Accurately counting dense objects in complex and diverse backgrounds is a significant challenge in computer vision,with applications ranging from crowd counting to various other object counting tasks.To address this,w...Accurately counting dense objects in complex and diverse backgrounds is a significant challenge in computer vision,with applications ranging from crowd counting to various other object counting tasks.To address this,we propose HUANNet(High-Resolution Unified Attention Network),a convolutional neural network designed to capture both local features and rich semantic information through a high-resolution representation learning framework,while optimizing computational distribution across parallel branches.HUANNet introduces three core modules:the High-Resolution Attention Module(HRAM),which enhances feature extraction by optimizing multiresolution feature fusion;the Unified Multi-Scale Attention Module(UMAM),which integrates spatial,channel,and convolutional kernel information through an attention mechanism applied across multiple levels of the network;and the Grid-Assisted Point Matching Module(GPMM),which stabilizes and improves point-to-point matching by leveraging grid-based mechanisms.Extensive experiments show that HUANNet achieves competitive results on the ShanghaiTech Part A/B crowd counting datasets and sets new state-of-the-art performance on dense object counting datasets such as CARPK and XRAY-IECCD,demonstrating the effectiveness and versatility of HUANNet.展开更多
Background:Diabetic foot,a severe complication of diabetes,is characterized by chronic refractory wounds.Sanhuang Oil,a topical herbal formula,demonstrates significant therapeutic effects including antibacterial,anti-...Background:Diabetic foot,a severe complication of diabetes,is characterized by chronic refractory wounds.Sanhuang Oil,a topical herbal formula,demonstrates significant therapeutic effects including antibacterial,anti-inflammatory,and immunomodulatory activities.However,its active constituents and mechanisms of action against diabetic foot remain to be elucidated.Methods:In this study,the chemical constituents of Sanhuang Oil were identified using UPLC-QE-Orbitrap-MS.Subsequently,the mechanism by which Sanhuang Oil promotes diabetic foot ulcer healing was predicted by integrating network pharmacology and molecular docking.Additionally,diabetic mouse model was established in ICR mice using a combination of a high-fat diet(HFD)and streptozotocin(STZ)chemical induction.A full-thickness skin defect was created on the dorsum of the mice.Wound healing and the healing rate were observed following Sanhuang Oil intervention.The mechanism underlying Sanhuang Oil’s promotion of diabetic ulcer healing was further investigated using transcriptomics and histopathological examination(H&E staining).Results:A total of 97 active ingredients were identified from Sanhuang Oil.Network pharmacology analysis predicted 543 common targets,and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis identified 203 relevant pathways.Molecular docking further confirmed high binding affinity(binding energy≤−5.0 kcal/mol)between specific active components in Sanhuang Oil(e.g.,coptisine,phellodendrine,baicalein)and key targets associated with diabetic foot ulcers(e.g.,EGFR,AKT1,STAT3).In vivo experimental results demonstrated that the wound healing rate was significantly higher in Sanhuang Oil-treated groups compared to the model group(P<0.001).HE staining revealed that the high-dose Sanhuang Oil group exhibited more pronounced epithelial tissue coverage over the wound,reduced inflammatory cell infiltration,and increased collagen deposition and fibroblast proliferation.transcriptomic analysis identified Pdk4,Ttn,Csrp3,Actn2,Myoz2,Tnnc2,Myod1,Myog,Myot,and Myf6 as key regulatory proteins involved in promoting wound healing.Conclusion:Sanhuang Oil promotes wound healing in diabetic ulcer mice,potentially by mitigating inflammation and regulating key targets such as Pdk4 to enhance fibroblast function.These findings provide novel insights into the multi-target,multi-pathway mechanism of Sanhuang Oil for treating diabetic foot ulcers.展开更多
The Ordos Basin is a large superimposed hydrocarbon-bearing basin in China,and further research on the sedimentary characteristics and sedimentary evolution of the sequence framework of target layers is of great theor...The Ordos Basin is a large superimposed hydrocarbon-bearing basin in China,and further research on the sedimentary characteristics and sedimentary evolution of the sequence framework of target layers is of great theoretical and practical significance for guiding oil and gas exploration.The sedimentary facies and sedimentary evolution of the high-resolution sequence framework of the Carboniferous Taiyuan Formation in the Hangjinqi area have been systematically analyzed for the first time by drilling,logging and seismic data.The results show that four types of sequence interfaces can be identified in the Taiyuan Formation:regional unconformity surfaces,scour surfaces,lithologic-lithofacies transformation surfaces and flooding surfaces.According to the sedimentary response caused by the upward and downward movements of the base level at different levels,the Taiyuan Formation can be divided into 2 long-term cycles(LSC_(1)-LSC_(2)),4 mid-term cycles(MSC_(1)-MSC4)and 7 short-term cycles(SSC_(1)-SSC7).The long-and mid-term cycles correspond to members T_(1)and T_(2)and layers T_(1)-1,T_(1-2),T_(2-1),and T_(2)-2,respectively.Long-term cycles are dominated by C_(1);mid-term cycles are dominated by C_(1)and C_(2),followed by A2;and short-term cycles are dominated by C_(1),C_(2),A1 and A2.Under the high-resolution sequence stratigraphic framework,the Hangjinqi area underwent a transformation of fan delta and tidal flat depositional systems during the Taiyuan Formation sedimentary period.In the MSC_(1)-MSC_(2)stage,owing to a large-scale paleocontinent,the fan delta sedimentary body,which was limited in scale and scope,developed only in the southeastern corner and gradually transitioned basinward to tidal flat facies.In the MSC3-MSC4 stage,as the paleocontinent continuously decreased and the sedimentary range expanded,fan-delta plain sedimentation began in the study area.Several braided distributary channels with poor connectivity developed on the fan-delta plain,and between them were floodplains and peat swamps.展开更多
High-resolution transmission electron microscopy(HRTEM)promises rapid atomic-scale dynamic structure imaging.Yet,the precision limitations of aberration parameters and the challenge of eliminating aberrations in Cs-co...High-resolution transmission electron microscopy(HRTEM)promises rapid atomic-scale dynamic structure imaging.Yet,the precision limitations of aberration parameters and the challenge of eliminating aberrations in Cs-corrected transmission electron microscopy constrain resolution.A machine learning algorithm is developed to determine the aberration parameters with higher precision from small,lattice-periodic crystal images.The proposed algorithm is then validated with simulated HRTEM images of graphene and applied to the experimental images of a molybdenum disulfide(MoS_(2))monolayer with 25 variables(14 aberrations)resolved in wide ranges.Using these measured parameters,the phases of the exit-wave functions are reconstructed for each image in a focal series of MoS_(2)monolayers.The images were acquired due to the unexpected movement of the specimen holder.Four-dimensional data extraction reveals time-varying atomic structures and ripple.In particular,the atomic evolution of the sulfur-vacancy point and line defects,as well as the edge structure near the amorphous,is visualized as the resolution has been improved from about 1.75?to 0.9 A.This method can help salvage important transmission electron microscope images and is beneficial for the images obtained from electron microscopes with average stability.展开更多
