Smart farming with outdoor monitoring systems is critical to address food shortages and sustainability challenges.These systems facilitate informed decisions that enhance efficiency in broader environmental management...Smart farming with outdoor monitoring systems is critical to address food shortages and sustainability challenges.These systems facilitate informed decisions that enhance efficiency in broader environmental management.Existing outdoor systems equipped with energy harvesters and self-powered sensors often struggle with fluctuating energy sources,low durability under harsh conditions,non-transparent or non-biocompatible materials,and complex structures.Herein,a multifunctional hydrogel is developed,which can fulfill all the above requirements and build selfsustainable outdoor monitoring systems solely by it.It can serve as a stable energy harvester that continuously generates direct current output with an average power density of 1.9 W m^(-3)for nearly 60 days of operation in normal environments(24℃,60%RH),with an energy density of around 1.36×10^(7)J m^(-3).It also shows good self-recoverability in severe environments(45℃,30%RH)in nearly 40 days of continuous operation.Moreover,this hydrogel enables noninvasive and self-powered monitoring of leaf relative water content,providing critical data on evaluating plant health,previously obtainable only through invasive or high-power consumption methods.Its potential extends to acting as other self-powered environmental sensors.This multifunctional hydrogel enables self-sustainable outdoor systems with scalable and low-cost production,paving the way for future agriculture.展开更多
UAV-based object detection is rapidly expanding in both civilian and military applications,including security surveillance,disaster assessment,and border patrol.However,challenges such as small objects,occlusions,comp...UAV-based object detection is rapidly expanding in both civilian and military applications,including security surveillance,disaster assessment,and border patrol.However,challenges such as small objects,occlusions,complex backgrounds,and variable lighting persist due to the unique perspective of UAV imagery.To address these issues,this paper introduces DAFPN-YOLO,an innovative model based on YOLOv8s(You Only Look Once version 8s).Themodel strikes a balance between detection accuracy and speed while reducing parameters,making itwell-suited for multi-object detection tasks from drone perspectives.A key feature of DAFPN-YOLO is the enhanced Drone-AFPN(Adaptive Feature Pyramid Network),which adaptively fuses multi-scale features to optimize feature extraction and enhance spatial and small-object information.To leverage Drone-AFPN’smulti-scale capabilities fully,a dedicated 160×160 small-object detection head was added,significantly boosting detection accuracy for small targets.In the backbone,the C2f_Dual(Cross Stage Partial with Cross-Stage Feature Fusion Dual)module and SPPELAN(Spatial Pyramid Pooling with Enhanced LocalAttentionNetwork)modulewere integrated.These components improve feature extraction and information aggregationwhile reducing parameters and computational complexity,enhancing inference efficiency.Additionally,Shape-IoU(Shape Intersection over Union)is used as the loss function for bounding box regression,enabling more precise shape-based object matching.Experimental results on the VisDrone 2019 dataset demonstrate the effectiveness ofDAFPN-YOLO.Compared to YOLOv8s,the proposedmodel achieves a 5.4 percentage point increase inmAP@0.5,a 3.8 percentage point improvement in mAP@0.5:0.95,and a 17.2%reduction in parameter count.These results highlight DAFPN-YOLO’s advantages in UAV-based object detection,offering valuable insights for applying deep learning to UAV-specific multi-object detection tasks.展开更多
In foggy traffic scenarios, existing object detection algorithms face challenges such as low detection accuracy, poor robustness, occlusion, missed detections, and false detections. To address this issue, a multi-scal...In foggy traffic scenarios, existing object detection algorithms face challenges such as low detection accuracy, poor robustness, occlusion, missed detections, and false detections. To address this issue, a multi-scale object detection algorithm based on an improved YOLOv8 has been proposed. Firstly, a lightweight attention mechanism, Triplet Attention, is introduced to enhance the algorithm’s ability to extract multi-dimensional and multi-scale features, thereby improving the receptive capability of the feature maps. Secondly, the Diverse Branch Block (DBB) is integrated into the CSP Bottleneck with two Convolutions (C2F) module to strengthen the fusion of semantic information across different layers. Thirdly, a new decoupled detection head is proposed by redesigning the original network