Chemical synthesis is essential in industries such as petrochemicals, fine chemicals, and pharmaceuticals, driving economic and social development. The increasing demand for new molecules and materials calls for novel...Chemical synthesis is essential in industries such as petrochemicals, fine chemicals, and pharmaceuticals, driving economic and social development. The increasing demand for new molecules and materials calls for novel chemical reactions;however, manual experimental screening is time-consuming. Artificial intelligence (AI) offers a promising solution by leveraging large-scale experimental data to model chemical reactions, although challenges such as the lack of standardization and predictability in chemical synthesis hinder AI applications. Additionally, the multi-scale nature of chemical reactions, along with complex multiphase processes, further complicates the task. Recent advances in microchemical systems, particularly continuous flow methods using microreactors, provide precise control over reaction conditions, enhancing reproducibility and enabling high-throughput experimentation. These systems minimize transport-related inconsistencies and facilitate scalable industrial applications. This review systematically explores recent developments in intelligent synthesis based on microchemical systems, focusing on reaction system design, synthesis robots, closed-loop optimization, and high-throughput experimentation, while identifying key areas for future research.展开更多
Diabetes mellitus(DM),an increasingly prevalent chronic metabolic disease,is characterised by prolonged hyperglycaemia,which leads to long-term health consequences.Although much effort has been put into understanding ...Diabetes mellitus(DM),an increasingly prevalent chronic metabolic disease,is characterised by prolonged hyperglycaemia,which leads to long-term health consequences.Although much effort has been put into understanding the pathogenesis of diabetic wounds,the underlying mechanisms remain unclear.The advent of single-cell RNA sequencing(scRNAseq)has revolutionised biological research by enabling the identification of novel cell types,the discovery of cellular markers,the analysis of gene expression patterns and the prediction of develop-mental trajectories.This powerful tool allows for an in-depth exploration of pathogenesis at the cellular and molecular levels.In this editorial,we focus on progenitor-based repair strategies for diabetic wound healing as revealed by scRNAseq and highlight the biological behaviour of various healing-related cells and the alteration of signalling pathways in the process of diabetic wound healing.ScRNAseq could not only deepen our understanding of the complex biology of diabetic wounds but also identify and validate new targets for inter-vention,offering hope for improved patient outcomes in the management of this challenging complication of DM.展开更多
Face liveness detection is essential for securing biometric authentication systems against spoofing attacks,including printed photos,replay videos,and 3D masks.This study systematically evaluates pre-trained CNN model...Face liveness detection is essential for securing biometric authentication systems against spoofing attacks,including printed photos,replay videos,and 3D masks.This study systematically evaluates pre-trained CNN models—DenseNet201,VGG16,InceptionV3,ResNet50,VGG19,MobileNetV2,Xception,and InceptionResNetV2—leveraging transfer learning and fine-tuning to enhance liveness detection performance.The models were trained and tested on NUAA and Replay-Attack datasets,with cross-dataset generalization validated on SiW-MV2 to assess real-world adaptability.Performance was evaluated using accuracy,precision,recall,FAR,FRR,HTER,and specialized spoof detection metrics(APCER,NPCER,ACER).Fine-tuning significantly improved detection accuracy,with DenseNet201 achieving the highest performance(98.5%on NUAA,97.71%on Replay-Attack),while MobileNetV2 proved the most efficient model for real-time applications(latency:15 ms,memory usage:45 MB,energy consumption:30 mJ).A statistical significance analysis(paired t-tests,confidence intervals)validated these improvements.Cross-dataset experiments identified DenseNet201 and MobileNetV2 as the most generalizable architectures,with DenseNet201 achieving 86.4%accuracy on Replay-Attack when trained on NUAA,demonstrating robust feature extraction and adaptability.In contrast,ResNet50 showed lower generalization capabilities,struggling with dataset variability and complex spoofing attacks.These findings suggest that MobileNetV2 is well-suited for low-power applications,while DenseNet201 is ideal for high-security environments requiring superior accuracy.This research provides a framework for improving real-time face liveness detection,enhancing biometric security,and guiding future advancements in AI-driven anti-spoofing techniques.展开更多
The stable nanobubbles adhered to mineral surfaces may facilitate their efficient separation via flotation in the mining industry.However,the state of nanobubbles on mineral solid surfaces is still elusive.In this stu...The stable nanobubbles adhered to mineral surfaces may facilitate their efficient separation via flotation in the mining industry.However,the state of nanobubbles on mineral solid surfaces is still elusive.In this study,molecular dynamics(MD)simulations are employed to examine mineral-like model surfaces with varying degrees of hydrophobicity,modulated by surface charges,to elucidate the adsorption behavior of nanobubbles at the interface.Our findings not only contribute to the fundamental understanding of nanobubbles but also have potential applications in the mining industry.We observed that as the surface charge increases,the contact angle of the nanobubbles increases accordingly with shape transformation from a pancake-like gas film to a cap-like shape,and ultimately forming a stable nanobubble upon an ordered water monolayer.When the solid–water interactions are weak with a small partial charge,the hydrophobic gas(N_(2))molecules accumulate near the solid surfaces.However,we have found,for the first time,that gas molecules assemble a nanobubble on the water monolayer adjacent to the solid surfaces with large partial charges.Such phenomena are attributed to the formation of a hydrophobic water monolayer with a hydrogen bond network structure near the surface.展开更多
Energy storage is a key factor in the drive for carbon neutrality and carbon nanotubes(CNTs)may have an important role in this.Their intrinsic sp2 covalent structure gives them excellent electrical conductivity,mechan...Energy storage is a key factor in the drive for carbon neutrality and carbon nanotubes(CNTs)may have an important role in this.Their intrinsic sp2 covalent structure gives them excellent electrical conductivity,mechanical strength,and chemical stability,making them suitable for many uses in energy storage,such as lithium-ion batteries(LIBs).Currently,their use in LIBs mainly focuses on conductive networks,current collectors,and dry electrodes.The review outlines advances in the use of CNTs in the cathodes and anodes of LIBs,especially in the electrode fabrication and mechanical sensors,as well as providing insights into their future development.展开更多
