Diabetes mellitus represents a major global health issue,driving the need for noninvasive alternatives to traditional blood glucose monitoring methods.Recent advancements in wearable technology have introduced skin-in...Diabetes mellitus represents a major global health issue,driving the need for noninvasive alternatives to traditional blood glucose monitoring methods.Recent advancements in wearable technology have introduced skin-interfaced biosensors capable of analyzing sweat and skin biomarkers,providing innovative solutions for diabetes diagnosis and monitoring.This review comprehensively discusses the current developments in noninvasive wearable biosensors,emphasizing simultaneous detection of biochemical biomarkers(such as glucose,cortisol,lactate,branched-chain amino acids,and cytokines)and physiological signals(including heart rate,blood pressure,and sweat rate)for accurate,personalized diabetes management.We explore innovations in multimodal sensor design,materials science,biorecognition elements,and integration techniques,highlighting the importance of advanced data analytics,artificial intelligence-driven predictive algorithms,and closed-loop therapeutic systems.Additionally,the review addresses ongoing challenges in biomarker validation,sensor stability,user compliance,data privacy,and regulatory considerations.A holistic,multimodal approach enabled by these next-generation wearable biosensors holds significant potential for improving patient outcomes and facilitating proactive healthcare interventions in diabetes management.展开更多
Human action recognition(HAR)is crucial for the development of efficient computer vision,where bioinspired neuromorphic perception visual systems have emerged as a vital solution to address transmission bottlenecks ac...Human action recognition(HAR)is crucial for the development of efficient computer vision,where bioinspired neuromorphic perception visual systems have emerged as a vital solution to address transmission bottlenecks across sensor-processor interfaces.However,the absence of interactions among versatile biomimicking functionalities within a single device,which was developed for specific vision tasks,restricts the computational capacity,practicality,and scalability of in-sensor vision computing.Here,we propose a bioinspired vision sensor composed of a Ga N/Al N-based ultrathin quantum-disks-in-nanowires(QD-NWs)array to mimic not only Parvo cells for high-contrast vision and Magno cells for dynamic vision in the human retina but also the synergistic activity between the two cells for in-sensor vision computing.By simply tuning the applied bias voltage on each QD-NW-array-based pixel,we achieve two biosimilar photoresponse characteristics with slow and fast reactions to light stimuli that enhance the in-sensor image quality and HAR efficiency,respectively.Strikingly,the interplay and synergistic interaction of the two photoresponse modes within a single device markedly increased the HAR recognition accuracy from 51.4%to 81.4%owing to the integrated artificial vision system.The demonstration of an intelligent vision sensor offers a promising device platform for the development of highly efficient HAR systems and future smart optoelectronics.展开更多
The fast growth of mobile autonomous machines from traditional equipment to unmanned autonomous vehicles has fueled the demand for accurate and reliable localization solutions in diverse application domains.Ultra Wide...The fast growth of mobile autonomous machines from traditional equipment to unmanned autonomous vehicles has fueled the demand for accurate and reliable localization solutions in diverse application domains.Ultra Wide Band(UWB)technology has emerged as a promising candidate for addressing this need,offering high precision,immunity to multipath interference,and robust performance in challenging environments.In this comprehensive survey,we systematically explore UWB-based localization for mobile autonomous machines,spanning from fundamental principles to future trends.To the best of our knowledge,this review paper stands as the pioneer in systematically dissecting the algorithms of UWB-based localization for mobile autonomous machines,covering a spectrum from bottom-ranging schemes to advanced sensor fusion,error mitigation,and optimization techniques.By synthesizing existing knowledge,evaluating current methodologies,and highlighting future trends,this review aims to catalyze progress and innovation in the field,unlocking new opportunities for mobile autonomous machine applications across diverse industries and domains.Thus,it serves as a valuable resource for researchers,practitioners,and stakeholders interested in advancing the state-of-the-art UWB-based localization for mobile autonomous machines.展开更多
The knapsack problem is a classical combinatorial optimization problem widely encountered in areas such as logistics,resource allocation,and portfolio optimization.Traditional methods,including dynamic program-ming(DP...The knapsack problem is a classical combinatorial optimization problem widely encountered in areas such as logistics,resource allocation,and portfolio optimization.Traditional methods,including dynamic program-ming(DP)and greedy algorithms,have been effective in solving small problem instances but often struggle with scalability and efficiency as the problem size increases.DP,for instance,has exponential time complexity and can become computationally prohibitive for large problem instances.On the other hand,greedy algorithms offer faster solutions but may not always yield the optimal results,especially when the problem involves complex constraints or large numbers of items.This paper introduces a novel reinforcement learning(RL)approach to solve the knapsack problem by enhancing the state representation within the learning environment.We propose a representation where item weights and volumes are expressed as ratios relative to the knapsack’s capacity,and item values are normalized to represent their percentage of the total value across all items.This novel state modification leads to a 5%improvement in accuracy compared to the state-of-the-art RL-based algorithms,while significantly reducing execution time.Our RL-based method outperforms DP by over 9000 times in terms of speed,making it highly scalable for larger problem instances.Furthermore,we improve the performance of the RL model by incorporating Noisy layers into the neural network architecture.The addition of Noisy layers enhances the exploration capabilities of the agent,resulting in an additional accuracy boost of 0.2%–0.5%.The results demonstrate that our approach not only outperforms existing RL techniques,such as the Transformer model in terms of accuracy,but also provides a substantial improvement than DP in computational efficiency.This combination of enhanced accuracy and speed presents a promising solution for tackling large-scale optimization problems in real-world applications,where both precision and time are critical factors.展开更多