head based on the Diverse Branch Block module to improve detection accuracy and reduce missed and false detections. Finally, the Minimum Point Distance based Intersection-over-Union (MPDIoU) is used to replace the original YOLOv8 Complete Intersection-over-Union (CIoU) to accelerate the network’s training convergence. Comparative experiments and dehazing pre-processing tests were conducted on the RTTS and VOC-Fog datasets. Compared to the baseline YOLOv8 model, the improved algorithm achieved mean Average Precision (mAP) improvements of 4.6% and 3.8%, respectively. After defogging pre-processing, the mAP increased by 5.3% and 4.4%, respectively. The experimental results demonstrate that the improved algorithm exhibits high practicality and effectiveness in foggy traffic scenarios.展开更多
Forest fires pose a serious threat to ecological balance, air quality, and the safety of both humans and wildlife. This paper presents an improved model based on You Only Look Once version 5 (YOLOv5), named YOLO Light...Forest fires pose a serious threat to ecological balance, air quality, and the safety of both humans and wildlife. This paper presents an improved model based on You Only Look Once version 5 (YOLOv5), named YOLO Lightweight Fire Detector (YOLO-LFD), to address the limitations of traditional sensor-based fire detection methods in terms of real-time performance and accuracy. The proposed model is designed to enhance inference speed while maintaining high detection accuracy on resource-constrained devices such as drones and embedded systems. Firstly, we introduce Depthwise Separable Convolutions (DSConv) to reduce the complexity of the feature extraction network. Secondly, we design and implement the Lightweight Faster Implementation of Cross Stage Partial (CSP) Bottleneck with 2 Convolutions (C2f-Light) and the CSP Structure with 3 Compact Inverted Blocks (C3CIB) modules to replace the traditional C3 modules. This optimization enhances deep feature extraction and semantic information processing, thereby significantly increasing inference speed. To enhance the detection capability for small fires, the model employs a Normalized Wasserstein Distance (NWD) loss function, which effectively reduces the missed detection rate and improves the accuracy of detecting small fire sources. Experimental results demonstrate that compared to the baseline YOLOv5s model, the YOLO-LFD model not only increases inference speed by 19.3% but also significantly improves the detection accuracy for small fire targets, with only a 1.6% reduction in overall mean average precision (mAP)@0.5. Through these innovative improvements to YOLOv5s, the YOLO-LFD model achieves a balance between speed and accuracy, making it particularly suitable for real-time detection tasks on mobile and embedded devices.展开更多
Medical image classification is crucial in disease diagnosis,treatment planning,and clinical decisionmaking.We introduced a novel medical image classification approach that integrates Bayesian Random Semantic Data Aug...Medical image classification is crucial in disease diagnosis,treatment planning,and clinical decisionmaking.We introduced a novel medical image classification approach that integrates Bayesian Random Semantic Data Augmentation(BSDA)with a Vision Mamba-based model for medical image classification(MedMamba),enhanced by residual connection blocks,we named the model BSDA-Mamba.BSDA augments medical image data semantically,enhancing the model’s generalization ability and classification performance.MedMamba,a deep learning-based state space model,excels in capturing long-range dependencies in medical images.By incorporating residual connections,BSDA-Mamba further improves feature extraction capabilities.Through comprehensive experiments on eight medical image datasets,we demonstrate that BSDA-Mamba outperforms existing models in accuracy,area under the curve,and F1-score.Our results highlight BSDA-Mamba’s potential as a reliable tool for medical image analysis,particularly in handling diverse imaging modalities from X-rays to MRI.The open-sourcing of our model’s code and datasets,will facilitate the reproduction and extension of our work.展开更多