Adaptive robust secure framework plays a vital role in implementing intelligent automation and decentralized decision making of Industry 5.0.Latency,privacy risks and the complexity of industrial networks have been pr...Adaptive robust secure framework plays a vital role in implementing intelligent automation and decentralized decision making of Industry 5.0.Latency,privacy risks and the complexity of industrial networks have been preventing attempts at traditional cloud-based learning systems.We demonstrate that,to overcome these challenges,for instance,the EdgeGuard-IoT framework,a 6G edge intelligence framework enhancing cybersecurity and operational resilience of the smart grid,is needed on the edge to integrate Secure Federated Learning(SFL)and Adaptive Anomaly Detection(AAD).With ultra-reliable low latency communication(URLLC)of 6G,artificial intelligence-based network orchestration,and massive machine type communication(mMTC),EdgeGuard-IoT brings real-time,distributed intelligence on the edge,and mitigates risks in data transmission and enhances privacy.EdgeGuard-IoT,with a hierarchical federated learning framework,helps edge devices to collaboratively train models without revealing the sensitive grid data,which is crucial in the smart grid where real-time power anomaly detection and the decentralization of the energy management are a big deal.The hybrid AI models driven adaptive anomaly detection mechanism immediately raises the thumb if the grid stability and strength are negatively affected due to cyber threats,faults,and energy distribution,thereby keeping the grid stable with resilience.The proposed framework also adopts various security means within the blockchain and zero-trust authentication techniques to reduce the adversarial attack risks and model poisoning during federated learning.EdgeGuard-IoT shows superior detection accuracy,response time,and scalability performance at a much reduced communication overhead via extensive simulations and deployment in real-world case studies in smart grids.This research pioneers a 6G-driven federated intelligence model designed for secure,self-optimizing,and resilient Industry 5.0 ecosystems,paving the way for next-generation autonomous smart grids and industrial cyber-physical systems.展开更多
Background: Erythrodermic psoriasis (EP) is a rare, severe variant of psoriasis characterized by widespread erythema, scaling, and systemic complications. Despite advances in systemic treatments, the management of EP ...Background: Erythrodermic psoriasis (EP) is a rare, severe variant of psoriasis characterized by widespread erythema, scaling, and systemic complications. Despite advances in systemic treatments, the management of EP remains challenging, particularly in patients with comorbidities or contraindications to standard therapies. Objectives: To evaluate the effectiveness of ozonated water as an adjunctive treatment for EP, delivered using a patented robotic therapy system designed for hygiene and infection prevention in non-self-sufficient patients. Methods: We report the case of a 90-year-old male patient with acute EP who received daily skin treatments with ozonated water in conjunction with supportive care, including rehydration and antibiotics. The intervention was facilitated by the robotic system “COPERNICO Surveillance & Prevention,” which ensured standardized hygiene practices and clinical documentation. Results: Within one week of treatment, the patient showed complete desquamation of necrotic skin, resolution of erythema, and significant metabolic recovery. Fever subsided, renal function improved, and the patient was discharged in stable condition. Follow-up confirmed sustained clinical improvement, and no adverse events were reported. Conclusions: Ozonated water demonstrated efficacy in alleviating the dermatological and systemic manifestations of EP in a high-risk elderly patient. This case highlights the potential of ozone therapy as a safe, cost-effective adjunctive treatment for EP and underscores the utility of robotic systems in managing complex dermatological conditions. Further research is warranted to validate these findings in larger cohorts.展开更多
Low-altitude economy opens up a completely new aerial space for economic growth by enabling brand new services such as fast logistics delivery,timely emergency rescue,and wide-area,high-definition environmental monito...Low-altitude economy opens up a completely new aerial space for economic growth by enabling brand new services such as fast logistics delivery,timely emergency rescue,and wide-area,high-definition environmental monitoring.This new space has many distinct features and therefore faces many new challenges compared with ground-and high-altitude-based information infrastructures.As a result,the rapid and mass development of unmanned aerial vehicles(UAVs)in low-altitude space will inevitably necessitate research on providing ultra-reliable,low-latency,high-capacity.展开更多
Background:Previous experiments have demonstrated that hypofractionated radiation therapy(HFRT),low-dose radiation therapy(LDRT),and combined anti-programmed cell death protein 1(αPD-1)can enhance the abscopal effect...Background:Previous experiments have demonstrated that hypofractionated radiation therapy(HFRT),low-dose radiation therapy(LDRT),and combined anti-programmed cell death protein 1(αPD-1)can enhance the abscopal effect.Combined with the phenomenon of low prognosis in patients with breast cancer lung metastasis,our study establishes a mouse model and changes the irradiation regimen of LDRT to explore its preventive effect on breast cancer lung metastasis.Methods:The breast cancer subcutaneous graft tumor model was developed.Two-lung prophylactic LDRT was performed prior to the onset of lung metastases,in combination with HFRT(8 Gy,3f),andαPD-1(200μg,4f)therapy.We watched and documented the tumor volume,survival duration,and number of lung metastases.Furthermore,after labeling the corresponding cells using markers,we detected immune-related cell infiltration by immunohistochemistry and flow cytometry,such as T cells.We also determined the expression of cytokines(IFN-γand TNF-α)by enzyme-linked immunosorbent assay.Result:The triple therapy(HFRT+LDRT+αPD-1)resulted in tumor shrinkage and prolonged survival in mice,with median survival extending from 35 to 52 days.The most notable decrease in the quantity of advanced lung metastatic nodules in breast cancer was observed with the triple therapy(HFRT+LDRT+αPD-1)(p<0.05).Furthermore,according to immunohistochemistry and flow cytometry,the triple treatment(HFRT+LDRT+αPD-1)showed the greatest expression of CD8^(+)T cells.Additionally,the ratio of CD8^(+)/CD4^(+)T cells was considerably greater than that of the groups(p<0.0001).Triple therapy(HFRT+LDRT+αPD-1)increased the recruitment of DCs cells,promoted IFN-γand TNF-αexpression,and curbed the aggregation of MDSCs cells(p<0.05).Conclusion:Prophylactic LDRT to the lungs,based on HFRT andαPD-1,can enhance anti-tumor efficacy and prevent advanced lung metastases from breast cancer.The process involves boosting the recruitment of DCs and CD8^(+)T cells,preventing MDSC cell aggregation,and lessening the tumor microenvironment’s immunosuppressive effects.展开更多
All-solid-state lithium batteries(ASSLBs)are strongly considered as the next-generation energy storage devices for their high energy density and intrinsic safety.The solid-solid contact between lithium metal and solid...All-solid-state lithium batteries(ASSLBs)are strongly considered as the next-generation energy storage devices for their high energy density and intrinsic safety.The solid-solid contact between lithium metal and solid electrolyte plays a vital role in the performance of working ASSLBs,which is challenging to investigate quantitatively by experimental approach.This work proposed a quantitative model based on the finite element method for electrochemical impedance spectroscopy simulation of different solid-solid contact states in ASSLBs.With the assistance of an equivalent circuit model and distribution of relaxation times,it is discovered that as the number of voids and the sharpness of cracks increase,the contact resistance Rcgrows and ultimately dominates the battery impedance.Through accurate fitting,inverse proportional relations between contact resistance Rcand(1-porosity)as well as crack angle was disclosed.This contribution affords a fresh insight into clarifying solid-solid contact states in ASSLBs.展开更多