The objective of image-based virtual try-on is to seamlessly integrate clothing onto a target image, generating a realistic representation of the character in the specified attire. However, existing virtual try-on met...The objective of image-based virtual try-on is to seamlessly integrate clothing onto a target image, generating a realistic representation of the character in the specified attire. However, existing virtual try-on methods frequently encounter challenges, including misalignment between the body and clothing, noticeable artifacts, and the loss of intricate garment details. To overcome these challenges, we introduce a two-stage high-resolution virtual try-on framework that integrates an attention mechanism, comprising a garment warping stage and an image generation stage. During the garment warping stage, we incorporate a channel attention mechanism to effectively retain the critical features of the garment, addressing challenges such as the loss of patterns, colors, and other essential details commonly observed in virtual try-on images produced by existing methods. During the image generation stage, with the aim of maximizing the utilization of the information proffered by the input image, the input features undergo double sampling within the normalization procedure, thereby enhancing the detail fidelity and clothing alignment efficacy of the output image. Experimental evaluations conducted on high-resolution datasets validate the effectiveness of the proposed method. Results demonstrate significant improvements in preserving garment details, reducing artifacts, and achieving superior alignment between the clothing and body compared to baseline methods, establishing its advantage in generating realistic and high-quality virtual try-on images.展开更多
[Objective]The paper aimed to effectively reduce the occurrence of bacterial resistance associated with breeding practices and to mitigate food safety risks by controlling the illegal use of veterinary drugs in self-f...[Objective]The paper aimed to effectively reduce the occurrence of bacterial resistance associated with breeding practices and to mitigate food safety risks by controlling the illegal use of veterinary drugs in self-formulated feed at the source.[Method]A screening database comprising 274 illegally added chemical drugs in self-formulated feed was established utilizing ultra-performance liquid chromatography coupled with quadrupole/electrostatic field orbitrap high-resolution mass spectrometry(HPLC-Q-Exactive Focus/MS).Subsequently,253 batches of self-formulated feed samples from various farms in Hebei Province were screened and quantitatively analyzed.[Result]The screening results indicated the presence of 8 pharmaceutical components across 10 batches of self-formulated feed samples,with a detection rate of 3.2%and concentrations ranging from 0.06 to 28851.8μg/g.[Conclusion]The application of high-resolution mass spectrometry is feasible and highly significant for the risk monitoring of illegally added drugs in self-formulated feed.展开更多
In oceanic and atmospheric science,finer resolutions have become a prevailing trend in all aspects of development.For high-resolution fluid flow simulations,the computational costs of widely used numerical models incr...In oceanic and atmospheric science,finer resolutions have become a prevailing trend in all aspects of development.For high-resolution fluid flow simulations,the computational costs of widely used numerical models increase significantly with the resolution.Artificial intelligence methods have attracted increasing attention because of their high precision and fast computing speeds compared with traditional numerical model methods.The resolution-independent Fourier neural operator(FNO)presents a promising solution to the still challenging problem of high-resolution fluid flow simulations based on low-resolution data.Accordingly,we assess the potential of FNO for high-resolution fluid flow simulations using the vorticity equation as an example.We assess and compare the performance of FNO in multiple high-resolution tests varying the amounts of data and the evolution durations.When assessed with finer resolution data(even up to number of grid points with 1280×1280),the FNO model,trained at low resolution(number of grid points with 64×64)and with limited data,exhibits a stable overall error and good accuracy.Additionally,our work demonstrates that the FNO model takes less time than the traditional numerical method for high-resolution simulations.This suggests that FNO has the prospect of becoming a cost-effective and highly precise model for high-resolution simulations in the future.Moreover,FNO can make longer high-resolution predictions while training with less data by superimposing vorticity fields from previous time steps as input.A suitable initial learning rate can be set according to the frequency principle,and the time intervals of the dataset need to be adjusted according to the spatial resolution of the input when training the FNO model.Our findings can help optimize FNO for future fluid flow simulations.展开更多
While algorithms have been created for land usage in urban settings,there have been few investigations into the extraction of urban footprint(UF).To address this research gap,the study employs several widely used imag...While algorithms have been created for land usage in urban settings,there have been few investigations into the extraction of urban footprint(UF).To address this research gap,the study employs several widely used image classification method classified into three categories to evaluate their segmentation capabilities for extracting UF across eight cities.The results indicate that pixel-based methods only excel in clear urban environments,and their overall accuracy is not consistently high.RF and SVM perform well but lack stability in object-based UF extraction,influenced by feature selection and classifier performance.Deep learning enhances feature extraction but requires powerful computing and faces challenges with complex urban layouts.SAM excels in medium-sized urban areas but falters in intricate layouts.Integrating traditional and deep learning methods optimizes UF extraction,balancing accuracy and processing efficiency.Future research should focus on adapting algorithms for diverse urban landscapes to enhance UF extraction accuracy and applicability.展开更多