The efficient storage and application of sustainable solar energy has drawn significant attention from both academic and industrial points of view.However,most developed catalytic materials still suffer from insuffici...The efficient storage and application of sustainable solar energy has drawn significant attention from both academic and industrial points of view.However,most developed catalytic materials still suffer from insufficient mass diffusion and unsatisfactory durability due to the lack of interconnected and regulatable porosity.Developing catalytic architectures with engineered active sites and prominent stability through rational synthesis strategies has become one of the core projects in solar-driven applications.The unique properties of mesoporous silicas render them among the most valuable functional materials for industrial applications,such as high specific surface area,regulatable porosity,adjustable surface properties,tunable particle sizes,and great thermal and mechanical stability.Mesoporous silicas serve as structural templates or catalytic supports to enhance light harvesting via the scattering effect and provide large surface areas for active site generation.These advantages have been widely utilized in solar applications,including hydrogen production,CO_(2)conversion,photovoltaics,biomass utilization,and pollutant degradation.To achieve the specific functionalities and desired activity,various types of mesoporous silicas from different synthesis methods have been customized and synthesized.Moreover,morphology regulation and component modification strategies have also been performed to endow mesoporous silica-based materials with unprecedented efficiency for solar energy storage and utilization.Nevertheless,reviews about synthesis,morphology regulation,and component modification strategies for mesoporous silica-based catalyst design in solar-driven applications are still limited.Herein,the latest progress concerning mesoporous silica-based catalysis in solar-driven applications is comprehensively reviewed.Synthesis principles,formation mechanisms,and rational functionalities of mesoporous silica are systematically summarized.Some typical catalysts with impressive activities in different solar-driven applications are highlighted.Furthermore,challenges and future potential opportunities in this study field are also discussed and proposed.This present review guides the design of mesoporous silica catalysts for efficient solar energy management for solar energy storage and conversion applications.展开更多
Following global catastrophic infrastructure loss(GCIL),traditional electricity networks would be damaged and unavailable for energy supply,necessitating alternative solutions to sustain critical services.These altern...Following global catastrophic infrastructure loss(GCIL),traditional electricity networks would be damaged and unavailable for energy supply,necessitating alternative solutions to sustain critical services.These alternative solutions would need to run without damaged infrastructure and would likely need to be located at the point of use,such as decentralized electricity generation from wood gas.This study explores the feasibility of using modified light duty vehicles to self-sustain electricity generation by producing wood chips for wood gasification.A 2004 Ford Falcon Fairmont was modified to power a woodchipper and an electrical generator.The vehicle successfully produced wood chips suitable for gasification with an energy return on investment(EROI)of 3.7 and sustained a stable output of 20 kW electrical power.Scalability analyses suggest such solutions could provide electricity to the critical water sanitation sector,equivalent to 4%of global electricity demand,if production of woodchippers was increased postcatastrophe.Future research could investigate the long-term durability of modified vehicles and alternative electricity generation,and quantify the scalability of wood gasification in GCIL scenarios.This work provides a foundation for developing resilient,decentralized energy systems to ensure the continuity of critical services during catastrophic events,leveraging existing vehicle infrastructure to enhance disaster preparedness.展开更多
Hydrogen is emerging as a promising alternative to fossil fuels in the transportation sector.This study evaluated the feasibility of estab-lishing hydrogen refueling stations in five cities in Oman,Duqm,Haima,Sur,Al B...Hydrogen is emerging as a promising alternative to fossil fuels in the transportation sector.This study evaluated the feasibility of estab-lishing hydrogen refueling stations in five cities in Oman,Duqm,Haima,Sur,Al Buraymi,and Salalah,using Hybrid Optimization of Multiple Electric Renewables(HOMER)software.Three hybrid energy systems,photovoltaic-wind turbine-battery,photovoltaic-battery,and wind turbine-battery were analyzed for each city.Results indicated that Duqm offers the lowest net present cost(NPC),levelized cost of energy,and levelized cost of hydrogen,making it the most cost-effective location.Additionally,Sensitivity analysis showed that as the life of electrolyzer increases during operation,the initial capital expenditure is distributed over a longer operational period,leading to a reduction in the NPC.More so,renewable energy systems produced no emissions which supports Oman’s mission target.This comprehensive analysis confirms the feasibility of establishing a hydrogen refueling station in Duqm,Oman,and highlights advanced optimization techniques’superior capability in designing cost-effective,sustainable energy systems.展开更多