Blockchain technologies have been used to facilitate Web 3.0 and FinTech applications.However,conventional blockchain technologies suffer from long transaction delays and low transaction success rates in some Web 3.0 ...Blockchain technologies have been used to facilitate Web 3.0 and FinTech applications.However,conventional blockchain technologies suffer from long transaction delays and low transaction success rates in some Web 3.0 and FinTech applications such as Supply Chain Finance(SCF).Blockchain sharding has been proposed to improve blockchain performance.However,the existing sharding methods either use a static sharding strategy,which lacks the adaptability for the dynamic SCF environment,or are designed for public chains,which are not applicable to consortium blockchain-based SCF.To address these issues,we propose an adaptive consortium blockchain sharding framework named ACSarF,which is based on the deep reinforcement learning algorithm.The proposed framework can improve consortium blockchain sharding to effectively reduce transaction delay and adaptively adjust the sharding and blockout strategies to increase the transaction success rate in a dynamic SCF environment.Furthermore,we propose to use a consistent hash algorithm in the ACSarF framework to ensure transaction load balancing in the adaptive sharding system to further improve the performance of blockchain sharding in dynamic SCF scenarios.To evaluate the proposed framework,we conducted extensive experiments in a typical SCF scenario.The obtained experimental results show that the ACSarF framework achieves a more than 60%improvement in user experience compared to other state-of-the-art blockchain systems.展开更多
Due to the continuous increase in global energy demand,photovoltaic solar energy generation and associated maintenance requirements have significantly expanded.One critical maintenance challenge in photovoltaic instal...Due to the continuous increase in global energy demand,photovoltaic solar energy generation and associated maintenance requirements have significantly expanded.One critical maintenance challenge in photovoltaic installations is detecting hot spots,localized overheating defects in solar cells that drastically reduce efficiency and can lead to permanent damage.Traditional methods for detecting these defects rely on manual inspections using thermal imaging,which are costly,labor-intensive,and impractical for large-scale installations.This research introduces an automated hybrid system based on two specialized convolutional neural networks deployed in a cascaded architecture.The first convolutional neural network efficiently detects and isolates individual solar panels from high-resolution aerial thermal images captured by drones.Subsequently,a second,more advanced convolutional neural network accurately classifies each isolated panel as either defective or healthy,effectively distinguishing genuine thermal anomalies from false positives caused by reflections or glare.Experimental validation on a real-world dataset comprising thousands of thermal images yielded exceptional accuracy,significantly reducing inspection time,costs,and the likelihood of false defect detections.This proposed system enhances the reliability and efficiency of photovoltaic plant inspections,thus contributing to improved operational performance and economic viability.展开更多
Port structures constitute the main link in the maritime transport chain of coastal countries and therefore contribute to their economic development. But it should be noted that the installation of said works is not w...Port structures constitute the main link in the maritime transport chain of coastal countries and therefore contribute to their economic development. But it should be noted that the installation of said works is not without consequences for the countries concerned. Benin, a country in the Gulf of Guinea, is no exception to this phenomenon because, due to its maritime history, it has a heritage of port structures. These structures, built on its coastline, cause a wide variety of environmental problems such as silting and erosion on either side of them. The general objective of this article is to contribute to the proper functionality of port facilities while minimizing environmental problems that may arise. It aims to provide managers with a tool allowing them to fully understand the state of their assets in order to rationalize maintenance actions. In order to achieve this objective, an assessment of the state of the structure, and then a structural diagnosis are necessary and recommendations can be established to restore the level of service of the latter. As a result, two examples were presented: the wharf of the Sèmè-Podji pipeline project and the maritime piles project of the Wasco de Gama bridge (control project), and recommendations adapted to this objective were established. The comparative analysis of the two examples, both maritime works, revealed an under-sizing at the level of the spans of the wharf bridge of the Sèmè-Podji pipeline project (spans of 7 m in length), while these spans vary on average by 45 m to 62 m for the Wasco da Gama bridge. Bringing the piles closer together at the Sèmè-Podji wharf reduces the energy of the current which promotes the accumulation of sediment. The structure no longer experiences a flow capable of setting in motion the sands accumulated since at least 2022. This element appears to be a fundamental characteristic explaining the erosion observed to the east of the structure.展开更多
The stability and electrocatalytic efficiency of transition metal oxides for water splitting is determined by geometric and electronic structure,especially under high current densities.Herein,a newly designed lamella-...The stability and electrocatalytic efficiency of transition metal oxides for water splitting is determined by geometric and electronic structure,especially under high current densities.Herein,a newly designed lamella-heterostructured nanoporous CoFe/CoFe_(2)O_(4) and CeO_(2−x),in situ grown on nickel foam(NF),holds great promise as a high-efficient bifunctional electrocatalyst(named R-CoFe/Ce/NF)for water splitting.Experimental characterization verifies surface reconstruction from CoFe alloy/oxide to highly active CoFeOOH during in situ electrochemical polarization.By virtues of three-dimensional nanoporous architecture and abundant electroactive CoFeOOH/CeO_(2−x) heterostructure interfaces,the R-CoFe/Ce/NF electrode achieves low overpotentials for oxygen evolution(η_(10)=227 mV;η_(500)=450 mV)and hydrogen evolution(η_(10)=35 mV;η_(408)=560 mV)reactions with high normalized electrochemical active surface areas,respectively.Additionally,the alkaline full water splitting electrolyzer of R-CoFe/Ce/NF||R-CoFe/Ce/NF achieves a current density of 50 mA·cm^(−2) only at 1.75 V;the decline of activity is satisfactory after 100-h durability test at 300 mA·cm^(−2).Density functional theory also demonstrates that the electron can transfer from CeO_(2−x) by virtue of O atom to CoFeOOH at CoFeOOH/CeO_(2−x) heterointerfaces and enhancing the adsorption of reactant,thus optimizing electronic structure and Gibbs free energies for the improvement of the activity for water splitting.展开更多
In optical metro-access networks,Access Points(APs)and Data Centers(DCs)are located on the fiber ring.In the cloud-centric solution,a large number of Internet of Things(IoT)data pose an enormous burden on DCs,so the V...In optical metro-access networks,Access Points(APs)and Data Centers(DCs)are located on the fiber ring.In the cloud-centric solution,a large number of Internet of Things(IoT)data pose an enormous burden on DCs,so the Virtual Machines(VMs)cannot be successfully launched due to the server overload.In addition,transferring the data from the AP to the remote DC may cause an undesirable delivery delay.For this end,we propose a promising solution considering the interplay between the cloud DC and edge APs.More specifically,bringing the partial capability of computing in APs close to things can reduce the pressure of DCs while guaranteeing the expected Quality of Service(QoS).In this work,when the cloud DC resource becomes limited,especially for delay sensitive but not computing-dependent IoT applications,we degrade their VMs and migrate them to edge APs instead of the remote DC.To avoid excessive VM degradation and computing offloading,we derive appropriate VM degradation coefficients based on classic microeconomic theory.Simulation results demonstrate that our algorithms improve the service providers'utility with the ratio from 34%to 89%over traditional cloud-centric solutions.展开更多
