Yongtao Yu,Yuelin Yu et al.Solvent-Resistant Wearable Triboelectric Nanogenerator for Energy-Harvesting and Self-Powered Sensors.Energy Environ.Mater.2024,7,e12700.On page 4 of this article,the first paragraph of 2.4,...Yongtao Yu,Yuelin Yu et al.Solvent-Resistant Wearable Triboelectric Nanogenerator for Energy-Harvesting and Self-Powered Sensors.Energy Environ.Mater.2024,7,e12700.On page 4 of this article,the first paragraph of 2.4,line 14(PDF version,same below),there is a spelling mistake of“sui,”.It should be changed to“suitable”.The denominator“dt”in the Equation(3)should be changed to“dt”.展开更多
Approximately 30%of the global population struggles with access to potable water,and 60%lacks adequate sanitation.Effective disinfection is crucial,however,heterogeneous systems,despite their benefits,often exhibit lo...Approximately 30%of the global population struggles with access to potable water,and 60%lacks adequate sanitation.Effective disinfection is crucial,however,heterogeneous systems,despite their benefits,often exhibit lower efficacy compared to homogeneous method,presenting a significant challenge[1].In heterogeneous catalysis,photocatalytic disinfection holds immense promise for various applications.However,two key factors significantly impact the efficacy of photocatalytic disinfection:the generation of reactive oxygen species(ROSs)by the photocatalyst and the interaction between ROS and bacteria.展开更多
In energy constrained application scenarios, self‐powered systems (SPSs) are gradually emerging as a core technological pathway for enabling distributed intelligent sensing. High‐entropy energy, such as micro‐wind,...In energy constrained application scenarios, self‐powered systems (SPSs) are gradually emerging as a core technological pathway for enabling distributed intelligent sensing. High‐entropy energy, such as micro‐wind, vibrations, water motion, and human activity, is widely available but difficult to harness due to its low density, randomness, and spatiotemporal fragmentation. Triboelectric nanogenerators (TENGs), with high efficiency to low‐frequency and irregular mechanical stimuli, offer a promising solution for efficient energy harvesting, driving the advancement of SPSs with high‐entropy distribution. This review outlines the basic concepts and recent developments of TENG‐driven SPSs, focusing on strategies for energy harvesting, power management, and system integration. It highlights structural optimization and performance enhancement under typical highentropy scenarios and analyzes key challenges in energy conversion, power regulation, and load management. Finally, the potential applications of TENG‐driven SPSs are discussed in emerging smart fields such as infrastructure monitoring, lowaltitude economy, mobile intelligent devices, and ocean sensing networks.展开更多
Flexible and wearable electronics are attracting surging attention due to their potential applications in human health monitoring and precision therapies.Safety hazards including strong magnetic field and electric lea...Flexible and wearable electronics are attracting surging attention due to their potential applications in human health monitoring and precision therapies.Safety hazards including strong magnetic field and electric leakage are big risk factors for human health.It remains challenging to develop self‐powered and wearable safety hazard sensors that could not only be able to monitor human motions but also have functions for detecting potential hazards.In this work,we fabricated a self‐powered,shapeable,and wearable magnetic triboelectric nanogenerator(MTENG)based on ferrofluid,Ecoflex,and carbonized silk fabric that possessed effective hazard prevention and biomechanical motion sensing ability.A peak open‐circuit voltage of 0.7 V and short‐circuit current of 10μA m^(−2)can be achieved when magnetic field is changed between 3.5 and 37.1 mT.As a component of triboelectric layer of the MTENG,ferrofluid can substantially extend the range of its sensing capabilities to many hazardous cues such as dangerous magnetic field.Furtherly,the developed multifunctional and self‐powered sensor can be used to monitor human activities such as drinking water and bending finger.This effort opens up a new design opportunity for hazard avoidance wearable electronics and self‐powered sensors.展开更多
Colorectal cancer is a major cause of cancer-related mortality worldwide,under-scoring the importance of early and effective colorectal cancer screening to im-prove survival rates.Traditional colorectal cancer screeni...Colorectal cancer is a major cause of cancer-related mortality worldwide,under-scoring the importance of early and effective colorectal cancer screening to im-prove survival rates.Traditional colorectal cancer screening methods include non-invasive tests,such as the fecal immunochemical test(FIT),as well as diagnostic procedures like colonoscopy.Colonoscopy remains the gold standard for detec-ting and treating precancerous polyps and early-stage cancer,regardless of whe-ther it is used as the first screening test or the second test following a positive FIT.However,its effectiveness can be affected by factors such as operator skill,patient variability,and limited lesion visibility,resulting in a significant rate of missed lesion rates and highlighting the need for more efficient and accurate screening techniques.This review is aimed to assess the current challenges of traditional screening methods with the impact of artificial intelligence(AI)in the diagnostic flow.The literature on AI-powered tools for colorectal cancer screening,including novel applications,emerging programs,and recent guidelines,has been reviewed to highlight both the advantages and limitations of implementing this technology in healthcare.Recent advances in AI have introduced soft AI colonoscopy,with the purpose of improving lesion recognition(computer-aided detection)and/or improving optical diagnosis(computer-aided diagnosis).AI-powered colono-scopy systems employ deep learning algorithms to analyze real-time endoscopic images,enhancing detection rates for adenomas,serrated lesions and cancer by reducing human error.AI-assisted colonoscopy enhances adenoma detection,enabling earlier intervention and improved patient outcomes.The benefits are particularly pronounced for less-experienced practitioners,as the detection rates for AI-assisted colonoscopy are similar to experts.AI integration also helps in the teaching process,in developing standardized procedures,and improving screening procedure accuracy and efficiency across different healthcare providers.However,there are challenges and limitations,such as the cost of AI implementation,data privacy concerns,and the need for extensive clinical validation.As AI technology continues to evolve,its transformation of the colorectal cancer screening system could revolutionize the field,making early detection more accessible and reducing mortality,on the condition that the above issues are addressed before widespread use.展开更多