Understanding vegetation water availability can be important for managing vegetation and combating climate change.Changes in vegetation water availability throughout China remains poorly understood,especially at a hig...Understanding vegetation water availability can be important for managing vegetation and combating climate change.Changes in vegetation water availability throughout China remains poorly understood,especially at a high spatial resolution.Standardized Precipitation Evapotranspiration Index(SPEI)is an ideal water availability index for assessing the spatiotemporal characteristics of drought and investigating the vegetation-water availability relationship.However,no high-resolution and long-term SPEI datasets over China are available.To fill this gap,we developed a new model based on machine learning to obtain high-resolution(1 km)SPEI data by combining climate variables with topographical and geographical features.Here,we analyzed the long-term drought over the past century(1901–2020)and vegetation-water availability relationship in the past two decades(2000–2020).The century-long drought trend analyses indicated an overall drying trend across China with increasing drought frequency,duration,and severity during the past century.We found that drought events in 1901–1961 showed a larger increase than that in 1961–2020,with the Qinghai-Xizang Plateau showing a significant drying trend during 1901–1960 but a wetting trend during 1961–2020.There were 13.90%and 28.21%of vegetation in China showing water deficit and water surplus respectively during 2000–2020.The water deficit area significantly shrank from 2000 to 2020 across China,which is dominated by the significant decrease in water deficit areas in South China.Among temperature,precipitation,and vegetation abundance,temperature is the most important factor for the vegetation-water availability dynamics in China over the past two decades,with high temperature contributing to water deficit.Our findings are important for water and vegetation management under a warming climate.展开更多
BACKGROUND Rectal cancer is a common malignant tumor of the digestive system,with older patients representing the predominantly affected population.Magnetic resonance imaging(MRI)has been widely applied in preoperativ...BACKGROUND Rectal cancer is a common malignant tumor of the digestive system,with older patients representing the predominantly affected population.Magnetic resonance imaging(MRI)has been widely applied in preoperative tumor assessment;however,the value of high-resolution MRI(HR-MRI)combined with dynamic contrast-enhanced(DCE)scanning in the preoperative diagnosis of rectal cancer in older patients remains unclear.AIM To evaluate the value of HR-MRI combined with DCE scanning in the preoperative diagnosis of rectal cancer in older patients.METHODS This retrospective study included 148 consecutive older female patients with rectal cancer who were treated at our hospital between December 2020 and December 2024.Clinical data and HR-MRI and DCE scan findings were collected.Histopathological examination after surgical resection served as the gold standard.The diagnostic accuracy of MRI for preoperative T and N staging was calculated.Consistency,sensitivity,and specificity between HR-MRI combined with DCE scanning and pathological staging were analyzed using the k test.Among the 148 patients,the overall accuracy of T staging was 84.5%.Sensitivity for T1,T2,T3,and T4 staging was 75.00%,62.50%,89.47%,and 90.48%,respectively,whereas specificity was 100.00%,94.35%,79.25%,and 96.06%,respectively.T staging based on HR-MRI combined with DCE scanning showed good agreement with pathological staging(k=0.8176,P<0.001).For N staging,sensitivity and specificity were 54.88%and 84.85%for N0,36.96%and 72.55%for N1,and 70.00%and 73.44%for N2,respectively;agreement with pathological N staging was poor(k=0.259,P<0.001).CONCLUSION HR-MRI combined with DCE scanning demonstrates high diagnostic accuracy for T staging of rectal cancer in older patients and can provide a theoretical basis for treatment planning.However,its diagnostic accuracy for N staging requires improvement.展开更多
High-resolution sub-meter satellite data play an increasingly crucial role in the 3D real-scene China construction initiative.Current research on 3D reconstruction using high-resolution satellite data primarily focuse...High-resolution sub-meter satellite data play an increasingly crucial role in the 3D real-scene China construction initiative.Current research on 3D reconstruction using high-resolution satellite data primarily focuses on two approaches:Multi-stereo fusion and multi-view matching.While algorithms based on these two methodologies for multi-view image 3D reconstruction have reached relative maturity,no systematic comparison has been conducted specifically on satellite data to evaluate the relative merits of multi-stereo fusion versus multi-view matching methods.This paper conducts a comparative analysis of the practical accuracy of both approaches using high-resolution satellite datasets from diverse geographical regions.To ensure fairness in accuracy comparison,both methodologies employ non-local dense matching for cost optimization.Results demonstrate that the multi-stereo fusion method outperforms multi-view matching in all evaluation metrics,exhibiting approximately 1.2%higher average matching accuracy and 10.7%superior elevation precision in the experimental datasets.Therefore,for 3D modeling applications using satellite data,we recommend adopting the multi-stereo fusion approach for digital surface model(DSM)product generation.展开更多
During drilling operations,the low resolution of seismic data often limits the accurate characterization of small-scale geological bodies near the borehole and ahead of the drill bit.This study investigates high-resol...During drilling operations,the low resolution of seismic data often limits the accurate characterization of small-scale geological bodies near the borehole and ahead of the drill bit.This study investigates high-resolution seismic data processing technologies and methods tailored for drilling scenarios.The high-resolution processing of seismic data is divided into three stages:pre-drilling processing,post-drilling correction,and while-drilling updating.By integrating seismic data from different stages,spatial ranges,and frequencies,together with information from drilled wells and while-drilling data,and applying artificial intelligence modeling techniques,a progressive high-resolution processing technology of seismic data based on multi-source information fusion is developed,which performs simple and efficient seismic information updates during drilling.Case studies show that,with the gradual integration of multi-source information,the resolution and accuracy of seismic data are significantly improved,and thin-bed weak reflections are more clearly imaged.The updated seismic information while-drilling demonstrates high value in predicting geological bodies ahead of the drill bit.Validation using logging,mud logging,and drilling engineering data ensures the fidelity of the processing results of high-resolution seismic data.This provides clearer and more accurate stratigraphic information for drilling operations,enhancing both drilling safety and efficiency.展开更多