Extensive research efforts have been dedicated to enhancing the corrosion resistance properties and mitigating the reactivity of metals.Various surface modification techniques,including anodizing,electrochemical depos...Extensive research efforts have been dedicated to enhancing the corrosion resistance properties and mitigating the reactivity of metals.Various surface modification techniques,including anodizing,electrochemical deposition,non-electric current-based coating,ion implantation,conversion coatings,and organic coatings,have been explored for these objectives.Among these surface modification methodologies,conversion coatings have garnered substantial attention due to their capacity of depositing onto metal surfaces uniformly,enhancing adhesion for subsequent layers,facilitating ease of application,and offering cost-effectiveness.Different coatings,each possessing distinct properties,have been employed for diverse metals.Nonetheless,in light of hexavalent chromium compounds'elevated toxicity and carcinogenic nature,environmentally compatible conversion coatings are actively sought as viable alternatives.The deposition of conversion coatings is an intricate process influenced by several variables.Consequently,laboratory optimization of conditions is imperative for commercializing specialized conversion coatings.Further research endeavors are warranted to expand the standards and requisite characteristics for specific applications.Composite conversion coatings,formulated with rare earth elements and innovative mineral and organic transformative agents,hold promise as a subject of investigation.Advanced studies in theoretical research and computer modeling about the mechanism of crack prevention in rare earth element-based co nversion coatings devoid of defects will yield valuable insights.The present work furnishes a comprehensive review of this subject,marking its inaugural examination.展开更多
Over more than a decade of development,medium to deep shale gas reservoirs have faced rapid production declines,making sustained output challenging.To harness remaining reserves effectively,advanced fracturing techniq...Over more than a decade of development,medium to deep shale gas reservoirs have faced rapid production declines,making sustained output challenging.To harness remaining reserves effectively,advanced fracturing techniques such as infill drilling are essential.This study develops a complex fracture network model for dual horizontal wells and a four-dimensional in-situ stress evolution model,grounded in elastic porous media theory.These models simulate and analyze the evolution of formation pore pressure and in-situ stress during production.The investigation focuses on the influence of infill well fracturing timing on fracture propagation patterns,individual well productivity,and the overall productivity of well clusters.The findings reveal that,at infill well locations,the maximum horizontal principal stress undergoes the most significant reduction,while changes in the minimum horizontal principal stress and vertical stress remain minimal.The horizontal stress surrounding the infill well may reorient,potentially transitioning the stress regime from strike-slip to normal faulting.Delays in infill well fracturing increase lateral fracture deflection and diminish fracture propagation between wells.Considering the stable production phase and cumulative gas output of the well group,the study identifies an optimal timing for infill fracturing.Notably,larger well spacing shifts the optimal timing to a later stage.展开更多
The problems noted in the structures built on wooden foundation piles in a lake environment required various works to strengthen over time.This work mainly consists of the recovery of the foundation mass by micropiles...The problems noted in the structures built on wooden foundation piles in a lake environment required various works to strengthen over time.This work mainly consists of the recovery of the foundation mass by micropiles due to the increase in loads on the structures,or the recovery of the foundation mass by injection,which is carried out when voids form between the ground and the wooden foundation elements.The high cost of foundation reinforcement methods led the National Agency for the Development of Tourist Heritage in Benin(ANPT)to replace the wooden foundation piles with reinforced concrete piles in the implementation of the project“reinventing the lakeside city of Ganvié”.This article presents an artisanal technology for the creation of reinforced concrete foundation piles in a lake environment.On-site examples made it possible to evaluate the performance of this artisanal implementation technique.The installation of these piles is carried out following manual drilling,followed by the installation of reinforcement and the pouring of concrete on site.The implementation of reinforced concrete foundation piles in place of the wooden ones studied in this article only impacted the infrastructure of the homes of the lakeside town of Ganviébut not the superstructure,which preserved the old traditional wooden architecture and thatched roofs.Thus,the ambition to move this city of Ganviéfrom the stage of a lake village to that of a floating city is very successful.This will contribute to improving the environment and living conditions of the populations and will promote economic development through tourism.展开更多
Responding to the stochasticity and uncertainty in the power height of distributed photovoltaic power generation.This paper presents a distributed photovoltaic ultra-short-term power forecasting method based on Variat...Responding to the stochasticity and uncertainty in the power height of distributed photovoltaic power generation.This paper presents a distributed photovoltaic ultra-short-term power forecasting method based on Variational Mode Decomposition(VMD)and Channel Attention Mechanism.First,Pearson’s correlation coefficient was utilized to filter out the meteorological factors that had a high impact on historical power.Second,the distributed PV power data were decomposed into a relatively smooth power series with different fluctuation patterns using variational modal decomposition(VMD).Finally,the reconstructed distributed PV power as well as other features are input into the combined CNN-SENet-BiLSTM model.In this model,the convolutional neural network(CNN)and channel attention mechanism dynamically adjust the weights while capturing the spatial features of the input data to improve the discriminative ability of key features.The extracted data is then fed into the bidirectional long short-term memory network(BiLSTM)to capture the time-series features,and the final output is the prediction result.The verification is conducted using a dataset from a distributed photovoltaic power station in the Northwest region of China.The results show that compared with other prediction methods,the method proposed in this paper has a higher prediction accuracy,which helps to improve the proportion of distributed PV access to the grid,and can guarantee the safe and stable operation of the power grid.展开更多
In recent years,many phase space distributions have been proposed,and one of the more independently interesting is the Bai distribution function(BDF).The BDF has been shown to interpolate between the instantaneous aut...In recent years,many phase space distributions have been proposed,and one of the more independently interesting is the Bai distribution function(BDF).The BDF has been shown to interpolate between the instantaneous auto-correlation function and the Wigner distribution function,and be applied in linear frequency modulated signal parameter estimation and optical partial coherence areas.Currently,the BDF is only defined for continuous signals.However,for both simulation and experimental purposes,the signals must be discrete.This necessitates the development of a BDF analysis workflow for discrete signals.In this work,we analyze the sampling requirements imposed by the BDF and demonstrate their correctness by comparing the continuous BDFs of continuous test signals with their numerically approximated counterparts.Our results permit more accurate simulations using BDFs,which will be useful in applying them to problems such as partial coherence.展开更多
基金supported by the National Natural Science Foundation of China(22378227)Shijiazhuang Science and Technology Bureau(231790163A).