According to the second law of thermodynamics,spontaneous chemical processes will ultimately reach the equilibrium state with the lowest energy.However,in biological systems,there are numerous highenergy states far fr...According to the second law of thermodynamics,spontaneous chemical processes will ultimately reach the equilibrium state with the lowest energy.However,in biological systems,there are numerous highenergy states far from equilibrium.One typical example is the transmembrane ion-concentration gradient,which plays crucial roles in maintaining homeostasis,regulating cell volume,and enabling cell signaling.Transmembrane ion-concentration gradient is achieved by an active transport process that requires the input of energy and the action of pump proteins.Replicating this process with synthetic supramolecular systems is particularly challenging,requiring both the input of energy and very specific,spatiotemporal control over ion uptake and release.In nature,pump proteins,such as protein-based ion channels,have evolved highly intricate architectures to perform this function.In contrast,Aprahamian and coworkers recently developed a much simpler smallmolecule system that functions as a molecular ion pump,utilizing light energy to pump chloride ions across a hydrophobic barrier against the concentration gradient[1].展开更多
The global healthcare landscape is increasingly challenged by the rising prevalence of chronic diseases and the demographic shift towards an aging population,necessitating the development of innovative and sustainable...The global healthcare landscape is increasingly challenged by the rising prevalence of chronic diseases and the demographic shift towards an aging population,necessitating the development of innovative and sustainable healthcare solutions.In this context,the emergence of triboelectric energy harvesters as a key technological breakthrough offers a viable pathway towards self-powered,efficient,and sustainable personal health management.This review critically examines the transformative potential of triboelectric nanogenerators(TENGs)in addressing the pressing challenges of modern healthcare,underscoring their unique benefits such as being battery-free,easy to fabricate,and cost-efficient.We begin by reviewing the fundamental mechanisms of triboelectrification at the atomic scale and presenting the contact electrification among various materials,such as metals,polymers,and semiconductors.The discussion subsequently extends to the commonly used materials for TENGs and explores advancements in their design and functionality,with an emphasis on structural and chemical innovations.Furthermore,the application spectrum of TENGs in personal health management is extensively reviewed,covering aspects including health monitoring,therapeutic intervention,health protection,and device powering,while highlighting their capacity for self-sustainability.The review concludes by addressing existing challenges while mapping out the latest significant contributions and prospective directions in TENG-based healthcare innovations.By facilitating a paradigm shift towards a more autonomous,cost-effective,and personalized healthcare model,independent of external power sources,TENGs are poised to markedly enhance the quality of care and overall well-being,marking the dawn of a new era in integrated personal health management.展开更多
To address the challenges of long commuting times,traffic congestion,high energy consumption,and emissions in inter-city travel,a new type of flying coach has been developed.This innovation aims to significantly short...To address the challenges of long commuting times,traffic congestion,high energy consumption,and emissions in inter-city travel,a new type of flying coach has been developed.This innovation aims to significantly shorten inter-city commuting times,enhance travel efficiency,and simultaneously reduce energy consumption and emissions.The flying coach integrates rail power supply technology,an intelligent operating system,and advanced new materials,comprising a catenary power supply guide rod and various sensor components.Based on analysis of traditional aircraft design principles,the research team simulated the design of the rail-powered flying coach using software such as AutoCAD and SolidWorks for three-dimensional modeling.The analysis results indicate that,compared to traditional aircraft and rail trains,the design of the new flying coach reduces its overall weight while maintaining carrying capacity,thereby improving commuting efficiency and environmental performance.This development lays a solid foundation for creating a greener,more efficient,and convenient inter-city transportation network.展开更多
The need for efficient thermal energy systems has gained significant attention due to the growing global concern about renewable energy resources,particularly in residential buildings.One of the biggest challenges in ...The need for efficient thermal energy systems has gained significant attention due to the growing global concern about renewable energy resources,particularly in residential buildings.One of the biggest challenges in this area is capturing and converting solar energy at maximum efficiency.This requires the use of strong materials and advanced fluids to enhance conversion efficiency while minimizing energy losses.Despite extensive research on thermal energy systems,there remains a limited understanding of how the combined effects of thermal radiation,irreversibility processes,and advanced heat flux models contribute to optimizing solar power performance in residential applications.Addressing these knowledge gaps is critical for advancing the design and implementation of highly efficient thermal energy systems.Owing to its usage,this study investigates the thermal energy and irreversibility processes in the context of solar power systems for residential buildings.Specifically,it explores the influence of thermal radiation and the Cattaneo–Christov heat flux model,considering the interactions over a stretching surface.The study incorporates cross fluid and Maxwell fluid effects into the governing model equations.Utilizing the Galerkin-weighted residual method,the transformed model is solved to understand the impacts on heat distribution.The findings reveal that increased thermal radiation and thermal conductivity significantly enhance heat distribution,offering valuable insights for optimizing solar power system efficiency in residential applications.展开更多
With the rapid advancement of robotics and Artificial Intelligence(AI),aerobics training companion robots now support eco-friendly fitness by reducing reliance on nonrenewable energy.This study presents a solar-powere...With the rapid advancement of robotics and Artificial Intelligence(AI),aerobics training companion robots now support eco-friendly fitness by reducing reliance on nonrenewable energy.This study presents a solar-powered aerobics training robot featuring an adaptive energy management system designed for sustainability and efficiency.The robot integrates machine vision with an enhanced Dynamic Cheetah Optimizer and Bayesian Neural Network(DynCO-BNN)to enable precise exercise monitoring and real-time feedback.Solar tracking technology ensures optimal energy absorption,while a microcontroller-based regulator manages power distribution and robotic movement.Dual-battery switching ensures uninterrupted operation,aided by light and I/V sensors for energy optimization.Using the INSIGHT-LME IMU dataset,which includes motion data from 76 individuals performing Local Muscular Endurance(LME)exercises,the system detects activities,counts repetitions,and recognizes human movements.To minimize energy use during data processing,Min-Max normalization and two-dimensional Discrete Fourier Transform(2D-DFT)are applied,boosting computational efficiency.The robot accurately identifies upper and lower limb movements,delivering effective exercise guidance.The DynCO-BNN model achieved a high tracking accuracy of 96.8%.Results confirm improved solar utilization,ecological sustainability,and reduced dependence on fossil fuels—positioning the robot as a smart,energy-efficient solution for next-generation fitness technology.展开更多