In the plethora of conceptual and algorithmic developments supporting data analytics and system modeling,humancentric pursuits assume a particular position owing to ways they emphasize and realize interaction between ...In the plethora of conceptual and algorithmic developments supporting data analytics and system modeling,humancentric pursuits assume a particular position owing to ways they emphasize and realize interaction between users and the data.We advocate that the level of abstraction,which can be flexibly adjusted,is conveniently realized through Granular Computing.Granular Computing is concerned with the development and processing information granules–formal entities which facilitate a way of organizing knowledge about the available data and relationships existing there.This study identifies the principles of Granular Computing,shows how information granules are constructed and subsequently used in describing relationships present among the data.展开更多
This paper addresses a target-enclosing problem for multiple spacecraft systems by proposing a two-layer affine formation control strategy. Compared with the existing methods,the adopted two-layer network structure in...This paper addresses a target-enclosing problem for multiple spacecraft systems by proposing a two-layer affine formation control strategy. Compared with the existing methods,the adopted two-layer network structure in this paper is generally directed, which is suitable for practical space missions. Firstly, distributed finite-time sliding-mode estimators and formation controllers in both layers are designed separately to improve the flexibility of the formation control system. By introducing the properties of affine transformation into formation control protocol design,the controllers can be used to track different time-varying target formation patterns. Besides, multilayer time-varying encirclements can be achieved with particular shapes to surround the moving target. In the sequel, by integrating adaptive neural networks and specialized artificial potential functions into backstepping controllers, the problems of uncertain Euler-Lagrange models, collision avoidance as well as formation reconfiguration are solved simultaneously. The stability of the proposed controllers is verified by the Lyapunov direct method. Finally, two simulation examples of triangle formation and more complex hexagon formation are presented to illustrate the feasibility of the theoretical results.展开更多
Sensitive and reliable X-ray detectors are essential for medical radiography,industrial inspection and security screening.Lowering the radiation dose allows reduced health risks and increased frequency and fidelity of...Sensitive and reliable X-ray detectors are essential for medical radiography,industrial inspection and security screening.Lowering the radiation dose allows reduced health risks and increased frequency and fidelity of diagnostic technologies for earlier detection of disease and its recurrence.Three-dimensional(3 D)organic-inorganic hybrid lead halide perovskites are promising for direct X-ray detection-they show improved sensitivity compared to conventional X-ray detectors.However,their high and unstable dark current,caused by ion migration and high dark carrier concentration in the 3 D hybrid perovskites,limits their performance and long-term operation stability.Here we report ultrasensitive,stable X-ray detectors made using zero-dimensional(0 D)methylammonium bismuth iodide perovskite(MA3Bi2I9)single crystals.The 0 D crystal structure leads to a high activation energy(Ea)for ion migration(0.46 e V)and is also accompanied by a low dark carrier concentration(~10^6 cm^-3).The X-ray detectors exhibit sensitivity of 10,620μC Gy-1 air cm-2,a limit of detection(Lo D)of 0.62 nG yairs-1,and stable operation even under high applied biases;no deterioration in detection performance was observed following sensing of an integrated X-ray irradiation dose of^23,800 m Gyair,equivalent to>200,000 times the dose required for a single commercial X-ray chest radiograph.Regulating the ion migration channels and decreasing the dark carrier concentration in perovskites provide routes for stable and ultrasensitive X-ray detectors.展开更多