文摘Chemical synthesis is essential in industries such as petrochemicals, fine chemicals, and pharmaceuticals, driving economic and social development. The increasing demand for new molecules and materials calls for novel chemical reactions;however, manual experimental screening is time-consuming. Artificial intelligence (AI) offers a promising solution by leveraging large-scale experimental data to model chemical reactions, although challenges such as the lack of standardization and predictability in chemical synthesis hinder AI applications. Additionally, the multi-scale nature of chemical reactions, along with complex multiphase processes, further complicates the task. Recent advances in microchemical systems, particularly continuous flow methods using microreactors, provide precise control over reaction conditions, enhancing reproducibility and enabling high-throughput experimentation. These systems minimize transport-related inconsistencies and facilitate scalable industrial applications. This review systematically explores recent developments in intelligent synthesis based on microchemical systems, focusing on reaction system design, synthesis robots, closed-loop optimization, and high-throughput experimentation, while identifying key areas for future research.
基金Supported by Shenzhen Science and Technology Program,No.GJHZ20210705142543019Guangdong Basic and Applied Basic Research Foundation,No.2023A1515220074.
文摘Diabetes mellitus(DM),an increasingly prevalent chronic metabolic disease,is characterised by prolonged hyperglycaemia,which leads to long-term health consequences.Although much effort has been put into understanding the pathogenesis of diabetic wounds,the underlying mechanisms remain unclear.The advent of single-cell RNA sequencing(scRNAseq)has revolutionised biological research by enabling the identification of novel cell types,the discovery of cellular markers,the analysis of gene expression patterns and the prediction of develop-mental trajectories.This powerful tool allows for an in-depth exploration of pathogenesis at the cellular and molecular levels.In this editorial,we focus on progenitor-based repair strategies for diabetic wound healing as revealed by scRNAseq and highlight the biological behaviour of various healing-related cells and the alteration of signalling pathways in the process of diabetic wound healing.ScRNAseq could not only deepen our understanding of the complex biology of diabetic wounds but also identify and validate new targets for inter-vention,offering hope for improved patient outcomes in the management of this challenging complication of DM.
基金funded by Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS),Faculty of Engineering and IT,University of Technology Sydney.Moreover,Ongoing Research Funding Program(ORF-2025-14)King Saud University,Riyadh,Saudi Arabia,under Project ORF-2025-。
文摘Face liveness detection is essential for securing biometric authentication systems against spoofing attacks,including printed photos,replay videos,and 3D masks.This study systematically evaluates pre-trained CNN models—DenseNet201,VGG16,InceptionV3,ResNet50,VGG19,MobileNetV2,Xception,and InceptionResNetV2—leveraging transfer learning and fine-tuning to enhance liveness detection performance.The models were trained and tested on NUAA and Replay-Attack datasets,with cross-dataset generalization validated on SiW-MV2 to assess real-world adaptability.Performance was evaluated using accuracy,precision,recall,FAR,FRR,HTER,and specialized spoof detection metrics(APCER,NPCER,ACER).Fine-tuning significantly improved detection accuracy,with DenseNet201 achieving the highest performance(98.5%on NUAA,97.71%on Replay-Attack),while MobileNetV2 proved the most efficient model for real-time applications(latency:15 ms,memory usage:45 MB,energy consumption:30 mJ).A statistical significance analysis(paired t-tests,confidence intervals)validated these improvements.Cross-dataset experiments identified DenseNet201 and MobileNetV2 as the most generalizable architectures,with DenseNet201 achieving 86.4%accuracy on Replay-Attack when trained on NUAA,demonstrating robust feature extraction and adaptability.In contrast,ResNet50 showed lower generalization capabilities,struggling with dataset variability and complex spoofing attacks.These findings suggest that MobileNetV2 is well-suited for low-power applications,while DenseNet201 is ideal for high-security environments requiring superior accuracy.This research provides a framework for improving real-time face liveness detection,enhancing biometric security,and guiding future advancements in AI-driven anti-spoofing techniques.
基金supported by the National Natural Science Foundation of China(Grant Nos.12022508,12074394,and 22125604)Shanghai Supercomputer Center of ChinaShanghai Snowlake Technology Co.Ltd.
文摘The stable nanobubbles adhered to mineral surfaces may facilitate their efficient separation via flotation in the mining industry.However,the state of nanobubbles on mineral solid surfaces is still elusive.In this study,molecular dynamics(MD)simulations are employed to examine mineral-like model surfaces with varying degrees of hydrophobicity,modulated by surface charges,to elucidate the adsorption behavior of nanobubbles at the interface.Our findings not only contribute to the fundamental understanding of nanobubbles but also have potential applications in the mining industry.We observed that as the surface charge increases,the contact angle of the nanobubbles increases accordingly with shape transformation from a pancake-like gas film to a cap-like shape,and ultimately forming a stable nanobubble upon an ordered water monolayer.When the solid–water interactions are weak with a small partial charge,the hydrophobic gas(N_(2))molecules accumulate near the solid surfaces.However,we have found,for the first time,that gas molecules assemble a nanobubble on the water monolayer adjacent to the solid surfaces with large partial charges.Such phenomena are attributed to the formation of a hydrophobic water monolayer with a hydrogen bond network structure near the surface.
文摘Energy storage is a key factor in the drive for carbon neutrality and carbon nanotubes(CNTs)may have an important role in this.Their intrinsic sp2 covalent structure gives them excellent electrical conductivity,mechanical strength,and chemical stability,making them suitable for many uses in energy storage,such as lithium-ion batteries(LIBs).Currently,their use in LIBs mainly focuses on conductive networks,current collectors,and dry electrodes.The review outlines advances in the use of CNTs in the cathodes and anodes of LIBs,especially in the electrode fabrication and mechanical sensors,as well as providing insights into their future development.
基金supported by Department of Information Technology,University of Tabuk,Tabuk,71491,Saudi Arabia.