The intelligent operation management of distribution services is crucial for the stability of power systems.Integrating the large language model(LLM)with 6G edge intelligence provides customized management solutions.H...The intelligent operation management of distribution services is crucial for the stability of power systems.Integrating the large language model(LLM)with 6G edge intelligence provides customized management solutions.However,the adverse effects of false data injection(FDI)attacks on the performance of LLMs cannot be overlooked.Therefore,we propose an FDI attack detection and LLM-assisted resource allocation algorithm for 6G edge intelligenceempowered distribution power grids.First,we formulate a resource allocation optimization problem.The objective is to minimize the weighted sum of the global loss function and total LLM fine-tuning delay under constraints of long-term privacy entropy and energy consumption.Then,we decouple it based on virtual queues.We utilize an LLM-assisted deep Q network(DQN)to learn the resource allocation strategy and design an FDI attack detection mechanism to ensure that fine-tuning remains on the correct path.Simulations demonstrate that the proposed algorithm has excellent performance in convergence,delay,and security.展开更多
In this paper,we develop a 6G wireless powered Internet of Things(IoT)system assisted by unmanned aerial vehicles(UAVs)to intelligently supply energy and collect data at the same time.In our dual-UAV scheme,UAV-E,with...In this paper,we develop a 6G wireless powered Internet of Things(IoT)system assisted by unmanned aerial vehicles(UAVs)to intelligently supply energy and collect data at the same time.In our dual-UAV scheme,UAV-E,with a constant power supply,transmits energy to charge the IoT devices on the ground,whereas UAV-B serves the IoT devices by data collection as a base station.In this framework,the system's energy efficiency is maximized,which we define as a ratio of the sum rate of IoT devices to the energy consumption of two UAVs during a fixed working duration.With the constraints of duration,transmit power,energy,and mobility,a difficult non-convex issue is presented by optimizing the trajectory,time duration allocation,and uplink transmit power of concurrently.To tackle the non-convex fractional optimization issue,we deconstruct it into three subproblems and we solve each of them iteratively using the descent method in conjunction with sequential convex approximation(SCA)approaches and the Dinkelbach algorithm.The simulation findings indicate that the suggested cooperative design has the potential to greatly increase the energy efficiency of the 6G intelligent UAV-assisted wireless powered IoT system when compared to previous benchmark systems.展开更多
In this paper,Let M_(n)denote the maximum of logarithmic general error distribution with parameter v≥1.Higher-order expansions for distributions of powered extremes M_(n)^(p)are derived under an optimal choice of nor...In this paper,Let M_(n)denote the maximum of logarithmic general error distribution with parameter v≥1.Higher-order expansions for distributions of powered extremes M_(n)^(p)are derived under an optimal choice of normalizing constants.It is shown that M_(n)^(p),when v=1,converges to the Frechet extreme value distribution at the rate of 1/n,and if v>1 then M_(n)^(p)converges to the Gumbel extreme value distribution at the rate of(loglogn)^(2)=(log n)^(1-1/v).展开更多
This paper investigates a wireless powered and backscattering enabled sensor network based on the non-linear energy harvesting model, where the power beacon(PB) delivers energy signals to wireless sensors to enable th...This paper investigates a wireless powered and backscattering enabled sensor network based on the non-linear energy harvesting model, where the power beacon(PB) delivers energy signals to wireless sensors to enable their passive backscattering and active transmission to the access point(AP). We propose an efficient time scheduling scheme for network performance enhancement, based on which each sensor can always harvest energy from the PB over the entire block except its time slots allocated for passive and active information delivery. Considering the PB and wireless sensors are from two selfish service providers, we use the Stackelberg game to model the energy interaction among them. To address the non-convexity of the leader-level problem, we propose to decompose the original problem into two subproblems and solve them iteratively in an alternating manner. Specifically, the successive convex approximation, semi-definite relaxation(SDR) and variable substitution techniques are applied to find a nearoptimal solution. To evaluate the performance loss caused by the interaction between two providers, we further investigate the social welfare maximization problem. Numerical results demonstrate that compared to the benchmark schemes, the proposed scheme can achieve up to 35.4% and 38.7% utility gain for the leader and the follower, respectively.展开更多
In this paper,we investigate IRS-aided user cooperation(UC)scheme in millimeter wave(mmWave)wirelesspowered sensor networks(WPSN),where two single-antenna users are wireless powered in the wireless energy transfer(WET...In this paper,we investigate IRS-aided user cooperation(UC)scheme in millimeter wave(mmWave)wirelesspowered sensor networks(WPSN),where two single-antenna users are wireless powered in the wireless energy transfer(WET)phase first and then cooperatively transmit information to a hybrid access point(AP)in the wireless information transmission(WIT)phase,following which the IRS is deployed to enhance the system performance of theWET andWIT.We maximized the weighted sum-rate problem by jointly optimizing the transmit time slots,power allocations,and the phase shifts of the IRS.Due to the non-convexity of the original problem,a semidefinite programming relaxation-based approach is proposed to convert the formulated problem to a convex optimization framework,which can obtain the optimal global solution.Simulation results demonstrate that the weighted sum throughput of the proposed UC scheme outperforms the non-UC scheme whether equipped with IRS or not.展开更多
Wearable triboelectric nanogenerators(TENGs)have attracted attention owing to their ability to harvest energy from the surrounding environment without maintenance.Herein,polyetherimide-Al_(2)O_(3)(PAl)and polyvinylide...Wearable triboelectric nanogenerators(TENGs)have attracted attention owing to their ability to harvest energy from the surrounding environment without maintenance.Herein,polyetherimide-Al_(2)O_(3)(PAl)and polyvinylidene fluoride-co-hexafluoropropylene(PVDF-HFP,PH)nanofiber membranes were used as tribo-positive and tribo-negative materials,respectively.Phytic acid-doped polyaniline(PANI)/cotton fabric(PPCF)and ethylenediamine(EDA)-crosslinked PAl(EPAl)nanofiber membranes were used as triboelectrode and triboencapsulation materials,respectively.The result showed that when the PAl-PH-based TENG was shaped as a circle with a radius of 1 cm,under the pressure of 50 N,and the frequency of 0.5 Hz,the open-circuit voltage(V_(oc))and short-circuit current(I_(sc))reached the highest value of 66.6 V and-93.4 to 110.1 nA,respectively.Moreover,the PH-based TENG could be used as a fabric sensor to detect fabric composition and as a sensor-inductive switch for light bulbs or beeping warning devices.When the PAl-PH-based TENG was shaped as a 5×5 cm^(2)rectangle,a 33 pF capacitor could be charged to 15 V in 28 s.Interestingly,compared to PAl nanofiber membranes,EPAl nanofiber membranes exhibited good dyeing properties and excellent solvent resistance.The PPCF exhibited<5%resistance change after washing,bending,and stretching.展开更多