Flexible temperature sensors have been extensively investigated due to their prospect of wide application in various flexible electronic products.However,most of the current flexible temperature sensors only work well...Flexible temperature sensors have been extensively investigated due to their prospect of wide application in various flexible electronic products.However,most of the current flexible temperature sensors only work well in a narrow temperature range,with their application at high or low temperatures still being a big challenge.This work proposes a flexible thermocouple temperature sensor based on aerogel blanket substrate,the temperature-sensitive layer of which uses the screen-printing technology to prepare indium oxide and indium tin oxide.It has good temperature sensitivity,with the test sensitivity reaching 226.7μV℃^(−1).Most importantly,it can work in a wide temperature range,from extremely low temperatures down to liquid nitrogen temperature to high temperatures up to 1200℃,which is difficult to be achieved by other existing flexible temperature sensors.This temperature sensor has huge application potential in biomedicine,aerospace and other fields.展开更多
基金supported by the Research Platform for biomedical and Health Technology, NUS (Suzhou) Research Institute (RP-BHT-Prof. LEE Chengkuo)RIE Advanced Manufacturing and Engineering (AME) Programmatic Grant (Grant A18A4b0055)+1 种基金RIE 2025-Industry Alignment Fund – Industry Collaboration Projects (IAF-ICP) (Grant I2301E0027)Reimagine Research Scheme projects, National University of Singapore, A-0009037-03-00 and A-0009454-01-00 and Reimagine Research Scheme projects, National University of Singapore, A-0004772-00-00 and A-0004772-01-00。
文摘Smart farming with outdoor monitoring systems is critical to address food shortages and sustainability challenges.These systems facilitate informed decisions that enhance efficiency in broader environmental management.Existing outdoor systems equipped with energy harvesters and self-powered sensors often struggle with fluctuating energy sources,low durability under harsh conditions,non-transparent or non-biocompatible materials,and complex structures.Herein,a multifunctional hydrogel is developed,which can fulfill all the above requirements and build selfsustainable outdoor monitoring systems solely by it.It can serve as a stable energy harvester that continuously generates direct current output with an average power density of 1.9 W m^(-3)for nearly 60 days of operation in normal environments(24℃,60%RH),with an energy density of around 1.36×10^(7)J m^(-3).It also shows good self-recoverability in severe environments(45℃,30%RH)in nearly 40 days of continuous operation.Moreover,this hydrogel enables noninvasive and self-powered monitoring of leaf relative water content,providing critical data on evaluating plant health,previously obtainable only through invasive or high-power consumption methods.Its potential extends to acting as other self-powered environmental sensors.This multifunctional hydrogel enables self-sustainable outdoor systems with scalable and low-cost production,paving the way for future agriculture.
基金supported by the National Natural Science Foundation of China(Grant Nos.62101275 and 62101274).
文摘UAV-based object detection is rapidly expanding in both civilian and military applications,including security surveillance,disaster assessment,and border patrol.However,challenges such as small objects,occlusions,complex backgrounds,and variable lighting persist due to the unique perspective of UAV imagery.To address these issues,this paper introduces DAFPN-YOLO,an innovative model based on YOLOv8s(You Only Look Once version 8s).Themodel strikes a balance between detection accuracy and speed while reducing parameters,making itwell-suited for multi-object detection tasks from drone perspectives.A key feature of DAFPN-YOLO is the enhanced Drone-AFPN(Adaptive Feature Pyramid Network),which adaptively fuses multi-scale features to optimize feature extraction and enhance spatial and small-object information.To leverage Drone-AFPN’smulti-scale capabilities fully,a dedicated 160×160 small-object detection head was added,significantly boosting detection accuracy for small targets.In the backbone,the C2f_Dual(Cross Stage Partial with Cross-Stage Feature Fusion Dual)module and SPPELAN(Spatial Pyramid Pooling with Enhanced LocalAttentionNetwork)modulewere integrated.These components improve feature extraction and information aggregationwhile reducing parameters and computational complexity,enhancing inference efficiency.Additionally,Shape-IoU(Shape Intersection over Union)is used as the loss function for bounding box regression,enabling more precise shape-based object matching.Experimental results on the VisDrone 2019 dataset demonstrate the effectiveness ofDAFPN-YOLO.Compared to YOLOv8s,the proposedmodel achieves a 5.4 percentage point increase inmAP@0.5,a 3.8 percentage point improvement in mAP@0.5:0.95,and a 17.2%reduction in parameter count.These results highlight DAFPN-YOLO’s advantages in UAV-based object detection,offering valuable insights for applying deep learning to UAV-specific multi-object detection tasks.
基金supported by the National Natural Science Foundation of China(Grant Nos.62101275 and 62101274).