文摘Adaptive robust secure framework plays a vital role in implementing intelligent automation and decentralized decision making of Industry 5.0.Latency,privacy risks and the complexity of industrial networks have been preventing attempts at traditional cloud-based learning systems.We demonstrate that,to overcome these challenges,for instance,the EdgeGuard-IoT framework,a 6G edge intelligence framework enhancing cybersecurity and operational resilience of the smart grid,is needed on the edge to integrate Secure Federated Learning(SFL)and Adaptive Anomaly Detection(AAD).With ultra-reliable low latency communication(URLLC)of 6G,artificial intelligence-based network orchestration,and massive machine type communication(mMTC),EdgeGuard-IoT brings real-time,distributed intelligence on the edge,and mitigates risks in data transmission and enhances privacy.EdgeGuard-IoT,with a hierarchical federated learning framework,helps edge devices to collaboratively train models without revealing the sensitive grid data,which is crucial in the smart grid where real-time power anomaly detection and the decentralization of the energy management are a big deal.The hybrid AI models driven adaptive anomaly detection mechanism immediately raises the thumb if the grid stability and strength are negatively affected due to cyber threats,faults,and energy distribution,thereby keeping the grid stable with resilience.The proposed framework also adopts various security means within the blockchain and zero-trust authentication techniques to reduce the adversarial attack risks and model poisoning during federated learning.EdgeGuard-IoT shows superior detection accuracy,response time,and scalability performance at a much reduced communication overhead via extensive simulations and deployment in real-world case studies in smart grids.This research pioneers a 6G-driven federated intelligence model designed for secure,self-optimizing,and resilient Industry 5.0 ecosystems,paving the way for next-generation autonomous smart grids and industrial cyber-physical systems.
文摘Background: Erythrodermic psoriasis (EP) is a rare, severe variant of psoriasis characterized by widespread erythema, scaling, and systemic complications. Despite advances in systemic treatments, the management of EP remains challenging, particularly in patients with comorbidities or contraindications to standard therapies. Objectives: To evaluate the effectiveness of ozonated water as an adjunctive treatment for EP, delivered using a patented robotic therapy system designed for hygiene and infection prevention in non-self-sufficient patients. Methods: We report the case of a 90-year-old male patient with acute EP who received daily skin treatments with ozonated water in conjunction with supportive care, including rehydration and antibiotics. The intervention was facilitated by the robotic system “COPERNICO Surveillance & Prevention,” which ensured standardized hygiene practices and clinical documentation. Results: Within one week of treatment, the patient showed complete desquamation of necrotic skin, resolution of erythema, and significant metabolic recovery. Fever subsided, renal function improved, and the patient was discharged in stable condition. Follow-up confirmed sustained clinical improvement, and no adverse events were reported. Conclusions: Ozonated water demonstrated efficacy in alleviating the dermatological and systemic manifestations of EP in a high-risk elderly patient. This case highlights the potential of ozone therapy as a safe, cost-effective adjunctive treatment for EP and underscores the utility of robotic systems in managing complex dermatological conditions. Further research is warranted to validate these findings in larger cohorts.
文摘Low-altitude economy opens up a completely new aerial space for economic growth by enabling brand new services such as fast logistics delivery,timely emergency rescue,and wide-area,high-definition environmental monitoring.This new space has many distinct features and therefore faces many new challenges compared with ground-and high-altitude-based information infrastructures.As a result,the rapid and mass development of unmanned aerial vehicles(UAVs)in low-altitude space will inevitably necessitate research on providing ultra-reliable,low-latency,high-capacity.
基金supported by a grant from the Southwest Medical University’s Program for Creating Popular Science Works(No.00160580).
文摘Background:Previous experiments have demonstrated that hypofractionated radiation therapy(HFRT),low-dose radiation therapy(LDRT),and combined anti-programmed cell death protein 1(αPD-1)can enhance the abscopal effect.Combined with the phenomenon of low prognosis in patients with breast cancer lung metastasis,our study establishes a mouse model and changes the irradiation regimen of LDRT to explore its preventive effect on breast cancer lung metastasis.Methods:The breast cancer subcutaneous graft tumor model was developed.Two-lung prophylactic LDRT was performed prior to the onset of lung metastases,in combination with HFRT(8 Gy,3f),andαPD-1(200μg,4f)therapy.We watched and documented the tumor volume,survival duration,and number of lung metastases.Furthermore,after labeling the corresponding cells using markers,we detected immune-related cell infiltration by immunohistochemistry and flow cytometry,such as T cells.We also determined the expression of cytokines(IFN-γand TNF-α)by enzyme-linked immunosorbent assay.Result:The triple therapy(HFRT+LDRT+αPD-1)resulted in tumor shrinkage and prolonged survival in mice,with median survival extending from 35 to 52 days.The most notable decrease in the quantity of advanced lung metastatic nodules in breast cancer was observed with the triple therapy(HFRT+LDRT+αPD-1)(p<0.05).Furthermore,according to immunohistochemistry and flow cytometry,the triple treatment(HFRT+LDRT+αPD-1)showed the greatest expression of CD8^(+)T cells.Additionally,the ratio of CD8^(+)/CD4^(+)T cells was considerably greater than that of the groups(p<0.0001).Triple therapy(HFRT+LDRT+αPD-1)increased the recruitment of DCs cells,promoted IFN-γand TNF-αexpression,and curbed the aggregation of MDSCs cells(p<0.05).Conclusion:Prophylactic LDRT to the lungs,based on HFRT andαPD-1,can enhance anti-tumor efficacy and prevent advanced lung metastases from breast cancer.The process involves boosting the recruitment of DCs and CD8^(+)T cells,preventing MDSC cell aggregation,and lessening the tumor microenvironment’s immunosuppressive effects.
基金supported by the Beijing Natural Science Foundation(Z200011,L233004)the National Key Research and Development Program(2021YFB2500300)+3 种基金the National Natural Science Foundation of China(52394170,52394171,22109011,22393900,and 22108151)the Tsinghua-Jiangyin Innovation Special Fund(TJISF)(2022JYTH0101)the S&T Program of Hebei(22344402D)the Tsinghua University Initiative Scientific Research Program.
文摘All-solid-state lithium batteries(ASSLBs)are strongly considered as the next-generation energy storage devices for their high energy density and intrinsic safety.The solid-solid contact between lithium metal and solid electrolyte plays a vital role in the performance of working ASSLBs,which is challenging to investigate quantitatively by experimental approach.This work proposed a quantitative model based on the finite element method for electrochemical impedance spectroscopy simulation of different solid-solid contact states in ASSLBs.With the assistance of an equivalent circuit model and distribution of relaxation times,it is discovered that as the number of voids and the sharpness of cracks increase,the contact resistance Rcgrows and ultimately dominates the battery impedance.Through accurate fitting,inverse proportional relations between contact resistance Rcand(1-porosity)as well as crack angle was disclosed.This contribution affords a fresh insight into clarifying solid-solid contact states in ASSLBs.