Electron beam injectors are pivotal components of large-scale scientific instruments,such as synchrotron radiation sources,free-electron lasers,and electron-positron colliders.The quality of the electron beam produced...Electron beam injectors are pivotal components of large-scale scientific instruments,such as synchrotron radiation sources,free-electron lasers,and electron-positron colliders.The quality of the electron beam produced by the injector critically influences the performance of the entire accelerator-based scientific research apparatus.The injectors of such facilities usually use photocathode and thermionic-cathode electron guns.Although the photocathode injector can produce electron beams of excellent quality,its associated laser system is massive and intricate.The thermionic-cathode electron gun,especially the gridded electron gun injector,has a simple structure capable of generating numerous electron beams.However,its emittance is typically high.In this study,methods to reduce beam emittance are explored through a comprehensive analysis of various grid structures and preliminary design results,examining the evolution of beam phase space at different grid positions.An optimization method for reducing the emittance of a gridded thermionic-cathode electron gun is proposed through theoretical derivation,electromagnetic-field simulation,and beam-dynamics simulation.A 50%reduction in emittance was achieved for a 50 keV,1.7 A electron gun,laying the foundation for the subsequent design of a high-current,low-emittance injector.展开更多
In December 2025,the ASEAN Centre for Energy(ACE)convened the third ASEAN Power Grid Partnership Meeting,bringing partners together for consultations on key issues.After more than two decades of planning and explorati...In December 2025,the ASEAN Centre for Energy(ACE)convened the third ASEAN Power Grid Partnership Meeting,bringing partners together for consultations on key issues.After more than two decades of planning and exploration,the ASEAN Power Grid is now entering a new phase—shifting from predominantly bilateral,one-way connections toward a multilateral,multidirectional network.展开更多
Managing massive data flows effectively and resolving spectrum shortages are two challenges that smart grid communication networks(SGCN)must overcome.To address these problems,we provide a combined optimization approa...Managing massive data flows effectively and resolving spectrum shortages are two challenges that smart grid communication networks(SGCN)must overcome.To address these problems,we provide a combined optimization approach that makes use of cognitive radio(CR)and non-orthogonal multiple access(NOMA)technologies.Our work focuses on using user pairing(UP)and power allocation(PA)techniques to maximize energy efficiency(EE)in SGCN,particularly within neighbourhood area networks(NANs).We develop a joint optimization problem that takes into account the real-world limitations of a CR-NOMA setting.This problem is NP-hard,nonlinear,and nonconvex by nature.To address the computational complexity of the problem,we use the block coordinate descent(BCD)method,which breaks the problem into UP and PA subproblems.Initially,we proposed the zebra-optimization user pairing(ZOUP)algorithm to tackle the UP problem,which outperforms both orthogonal multiple access(OMA)and non-optimized NOMA(UPWO)by 78.8%and13.6%,respectively,at a SNR of 15 dB.Based on the ZOUP pairs,we subsequently proposed the PA approach,i.e.,ZOUPPA,which significantly outperforms UPWO and ZOUP by 53.2%and 25.4%,respectively,at an SNR of 15 dB.A detailed analysis of key parameters,including varying SNRs,power allocation constants,path loss exponents,user density,channel availability,and coverage radius,underscores the superiority of our approach.By facilitating the effective use of communication resources in SGCN,our research opens the door to more intelligent and energy-efficient grid systems.Our work tackles important issues in SGCN and lays the groundwork for future developments in smart grid communication technologies by combining modern optimization approaches with CR-NOMA.展开更多
Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the ...Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the configuration of photovoltaic(3.8 MW),wind power(2.5 MW),energy storage(2.2 MWh),and SVC(1.2 Mvar)through interaction between upper and lower layers,and modifies lines 2–3,8–9,etc.to improve transmission capacity and voltage stability.The author uses normal distribution and Monte Carlo method to model load uncertainty,and combines Weibull distribution to describe wind speed characteristics.Compared to the traditional three-layer model(TLM),Benders decomposition-based two-layer model(BLBD)has a 58.1%reduction in convergence time(5.36 vs.12.78 h),a 51.1%reduction in iteration times(23 vs.47 times),a 8.07%reduction in total cost(12.436 vs.13.528 million yuan),and a 9.62%reduction in carbon emissions(12,456 vs.13,782 t).After optimization,the peak valley difference decreased from4.1 to 2.9MW,the renewable energy consumption rate reached 93.4%,and the energy storage efficiency was 87.6%.Themodel has been validated in the IEEE 33 node system,demonstrating its superiority in terms of economy,low-carbon,and reliability.展开更多
Owing to the development of communication technologies and control systems,the integration of numerous Internet of Things(IoT)nodes into the power grid has become increasingly prevalent.These nodes are deployed to gat...Owing to the development of communication technologies and control systems,the integration of numerous Internet of Things(IoT)nodes into the power grid has become increasingly prevalent.These nodes are deployed to gather operational data from various distributed energy sources and monitor real-time energy consumption,thereby transforming the traditional power grid into a smart grid(SG).However,the openness of wireless communication channels introduces vulnerabilities,as it allows potential eavesdroppers to intercept sensitive information.This poses threats to the secure and efficient operation of the IoT-driven smart grid.To address these challenges,we propose a novel scenario that incorporates an Unmanned Aerial Vehicle(UAV)as a relay gateway for multiple authorized smart meters.This scenario is further enhanced by the integration of Reconfigurable Intelligent Surface(RIS)technology,which dynamically adjusts the direction of information transmission.Our objective is to maximize the secure rate within this UAV-RIS-aided system with multiple authorized smart meters and an eavesdropper based on physical layer security(PLS)techniques.We formulate the problem of secure rate maximization by jointly optimizing the active beamforming of the UAV,the passive beamforming of the RIS,and the UAV’s trajectory.To solve this complex optimization problem,we introduce the Twin Soft Actor-Critic(TSAC)algorithm.This algorithm employs a dual-agent framework,where Agent 1 focuses on optimizing the beamforming for both the UAV and the RIS,while Agent 2 concurrently searches for the optimal trajectory of the UAV.Simulation results demonstrate the TSAC algorithm significantly enhances the secure rate of the system,achieving faster convergence and higher rewards under the worst communication conditions.The TSAC algorithm consistently outperforms the Twin Deep Deterministic Policy Gradient(TDDPG)and Twin Delayed Deep Deterministic Policy Gradient(TTD3)algorithms.Furthermore,the TSAC algorithm exhibits robust performance when the distribution of smart meters follows a Gaussian distribution,further validating its practical applicability and effectiveness in real-world scenarios.展开更多
Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The prese...Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The present article outlines the TransCarbonNet,a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory(Bi-LSTM)network to forecast the carbon intensity of the grid several days.The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data;hence,it is able to give suitable forecasts over a period of seven days.TransCarbonNet takes advantage of a multi-head self-attention element to identify significant temporal connections,which means the Bi-LSTM element calculates sequential dependencies in both directions.Massive tests on two actual data sets indicate much improved results in comparison with the existing results,with mean relative errors of 15.3 percent and 12.7 percent,respectively.The framework has given explicable weights of attention that reveal critical periods that influence carbon intensity alterations,and informed decisions on the management of carbon sustainability.The effectiveness of the proposed solution has been validated in numerous cases of operations,and TransCarbonNet is established to be an effective tool when it comes to carbon-friendly optimization of the grid.展开更多
Modern power systems increasingly depend on interconnected microgrids to enhance reliability and renewable energy utilization.However,the high penetration of intermittent renewable sources often causes frequency devia...Modern power systems increasingly depend on interconnected microgrids to enhance reliability and renewable energy utilization.However,the high penetration of intermittent renewable sources often causes frequency deviations,voltage fluctuations,and poor reactive power coordination,posing serious challenges to grid stability.Conventional Interconnection FlowControllers(IFCs)primarily regulate active power flowand fail to effectively handle dynamic frequency variations or reactive power sharing in multi-microgrid networks.To overcome these limitations,this study proposes an enhanced Interconnection Flow Controller(e-IFC)that integrates frequency response balancing and an Interconnection Reactive Power Flow Controller(IRFC)within a unified adaptive control structure.The proposed e-IFC is implemented and analyzed in DIgSILENT PowerFactory to evaluate its performance under various grid disturbances,including frequency drops,load changes,and reactive power fluctuations.Simulation results reveal that the e-IFC achieves 27.4% higher active power sharing accuracy,19.6% lower reactive power deviation,and 18.2% improved frequency stability compared to the conventional IFC.The adaptive controller ensures seamless transitions between grid-connected and islanded modes and maintains stable operation even under communication delays and data noise.Overall,the proposed e-IFCsignificantly enhances active-reactive power coordination and dynamic stability in renewable-integrated multi-microgrid systems.Future research will focus on coupling the e-IFC with tertiary-level optimization frameworks and conducting hardware-in-the-loop validation to enable its application in large-scale smart microgrid environments.展开更多
基金funded by the National Natural Science Foundation of China(62273213,62472262,62572287)Natural Science Foundation of Shandong Province(ZR2024MF144)+1 种基金Natural Science Foundation of Shandong Province for Innovation and Development Joint Funds(ZR2022LZH001)Taishan Scholarship Construction Engineering.
文摘Accurately counting dense objects in complex and diverse backgrounds is a significant challenge in computer vision,with applications ranging from crowd counting to various other object counting tasks.To address this,we propose HUANNet(High-Resolution Unified Attention Network),a convolutional neural network designed to capture both local features and rich semantic information through a high-resolution representation learning framework,while optimizing computational distribution across parallel branches.HUANNet introduces three core modules:the High-Resolution Attention Module(HRAM),which enhances feature extraction by optimizing multiresolution feature fusion;the Unified Multi-Scale Attention Module(UMAM),which integrates spatial,channel,and convolutional kernel information through an attention mechanism applied across multiple levels of the network;and the Grid-Assisted Point Matching Module(GPMM),which stabilizes and improves point-to-point matching by leveraging grid-based mechanisms.Extensive experiments show that HUANNet achieves competitive results on the ShanghaiTech Part A/B crowd counting datasets and sets new state-of-the-art performance on dense object counting datasets such as CARPK and XRAY-IECCD,demonstrating the effectiveness and versatility of HUANNet.
基金supported by the Natural Science Foundation of Hubei Provincial Department of Education(D20232101)Shandong Second Medical University 2024 Affiliated Hospital(Teaching Hospital)Scientific Research Development Fund Project(2024FYQ026)+3 种基金the innovative Research Programme of Xiangyang No.1 People’s Hospital(XYY2023ZY01)Faculty Development Grants of Xiangyang No.1 People’s Hospital Affiliated to Hubei University of Medicine(XYY2023D05)Joint supported by Hubei Provincial Natural Science Foundation and Xiangyang of China(2025AFD091)Traditional Chinese Medicine Scientific Research Project of Hubei Provincial Administration of Traditional Chinese Medicine(ZY2025D019).
文摘Background:Diabetic foot,a severe complication of diabetes,is characterized by chronic refractory wounds.Sanhuang Oil,a topical herbal formula,demonstrates significant therapeutic effects including antibacterial,anti-inflammatory,and immunomodulatory activities.However,its active constituents and mechanisms of action against diabetic foot remain to be elucidated.Methods:In this study,the chemical constituents of Sanhuang Oil were identified using UPLC-QE-Orbitrap-MS.Subsequently,the mechanism by which Sanhuang Oil promotes diabetic foot ulcer healing was predicted by integrating network pharmacology and molecular docking.Additionally,diabetic mouse model was established in ICR mice using a combination of a high-fat diet(HFD)and streptozotocin(STZ)chemical induction.A full-thickness skin defect was created on the dorsum of the mice.Wound healing and the healing rate were observed following Sanhuang Oil intervention.The mechanism underlying Sanhuang Oil’s promotion of diabetic ulcer healing was further investigated using transcriptomics and histopathological examination(H&E staining).Results:A total of 97 active ingredients were identified from Sanhuang Oil.Network pharmacology analysis predicted 543 common targets,and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis identified 203 relevant pathways.Molecular docking further confirmed high binding affinity(binding energy≤−5.0 kcal/mol)between specific active components in Sanhuang Oil(e.g.,coptisine,phellodendrine,baicalein)and key targets associated with diabetic foot ulcers(e.g.,EGFR,AKT1,STAT3).In vivo experimental results demonstrated that the wound healing rate was significantly higher in Sanhuang Oil-treated groups compared to the model group(P<0.001).HE staining revealed that the high-dose Sanhuang Oil group exhibited more pronounced epithelial tissue coverage over the wound,reduced inflammatory cell infiltration,and increased collagen deposition and fibroblast proliferation.transcriptomic analysis identified Pdk4,Ttn,Csrp3,Actn2,Myoz2,Tnnc2,Myod1,Myog,Myot,and Myf6 as key regulatory proteins involved in promoting wound healing.Conclusion:Sanhuang Oil promotes wound healing in diabetic ulcer mice,potentially by mitigating inflammation and regulating key targets such as Pdk4 to enhance fibroblast function.These findings provide novel insights into the multi-target,multi-pathway mechanism of Sanhuang Oil for treating diabetic foot ulcers.