文摘In foggy traffic scenarios, existing object detection algorithms face challenges such as low detection accuracy, poor robustness, occlusion, missed detections, and false detections. To address this issue, a multi-scale object detection algorithm based on an improved YOLOv8 has been proposed. Firstly, a lightweight attention mechanism, Triplet Attention, is introduced to enhance the algorithm’s ability to extract multi-dimensional and multi-scale features, thereby improving the receptive capability of the feature maps. Secondly, the Diverse Branch Block (DBB) is integrated into the CSP Bottleneck with two Convolutions (C2F) module to strengthen the fusion of semantic information across different layers. Thirdly, a new decoupled detection head is proposed by redesigning the original network head based on the Diverse Branch Block module to improve detection accuracy and reduce missed and false detections. Finally, the Minimum Point Distance based Intersection-over-Union (MPDIoU) is used to replace the original YOLOv8 Complete Intersection-over-Union (CIoU) to accelerate the network’s training convergence. Comparative experiments and dehazing pre-processing tests were conducted on the RTTS and VOC-Fog datasets. Compared to the baseline YOLOv8 model, the improved algorithm achieved mean Average Precision (mAP) improvements of 4.6% and 3.8%, respectively. After defogging pre-processing, the mAP increased by 5.3% and 4.4%, respectively. The experimental results demonstrate that the improved algorithm exhibits high practicality and effectiveness in foggy traffic scenarios.
基金supported by the National Natural Science Foundation of China(Grant Nos.62101275 and 62101274).
文摘Forest fires pose a serious threat to ecological balance, air quality, and the safety of both humans and wildlife. This paper presents an improved model based on You Only Look Once version 5 (YOLOv5), named YOLO Lightweight Fire Detector (YOLO-LFD), to address the limitations of traditional sensor-based fire detection methods in terms of real-time performance and accuracy. The proposed model is designed to enhance inference speed while maintaining high detection accuracy on resource-constrained devices such as drones and embedded systems. Firstly, we introduce Depthwise Separable Convolutions (DSConv) to reduce the complexity of the feature extraction network. Secondly, we design and implement the Lightweight Faster Implementation of Cross Stage Partial (CSP) Bottleneck with 2 Convolutions (C2f-Light) and the CSP Structure with 3 Compact Inverted Blocks (C3CIB) modules to replace the traditional C3 modules. This optimization enhances deep feature extraction and semantic information processing, thereby significantly increasing inference speed. To enhance the detection capability for small fires, the model employs a Normalized Wasserstein Distance (NWD) loss function, which effectively reduces the missed detection rate and improves the accuracy of detecting small fire sources. Experimental results demonstrate that compared to the baseline YOLOv5s model, the YOLO-LFD model not only increases inference speed by 19.3% but also significantly improves the detection accuracy for small fire targets, with only a 1.6% reduction in overall mean average precision (mAP)@0.5. Through these innovative improvements to YOLOv5s, the YOLO-LFD model achieves a balance between speed and accuracy, making it particularly suitable for real-time detection tasks on mobile and embedded devices.
文摘Medical image classification is crucial in disease diagnosis,treatment planning,and clinical decisionmaking.We introduced a novel medical image classification approach that integrates Bayesian Random Semantic Data Augmentation(BSDA)with a Vision Mamba-based model for medical image classification(MedMamba),enhanced by residual connection blocks,we named the model BSDA-Mamba.BSDA augments medical image data semantically,enhancing the model’s generalization ability and classification performance.MedMamba,a deep learning-based state space model,excels in capturing long-range dependencies in medical images.By incorporating residual connections,BSDA-Mamba further improves feature extraction capabilities.Through comprehensive experiments on eight medical image datasets,we demonstrate that BSDA-Mamba outperforms existing models in accuracy,area under the curve,and F1-score.Our results highlight BSDA-Mamba’s potential as a reliable tool for medical image analysis,particularly in handling diverse imaging modalities from X-rays to MRI.The open-sourcing of our model’s code and datasets,will facilitate the reproduction and extension of our work.
基金financially supported by the Ningbo Institute of Digital Twin,Eastern Institute of Technology,Ningbo.We also acknowledge supportfrom the Young Innovative Talent of Yongjiang Talent Project(2023A‐387‐G).