Paired electrolysis of waste feedstocks holds an energy-efficient alternative for chemical production;however,the sluggish anodic oxidation limited the total efficiency under larger current density.Herein,we construct...Paired electrolysis of waste feedstocks holds an energy-efficient alternative for chemical production;however,the sluggish anodic oxidation limited the total efficiency under larger current density.Herein,we constructed ultralow-coordinated Ni species with Ni–O coordination number of∼3 via a hydrothermal synthesis-sulfidation-annealing process and electrochemical activation and demonstrated the vital role in accelerating the proton deintercalation and reactive oxygen intermediate·OH formation during electro-reforming polyethylene terephthalate hydrolysate(POR).The target catalyst NiCoSx/NF afforded a high formate productivity of 7.4 mmol cm^(−2)h^(−1)at∼600 mA cm^(−2)with a formate Faradic efficiency(FE_(formate))of 92.4%in POR and maintained a FE_(formate)of∼90%for 100 h at 2 A in a membrane electrode assembly electrolyzer.Coupling POR on NiCoSx/NF with carbon dioxide reduction reaction on oxygen vacancies enriched Vo-BiSnO reached effective concurrent formate production with 172.7%of FE_(formate)at 500 mA cm^(−2)and long-term stability.Such excellent performance shows the great prospect of electrocatalyst design by regulating the local metal environment.展开更多
Rechargeable zinc-air batteries(ZABs) have recently drawn great attention in energy research due to their high theoretical capacity,low costs, and inherently safe nature [1–3]. However, the sluggish cathode reactions...Rechargeable zinc-air batteries(ZABs) have recently drawn great attention in energy research due to their high theoretical capacity,low costs, and inherently safe nature [1–3]. However, the sluggish cathode reactions necessitate the development of bifunctional oxygen electrocatalysts with lower ΔE indicator values. The ΔE indicator is commonly employed to quantitatively evaluate the electrocatalytic activity of a bifunctional oxygen electrocatalyst,representing the overall overpotential from oxygen reduction reaction(ORR) to oxygen evolution reaction(OER).展开更多
Single-atom catalysts(SACs)offer a promising approach for maximizing noble metals utilization in catalytic processes.However,their performance in CO_(2)hydrogenation is often constrained by the nature of metal-support...Single-atom catalysts(SACs)offer a promising approach for maximizing noble metals utilization in catalytic processes.However,their performance in CO_(2)hydrogenation is often constrained by the nature of metal-support interactions.In this study,we synthesized TiO_(2)supported Pt SACs(Pt1/TiO_(2)),with Pt single atoms dispersed on rutile(Pt1/R)and anatase(Pt1/A)phases of TiO_(2)for the reverse water-gas shift(RWGS)reaction.While both catalysts maintained 100%CO selectivity over time,Pt1/A achieved a CO_(2)conversion of 7.5%,significantly outperforming Pt1/R(3.6%).In situ diffuse reflectance infrared Fourier-transform spectroscopy and X-ray photoelectron spectroscopy revealed distinct reaction pathways:the COOH pathway was dominant on Pt1/A,whereas the–OH+HCO pathway was more competitive on Pt1/R.Analysis of electron metal-support interactions and energy barrier calculations indicated that Pt1/A better stabilized metallic Pt species and facilitates more favorable reaction pathways with lower energy barriers.These findings provide valuable insights for the design of more efficient SAC systems in CO_(2)hydrogenation processes.展开更多
High-voltage solid-state lithium-ion batteries(SSLIBs)have attracted considerable research attention in recent years due to their high-energy-density and superior safety characteristics.However,the integration of high...High-voltage solid-state lithium-ion batteries(SSLIBs)have attracted considerable research attention in recent years due to their high-energy-density and superior safety characteristics.However,the integration of high-voltage cathodes with solid electrolytes(SEs)presents multiple challenges,including the formation of high-impedance layers from spontaneous chemical reactions,electrochemical instability,insufficient interfacial contact,and lattice expansion.These issues significantly impair battery performance and potentially lead to battery failure,thus impeding the commercialization of high-voltage SSLIBs.The incorporation of fluorides,known for their robust bond strength and high free energy of formation,has emerged as an effective strategy to address these challenges.Fluorinated electrolytes and electrode/electrolyte interfaces have been demonstrated to significantly influence the reaction reversibility/kinetics,safety,and stability of rechargeable batteries,particularly under high voltage.This review summarizes recent advancements in fluorination treatment for high-voltage SEs,focusing on solid polymer electrolytes(SPEs),inorganic solid electrolytes(ISEs),and composite solid electrolytes(CSEs),along with the performance enhancements these strategies afford.This review aims to provide a comprehensive understanding of the structure-property relationships,the characteristics of fluorinated interfaces,and the application of fluorinated SEs in high-voltage SSLIBs.Further,the impacts of residual moisture and the challenges of fluorinated SEs are discussed.Finally,the review explores potential future directions for the development of fluorinated SSLIBs.展开更多
The synergy of single atoms(SAs)and nanoparticles(NPs)has demonstrated great potential in promoting the electrocatalytic carbon dioxide reduction reaction(CO_(2)RR);however,the rationalization of the SAs/NPs proportio...The synergy of single atoms(SAs)and nanoparticles(NPs)has demonstrated great potential in promoting the electrocatalytic carbon dioxide reduction reaction(CO_(2)RR);however,the rationalization of the SAs/NPs proportion remains one challenge for the catalyst design.Herein,a Ni2+-loaded porous poly(ionic liquids)(PIL)precursor synthesized through the free radical self-polymerization of the ionic liquid monomer,1-allyl-3-vinylimidazolium chloride,was pyrolyzed to prepare the Ni,N co-doped carbon materials,in which the proportion of Ni SAs and NPs could be facilely modulated by controlling the annealing temperature.The catalyst Ni-NC-1000 with a moderate proportion of Ni SAs and NPs exhibited high efficiency in the electrocatalytic conversion of CO_(2)into CO.Operando Ni K-edge X-ray absorption near-edge structure(XANES)spectra and theoretical calculations were conducted to gain insight into the synergy of Ni SAs and NPs.The charge transfer from Ni NPs to the surrounding carbon layer and then to the Ni SAs resulted in the electron-enriched Ni SAs active sites.In the electroreduction of CO_(2),the coexistence of Ni SAs and NPs strengthened the CO_(2)activation and the affinity towards the key intermediate of*COOH,lowering the free energy for the potential-determining*CO_(2)→*COOH step,and therefore promoted the catalysis efficiency.展开更多
The dynamic mechanical response and deformation mechanism of magnesium-yttrium alloy at high strain rate were investigated using split-Hopkinson pressure bar(SHPB)impact,and the microstructure evolution and crack form...The dynamic mechanical response and deformation mechanism of magnesium-yttrium alloy at high strain rate were investigated using split-Hopkinson pressure bar(SHPB)impact,and the microstructure evolution and crack formation mechanism were revealed.The yield strength and work hardening rate increase significantly with increasing impact strain rate.Deformation twinning and non-basal dislocation slip are the primary deformation mechanisms during testing.Contrary to crack initiation mechanism facilitated by adiabatic shear bands,we find that high-density co-axial nanocrystalline grains form near cracks,which leads to local softening and promotes crack initiation and rapid propagation.Most grains have similar<1^(-)21^(-)0>orientations,with unique misorientation of 24°,32°,62°,78°and 90°between adjacent grains,suggesting that these grains are primarily formed by interface transformation,which exhibits distinct differences from recrystallized grains.Our results shed light upon the dynamic mechanical response and crack formation mechanism in magnesium alloys under impact deformation.展开更多