As an open network architecture,Wireless Computing PowerNetworks(WCPN)pose newchallenges for achieving efficient and secure resource management in networks,because of issues such as insecure communication channels and...As an open network architecture,Wireless Computing PowerNetworks(WCPN)pose newchallenges for achieving efficient and secure resource management in networks,because of issues such as insecure communication channels and untrusted device terminals.Blockchain,as a shared,immutable distributed ledger,provides a secure resource management solution for WCPN.However,integrating blockchain into WCPN faces challenges like device heterogeneity,monitoring communication states,and dynamic network nature.Whereas Digital Twins(DT)can accurately maintain digital models of physical entities through real-time data updates and self-learning,enabling continuous optimization of WCPN,improving synchronization performance,ensuring real-time accuracy,and supporting smooth operation of WCPN services.In this paper,we propose a DT for blockchain-empowered WCPN architecture that guarantees real-time data transmission between physical entities and digital models.We adopt an enumeration-based optimal placement algorithm(EOPA)and an improved simulated annealing-based near-optimal placement algorithm(ISAPA)to achieve minimum average DT synchronization latency under the constraint of DT error.Numerical results show that the proposed solution in this paper outperforms benchmarks in terms of average synchronization latency.展开更多
One of the impacts of the Fukushima disaster was the shutdown of all nuclear power plants in Japan,reaching zero production in 2015.In response,the country started importing more fossil energy including coal,oil,and n...One of the impacts of the Fukushima disaster was the shutdown of all nuclear power plants in Japan,reaching zero production in 2015.In response,the country started importing more fossil energy including coal,oil,and natural gas to fill the energy gap.However,this led to a significant increase in carbon emissions,hindering the efforts to reduce its carbon footprint.In the current situation,Japan is actively working to balance its energy requirements with environmental considerations,including the utilization of hydrogen fuel.Therefore,this paper aims to explore the feasibility and implications of using hydrogen power plants as a means to reduce emissions,and this analysis will be conducted using the energy modeling of the MARKAL-TIMES Japan framework.The hydrogen scenario(HS)is assumed with the extensive integration of hydrogen into the power generation sector,supported by a hydrogen import scheme.Additionally,this scenario will be compared with the Business as Usual(BAU)scenario.The results showed that the generation capacities of the BAU and HS scenarios have significantly different primary energy supplies.The BAU scenario is highly dependent on fossil fuels,while the HS scenario integrates hydrogen contribution along with an increase in renewable energy,reaching a peak contribution of 2,160 PJ in 2050.In the HS scenario,the target of reducing CO_(2) emissions by 80%is achieved through significant hydrogen penetration.By 2050,the total CO_(2) emissions are estimated to be 939 million tons for the BAU scenario and 261 million tons for the Hydrogen scenario.In addition,the contribution of hydrogen to electricity generation is expected to be 153 TWh,smaller than PV and wind power.展开更多
This paper investigates the multi-Unmanned Aerial Vehicle(UAV)-assisted wireless-powered Mobile Edge Computing(MEC)system,where UAVs provide computation and powering services to mobile terminals.We aim to maximize the...This paper investigates the multi-Unmanned Aerial Vehicle(UAV)-assisted wireless-powered Mobile Edge Computing(MEC)system,where UAVs provide computation and powering services to mobile terminals.We aim to maximize the number of completed computation tasks by jointly optimizing the offloading decisions of all terminals and the trajectory planning of all UAVs.The action space of the system is extremely large and grows exponentially with the number of UAVs.In this case,single-agent learning will require an overlarge neural network,resulting in insufficient exploration.However,the offloading decisions and trajectory planning are two subproblems performed by different executants,providing an opportunity for problem-solving.We thus adopt the idea of decomposition and propose a 2-Tiered Multi-agent Soft Actor-Critic(2T-MSAC)algorithm,decomposing a single neural network into multiple small-scale networks.In the first tier,a single agent is used for offloading decisions,and an online pretrained model based on imitation learning is specially designed to accelerate the training process of this agent.In the second tier,UAVs utilize multiple agents to plan their trajectories.Each agent exerts its influence on the parameter update of other agents through actions and rewards,thereby achieving joint optimization.Simulation results demonstrate that the proposed algorithm can be applied to scenarios with various location distributions of terminals,outperforming existing benchmarks that perform well only in specific scenarios.In particular,2T-MSAC increases the number of completed tasks by 45.5%in the scenario with uneven terminal distributions.Moreover,the pretrained model based on imitation learning reduces the convergence time of 2T-MSAC by 58.2%.展开更多
A set of water powered excavation test system was developed for the comprehensive performance testing and evaluation of water powered percussive rock drill indoors. The whole system contains hydraulic power section, e...A set of water powered excavation test system was developed for the comprehensive performance testing and evaluation of water powered percussive rock drill indoors. The whole system contains hydraulic power section, electronic control system, test and data acquisition system, and assistant devices, such as guideway and drilling bench. Parameters of the water powered percussive rock drill can be obtained by analyzing testing data, which contain impact energy, front and back cavity pressure, pressure and flow in each working part, drilling velocity, frequency and energy efficiency etc. The system is applied to test the self-designed water powered percussive rock drill SYYG65. The parameters of water powered percussive rock drill with impact pressure of about 8.9 MPa are 58.93 J for impact energy, and 8.97% for energy efficiency, which prove the effectiveness of system.展开更多
文摘Yongtao Yu,Yuelin Yu et al.Solvent-Resistant Wearable Triboelectric Nanogenerator for Energy-Harvesting and Self-Powered Sensors.Energy Environ.Mater.2024,7,e12700.On page 4 of this article,the first paragraph of 2.4,line 14(PDF version,same below),there is a spelling mistake of“sui,”.It should be changed to“suitable”.The denominator“dt”in the Equation(3)should be changed to“dt”.
