through finite element simulations for the combined motion,confirming the effectiveness of this strategy for a NAV weighing less than 40 mg with a wingspan of under 3 cm.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
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.
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).
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.
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partially supported by MRC(MC_PC_17171)Royal Society(RP202G0230)+8 种基金BHF(AA/18/3/34220)Hope Foundation for Cancer Research(RM60G0680)GCRF(P202PF11)Sino-UK Industrial Fund(RP202G0289)Sino-UK Education Fund(OP202006)LIAS(P202ED10,P202RE969)Data Science Enhancement Fund(P202RE237)Fight for Sight(24NN201)BBSRC(RM32G0178B8).
文摘Photovoltaic(PV)systems are electrical systems designed to convert solar energy into electrical energy.As a crucial component of PV systems,harsh weather conditions,photovoltaic panel temperature and solar irradiance influence the power output of photovoltaic cells.Therefore,accurately identifying the parameters of PV models is essential for simulating,controlling and evaluating PV systems.In this study,we propose an enhanced weighted-mean-of-vectors optimisation(EINFO)for efficiently determining the unknown parameters in PV systems.EINFO introduces a Lambert W-based explicit objective function for the PV model,enhancing the computational accuracy of the algorithm's population fitness.This addresses the challenge of improving the metaheuristic algorithms'identification accuracy for unknown parameter identification in PV models.We experimentally apply EINFO to three types of PV models(single-diode,double-diode and PV-module models)to validate its accuracy and stability in parameter identification.The results demonstrate that EINFO achieves root mean square errors(RMSEs)of 7.7301E-04,6.8553E-04 and 2.0608E-03 for the single-diode model,double-diode model and PV-module model,respectively,surpassing those obtained by using INFO algorithm as well as other methods in terms of convergence speed,accuracy and stability.Furthermore,comprehensive experimental findings on three commercial PV modules(ST40,SM55 and KC200GT)indicate that EINFO consistently maintains high accuracy across varying temperatures and irradiation levels.In conclusion,EINFO emerges as a highly competitive and practical approach for parameter identification in diverse types of PV models.
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.
文摘With an optimised hall layout,progressive design collaborations,inspiring trends and AIdriven innovations,Heimtextil 2026 reacts to the current market situation–and offers the industry a reliable constant in challenging times.Under the motto‘Lead the Change’,the leading trade fair for home and contract textiles and textile design shows how challenges can be turned into opportunities.From 13 to 16 January,more than 3,100 exhibitors from 65 countries will provide a comprehensive market overview with new collections and textile solutions.As a knowledge hub,Heimtextil delivers new strategies and concrete solutions for future business success.
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supported by the Natural Science Foundation of Guangdong Province(Grant 2023A1515011667)Science and Technology Major Project of Shenzhen(Grant KJZD20230923114809020)Key Basic Research Foundation of Shenzhen(Grant JCYJ20220818100205012).
文摘Estimating probability density functions(PDFs)is critical in data analysis,particularly for complex multimodal distributions.traditional kernel density estimator(KDE)methods often face challenges in accurately capturing multimodal structures due to their uniform weighting scheme,leading to mode loss and degraded estimation accuracy.This paper presents the flexible kernel density estimator(F-KDE),a novel nonparametric approach designed to address these limitations.F-KDE introduces the concept of kernel unit inequivalence,assigning adaptive weights to each kernel unit,which better models local density variations in multimodal data.The method optimises an objective function that integrates estimation error and log-likelihood,using a particle swarm optimisation(PSO)algorithm that automatically determines optimal weights and bandwidths.Through extensive experiments on synthetic and real-world datasets,we demonstrated that(1)the weights and bandwidths in F-KDE stabilise as the optimisation algorithm iterates,(2)F-KDE effectively captures the multimodal characteristics and(3)F-KDE outperforms state-of-the-art density estimation methods regarding accuracy and robustness.The results confirm that F-KDE provides a valuable solution for accurately estimating multimodal PDFs.