Developing highly active and stable oxygen evolution reaction(OER)catalysts necessitates the establishment of a comprehensive OER catalyst database.However,the absence of a standardized benchmarking protocol has hinde...Developing highly active and stable oxygen evolution reaction(OER)catalysts necessitates the establishment of a comprehensive OER catalyst database.However,the absence of a standardized benchmarking protocol has hindered this progress.In this work,we present a systematic protocol for electrochemical measurements to thoroughly evaluate the activity and stability of OER electrocatalysts.We begin with a detailed introduction to constructing the electrochemical system,encompassing experimental setup and the selection criteria for electrodes and electrolytes.Potential contaminants originating from electrolytes,cells,and electrodes are identified and their impacts are discussed.We also examine the effects of external factors,such as temperature,magnetic fields,and natural light,on OER measurements.The protocol outlines operational mechanisms and recommended settings for various electrochemical techniques,including cyclic voltammetry(CV),potentiostatic electrochemical impedance spectroscopy(PEIS),Tafel slope analysis,and pulse voltammetry(PV).We summarize existing evaluation methodologies for assessing intrinsic activities and long-term stabilities of catalysts.Based on these discussions,we propose a comprehensive protocol for evaluating OER electrocatalysts’performance.Finally,we offer perspectives on advancing OER catalysts from laboratory research to industrial applications.展开更多
Efficiently utilizing ammonia(carbon-free fuel)via low-temperature fuel cells is severely hindered by the sluggish kinetics of ammonia oxidation reaction(AOR).Herein,platinum-iridium-tungsten nanocubes(PtIrW-NCBs)with...Efficiently utilizing ammonia(carbon-free fuel)via low-temperature fuel cells is severely hindered by the sluggish kinetics of ammonia oxidation reaction(AOR).Herein,platinum-iridium-tungsten nanocubes(PtIrW-NCBs)with exposed{100}-rich facets were synthesized by a glucose-assisted solvent-thermal method,in which alloying W not only can facilitate the formation of such specific nanostructures to expose more active sites for AOR,but also modulate the electronic structure of PtIr to promote the kinetics of AOR.The PtIrW-NCBs featuring the small nanoparticle size of 5.05±0.07 nm exhibit superior AOR performance,wherein the onset potential is down to 0.319 V and the mass activity is 30.15 A g_((PGM=Pt,Ir))^(-1)at 0.50 V vs.RHE,significantly higher than those of reported majority of AOR catalysts and even commercial PtIr/C.Meanwhile,in situ Fourier transform infrared spectroscopy measurement further reveals that AOR on PtIrW-NCBs dominantly undergoes the dimerization path of NH_(x)(1≤x≤2).In addition,the theoretical calculations also identify that alloying W into PtIr can contribute additional electrons to 5d orbitals of PtIr,enabling the d-band center approaching the Femi level,which in turn induces the high-filling of bonding orbitals of N-N bond in^(*)N_(2)H_(4),promoting the dimerization of^(*)NH_(2)to^(*)N_(2)H_(4)and thus leading to high AOR activity of PtIrW.This work provides new insights for designing efficient AOR electrocatalysts.展开更多
The field of subwavelength optics has opened new avenues for investigating light–matter interactions by enabling the exploration of novel phenomena at the subwavelength scale. In recent decades,advancements in fundam...The field of subwavelength optics has opened new avenues for investigating light–matter interactions by enabling the exploration of novel phenomena at the subwavelength scale. In recent decades,advancements in fundamental understanding and micro–nanotechnologies have significantly propelled the development of subwavelength optics and its practical applications.展开更多
Efficient electrocatalysts for oxygen reduction reaction(ORR)show significant importance for advancing the performance and affordability of proton exchange membrane fuel cells and other energy conversion devices.Herei...Efficient electrocatalysts for oxygen reduction reaction(ORR)show significant importance for advancing the performance and affordability of proton exchange membrane fuel cells and other energy conversion devices.Herein,PtCo_(3)nanoalloys dispersed on a carbon black support,were prepared using ultrafast Joule heating method.By tuning the heating modes,such as high-temperature shock and heating for 2 s,two kinds of PtCo_(3)nanoalloys with varying crystallinities were obtained,referred to as PtCo_(3)-HTS(average size of 5.4 nm)and PtCo_(3)-HT-2 s(average size of 6.4 nm),respectively.Impressively,PtCo_(3)-HTS exhibited superior electrocatalytic ORR activity and stability(E_(1/2)=0.897 V vs.RHE and 36mV negative shift after 50,000 cycles),outperforming PtCo_(3)-HT-2 s(E_(1/2)=0.872 V and 16.2mV negative shift),as well as the commercial Pt/C(20 wt%)catalyst(E_(1/2)=0.847 V and 21.0mV negative shift).The enhanced ORR performance of PtCo_(3)-HTS may be attributed to its low crystallinity,which results in an active local electronic structure and chemical state,as confirmed by X-ray diffraction(XRD)and X-ray absorption fine structure(XAFS)analyses.The ultrafast Joule heating method showed great potential for crystallinity engineering,offering a promising pathway to revolutionize the manufacturing of cost-effective and environmentally friendly catalysts for clean energy applications.展开更多
Schizophrenia is a complex psychiatric disorder marked by positive and negative symptoms,leading to mood disturbances,cognitive impairments,and social withdrawal.While anti-psychotic medications remain the cornerstone...Schizophrenia is a complex psychiatric disorder marked by positive and negative symptoms,leading to mood disturbances,cognitive impairments,and social withdrawal.While anti-psychotic medications remain the cornerstone of treatment,they often fail to fully address certain symptoms.Additionally,treatment-resistant schizophrenia,affecting 30%-40%of patients,remains a substantial clinical challenge.Positive,negative symptoms and cognitive impairments have been linked to disruptions in the glutamatergic,serotonin,GABAergic,and muscarinic pathways in the brain.Recent advances using genome-wide association study and other approaches have uncovered a significant number of new schizophrenia risk genes that uncovered new,and reinforced prior,concepts on the genetic and neurological underpinnings of schizophrenia,including abnormalities in synaptic function,immune processes,and lipid metabolism.Concurrently,new therapeutics targeting different modalities,which are expected to address some of the limitations of anti-psychotic drugs currently being offered to patients,are currently being evaluated.Collectively,these efforts provide new momentum for the next phase of schizophrenia research and treatment.展开更多
Flexible job shop scheduling problems(FJSP)have received much attention from academia and industry for many years.Due to their exponential complexity,swarm intelligence(SI)and evolutionary algorithms(EA)are developed,...Flexible job shop scheduling problems(FJSP)have received much attention from academia and industry for many years.Due to their exponential complexity,swarm intelligence(SI)and evolutionary algorithms(EA)are developed,employed and improved for solving them.More than 60%of the publications are related to SI and EA.This paper intents to give a comprehensive literature review of SI and EA for solving FJSP.First,the mathematical model of FJSP is presented and the constraints in applications are summarized.Then,the encoding and decoding strategies for connecting the problem and algorithms are reviewed.The strategies for initializing algorithms?population and local search operators for improving convergence performance are summarized.Next,one classical hybrid genetic algorithm(GA)and one newest imperialist competitive algorithm(ICA)with variables neighborhood search(VNS)for solving FJSP are presented.Finally,we summarize,discus and analyze the status of SI and EA for solving FJSP and give insight into future research directions.展开更多