基金supported by the Taishan Scholar Project of Shandong Province(tsqn202211168)the National Natural Science Foundation of China(52422213,52272212)the Natural Science Foundation of Shandong Province(ZR2022JQ20).
文摘Approximately 30%of the global population struggles with access to potable water,and 60%lacks adequate sanitation.Effective disinfection is crucial,however,heterogeneous systems,despite their benefits,often exhibit lower efficacy compared to homogeneous method,presenting a significant challenge[1].In heterogeneous catalysis,photocatalytic disinfection holds immense promise for various applications.However,two key factors significantly impact the efficacy of photocatalytic disinfection:the generation of reactive oxygen species(ROSs)by the photocatalyst and the interaction between ROS and bacteria.
基金supported by The National Key Research and Development Program of China(Grant No.2023YFB2604600).
文摘In energy constrained application scenarios, self‐powered systems (SPSs) are gradually emerging as a core technological pathway for enabling distributed intelligent sensing. High‐entropy energy, such as micro‐wind, vibrations, water motion, and human activity, is widely available but difficult to harness due to its low density, randomness, and spatiotemporal fragmentation. Triboelectric nanogenerators (TENGs), with high efficiency to low‐frequency and irregular mechanical stimuli, offer a promising solution for efficient energy harvesting, driving the advancement of SPSs with high‐entropy distribution. This review outlines the basic concepts and recent developments of TENG‐driven SPSs, focusing on strategies for energy harvesting, power management, and system integration. It highlights structural optimization and performance enhancement under typical highentropy scenarios and analyzes key challenges in energy conversion, power regulation, and load management. Finally, the potential applications of TENG‐driven SPSs are discussed in emerging smart fields such as infrastructure monitoring, lowaltitude economy, mobile intelligent devices, and ocean sensing networks.
基金financially supported by the National Natural Science Foundation of China(No.52125201)Beijing Natural Science Foundation(No.Z240025)and the Beijing Municipal Science and Technology(No.Z221100002722015).
文摘Flexible and wearable electronics are attracting surging attention due to their potential applications in human health monitoring and precision therapies.Safety hazards including strong magnetic field and electric leakage are big risk factors for human health.It remains challenging to develop self‐powered and wearable safety hazard sensors that could not only be able to monitor human motions but also have functions for detecting potential hazards.In this work,we fabricated a self‐powered,shapeable,and wearable magnetic triboelectric nanogenerator(MTENG)based on ferrofluid,Ecoflex,and carbonized silk fabric that possessed effective hazard prevention and biomechanical motion sensing ability.A peak open‐circuit voltage of 0.7 V and short‐circuit current of 10μA m^(−2)can be achieved when magnetic field is changed between 3.5 and 37.1 mT.As a component of triboelectric layer of the MTENG,ferrofluid can substantially extend the range of its sensing capabilities to many hazardous cues such as dangerous magnetic field.Furtherly,the developed multifunctional and self‐powered sensor can be used to monitor human activities such as drinking water and bending finger.This effort opens up a new design opportunity for hazard avoidance wearable electronics and self‐powered sensors.
文摘Colorectal cancer is a major cause of cancer-related mortality worldwide,under-scoring the importance of early and effective colorectal cancer screening to im-prove survival rates.Traditional colorectal cancer screening methods include non-invasive tests,such as the fecal immunochemical test(FIT),as well as diagnostic procedures like colonoscopy.Colonoscopy remains the gold standard for detec-ting and treating precancerous polyps and early-stage cancer,regardless of whe-ther it is used as the first screening test or the second test following a positive FIT.However,its effectiveness can be affected by factors such as operator skill,patient variability,and limited lesion visibility,resulting in a significant rate of missed lesion rates and highlighting the need for more efficient and accurate screening techniques.This review is aimed to assess the current challenges of traditional screening methods with the impact of artificial intelligence(AI)in the diagnostic flow.The literature on AI-powered tools for colorectal cancer screening,including novel applications,emerging programs,and recent guidelines,has been reviewed to highlight both the advantages and limitations of implementing this technology in healthcare.Recent advances in AI have introduced soft AI colonoscopy,with the purpose of improving lesion recognition(computer-aided detection)and/or improving optical diagnosis(computer-aided diagnosis).AI-powered colono-scopy systems employ deep learning algorithms to analyze real-time endoscopic images,enhancing detection rates for adenomas,serrated lesions and cancer by reducing human error.AI-assisted colonoscopy enhances adenoma detection,enabling earlier intervention and improved patient outcomes.The benefits are particularly pronounced for less-experienced practitioners,as the detection rates for AI-assisted colonoscopy are similar to experts.AI integration also helps in the teaching process,in developing standardized procedures,and improving screening procedure accuracy and efficiency across different healthcare providers.However,there are challenges and limitations,such as the cost of AI implementation,data privacy concerns,and the need for extensive clinical validation.As AI technology continues to evolve,its transformation of the colorectal cancer screening system could revolutionize the field,making early detection more accessible and reducing mortality,on the condition that the above issues are addressed before widespread use.