基金supported by the Fundamental Research Funds for the Liaoning Universities(Grant No.LJ202410166012).
文摘The Ordos Basin is a large superimposed hydrocarbon-bearing basin in China,and further research on the sedimentary characteristics and sedimentary evolution of the sequence framework of target layers is of great theoretical and practical significance for guiding oil and gas exploration.The sedimentary facies and sedimentary evolution of the high-resolution sequence framework of the Carboniferous Taiyuan Formation in the Hangjinqi area have been systematically analyzed for the first time by drilling,logging and seismic data.The results show that four types of sequence interfaces can be identified in the Taiyuan Formation:regional unconformity surfaces,scour surfaces,lithologic-lithofacies transformation surfaces and flooding surfaces.According to the sedimentary response caused by the upward and downward movements of the base level at different levels,the Taiyuan Formation can be divided into 2 long-term cycles(LSC_(1)-LSC_(2)),4 mid-term cycles(MSC_(1)-MSC4)and 7 short-term cycles(SSC_(1)-SSC7).The long-and mid-term cycles correspond to members T_(1)and T_(2)and layers T_(1)-1,T_(1-2),T_(2-1),and T_(2)-2,respectively.Long-term cycles are dominated by C_(1);mid-term cycles are dominated by C_(1)and C_(2),followed by A2;and short-term cycles are dominated by C_(1),C_(2),A1 and A2.Under the high-resolution sequence stratigraphic framework,the Hangjinqi area underwent a transformation of fan delta and tidal flat depositional systems during the Taiyuan Formation sedimentary period.In the MSC_(1)-MSC_(2)stage,owing to a large-scale paleocontinent,the fan delta sedimentary body,which was limited in scale and scope,developed only in the southeastern corner and gradually transitioned basinward to tidal flat facies.In the MSC3-MSC4 stage,as the paleocontinent continuously decreased and the sedimentary range expanded,fan-delta plain sedimentation began in the study area.Several braided distributary channels with poor connectivity developed on the fan-delta plain,and between them were floodplains and peat swamps.
基金financial support from the National Natural Science Foundation of China(Grant No.61971201)。
文摘High-resolution transmission electron microscopy(HRTEM)promises rapid atomic-scale dynamic structure imaging.Yet,the precision limitations of aberration parameters and the challenge of eliminating aberrations in Cs-corrected transmission electron microscopy constrain resolution.A machine learning algorithm is developed to determine the aberration parameters with higher precision from small,lattice-periodic crystal images.The proposed algorithm is then validated with simulated HRTEM images of graphene and applied to the experimental images of a molybdenum disulfide(MoS_(2))monolayer with 25 variables(14 aberrations)resolved in wide ranges.Using these measured parameters,the phases of the exit-wave functions are reconstructed for each image in a focal series of MoS_(2)monolayers.The images were acquired due to the unexpected movement of the specimen holder.Four-dimensional data extraction reveals time-varying atomic structures and ripple.In particular,the atomic evolution of the sulfur-vacancy point and line defects,as well as the edge structure near the amorphous,is visualized as the resolution has been improved from about 1.75?to 0.9 A.This method can help salvage important transmission electron microscope images and is beneficial for the images obtained from electron microscopes with average stability.
基金supported by the National Natural Science Foundation of China(61772179)Hunan Provincial Natural Science Foundation of China(2022JJ50016,2023JJ50095)+1 种基金the Science and Technology Plan Project of Hunan Province(2016TP1020)Double First-Class University Project of Hunan Province(Xiangjiaotong[2018]469,[2020]248).
文摘The objective of image-based virtual try-on is to seamlessly integrate clothing onto a target image, generating a realistic representation of the character in the specified attire. However, existing virtual try-on methods frequently encounter challenges, including misalignment between the body and clothing, noticeable artifacts, and the loss of intricate garment details. To overcome these challenges, we introduce a two-stage high-resolution virtual try-on framework that integrates an attention mechanism, comprising a garment warping stage and an image generation stage. During the garment warping stage, we incorporate a channel attention mechanism to effectively retain the critical features of the garment, addressing challenges such as the loss of patterns, colors, and other essential details commonly observed in virtual try-on images produced by existing methods. During the image generation stage, with the aim of maximizing the utilization of the information proffered by the input image, the input features undergo double sampling within the normalization procedure, thereby enhancing the detail fidelity and clothing alignment efficacy of the output image. Experimental evaluations conducted on high-resolution datasets validate the effectiveness of the proposed method. Results demonstrate significant improvements in preserving garment details, reducing artifacts, and achieving superior alignment between the clothing and body compared to baseline methods, establishing its advantage in generating realistic and high-quality virtual try-on images.
基金Supported by the Earmarked Fund for Hebei Agriculture Research System(HBCT2024260407)。
文摘[Objective]The paper aimed to effectively reduce the occurrence of bacterial resistance associated with breeding practices and to mitigate food safety risks by controlling the illegal use of veterinary drugs in self-formulated feed at the source.[Method]A screening database comprising 274 illegally added chemical drugs in self-formulated feed was established utilizing ultra-performance liquid chromatography coupled with quadrupole/electrostatic field orbitrap high-resolution mass spectrometry(HPLC-Q-Exactive Focus/MS).Subsequently,253 batches of self-formulated feed samples from various farms in Hebei Province were screened and quantitatively analyzed.[Result]The screening results indicated the presence of 8 pharmaceutical components across 10 batches of self-formulated feed samples,with a detection rate of 3.2%and concentrations ranging from 0.06 to 28851.8μg/g.[Conclusion]The application of high-resolution mass spectrometry is feasible and highly significant for the risk monitoring of illegally added drugs in self-formulated feed.
基金The National Natural Science Foundation of China under contract No.42425606the Basic Scientific Fund for the National Public Research Institute of China(Shu-Xingbei Young Talent Program)under contract No.2023S01+1 种基金the Ocean Decade International Cooperation Center Scientific and Technological Cooperation Project under contract No.GHKJ2024005China-Korea Joint Ocean Research Center Project under contract Nos PI-20240101(China)and 20220407(Korea).