Focusing on the structural optimization of auxetic materials using data-driven methods,a back-propagation neural network(BPNN)based design framework is developed for petal-shaped auxetics using isogeometric analysis.A...Focusing on the structural optimization of auxetic materials using data-driven methods,a back-propagation neural network(BPNN)based design framework is developed for petal-shaped auxetics using isogeometric analysis.Adopting a NURBSbased parametric modelling scheme with a small number of design variables,the highly nonlinear relation between the input geometry variables and the effective material properties is obtained using BPNN-based fitting method,and demonstrated in this work to give high accuracy and efficiency.Such BPNN-based fitting functions also enable an easy analytical sensitivity analysis,in contrast to the generally complex procedures of typical shape and size sensitivity approaches.展开更多
Surface enhanced Raman scattering(SERS)is an efficient technique to detect low concentration molecules.In this work,periodical silicon nanowires(Si NWs)integrated with metal-insulator-metal(MIM)layers are employed as ...Surface enhanced Raman scattering(SERS)is an efficient technique to detect low concentration molecules.In this work,periodical silicon nanowires(Si NWs)integrated with metal-insulator-metal(MIM)layers are employed as SERS substrates.Laser interference lithography(LIL)combined with reactive ion etching(RIE)is used to fabricate large-area periodic nanostructures,followed by decorating the MIM layers.Compared to MIM disks array on Si surface,the SERS enhancement factor(EF)of the MIM structures on the Si NWs array can be increased up to 5 times,which is attributed to the enhanced electric field at the boundary of the MIM disks.Furthermore,high density of nanoparticles and nanogaps serving as hot spots on sidewall surfaces also contribute to the enhanced SERS signals.Via changing the thickness of the insulator layer,the plasmonic resonance can be tuned,which provides a new localized surface plasmon resonance(LSPR)characteristic for SERS applications.展开更多
Catalytic conversion of COinto chemicals and fuels is an alternative to alleviate climate change and ocean acidification.The catalytic reduction of COby Hcan lead to the formation of various products:carbon monoxide,c...Catalytic conversion of COinto chemicals and fuels is an alternative to alleviate climate change and ocean acidification.The catalytic reduction of COby Hcan lead to the formation of various products:carbon monoxide,carboxylic acids,aldehydes,alcohols and hydrocarbons.In this paper,a comprehensive thermodynamics analysis of COhydrogenation is conducted using the Gibbs free energy minimization method.The results show that COreduction to CO needs a high temperature and H/COratio to achieve a high COconversion.However,synthesis of methanol from COneeds a relatively high pressure and low temperature to minimize the reverse water-gas shift reaction.Direct COhydrogenation to formic acid or formaldehyde is thermodynamically limited.On the contrary,production of CHfrom COhydrogenation is the thermodynamically easiest reaction with nearly 100%CH4 yield at moderate conditions.In addition,complex reactions with more than one product are also calculated in this work.Among the considered carboxylic acids（HCOOH,CHCOOH and CHCOOH）,propionic acid dominates in the product stream（selectivity above 90%）.The same trend can also be found in the hydrogenation of COto aldehydes and alcohols with the major product of propionaldehyde and butanol,respectively.In the process of COhydrogenation to alkenes,low temperature,high pressure,and high Hpartial pressure favor the COconversion.CHis the most thermodynamically favorable among all considered alkynes under different temperatures and pressures.The thermodynamic calculations are validated with experimental results,suggesting that the Gibbs free energy minimization method is effective for thermodynamically understanding the reaction network involved in the COhydrogenation process,which is helpful for the development of high-performance catalysts.展开更多
Accurate remaining useful life(RUL)prediction is important in industrial systems.It prevents machines from working under failure conditions,and ensures that the industrial system works reliably and efficiently.Recentl...Accurate remaining useful life(RUL)prediction is important in industrial systems.It prevents machines from working under failure conditions,and ensures that the industrial system works reliably and efficiently.Recently,many deep learning based methods have been proposed to predict RUL.Among these methods,recurrent neural network(RNN)based approaches show a strong capability of capturing sequential information.This allows RNN based methods to perform better than convolutional neural network(CNN)based approaches on the RUL prediction task.In this paper,we question this common paradigm and argue that existing CNN based approaches are not designed according to the classic principles of CNN,which reduces their performances.Additionally,the capacity of capturing sequential information is highly affected by the receptive field of CNN,which is neglected by existing CNN based methods.To solve these problems,we propose a series of new CNNs,which show competitive results to RNN based methods.Compared with RNN,CNN processes the input signals in parallel so that the temporal sequence is not easily determined.To alleviate this issue,a position encoding scheme is developed to enhance the sequential information encoded by a CNN.Hence,our proposed position encoding based CNN called PE-Net is further improved and even performs better than RNN based methods.Extensive experiments are conducted on the C-MAPSS dataset,where our PE-Net shows state-of-the-art performance.展开更多
文摘Diabetes mellitus represents a major global health issue,driving the need for noninvasive alternatives to traditional blood glucose monitoring methods.Recent advancements in wearable technology have introduced skin-interfaced biosensors capable of analyzing sweat and skin biomarkers,providing innovative solutions for diabetes diagnosis and monitoring.This review comprehensively discusses the current developments in noninvasive wearable biosensors,emphasizing simultaneous detection of biochemical biomarkers(such as glucose,cortisol,lactate,branched-chain amino acids,and cytokines)and physiological signals(including heart rate,blood pressure,and sweat rate)for accurate,personalized diabetes management.We explore innovations in multimodal sensor design,materials science,biorecognition elements,and integration techniques,highlighting the importance of advanced data analytics,artificial intelligence-driven predictive algorithms,and closed-loop therapeutic systems.Additionally,the review addresses ongoing challenges in biomarker validation,sensor stability,user compliance,data privacy,and regulatory considerations.A holistic,multimodal approach enabled by these next-generation wearable biosensors holds significant potential for improving patient outcomes and facilitating proactive healthcare interventions in diabetes management.