基金financial supports of National Natural Science Foundation of China(22171226)Natural Science Basic Research Program of Shaanxi(2022JC-06).
文摘According to the second law of thermodynamics,spontaneous chemical processes will ultimately reach the equilibrium state with the lowest energy.However,in biological systems,there are numerous highenergy states far from equilibrium.One typical example is the transmembrane ion-concentration gradient,which plays crucial roles in maintaining homeostasis,regulating cell volume,and enabling cell signaling.Transmembrane ion-concentration gradient is achieved by an active transport process that requires the input of energy and the action of pump proteins.Replicating this process with synthetic supramolecular systems is particularly challenging,requiring both the input of energy and very specific,spatiotemporal control over ion uptake and release.In nature,pump proteins,such as protein-based ion channels,have evolved highly intricate architectures to perform this function.In contrast,Aprahamian and coworkers recently developed a much simpler smallmolecule system that functions as a molecular ion pump,utilizing light energy to pump chloride ions across a hydrophobic barrier against the concentration gradient[1].
基金supported by the National Research Foundation of Korea(NRF)Grant funded by the Korea government(MSIT)(Nos.2020R1A5A1019131 and 2022M3D1A2054488)。
文摘The global healthcare landscape is increasingly challenged by the rising prevalence of chronic diseases and the demographic shift towards an aging population,necessitating the development of innovative and sustainable healthcare solutions.In this context,the emergence of triboelectric energy harvesters as a key technological breakthrough offers a viable pathway towards self-powered,efficient,and sustainable personal health management.This review critically examines the transformative potential of triboelectric nanogenerators(TENGs)in addressing the pressing challenges of modern healthcare,underscoring their unique benefits such as being battery-free,easy to fabricate,and cost-efficient.We begin by reviewing the fundamental mechanisms of triboelectrification at the atomic scale and presenting the contact electrification among various materials,such as metals,polymers,and semiconductors.The discussion subsequently extends to the commonly used materials for TENGs and explores advancements in their design and functionality,with an emphasis on structural and chemical innovations.Furthermore,the application spectrum of TENGs in personal health management is extensively reviewed,covering aspects including health monitoring,therapeutic intervention,health protection,and device powering,while highlighting their capacity for self-sustainability.The review concludes by addressing existing challenges while mapping out the latest significant contributions and prospective directions in TENG-based healthcare innovations.By facilitating a paradigm shift towards a more autonomous,cost-effective,and personalized healthcare model,independent of external power sources,TENGs are poised to markedly enhance the quality of care and overall well-being,marking the dawn of a new era in integrated personal health management.
基金College Student Innovation Training Program Project(S202410225147)。
文摘To address the challenges of long commuting times,traffic congestion,high energy consumption,and emissions in inter-city travel,a new type of flying coach has been developed.This innovation aims to significantly shorten inter-city commuting times,enhance travel efficiency,and simultaneously reduce energy consumption and emissions.The flying coach integrates rail power supply technology,an intelligent operating system,and advanced new materials,comprising a catenary power supply guide rod and various sensor components.Based on analysis of traditional aircraft design principles,the research team simulated the design of the rail-powered flying coach using software such as AutoCAD and SolidWorks for three-dimensional modeling.The analysis results indicate that,compared to traditional aircraft and rail trains,the design of the new flying coach reduces its overall weight while maintaining carrying capacity,thereby improving commuting efficiency and environmental performance.This development lays a solid foundation for creating a greener,more efficient,and convenient inter-city transportation network.
基金funded by Universiti Teknikal Malaysia Melaka through the Tabung Penerbitan Jurnal(S11017).
文摘The need for efficient thermal energy systems has gained significant attention due to the growing global concern about renewable energy resources,particularly in residential buildings.One of the biggest challenges in this area is capturing and converting solar energy at maximum efficiency.This requires the use of strong materials and advanced fluids to enhance conversion efficiency while minimizing energy losses.Despite extensive research on thermal energy systems,there remains a limited understanding of how the combined effects of thermal radiation,irreversibility processes,and advanced heat flux models contribute to optimizing solar power performance in residential applications.Addressing these knowledge gaps is critical for advancing the design and implementation of highly efficient thermal energy systems.Owing to its usage,this study investigates the thermal energy and irreversibility processes in the context of solar power systems for residential buildings.Specifically,it explores the influence of thermal radiation and the Cattaneo–Christov heat flux model,considering the interactions over a stretching surface.The study incorporates cross fluid and Maxwell fluid effects into the governing model equations.Utilizing the Galerkin-weighted residual method,the transformed model is solved to understand the impacts on heat distribution.The findings reveal that increased thermal radiation and thermal conductivity significantly enhance heat distribution,offering valuable insights for optimizing solar power system efficiency in residential applications.
文摘With the rapid advancement of robotics and Artificial Intelligence(AI),aerobics training companion robots now support eco-friendly fitness by reducing reliance on nonrenewable energy.This study presents a solar-powered aerobics training robot featuring an adaptive energy management system designed for sustainability and efficiency.The robot integrates machine vision with an enhanced Dynamic Cheetah Optimizer and Bayesian Neural Network(DynCO-BNN)to enable precise exercise monitoring and real-time feedback.Solar tracking technology ensures optimal energy absorption,while a microcontroller-based regulator manages power distribution and robotic movement.Dual-battery switching ensures uninterrupted operation,aided by light and I/V sensors for energy optimization.Using the INSIGHT-LME IMU dataset,which includes motion data from 76 individuals performing Local Muscular Endurance(LME)exercises,the system detects activities,counts repetitions,and recognizes human movements.To minimize energy use during data processing,Min-Max normalization and two-dimensional Discrete Fourier Transform(2D-DFT)are applied,boosting computational efficiency.The robot accurately identifies upper and lower limb movements,delivering effective exercise guidance.The DynCO-BNN model achieved a high tracking accuracy of 96.8%.Results confirm improved solar utilization,ecological sustainability,and reduced dependence on fossil fuels—positioning the robot as a smart,energy-efficient solution for next-generation fitness technology.