基金supported by the National Key Research and Development Program of China (2022YFC3302300)National Natural Science Foundation of China under Grant (No.61873309,No.92046024,No.92146002)Shanghai Science and Technology Project under Grant (No.22510761000)。
文摘Blockchain technologies have been used to facilitate Web 3.0 and FinTech applications.However,conventional blockchain technologies suffer from long transaction delays and low transaction success rates in some Web 3.0 and FinTech applications such as Supply Chain Finance(SCF).Blockchain sharding has been proposed to improve blockchain performance.However,the existing sharding methods either use a static sharding strategy,which lacks the adaptability for the dynamic SCF environment,or are designed for public chains,which are not applicable to consortium blockchain-based SCF.To address these issues,we propose an adaptive consortium blockchain sharding framework named ACSarF,which is based on the deep reinforcement learning algorithm.The proposed framework can improve consortium blockchain sharding to effectively reduce transaction delay and adaptively adjust the sharding and blockout strategies to increase the transaction success rate in a dynamic SCF environment.Furthermore,we propose to use a consistent hash algorithm in the ACSarF framework to ensure transaction load balancing in the adaptive sharding system to further improve the performance of blockchain sharding in dynamic SCF scenarios.To evaluate the proposed framework,we conducted extensive experiments in a typical SCF scenario.The obtained experimental results show that the ACSarF framework achieves a more than 60%improvement in user experience compared to other state-of-the-art blockchain systems.
基金funded by the Spanish Ministerio de Ciencia,Innovación y Universidades,grant number RTC2019-007364-3(FPGM)by the Comunidad de Madrid through the direct grant with ref.SI4/PJI/2024-00233 for the promotion of research and technology transfer at the Universidad Autónoma de Madrid。
文摘Due to the continuous increase in global energy demand,photovoltaic solar energy generation and associated maintenance requirements have significantly expanded.One critical maintenance challenge in photovoltaic installations is detecting hot spots,localized overheating defects in solar cells that drastically reduce efficiency and can lead to permanent damage.Traditional methods for detecting these defects rely on manual inspections using thermal imaging,which are costly,labor-intensive,and impractical for large-scale installations.This research introduces an automated hybrid system based on two specialized convolutional neural networks deployed in a cascaded architecture.The first convolutional neural network efficiently detects and isolates individual solar panels from high-resolution aerial thermal images captured by drones.Subsequently,a second,more advanced convolutional neural network accurately classifies each isolated panel as either defective or healthy,effectively distinguishing genuine thermal anomalies from false positives caused by reflections or glare.Experimental validation on a real-world dataset comprising thousands of thermal images yielded exceptional accuracy,significantly reducing inspection time,costs,and the likelihood of false defect detections.This proposed system enhances the reliability and efficiency of photovoltaic plant inspections,thus contributing to improved operational performance and economic viability.
文摘Port structures constitute the main link in the maritime transport chain of coastal countries and therefore contribute to their economic development. But it should be noted that the installation of said works is not without consequences for the countries concerned. Benin, a country in the Gulf of Guinea, is no exception to this phenomenon because, due to its maritime history, it has a heritage of port structures. These structures, built on its coastline, cause a wide variety of environmental problems such as silting and erosion on either side of them. The general objective of this article is to contribute to the proper functionality of port facilities while minimizing environmental problems that may arise. It aims to provide managers with a tool allowing them to fully understand the state of their assets in order to rationalize maintenance actions. In order to achieve this objective, an assessment of the state of the structure, and then a structural diagnosis are necessary and recommendations can be established to restore the level of service of the latter. As a result, two examples were presented: the wharf of the Sèmè-Podji pipeline project and the maritime piles project of the Wasco de Gama bridge (control project), and recommendations adapted to this objective were established. The comparative analysis of the two examples, both maritime works, revealed an under-sizing at the level of the spans of the wharf bridge of the Sèmè-Podji pipeline project (spans of 7 m in length), while these spans vary on average by 45 m to 62 m for the Wasco da Gama bridge. Bringing the piles closer together at the Sèmè-Podji wharf reduces the energy of the current which promotes the accumulation of sediment. The structure no longer experiences a flow capable of setting in motion the sands accumulated since at least 2022. This element appears to be a fundamental characteristic explaining the erosion observed to the east of the structure.
基金sponsored by the National Natural Science Foundation of China(Nos.5210125 and 52375422)the Science Research Project of Hebei Education Department(No.BJK2023058)the Natural Science Foundation of Hebei Province(Nos.E2020208069,B2020208083 and E202320801).
文摘The stability and electrocatalytic efficiency of transition metal oxides for water splitting is determined by geometric and electronic structure,especially under high current densities.Herein,a newly designed lamella-heterostructured nanoporous CoFe/CoFe_(2)O_(4) and CeO_(2−x),in situ grown on nickel foam(NF),holds great promise as a high-efficient bifunctional electrocatalyst(named R-CoFe/Ce/NF)for water splitting.Experimental characterization verifies surface reconstruction from CoFe alloy/oxide to highly active CoFeOOH during in situ electrochemical polarization.By virtues of three-dimensional nanoporous architecture and abundant electroactive CoFeOOH/CeO_(2−x) heterostructure interfaces,the R-CoFe/Ce/NF electrode achieves low overpotentials for oxygen evolution(η_(10)=227 mV;η_(500)=450 mV)and hydrogen evolution(η_(10)=35 mV;η_(408)=560 mV)reactions with high normalized electrochemical active surface areas,respectively.Additionally,the alkaline full water splitting electrolyzer of R-CoFe/Ce/NF||R-CoFe/Ce/NF achieves a current density of 50 mA·cm^(−2) only at 1.75 V;the decline of activity is satisfactory after 100-h durability test at 300 mA·cm^(−2).Density functional theory also demonstrates that the electron can transfer from CeO_(2−x) by virtue of O atom to CoFeOOH at CoFeOOH/CeO_(2−x) heterointerfaces and enhancing the adsorption of reactant,thus optimizing electronic structure and Gibbs free energies for the improvement of the activity for water splitting.