基金funded by the National Natural Science Foundation of China(Grant Nos.62322410,52272168,624B2135,61804047)the Fundamental Research Funds for the Central Universities(No.WK2030000103)。
文摘Human action recognition(HAR)is crucial for the development of efficient computer vision,where bioinspired neuromorphic perception visual systems have emerged as a vital solution to address transmission bottlenecks across sensor-processor interfaces.However,the absence of interactions among versatile biomimicking functionalities within a single device,which was developed for specific vision tasks,restricts the computational capacity,practicality,and scalability of in-sensor vision computing.Here,we propose a bioinspired vision sensor composed of a Ga N/Al N-based ultrathin quantum-disks-in-nanowires(QD-NWs)array to mimic not only Parvo cells for high-contrast vision and Magno cells for dynamic vision in the human retina but also the synergistic activity between the two cells for in-sensor vision computing.By simply tuning the applied bias voltage on each QD-NW-array-based pixel,we achieve two biosimilar photoresponse characteristics with slow and fast reactions to light stimuli that enhance the in-sensor image quality and HAR efficiency,respectively.Strikingly,the interplay and synergistic interaction of the two photoresponse modes within a single device markedly increased the HAR recognition accuracy from 51.4%to 81.4%owing to the integrated artificial vision system.The demonstration of an intelligent vision sensor offers a promising device platform for the development of highly efficient HAR systems and future smart optoelectronics.
文摘The fast growth of mobile autonomous machines from traditional equipment to unmanned autonomous vehicles has fueled the demand for accurate and reliable localization solutions in diverse application domains.Ultra Wide Band(UWB)technology has emerged as a promising candidate for addressing this need,offering high precision,immunity to multipath interference,and robust performance in challenging environments.In this comprehensive survey,we systematically explore UWB-based localization for mobile autonomous machines,spanning from fundamental principles to future trends.To the best of our knowledge,this review paper stands as the pioneer in systematically dissecting the algorithms of UWB-based localization for mobile autonomous machines,covering a spectrum from bottom-ranging schemes to advanced sensor fusion,error mitigation,and optimization techniques.By synthesizing existing knowledge,evaluating current methodologies,and highlighting future trends,this review aims to catalyze progress and innovation in the field,unlocking new opportunities for mobile autonomous machine applications across diverse industries and domains.Thus,it serves as a valuable resource for researchers,practitioners,and stakeholders interested in advancing the state-of-the-art UWB-based localization for mobile autonomous machines.
基金supported in part by the Research Start-Up Funds of South-Central Minzu University under Grants YZZ23002,YZY23001,and YZZ18006in part by the Hubei Provincial Natural Science Foundation of China under Grants 2024AFB842 and 2023AFB202+3 种基金in part by the Knowledge Innovation Program of Wuhan Basic Research underGrant 2023010201010151in part by the Spring Sunshine Program of Ministry of Education of the People’s Republic of China under Grant HZKY20220331in part by the Funds for Academic Innovation Teams and Research Platformof South-CentralMinzu University Grant Number:XT224003,PTZ24001in part by the Career Development Fund(CDF)of the Agency for Science,Technology and Research(A*STAR)(Grant Number:C233312007).
文摘The knapsack problem is a classical combinatorial optimization problem widely encountered in areas such as logistics,resource allocation,and portfolio optimization.Traditional methods,including dynamic program-ming(DP)and greedy algorithms,have been effective in solving small problem instances but often struggle with scalability and efficiency as the problem size increases.DP,for instance,has exponential time complexity and can become computationally prohibitive for large problem instances.On the other hand,greedy algorithms offer faster solutions but may not always yield the optimal results,especially when the problem involves complex constraints or large numbers of items.This paper introduces a novel reinforcement learning(RL)approach to solve the knapsack problem by enhancing the state representation within the learning environment.We propose a representation where item weights and volumes are expressed as ratios relative to the knapsack’s capacity,and item values are normalized to represent their percentage of the total value across all items.This novel state modification leads to a 5%improvement in accuracy compared to the state-of-the-art RL-based algorithms,while significantly reducing execution time.Our RL-based method outperforms DP by over 9000 times in terms of speed,making it highly scalable for larger problem instances.Furthermore,we improve the performance of the RL model by incorporating Noisy layers into the neural network architecture.The addition of Noisy layers enhances the exploration capabilities of the agent,resulting in an additional accuracy boost of 0.2%–0.5%.The results demonstrate that our approach not only outperforms existing RL techniques,such as the Transformer model in terms of accuracy,but also provides a substantial improvement than DP in computational efficiency.This combination of enhanced accuracy and speed presents a promising solution for tackling large-scale optimization problems in real-world applications,where both precision and time are critical factors.
基金We highly thank the funding from the National Natural Science Foundation of China(grants 22222806,22178162,22072065,and 22408170)the Distinguished Youth Foundation of Jiangsu Province(BK20220053)+2 种基金the National Key Research and Development Program of China(2024YFE0206900)the Six Talent Peaks Project in Jiangsu Province(grant JNHB-035)Agency for Science,Technology and Research(A*STAR)through Low Carbon Energy Research Finding Initiative(LCERFI01-0033|U2102d2006).