基金supported by the Science and Technology Project of State Grid Corporation of China under Grant Number 52094021N010(5400-202199534A-0-5-ZN).
文摘The intelligent operation management of distribution services is crucial for the stability of power systems.Integrating the large language model(LLM)with 6G edge intelligence provides customized management solutions.However,the adverse effects of false data injection(FDI)attacks on the performance of LLMs cannot be overlooked.Therefore,we propose an FDI attack detection and LLM-assisted resource allocation algorithm for 6G edge intelligenceempowered distribution power grids.First,we formulate a resource allocation optimization problem.The objective is to minimize the weighted sum of the global loss function and total LLM fine-tuning delay under constraints of long-term privacy entropy and energy consumption.Then,we decouple it based on virtual queues.We utilize an LLM-assisted deep Q network(DQN)to learn the resource allocation strategy and design an FDI attack detection mechanism to ensure that fine-tuning remains on the correct path.Simulations demonstrate that the proposed algorithm has excellent performance in convergence,delay,and security.
基金supported by the Natural Science Foundation of Beijing Municipality under Grant L192034。
文摘In this paper,we develop a 6G wireless powered Internet of Things(IoT)system assisted by unmanned aerial vehicles(UAVs)to intelligently supply energy and collect data at the same time.In our dual-UAV scheme,UAV-E,with a constant power supply,transmits energy to charge the IoT devices on the ground,whereas UAV-B serves the IoT devices by data collection as a base station.In this framework,the system's energy efficiency is maximized,which we define as a ratio of the sum rate of IoT devices to the energy consumption of two UAVs during a fixed working duration.With the constraints of duration,transmit power,energy,and mobility,a difficult non-convex issue is presented by optimizing the trajectory,time duration allocation,and uplink transmit power of concurrently.To tackle the non-convex fractional optimization issue,we deconstruct it into three subproblems and we solve each of them iteratively using the descent method in conjunction with sequential convex approximation(SCA)approaches and the Dinkelbach algorithm.The simulation findings indicate that the suggested cooperative design has the potential to greatly increase the energy efficiency of the 6G intelligent UAV-assisted wireless powered IoT system when compared to previous benchmark systems.
文摘In this paper,Let M_(n)denote the maximum of logarithmic general error distribution with parameter v≥1.Higher-order expansions for distributions of powered extremes M_(n)^(p)are derived under an optimal choice of normalizing constants.It is shown that M_(n)^(p),when v=1,converges to the Frechet extreme value distribution at the rate of 1/n,and if v>1 then M_(n)^(p)converges to the Gumbel extreme value distribution at the rate of(loglogn)^(2)=(log n)^(1-1/v).
基金supported by National Natural Science Foundation of China(No.61901229 and No.62071242)the Project of Jiangsu Engineering Research Center of Novel Optical Fiber Technology and Communication Network(No.SDGC2234)+1 种基金the Open Research Project of Jiangsu Provincial Key Laboratory of Photonic and Electronic Materials Sciences and Technology(No.NJUZDS2022-008)the Post-Doctoral Research Supporting Program of Jiangsu Province(No.SBH20).
文摘This paper investigates a wireless powered and backscattering enabled sensor network based on the non-linear energy harvesting model, where the power beacon(PB) delivers energy signals to wireless sensors to enable their passive backscattering and active transmission to the access point(AP). We propose an efficient time scheduling scheme for network performance enhancement, based on which each sensor can always harvest energy from the PB over the entire block except its time slots allocated for passive and active information delivery. Considering the PB and wireless sensors are from two selfish service providers, we use the Stackelberg game to model the energy interaction among them. To address the non-convexity of the leader-level problem, we propose to decompose the original problem into two subproblems and solve them iteratively in an alternating manner. Specifically, the successive convex approximation, semi-definite relaxation(SDR) and variable substitution techniques are applied to find a nearoptimal solution. To evaluate the performance loss caused by the interaction between two providers, we further investigate the social welfare maximization problem. Numerical results demonstrate that compared to the benchmark schemes, the proposed scheme can achieve up to 35.4% and 38.7% utility gain for the leader and the follower, respectively.
基金This work was supported in part by the open research fund of National Mobile Communications Research Laboratory,Southeast University(No.2023D11)in part by Sponsored by program for Science&Technology Innovation Talents in Universities of Henan Province(23HASTIT019)+2 种基金in part by Natural Science Foundation of Henan Province(20232300421097)in part by the project funded by China Postdoctoral Science Foundation(2020M682345)in part by the Henan Postdoctoral Foundation(202001015).
文摘In this paper,we investigate IRS-aided user cooperation(UC)scheme in millimeter wave(mmWave)wirelesspowered sensor networks(WPSN),where two single-antenna users are wireless powered in the wireless energy transfer(WET)phase first and then cooperatively transmit information to a hybrid access point(AP)in the wireless information transmission(WIT)phase,following which the IRS is deployed to enhance the system performance of theWET andWIT.We maximized the weighted sum-rate problem by jointly optimizing the transmit time slots,power allocations,and the phase shifts of the IRS.Due to the non-convexity of the original problem,a semidefinite programming relaxation-based approach is proposed to convert the formulated problem to a convex optimization framework,which can obtain the optimal global solution.Simulation results demonstrate that the weighted sum throughput of the proposed UC scheme outperforms the non-UC scheme whether equipped with IRS or not.