文摘The efficient storage and application of sustainable solar energy has drawn significant attention from both academic and industrial points of view.However,most developed catalytic materials still suffer from insufficient mass diffusion and unsatisfactory durability due to the lack of interconnected and regulatable porosity.Developing catalytic architectures with engineered active sites and prominent stability through rational synthesis strategies has become one of the core projects in solar-driven applications.The unique properties of mesoporous silicas render them among the most valuable functional materials for industrial applications,such as high specific surface area,regulatable porosity,adjustable surface properties,tunable particle sizes,and great thermal and mechanical stability.Mesoporous silicas serve as structural templates or catalytic supports to enhance light harvesting via the scattering effect and provide large surface areas for active site generation.These advantages have been widely utilized in solar applications,including hydrogen production,CO_(2)conversion,photovoltaics,biomass utilization,and pollutant degradation.To achieve the specific functionalities and desired activity,various types of mesoporous silicas from different synthesis methods have been customized and synthesized.Moreover,morphology regulation and component modification strategies have also been performed to endow mesoporous silica-based materials with unprecedented efficiency for solar energy storage and utilization.Nevertheless,reviews about synthesis,morphology regulation,and component modification strategies for mesoporous silica-based catalyst design in solar-driven applications are still limited.Herein,the latest progress concerning mesoporous silica-based catalysis in solar-driven applications is comprehensively reviewed.Synthesis principles,formation mechanisms,and rational functionalities of mesoporous silica are systematically summarized.Some typical catalysts with impressive activities in different solar-driven applications are highlighted.Furthermore,challenges and future potential opportunities in this study field are also discussed and proposed.This present review guides the design of mesoporous silica catalysts for efficient solar energy management for solar energy storage and conversion applications.
基金This work was funded in part by the Alliance to Feed the Earth in Disasters(ALLFED).
文摘Following global catastrophic infrastructure loss(GCIL),traditional electricity networks would be damaged and unavailable for energy supply,necessitating alternative solutions to sustain critical services.These alternative solutions would need to run without damaged infrastructure and would likely need to be located at the point of use,such as decentralized electricity generation from wood gas.This study explores the feasibility of using modified light duty vehicles to self-sustain electricity generation by producing wood chips for wood gasification.A 2004 Ford Falcon Fairmont was modified to power a woodchipper and an electrical generator.The vehicle successfully produced wood chips suitable for gasification with an energy return on investment(EROI)of 3.7 and sustained a stable output of 20 kW electrical power.Scalability analyses suggest such solutions could provide electricity to the critical water sanitation sector,equivalent to 4%of global electricity demand,if production of woodchippers was increased postcatastrophe.Future research could investigate the long-term durability of modified vehicles and alternative electricity generation,and quantify the scalability of wood gasification in GCIL scenarios.This work provides a foundation for developing resilient,decentralized energy systems to ensure the continuity of critical services during catastrophic events,leveraging existing vehicle infrastructure to enhance disaster preparedness.
文摘Hydrogen is emerging as a promising alternative to fossil fuels in the transportation sector.This study evaluated the feasibility of estab-lishing hydrogen refueling stations in five cities in Oman,Duqm,Haima,Sur,Al Buraymi,and Salalah,using Hybrid Optimization of Multiple Electric Renewables(HOMER)software.Three hybrid energy systems,photovoltaic-wind turbine-battery,photovoltaic-battery,and wind turbine-battery were analyzed for each city.Results indicated that Duqm offers the lowest net present cost(NPC),levelized cost of energy,and levelized cost of hydrogen,making it the most cost-effective location.Additionally,Sensitivity analysis showed that as the life of electrolyzer increases during operation,the initial capital expenditure is distributed over a longer operational period,leading to a reduction in the NPC.More so,renewable energy systems produced no emissions which supports Oman’s mission target.This comprehensive analysis confirms the feasibility of establishing a hydrogen refueling station in Duqm,Oman,and highlights advanced optimization techniques’superior capability in designing cost-effective,sustainable energy systems.