文摘Paired electrolysis of waste feedstocks holds an energy-efficient alternative for chemical production;however,the sluggish anodic oxidation limited the total efficiency under larger current density.Herein,we constructed ultralow-coordinated Ni species with Ni–O coordination number of∼3 via a hydrothermal synthesis-sulfidation-annealing process and electrochemical activation and demonstrated the vital role in accelerating the proton deintercalation and reactive oxygen intermediate·OH formation during electro-reforming polyethylene terephthalate hydrolysate(POR).The target catalyst NiCoSx/NF afforded a high formate productivity of 7.4 mmol cm^(−2)h^(−1)at∼600 mA cm^(−2)with a formate Faradic efficiency(FE_(formate))of 92.4%in POR and maintained a FE_(formate)of∼90%for 100 h at 2 A in a membrane electrode assembly electrolyzer.Coupling POR on NiCoSx/NF with carbon dioxide reduction reaction on oxygen vacancies enriched Vo-BiSnO reached effective concurrent formate production with 172.7%of FE_(formate)at 500 mA cm^(−2)and long-term stability.Such excellent performance shows the great prospect of electrocatalyst design by regulating the local metal environment.
基金National Research Foundation (NRF Investigatorship NRF-NRFI09-0002)Agency for Science,Technology and Research (MTC Programmatic Fund M23L9b0052)。
文摘Rechargeable zinc-air batteries(ZABs) have recently drawn great attention in energy research due to their high theoretical capacity,low costs, and inherently safe nature [1–3]. However, the sluggish cathode reactions necessitate the development of bifunctional oxygen electrocatalysts with lower ΔE indicator values. The ΔE indicator is commonly employed to quantitatively evaluate the electrocatalytic activity of a bifunctional oxygen electrocatalyst,representing the overall overpotential from oxygen reduction reaction(ORR) to oxygen evolution reaction(OER).
文摘Single-atom catalysts(SACs)offer a promising approach for maximizing noble metals utilization in catalytic processes.However,their performance in CO_(2)hydrogenation is often constrained by the nature of metal-support interactions.In this study,we synthesized TiO_(2)supported Pt SACs(Pt1/TiO_(2)),with Pt single atoms dispersed on rutile(Pt1/R)and anatase(Pt1/A)phases of TiO_(2)for the reverse water-gas shift(RWGS)reaction.While both catalysts maintained 100%CO selectivity over time,Pt1/A achieved a CO_(2)conversion of 7.5%,significantly outperforming Pt1/R(3.6%).In situ diffuse reflectance infrared Fourier-transform spectroscopy and X-ray photoelectron spectroscopy revealed distinct reaction pathways:the COOH pathway was dominant on Pt1/A,whereas the–OH+HCO pathway was more competitive on Pt1/R.Analysis of electron metal-support interactions and energy barrier calculations indicated that Pt1/A better stabilized metallic Pt species and facilitates more favorable reaction pathways with lower energy barriers.These findings provide valuable insights for the design of more efficient SAC systems in CO_(2)hydrogenation processes.
基金supported by the A*STAR MTC Programmatic Project(No.M23L9b0052)the Indonesia-NTU Singapore Institute of Research for Sustainability and Innovation(INSPIRASI)(No.6635/E3/KL.02.02/2023)+2 种基金the Singapore NRF Singapore-China Flagship Program(No.023740-00001)the National Natural Science Foundation of China(Nos.11975043 and 11475300)the China Scholarship Council(No.202306460087)。
文摘High-voltage solid-state lithium-ion batteries(SSLIBs)have attracted considerable research attention in recent years due to their high-energy-density and superior safety characteristics.However,the integration of high-voltage cathodes with solid electrolytes(SEs)presents multiple challenges,including the formation of high-impedance layers from spontaneous chemical reactions,electrochemical instability,insufficient interfacial contact,and lattice expansion.These issues significantly impair battery performance and potentially lead to battery failure,thus impeding the commercialization of high-voltage SSLIBs.The incorporation of fluorides,known for their robust bond strength and high free energy of formation,has emerged as an effective strategy to address these challenges.Fluorinated electrolytes and electrode/electrolyte interfaces have been demonstrated to significantly influence the reaction reversibility/kinetics,safety,and stability of rechargeable batteries,particularly under high voltage.This review summarizes recent advancements in fluorination treatment for high-voltage SEs,focusing on solid polymer electrolytes(SPEs),inorganic solid electrolytes(ISEs),and composite solid electrolytes(CSEs),along with the performance enhancements these strategies afford.This review aims to provide a comprehensive understanding of the structure-property relationships,the characteristics of fluorinated interfaces,and the application of fluorinated SEs in high-voltage SSLIBs.Further,the impacts of residual moisture and the challenges of fluorinated SEs are discussed.Finally,the review explores potential future directions for the development of fluorinated SSLIBs.
基金National Natural Science Foundation of China(grants 22072065,22178162,and 22222806)Distinguished Youth Foundation of Jiangsu Province(grant BK20220053)Six talent peaks project in Jiangsu Province(grant JNHB-035)。
文摘The synergy of single atoms(SAs)and nanoparticles(NPs)has demonstrated great potential in promoting the electrocatalytic carbon dioxide reduction reaction(CO_(2)RR);however,the rationalization of the SAs/NPs proportion remains one challenge for the catalyst design.Herein,a Ni2+-loaded porous poly(ionic liquids)(PIL)precursor synthesized through the free radical self-polymerization of the ionic liquid monomer,1-allyl-3-vinylimidazolium chloride,was pyrolyzed to prepare the Ni,N co-doped carbon materials,in which the proportion of Ni SAs and NPs could be facilely modulated by controlling the annealing temperature.The catalyst Ni-NC-1000 with a moderate proportion of Ni SAs and NPs exhibited high efficiency in the electrocatalytic conversion of CO_(2)into CO.Operando Ni K-edge X-ray absorption near-edge structure(XANES)spectra and theoretical calculations were conducted to gain insight into the synergy of Ni SAs and NPs.The charge transfer from Ni NPs to the surrounding carbon layer and then to the Ni SAs resulted in the electron-enriched Ni SAs active sites.In the electroreduction of CO_(2),the coexistence of Ni SAs and NPs strengthened the CO_(2)activation and the affinity towards the key intermediate of*COOH,lowering the free energy for the potential-determining*CO_(2)→*COOH step,and therefore promoted the catalysis efficiency.
基金support from the National Natural Science Foundation of China(Grant Nos.52301137,51974097,52364050)the Natural Science Special Foundation of Guizhou University(No.(2023)20)+1 种基金Guizhou Province Science and Technology Project(Grant Nos.[2023]001,[2019]2163)Guiyang city Science and Technology Project(Grant No.[2023]48-16).