基金supported by the Researchers Supporting Project of King Saud University,Riyadh,Saudi Arabia,under Project RSPD2025R681。
文摘In optical metro-access networks,Access Points(APs)and Data Centers(DCs)are located on the fiber ring.In the cloud-centric solution,a large number of Internet of Things(IoT)data pose an enormous burden on DCs,so the Virtual Machines(VMs)cannot be successfully launched due to the server overload.In addition,transferring the data from the AP to the remote DC may cause an undesirable delivery delay.For this end,we propose a promising solution considering the interplay between the cloud DC and edge APs.More specifically,bringing the partial capability of computing in APs close to things can reduce the pressure of DCs while guaranteeing the expected Quality of Service(QoS).In this work,when the cloud DC resource becomes limited,especially for delay sensitive but not computing-dependent IoT applications,we degrade their VMs and migrate them to edge APs instead of the remote DC.To avoid excessive VM degradation and computing offloading,we derive appropriate VM degradation coefficients based on classic microeconomic theory.Simulation results demonstrate that our algorithms improve the service providers'utility with the ratio from 34%to 89%over traditional cloud-centric solutions.
文摘Extensive research efforts have been dedicated to enhancing the corrosion resistance properties and mitigating the reactivity of metals.Various surface modification techniques,including anodizing,electrochemical deposition,non-electric current-based coating,ion implantation,conversion coatings,and organic coatings,have been explored for these objectives.Among these surface modification methodologies,conversion coatings have garnered substantial attention due to their capacity of depositing onto metal surfaces uniformly,enhancing adhesion for subsequent layers,facilitating ease of application,and offering cost-effectiveness.Different coatings,each possessing distinct properties,have been employed for diverse metals.Nonetheless,in light of hexavalent chromium compounds'elevated toxicity and carcinogenic nature,environmentally compatible conversion coatings are actively sought as viable alternatives.The deposition of conversion coatings is an intricate process influenced by several variables.Consequently,laboratory optimization of conditions is imperative for commercializing specialized conversion coatings.Further research endeavors are warranted to expand the standards and requisite characteristics for specific applications.Composite conversion coatings,formulated with rare earth elements and innovative mineral and organic transformative agents,hold promise as a subject of investigation.Advanced studies in theoretical research and computer modeling about the mechanism of crack prevention in rare earth element-based co nversion coatings devoid of defects will yield valuable insights.The present work furnishes a comprehensive review of this subject,marking its inaugural examination.
基金supported by the National Natural Science Foundation of China(Grant No.52374043)the Southwest Oil&Gas Field Branch in PetroChina(Grant No.JS2023-115)。
文摘Over more than a decade of development,medium to deep shale gas reservoirs have faced rapid production declines,making sustained output challenging.To harness remaining reserves effectively,advanced fracturing techniques such as infill drilling are essential.This study develops a complex fracture network model for dual horizontal wells and a four-dimensional in-situ stress evolution model,grounded in elastic porous media theory.These models simulate and analyze the evolution of formation pore pressure and in-situ stress during production.The investigation focuses on the influence of infill well fracturing timing on fracture propagation patterns,individual well productivity,and the overall productivity of well clusters.The findings reveal that,at infill well locations,the maximum horizontal principal stress undergoes the most significant reduction,while changes in the minimum horizontal principal stress and vertical stress remain minimal.The horizontal stress surrounding the infill well may reorient,potentially transitioning the stress regime from strike-slip to normal faulting.Delays in infill well fracturing increase lateral fracture deflection and diminish fracture propagation between wells.Considering the stable production phase and cumulative gas output of the well group,the study identifies an optimal timing for infill fracturing.Notably,larger well spacing shifts the optimal timing to a later stage.
文摘The problems noted in the structures built on wooden foundation piles in a lake environment required various works to strengthen over time.This work mainly consists of the recovery of the foundation mass by micropiles due to the increase in loads on the structures,or the recovery of the foundation mass by injection,which is carried out when voids form between the ground and the wooden foundation elements.The high cost of foundation reinforcement methods led the National Agency for the Development of Tourist Heritage in Benin(ANPT)to replace the wooden foundation piles with reinforced concrete piles in the implementation of the project“reinventing the lakeside city of Ganvié”.This article presents an artisanal technology for the creation of reinforced concrete foundation piles in a lake environment.On-site examples made it possible to evaluate the performance of this artisanal implementation technique.The installation of these piles is carried out following manual drilling,followed by the installation of reinforcement and the pouring of concrete on site.The implementation of reinforced concrete foundation piles in place of the wooden ones studied in this article only impacted the infrastructure of the homes of the lakeside town of Ganviébut not the superstructure,which preserved the old traditional wooden architecture and thatched roofs.Thus,the ambition to move this city of Ganviéfrom the stage of a lake village to that of a floating city is very successful.This will contribute to improving the environment and living conditions of the populations and will promote economic development through tourism.
基金supported by the Inner Mongolia Power Company 2024 Staff Innovation Studio Innovation Project“Research on Cluster Output Prediction and Group Control Technology for County-Wide Distributed Photovoltaic Construction”.
文摘Responding to the stochasticity and uncertainty in the power height of distributed photovoltaic power generation.This paper presents a distributed photovoltaic ultra-short-term power forecasting method based on Variational Mode Decomposition(VMD)and Channel Attention Mechanism.First,Pearson’s correlation coefficient was utilized to filter out the meteorological factors that had a high impact on historical power.Second,the distributed PV power data were decomposed into a relatively smooth power series with different fluctuation patterns using variational modal decomposition(VMD).Finally,the reconstructed distributed PV power as well as other features are input into the combined CNN-SENet-BiLSTM model.In this model,the convolutional neural network(CNN)and channel attention mechanism dynamically adjust the weights while capturing the spatial features of the input data to improve the discriminative ability of key features.The extracted data is then fed into the bidirectional long short-term memory network(BiLSTM)to capture the time-series features,and the final output is the prediction result.The verification is conducted using a dataset from a distributed photovoltaic power station in the Northwest region of China.The results show that compared with other prediction methods,the method proposed in this paper has a higher prediction accuracy,which helps to improve the proportion of distributed PV access to the grid,and can guarantee the safe and stable operation of the power grid.
基金the support of the University College Dublin through a John Sheridan Scholarship.Min Wan thanks 4TU.RECENTRE program(Award No.OA102070)the National Growth Fund programme PhotonDelta in The Netherlands.
文摘In recent years,many phase space distributions have been proposed,and one of the more independently interesting is the Bai distribution function(BDF).The BDF has been shown to interpolate between the instantaneous auto-correlation function and the Wigner distribution function,and be applied in linear frequency modulated signal parameter estimation and optical partial coherence areas.Currently,the BDF is only defined for continuous signals.However,for both simulation and experimental purposes,the signals must be discrete.This necessitates the development of a BDF analysis workflow for discrete signals.In this work,we analyze the sampling requirements imposed by the BDF and demonstrate their correctness by comparing the continuous BDFs of continuous test signals with their numerically approximated counterparts.Our results permit more accurate simulations using BDFs,which will be useful in applying them to problems such as partial coherence.