基金supported by the JSPS KAKENHI(Grant numbers JP20H00288 and JP22K02136)
文摘Wearable triboelectric nanogenerators(TENGs)have attracted attention owing to their ability to harvest energy from the surrounding environment without maintenance.Herein,polyetherimide-Al_(2)O_(3)(PAl)and polyvinylidene fluoride-co-hexafluoropropylene(PVDF-HFP,PH)nanofiber membranes were used as tribo-positive and tribo-negative materials,respectively.Phytic acid-doped polyaniline(PANI)/cotton fabric(PPCF)and ethylenediamine(EDA)-crosslinked PAl(EPAl)nanofiber membranes were used as triboelectrode and triboencapsulation materials,respectively.The result showed that when the PAl-PH-based TENG was shaped as a circle with a radius of 1 cm,under the pressure of 50 N,and the frequency of 0.5 Hz,the open-circuit voltage(V_(oc))and short-circuit current(I_(sc))reached the highest value of 66.6 V and-93.4 to 110.1 nA,respectively.Moreover,the PH-based TENG could be used as a fabric sensor to detect fabric composition and as a sensor-inductive switch for light bulbs or beeping warning devices.When the PAl-PH-based TENG was shaped as a 5×5 cm^(2)rectangle,a 33 pF capacitor could be charged to 15 V in 28 s.Interestingly,compared to PAl nanofiber membranes,EPAl nanofiber membranes exhibited good dyeing properties and excellent solvent resistance.The PPCF exhibited<5%resistance change after washing,bending,and stretching.
基金supported by the National Natural Science Foundation of China under Grant 62272391in part by the Key Industry Innovation Chain of Shaanxi under Grant 2021ZDLGY05-08.
文摘As an open network architecture,Wireless Computing PowerNetworks(WCPN)pose newchallenges for achieving efficient and secure resource management in networks,because of issues such as insecure communication channels and untrusted device terminals.Blockchain,as a shared,immutable distributed ledger,provides a secure resource management solution for WCPN.However,integrating blockchain into WCPN faces challenges like device heterogeneity,monitoring communication states,and dynamic network nature.Whereas Digital Twins(DT)can accurately maintain digital models of physical entities through real-time data updates and self-learning,enabling continuous optimization of WCPN,improving synchronization performance,ensuring real-time accuracy,and supporting smooth operation of WCPN services.In this paper,we propose a DT for blockchain-empowered WCPN architecture that guarantees real-time data transmission between physical entities and digital models.We adopt an enumeration-based optimal placement algorithm(EOPA)and an improved simulated annealing-based near-optimal placement algorithm(ISAPA)to achieve minimum average DT synchronization latency under the constraint of DT error.Numerical results show that the proposed solution in this paper outperforms benchmarks in terms of average synchronization latency.
文摘One of the impacts of the Fukushima disaster was the shutdown of all nuclear power plants in Japan,reaching zero production in 2015.In response,the country started importing more fossil energy including coal,oil,and natural gas to fill the energy gap.However,this led to a significant increase in carbon emissions,hindering the efforts to reduce its carbon footprint.In the current situation,Japan is actively working to balance its energy requirements with environmental considerations,including the utilization of hydrogen fuel.Therefore,this paper aims to explore the feasibility and implications of using hydrogen power plants as a means to reduce emissions,and this analysis will be conducted using the energy modeling of the MARKAL-TIMES Japan framework.The hydrogen scenario(HS)is assumed with the extensive integration of hydrogen into the power generation sector,supported by a hydrogen import scheme.Additionally,this scenario will be compared with the Business as Usual(BAU)scenario.The results showed that the generation capacities of the BAU and HS scenarios have significantly different primary energy supplies.The BAU scenario is highly dependent on fossil fuels,while the HS scenario integrates hydrogen contribution along with an increase in renewable energy,reaching a peak contribution of 2,160 PJ in 2050.In the HS scenario,the target of reducing CO_(2) emissions by 80%is achieved through significant hydrogen penetration.By 2050,the total CO_(2) emissions are estimated to be 939 million tons for the BAU scenario and 261 million tons for the Hydrogen scenario.In addition,the contribution of hydrogen to electricity generation is expected to be 153 TWh,smaller than PV and wind power.
基金supported in part by the National Natural Science Foundation of China under Grant 62271306,Grant 62072410,and Grant 62331017in part by the Fundamental Research Funds for the Provincial Universities of Zhejiang under Grant RF-B2022002。
文摘This paper investigates the multi-Unmanned Aerial Vehicle(UAV)-assisted wireless-powered Mobile Edge Computing(MEC)system,where UAVs provide computation and powering services to mobile terminals.We aim to maximize the number of completed computation tasks by jointly optimizing the offloading decisions of all terminals and the trajectory planning of all UAVs.The action space of the system is extremely large and grows exponentially with the number of UAVs.In this case,single-agent learning will require an overlarge neural network,resulting in insufficient exploration.However,the offloading decisions and trajectory planning are two subproblems performed by different executants,providing an opportunity for problem-solving.We thus adopt the idea of decomposition and propose a 2-Tiered Multi-agent Soft Actor-Critic(2T-MSAC)algorithm,decomposing a single neural network into multiple small-scale networks.In the first tier,a single agent is used for offloading decisions,and an online pretrained model based on imitation learning is specially designed to accelerate the training process of this agent.In the second tier,UAVs utilize multiple agents to plan their trajectories.Each agent exerts its influence on the parameter update of other agents through actions and rewards,thereby achieving joint optimization.Simulation results demonstrate that the proposed algorithm can be applied to scenarios with various location distributions of terminals,outperforming existing benchmarks that perform well only in specific scenarios.In particular,2T-MSAC increases the number of completed tasks by 45.5%in the scenario with uneven terminal distributions.Moreover,the pretrained model based on imitation learning reduces the convergence time of 2T-MSAC by 58.2%.
基金Project(2006AA06Z134) supported by the National High Technology Research and Development Program of ChinaProjects(50934006, 50904079) supported by the National Natural Science Foundation of China
文摘A set of water powered excavation test system was developed for the comprehensive performance testing and evaluation of water powered percussive rock drill indoors. The whole system contains hydraulic power section, electronic control system, test and data acquisition system, and assistant devices, such as guideway and drilling bench. Parameters of the water powered percussive rock drill can be obtained by analyzing testing data, which contain impact energy, front and back cavity pressure, pressure and flow in each working part, drilling velocity, frequency and energy efficiency etc. The system is applied to test the self-designed water powered percussive rock drill SYYG65. The parameters of water powered percussive rock drill with impact pressure of about 8.9 MPa are 58.93 J for impact energy, and 8.97% for energy efficiency, which prove the effectiveness of system.