文摘The dynamic mechanical response and deformation mechanism of magnesium-yttrium alloy at high strain rate were investigated using split-Hopkinson pressure bar(SHPB)impact,and the microstructure evolution and crack formation mechanism were revealed.The yield strength and work hardening rate increase significantly with increasing impact strain rate.Deformation twinning and non-basal dislocation slip are the primary deformation mechanisms during testing.Contrary to crack initiation mechanism facilitated by adiabatic shear bands,we find that high-density co-axial nanocrystalline grains form near cracks,which leads to local softening and promotes crack initiation and rapid propagation.Most grains have similar<1^(-)21^(-)0>orientations,with unique misorientation of 24°,32°,62°,78°and 90°between adjacent grains,suggesting that these grains are primarily formed by interface transformation,which exhibits distinct differences from recrystallized grains.Our results shed light upon the dynamic mechanical response and crack formation mechanism in magnesium alloys under impact deformation.
基金supported by the Fundamental Research Funds for the Central Universities(20822041H4082)。
文摘Developing highly active and stable oxygen evolution reaction(OER)catalysts necessitates the establishment of a comprehensive OER catalyst database.However,the absence of a standardized benchmarking protocol has hindered this progress.In this work,we present a systematic protocol for electrochemical measurements to thoroughly evaluate the activity and stability of OER electrocatalysts.We begin with a detailed introduction to constructing the electrochemical system,encompassing experimental setup and the selection criteria for electrodes and electrolytes.Potential contaminants originating from electrolytes,cells,and electrodes are identified and their impacts are discussed.We also examine the effects of external factors,such as temperature,magnetic fields,and natural light,on OER measurements.The protocol outlines operational mechanisms and recommended settings for various electrochemical techniques,including cyclic voltammetry(CV),potentiostatic electrochemical impedance spectroscopy(PEIS),Tafel slope analysis,and pulse voltammetry(PV).We summarize existing evaluation methodologies for assessing intrinsic activities and long-term stabilities of catalysts.Based on these discussions,we propose a comprehensive protocol for evaluating OER electrocatalysts’performance.Finally,we offer perspectives on advancing OER catalysts from laboratory research to industrial applications.
基金supported by the National Natural Science Foundation of China(22379031)the Guangxi Science and Technology Project of China(AB16380030)+1 种基金the National Research Foundation,SingaporeA*STAR(Agency for Science,Technology and Research)under its LCER Phase 2 Programme Hydrogen&Emerging Technologies FI,Directed Hydrogen Programme(U2305D4003)。
文摘Efficiently utilizing ammonia(carbon-free fuel)via low-temperature fuel cells is severely hindered by the sluggish kinetics of ammonia oxidation reaction(AOR).Herein,platinum-iridium-tungsten nanocubes(PtIrW-NCBs)with exposed{100}-rich facets were synthesized by a glucose-assisted solvent-thermal method,in which alloying W not only can facilitate the formation of such specific nanostructures to expose more active sites for AOR,but also modulate the electronic structure of PtIr to promote the kinetics of AOR.The PtIrW-NCBs featuring the small nanoparticle size of 5.05±0.07 nm exhibit superior AOR performance,wherein the onset potential is down to 0.319 V and the mass activity is 30.15 A g_((PGM=Pt,Ir))^(-1)at 0.50 V vs.RHE,significantly higher than those of reported majority of AOR catalysts and even commercial PtIr/C.Meanwhile,in situ Fourier transform infrared spectroscopy measurement further reveals that AOR on PtIrW-NCBs dominantly undergoes the dimerization path of NH_(x)(1≤x≤2).In addition,the theoretical calculations also identify that alloying W into PtIr can contribute additional electrons to 5d orbitals of PtIr,enabling the d-band center approaching the Femi level,which in turn induces the high-filling of bonding orbitals of N-N bond in^(*)N_(2)H_(4),promoting the dimerization of^(*)NH_(2)to^(*)N_(2)H_(4)and thus leading to high AOR activity of PtIrW.This work provides new insights for designing efficient AOR electrocatalysts.
文摘The field of subwavelength optics has opened new avenues for investigating light–matter interactions by enabling the exploration of novel phenomena at the subwavelength scale. In recent decades,advancements in fundamental understanding and micro–nanotechnologies have significantly propelled the development of subwavelength optics and its practical applications.
基金supported by the National Natural Science Foundation of China(No.12205165).
文摘Efficient electrocatalysts for oxygen reduction reaction(ORR)show significant importance for advancing the performance and affordability of proton exchange membrane fuel cells and other energy conversion devices.Herein,PtCo_(3)nanoalloys dispersed on a carbon black support,were prepared using ultrafast Joule heating method.By tuning the heating modes,such as high-temperature shock and heating for 2 s,two kinds of PtCo_(3)nanoalloys with varying crystallinities were obtained,referred to as PtCo_(3)-HTS(average size of 5.4 nm)and PtCo_(3)-HT-2 s(average size of 6.4 nm),respectively.Impressively,PtCo_(3)-HTS exhibited superior electrocatalytic ORR activity and stability(E_(1/2)=0.897 V vs.RHE and 36mV negative shift after 50,000 cycles),outperforming PtCo_(3)-HT-2 s(E_(1/2)=0.872 V and 16.2mV negative shift),as well as the commercial Pt/C(20 wt%)catalyst(E_(1/2)=0.847 V and 21.0mV negative shift).The enhanced ORR performance of PtCo_(3)-HTS may be attributed to its low crystallinity,which results in an active local electronic structure and chemical state,as confirmed by X-ray diffraction(XRD)and X-ray absorption fine structure(XAFS)analyses.The ultrafast Joule heating method showed great potential for crystallinity engineering,offering a promising pathway to revolutionize the manufacturing of cost-effective and environmentally friendly catalysts for clean energy applications.