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.
基金supported by the Artificial Intelligence Innovation Project of Wuhan Science and Technology Bureau,2023010402040016the Natural Science Foundation of Hubei Province of China,2022CFB076,JSPS KAKENHI,JP25K15279,Natural Science Foundation of Hubei Province,2023AFB003+1 种基金the National Natural Science Foundation of China,52201363the Education Department Scientific Research Programme Project of Hubei Province of China,Q20222208.
文摘The challenge of optimising multimodal functions within high-dimensional domains constitutes a notable difficulty in evolutionary computation research.Addressing this issue,this study introduces the Deep Backtracking Bare-Bones Particle Swarm Optimisation(DBPSO)algorithm,an innovative approach built upon the integration of the Deep Memory Storage Mechanism(DMSM)and the Dynamic Memory Activation Strategy(DMAS).The DMSM enhances the memory retention for the globally optimal particle,promoting interaction between standard particles and their historically optimal counterparts.In parallel,DMAS assures the updated position of the globally optimal particle is appropriately aligned with the deep memory repository.The efficacy of DBPSO was rigorously assessed through a series of simulations employing the CEC2017 benchmark suite.A comparative analysis juxtaposed DBPSO's performance against five contemporary evolutionary algorithms across two experimental conditions:Dimension-50 and Dimension-100.In the 50D trials,DBPSO attained an average ranking of 2.03,whereas in the 100D scenarios,it improved to an average ranking of 1.9.Further examination utilising the CEC2019 benchmark functions revealed DBPSO's robustness,securing four first-place finishes,three second-place standings,and three third-place positions,culminating in an unmatched average ranking of 1.9 across all algorithms.These empirical results corroborate DBPSO's proficiency in delivering precise solutions for complex,high-dimensional optimisation challenges.
基金supported by the Natural Science Foundation of Henan Province,China.(No,222300420596).
文摘Impact ground pressure events occur frequently in coal mining processes,significantly affecting the personal safety of construction workers.Real-time microseismic monitoring of coal rock body rupture information can provide early warnings,and the seismic source location method is an essential indicator for evaluating a microseismic monitoring system.This paper proposes a nonlinear hybrid optimal particle swarm optimisation(PSO)microseismic positioning method based on this technique.The method first improves the PSO algorithm by using the global search performance of this method to quickly find a feasible solution and provide a better initial solution for the subsequent solution of the nonlinear optimal microseismic positioning method.This approach effectively prevents the problem of the microseismic positioning method falling into a local optimum because of an over-reliance on the initial value.In addition,the nonlinear optimal microseismic positioning method further narrows the localisation error based on the PSO algorithm.A simulation test demonstrates that the new method has a good positioning effect,and engineering application examples also show that the proposed method has high accuracy and strong positioning stability.The new method is better than the separate positioning method,both overall and in three directions,making it more suitable for solving the microseismic positioning problem.
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.
基金supported by ANR-ASTRID NANOFLY(ANR-19-ASTR-0023)and French AID(Defense Innovation Agency).
文摘This article presents the design of a microfabricated bio-inspired flapping-wing Nnano Aaerial Vvehicle(NAV),driven by an electromagnetic system.Our approach is based on artificial wings composed of rigid bodies connected by compliant links,which optimise aerodynamic forces though replicating the complex wing kinematics of insects.The originality of this article lies in a new design methodology based on a triple equivalence between a 3D model,a multibody model,and a mass/spring model(0D)which reduces the number of parameters in the problem.This approach facilitates NAV optimisation by using only the mass/spring model,thereby simplifying the design process while maintaining high accuracy.Two wing geometries are studied and optimised in this article to produce large-amplitude wing motions(approximately 40^\circ),and enabling flapping and twisting motion in quadrature.The results are validated thanks to experimental measurements for the large amplitude and through finite element simulations for the combined motion,confirming the effectiveness of this strategy for a NAV weighing less than 40 mg with a wingspan of under 3 cm.
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.