文摘In oceanic and atmospheric science,finer resolutions have become a prevailing trend in all aspects of development.For high-resolution fluid flow simulations,the computational costs of widely used numerical models increase significantly with the resolution.Artificial intelligence methods have attracted increasing attention because of their high precision and fast computing speeds compared with traditional numerical model methods.The resolution-independent Fourier neural operator(FNO)presents a promising solution to the still challenging problem of high-resolution fluid flow simulations based on low-resolution data.Accordingly,we assess the potential of FNO for high-resolution fluid flow simulations using the vorticity equation as an example.We assess and compare the performance of FNO in multiple high-resolution tests varying the amounts of data and the evolution durations.When assessed with finer resolution data(even up to number of grid points with 1280×1280),the FNO model,trained at low resolution(number of grid points with 64×64)and with limited data,exhibits a stable overall error and good accuracy.Additionally,our work demonstrates that the FNO model takes less time than the traditional numerical method for high-resolution simulations.This suggests that FNO has the prospect of becoming a cost-effective and highly precise model for high-resolution simulations in the future.Moreover,FNO can make longer high-resolution predictions while training with less data by superimposing vorticity fields from previous time steps as input.A suitable initial learning rate can be set according to the frequency principle,and the time intervals of the dataset need to be adjusted according to the spatial resolution of the input when training the FNO model.Our findings can help optimize FNO for future fluid flow simulations.
文摘While algorithms have been created for land usage in urban settings,there have been few investigations into the extraction of urban footprint(UF).To address this research gap,the study employs several widely used image classification method classified into three categories to evaluate their segmentation capabilities for extracting UF across eight cities.The results indicate that pixel-based methods only excel in clear urban environments,and their overall accuracy is not consistently high.RF and SVM perform well but lack stability in object-based UF extraction,influenced by feature selection and classifier performance.Deep learning enhances feature extraction but requires powerful computing and faces challenges with complex urban layouts.SAM excels in medium-sized urban areas but falters in intricate layouts.Integrating traditional and deep learning methods optimizes UF extraction,balancing accuracy and processing efficiency.Future research should focus on adapting algorithms for diverse urban landscapes to enhance UF extraction accuracy and applicability.
基金funded by the General Program of National Natural Science Foundation of China(Grant No.42377467).
文摘Understanding vegetation water availability can be important for managing vegetation and combating climate change.Changes in vegetation water availability throughout China remains poorly understood,especially at a high spatial resolution.Standardized Precipitation Evapotranspiration Index(SPEI)is an ideal water availability index for assessing the spatiotemporal characteristics of drought and investigating the vegetation-water availability relationship.However,no high-resolution and long-term SPEI datasets over China are available.To fill this gap,we developed a new model based on machine learning to obtain high-resolution(1 km)SPEI data by combining climate variables with topographical and geographical features.Here,we analyzed the long-term drought over the past century(1901–2020)and vegetation-water availability relationship in the past two decades(2000–2020).The century-long drought trend analyses indicated an overall drying trend across China with increasing drought frequency,duration,and severity during the past century.We found that drought events in 1901–1961 showed a larger increase than that in 1961–2020,with the Qinghai-Xizang Plateau showing a significant drying trend during 1901–1960 but a wetting trend during 1961–2020.There were 13.90%and 28.21%of vegetation in China showing water deficit and water surplus respectively during 2000–2020.The water deficit area significantly shrank from 2000 to 2020 across China,which is dominated by the significant decrease in water deficit areas in South China.Among temperature,precipitation,and vegetation abundance,temperature is the most important factor for the vegetation-water availability dynamics in China over the past two decades,with high temperature contributing to water deficit.Our findings are important for water and vegetation management under a warming climate.
基金Supported by Tianjin Key Medical Discipline(Specialty)Construction Project,No.TJYXZDXK-3-012B.
文摘BACKGROUND Rectal cancer is a common malignant tumor of the digestive system,with older patients representing the predominantly affected population.Magnetic resonance imaging(MRI)has been widely applied in preoperative tumor assessment;however,the value of high-resolution MRI(HR-MRI)combined with dynamic contrast-enhanced(DCE)scanning in the preoperative diagnosis of rectal cancer in older patients remains unclear.AIM To evaluate the value of HR-MRI combined with DCE scanning in the preoperative diagnosis of rectal cancer in older patients.METHODS This retrospective study included 148 consecutive older female patients with rectal cancer who were treated at our hospital between December 2020 and December 2024.Clinical data and HR-MRI and DCE scan findings were collected.Histopathological examination after surgical resection served as the gold standard.The diagnostic accuracy of MRI for preoperative T and N staging was calculated.Consistency,sensitivity,and specificity between HR-MRI combined with DCE scanning and pathological staging were analyzed using the k test.Among the 148 patients,the overall accuracy of T staging was 84.5%.Sensitivity for T1,T2,T3,and T4 staging was 75.00%,62.50%,89.47%,and 90.48%,respectively,whereas specificity was 100.00%,94.35%,79.25%,and 96.06%,respectively.T staging based on HR-MRI combined with DCE scanning showed good agreement with pathological staging(k=0.8176,P<0.001).For N staging,sensitivity and specificity were 54.88%and 84.85%for N0,36.96%and 72.55%for N1,and 70.00%and 73.44%for N2,respectively;agreement with pathological N staging was poor(k=0.259,P<0.001).CONCLUSION HR-MRI combined with DCE scanning demonstrates high diagnostic accuracy for T staging of rectal cancer in older patients and can provide a theoretical basis for treatment planning.However,its diagnostic accuracy for N staging requires improvement.
文摘High-resolution sub-meter satellite data play an increasingly crucial role in the 3D real-scene China construction initiative.Current research on 3D reconstruction using high-resolution satellite data primarily focuses on two approaches:Multi-stereo fusion and multi-view matching.While algorithms based on these two methodologies for multi-view image 3D reconstruction have reached relative maturity,no systematic comparison has been conducted specifically on satellite data to evaluate the relative merits of multi-stereo fusion versus multi-view matching methods.This paper conducts a comparative analysis of the practical accuracy of both approaches using high-resolution satellite datasets from diverse geographical regions.To ensure fairness in accuracy comparison,both methodologies employ non-local dense matching for cost optimization.Results demonstrate that the multi-stereo fusion method outperforms multi-view matching in all evaluation metrics,exhibiting approximately 1.2%higher average matching accuracy and 10.7%superior elevation precision in the experimental datasets.Therefore,for 3D modeling applications using satellite data,we recommend adopting the multi-stereo fusion approach for digital surface model(DSM)product generation.