基金supported by the Ministry of Health National Medical Research Council (to JL)the National University of Singapore (to JJEC)
文摘Schizophrenia is a complex psychiatric disorder marked by positive and negative symptoms,leading to mood disturbances,cognitive impairments,and social withdrawal.While anti-psychotic medications remain the cornerstone of treatment,they often fail to fully address certain symptoms.Additionally,treatment-resistant schizophrenia,affecting 30%-40%of patients,remains a substantial clinical challenge.Positive,negative symptoms and cognitive impairments have been linked to disruptions in the glutamatergic,serotonin,GABAergic,and muscarinic pathways in the brain.Recent advances using genome-wide association study and other approaches have uncovered a significant number of new schizophrenia risk genes that uncovered new,and reinforced prior,concepts on the genetic and neurological underpinnings of schizophrenia,including abnormalities in synaptic function,immune processes,and lipid metabolism.Concurrently,new therapeutics targeting different modalities,which are expected to address some of the limitations of anti-psychotic drugs currently being offered to patients,are currently being evaluated.Collectively,these efforts provide new momentum for the next phase of schizophrenia research and treatment.
基金supported in part by the National Natural Science Foundation of China(61603169,61773192,61803192)in part by the funding from Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technologyin part by Singapore National Research Foundation(NRF-RSS2016-004)
文摘Flexible job shop scheduling problems(FJSP)have received much attention from academia and industry for many years.Due to their exponential complexity,swarm intelligence(SI)and evolutionary algorithms(EA)are developed,employed and improved for solving them.More than 60%of the publications are related to SI and EA.This paper intents to give a comprehensive literature review of SI and EA for solving FJSP.First,the mathematical model of FJSP is presented and the constraints in applications are summarized.Then,the encoding and decoding strategies for connecting the problem and algorithms are reviewed.The strategies for initializing algorithms?population and local search operators for improving convergence performance are summarized.Next,one classical hybrid genetic algorithm(GA)and one newest imperialist competitive algorithm(ICA)with variables neighborhood search(VNS)for solving FJSP are presented.Finally,we summarize,discus and analyze the status of SI and EA for solving FJSP and give insight into future research directions.
基金National Natural Science Foundation of China(Grant Nos.51705158 and 51805174)the Fundamental Research Funds for the Central Universities(Grant Nos.2018MS45 and 2019MS059)。
文摘Focusing on the structural optimization of auxetic materials using data-driven methods,a back-propagation neural network(BPNN)based design framework is developed for petal-shaped auxetics using isogeometric analysis.Adopting a NURBSbased parametric modelling scheme with a small number of design variables,the highly nonlinear relation between the input geometry variables and the effective material properties is obtained using BPNN-based fitting method,and demonstrated in this work to give high accuracy and efficiency.Such BPNN-based fitting functions also enable an easy analytical sensitivity analysis,in contrast to the generally complex procedures of typical shape and size sensitivity approaches.
基金financial support from A*STAR,SERC 2014 Public Sector Research Funding (PSF) Grant (SERC Project No. 1421200080)
文摘Surface enhanced Raman scattering(SERS)is an efficient technique to detect low concentration molecules.In this work,periodical silicon nanowires(Si NWs)integrated with metal-insulator-metal(MIM)layers are employed as SERS substrates.Laser interference lithography(LIL)combined with reactive ion etching(RIE)is used to fabricate large-area periodic nanostructures,followed by decorating the MIM layers.Compared to MIM disks array on Si surface,the SERS enhancement factor(EF)of the MIM structures on the Si NWs array can be increased up to 5 times,which is attributed to the enhanced electric field at the boundary of the MIM disks.Furthermore,high density of nanoparticles and nanogaps serving as hot spots on sidewall surfaces also contribute to the enhanced SERS signals.Via changing the thickness of the insulator layer,the plasmonic resonance can be tuned,which provides a new localized surface plasmon resonance(LSPR)characteristic for SERS applications.
基金funded by the National Research Foundation(NRF)Prime Minister’s Office,Singapore under its Campus for Research Excellence and Technological Enterprise(CREATE)Program
文摘Catalytic conversion of COinto chemicals and fuels is an alternative to alleviate climate change and ocean acidification.The catalytic reduction of COby Hcan lead to the formation of various products:carbon monoxide,carboxylic acids,aldehydes,alcohols and hydrocarbons.In this paper,a comprehensive thermodynamics analysis of COhydrogenation is conducted using the Gibbs free energy minimization method.The results show that COreduction to CO needs a high temperature and H/COratio to achieve a high COconversion.However,synthesis of methanol from COneeds a relatively high pressure and low temperature to minimize the reverse water-gas shift reaction.Direct COhydrogenation to formic acid or formaldehyde is thermodynamically limited.On the contrary,production of CHfrom COhydrogenation is the thermodynamically easiest reaction with nearly 100%CH4 yield at moderate conditions.In addition,complex reactions with more than one product are also calculated in this work.Among the considered carboxylic acids（HCOOH,CHCOOH and CHCOOH）,propionic acid dominates in the product stream（selectivity above 90%）.The same trend can also be found in the hydrogenation of COto aldehydes and alcohols with the major product of propionaldehyde and butanol,respectively.In the process of COhydrogenation to alkenes,low temperature,high pressure,and high Hpartial pressure favor the COconversion.CHis the most thermodynamically favorable among all considered alkynes under different temperatures and pressures.The thermodynamic calculations are validated with experimental results,suggesting that the Gibbs free energy minimization method is effective for thermodynamically understanding the reaction network involved in the COhydrogenation process,which is helpful for the development of high-performance catalysts.
基金supported by National Research Foundation of Singapore,AME Young Individual Research Grant(A2084c0167)。
文摘Accurate remaining useful life(RUL)prediction is important in industrial systems.It prevents machines from working under failure conditions,and ensures that the industrial system works reliably and efficiently.Recently,many deep learning based methods have been proposed to predict RUL.Among these methods,recurrent neural network(RNN)based approaches show a strong capability of capturing sequential information.This allows RNN based methods to perform better than convolutional neural network(CNN)based approaches on the RUL prediction task.In this paper,we question this common paradigm and argue that existing CNN based approaches are not designed according to the classic principles of CNN,which reduces their performances.Additionally,the capacity of capturing sequential information is highly affected by the receptive field of CNN,which is neglected by existing CNN based methods.To solve these problems,we propose a series of new CNNs,which show competitive results to RNN based methods.Compared with RNN,CNN processes the input signals in parallel so that the temporal sequence is not easily determined.To alleviate this issue,a position encoding scheme is developed to enhance the sequential information encoded by a CNN.Hence,our proposed position encoding based CNN called PE-Net is further improved and even performs better than RNN based methods.Extensive experiments are conducted on the C-MAPSS dataset,where our PE-Net shows state-of-the-art performance.