文摘In the plethora of conceptual and algorithmic developments supporting data analytics and system modeling,humancentric pursuits assume a particular position owing to ways they emphasize and realize interaction between users and the data.We advocate that the level of abstraction,which can be flexibly adjusted,is conveniently realized through Granular Computing.Granular Computing is concerned with the development and processing information granules–formal entities which facilitate a way of organizing knowledge about the available data and relationships existing there.This study identifies the principles of Granular Computing,shows how information granules are constructed and subsequently used in describing relationships present among the data.
基金sponsored by National Natural Science Foundation of China (Nos. 61673327, 51606161, 11602209, 91441128)Natural Science Foundation of Fujian Province of China (No. 2016J06011)China Scholarship Council (No. 201606310153)
文摘This paper addresses a target-enclosing problem for multiple spacecraft systems by proposing a two-layer affine formation control strategy. Compared with the existing methods,the adopted two-layer network structure in this paper is generally directed, which is suitable for practical space missions. Firstly, distributed finite-time sliding-mode estimators and formation controllers in both layers are designed separately to improve the flexibility of the formation control system. By introducing the properties of affine transformation into formation control protocol design,the controllers can be used to track different time-varying target formation patterns. Besides, multilayer time-varying encirclements can be achieved with particular shapes to surround the moving target. In the sequel, by integrating adaptive neural networks and specialized artificial potential functions into backstepping controllers, the problems of uncertain Euler-Lagrange models, collision avoidance as well as formation reconfiguration are solved simultaneously. The stability of the proposed controllers is verified by the Lyapunov direct method. Finally, two simulation examples of triangle formation and more complex hexagon formation are presented to illustrate the feasibility of the theoretical results.
基金supported by the National Natural Science Foundation of China(Grant nos.21773218,61974063)the Sichuan Province(Grant no.2018JY0206)the China Academy of Engineering Physics(Grant no.YZJJLX2018007)。
文摘Sensitive and reliable X-ray detectors are essential for medical radiography,industrial inspection and security screening.Lowering the radiation dose allows reduced health risks and increased frequency and fidelity of diagnostic technologies for earlier detection of disease and its recurrence.Three-dimensional(3 D)organic-inorganic hybrid lead halide perovskites are promising for direct X-ray detection-they show improved sensitivity compared to conventional X-ray detectors.However,their high and unstable dark current,caused by ion migration and high dark carrier concentration in the 3 D hybrid perovskites,limits their performance and long-term operation stability.Here we report ultrasensitive,stable X-ray detectors made using zero-dimensional(0 D)methylammonium bismuth iodide perovskite(MA3Bi2I9)single crystals.The 0 D crystal structure leads to a high activation energy(Ea)for ion migration(0.46 e V)and is also accompanied by a low dark carrier concentration(~10^6 cm^-3).The X-ray detectors exhibit sensitivity of 10,620μC Gy-1 air cm-2,a limit of detection(Lo D)of 0.62 nG yairs-1,and stable operation even under high applied biases;no deterioration in detection performance was observed following sensing of an integrated X-ray irradiation dose of^23,800 m Gyair,equivalent to>200,000 times the dose required for a single commercial X-ray chest radiograph.Regulating the ion migration channels and decreasing the dark carrier concentration in perovskites provide routes for stable and ultrasensitive X-ray detectors.
基金supported by The National Key Research and Development Program of China(2020YFB2009100)Natural Science Basic Research Program of Shaanxi(Program No.2022JQ-508)National Science and Technology Major Project(Grant No.J2019-V-0006-0100),Open research fund of SKLMS(Grant No.sklms2021009).
文摘Flexible temperature sensors have been extensively investigated due to their prospect of wide application in various flexible electronic products.However,most of the current flexible temperature sensors only work well in a narrow temperature range,with their application at high or low temperatures still being a big challenge.This work proposes a flexible thermocouple temperature sensor based on aerogel blanket substrate,the temperature-sensitive layer of which uses the screen-printing technology to prepare indium oxide and indium tin oxide.It has good temperature sensitivity,with the test sensitivity reaching 226.7μV℃^(−1).Most importantly,it can work in a wide temperature range,from extremely low temperatures down to liquid nitrogen temperature to high temperatures up to 1200℃,which is difficult to be achieved by other existing flexible temperature sensors.This temperature sensor has huge application potential in biomedicine,aerospace and other fields.