基金Supported by the National Natural Science Foundation of China(U24B2031)National Key Research and Development Project(2018YFA0702504)"14th Five-Year Plan"Science and Technology Project of CNOOC(KJGG2022-0201)。
文摘During drilling operations,the low resolution of seismic data often limits the accurate characterization of small-scale geological bodies near the borehole and ahead of the drill bit.This study investigates high-resolution seismic data processing technologies and methods tailored for drilling scenarios.The high-resolution processing of seismic data is divided into three stages:pre-drilling processing,post-drilling correction,and while-drilling updating.By integrating seismic data from different stages,spatial ranges,and frequencies,together with information from drilled wells and while-drilling data,and applying artificial intelligence modeling techniques,a progressive high-resolution processing technology of seismic data based on multi-source information fusion is developed,which performs simple and efficient seismic information updates during drilling.Case studies show that,with the gradual integration of multi-source information,the resolution and accuracy of seismic data are significantly improved,and thin-bed weak reflections are more clearly imaged.The updated seismic information while-drilling demonstrates high value in predicting geological bodies ahead of the drill bit.Validation using logging,mud logging,and drilling engineering data ensures the fidelity of the processing results of high-resolution seismic data.This provides clearer and more accurate stratigraphic information for drilling operations,enhancing both drilling safety and efficiency.
基金supported by the Hundred-person Program of Chinese Academy of Sciences and the National Natural Science Foundation of China(No.11905074).
文摘Electron beam injectors are pivotal components of large-scale scientific instruments,such as synchrotron radiation sources,free-electron lasers,and electron-positron colliders.The quality of the electron beam produced by the injector critically influences the performance of the entire accelerator-based scientific research apparatus.The injectors of such facilities usually use photocathode and thermionic-cathode electron guns.Although the photocathode injector can produce electron beams of excellent quality,its associated laser system is massive and intricate.The thermionic-cathode electron gun,especially the gridded electron gun injector,has a simple structure capable of generating numerous electron beams.However,its emittance is typically high.In this study,methods to reduce beam emittance are explored through a comprehensive analysis of various grid structures and preliminary design results,examining the evolution of beam phase space at different grid positions.An optimization method for reducing the emittance of a gridded thermionic-cathode electron gun is proposed through theoretical derivation,electromagnetic-field simulation,and beam-dynamics simulation.A 50%reduction in emittance was achieved for a 50 keV,1.7 A electron gun,laying the foundation for the subsequent design of a high-current,low-emittance injector.
文摘In December 2025,the ASEAN Centre for Energy(ACE)convened the third ASEAN Power Grid Partnership Meeting,bringing partners together for consultations on key issues.After more than two decades of planning and exploration,the ASEAN Power Grid is now entering a new phase—shifting from predominantly bilateral,one-way connections toward a multilateral,multidirectional network.
文摘Managing massive data flows effectively and resolving spectrum shortages are two challenges that smart grid communication networks(SGCN)must overcome.To address these problems,we provide a combined optimization approach that makes use of cognitive radio(CR)and non-orthogonal multiple access(NOMA)technologies.Our work focuses on using user pairing(UP)and power allocation(PA)techniques to maximize energy efficiency(EE)in SGCN,particularly within neighbourhood area networks(NANs).We develop a joint optimization problem that takes into account the real-world limitations of a CR-NOMA setting.This problem is NP-hard,nonlinear,and nonconvex by nature.To address the computational complexity of the problem,we use the block coordinate descent(BCD)method,which breaks the problem into UP and PA subproblems.Initially,we proposed the zebra-optimization user pairing(ZOUP)algorithm to tackle the UP problem,which outperforms both orthogonal multiple access(OMA)and non-optimized NOMA(UPWO)by 78.8%and13.6%,respectively,at a SNR of 15 dB.Based on the ZOUP pairs,we subsequently proposed the PA approach,i.e.,ZOUPPA,which significantly outperforms UPWO and ZOUP by 53.2%and 25.4%,respectively,at an SNR of 15 dB.A detailed analysis of key parameters,including varying SNRs,power allocation constants,path loss exponents,user density,channel availability,and coverage radius,underscores the superiority of our approach.By facilitating the effective use of communication resources in SGCN,our research opens the door to more intelligent and energy-efficient grid systems.Our work tackles important issues in SGCN and lays the groundwork for future developments in smart grid communication technologies by combining modern optimization approaches with CR-NOMA.
文摘Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the configuration of photovoltaic(3.8 MW),wind power(2.5 MW),energy storage(2.2 MWh),and SVC(1.2 Mvar)through interaction between upper and lower layers,and modifies lines 2–3,8–9,etc.to improve transmission capacity and voltage stability.The author uses normal distribution and Monte Carlo method to model load uncertainty,and combines Weibull distribution to describe wind speed characteristics.Compared to the traditional three-layer model(TLM),Benders decomposition-based two-layer model(BLBD)has a 58.1%reduction in convergence time(5.36 vs.12.78 h),a 51.1%reduction in iteration times(23 vs.47 times),a 8.07%reduction in total cost(12.436 vs.13.528 million yuan),and a 9.62%reduction in carbon emissions(12,456 vs.13,782 t).After optimization,the peak valley difference decreased from4.1 to 2.9MW,the renewable energy consumption rate reached 93.4%,and the energy storage efficiency was 87.6%.Themodel has been validated in the IEEE 33 node system,demonstrating its superiority in terms of economy,low-carbon,and reliability.
基金supported by State Grid Shanxi Electric Power Company’s Science and Technology Projects(No.52051C230102).
文摘Owing to the development of communication technologies and control systems,the integration of numerous Internet of Things(IoT)nodes into the power grid has become increasingly prevalent.These nodes are deployed to gather operational data from various distributed energy sources and monitor real-time energy consumption,thereby transforming the traditional power grid into a smart grid(SG).However,the openness of wireless communication channels introduces vulnerabilities,as it allows potential eavesdroppers to intercept sensitive information.This poses threats to the secure and efficient operation of the IoT-driven smart grid.To address these challenges,we propose a novel scenario that incorporates an Unmanned Aerial Vehicle(UAV)as a relay gateway for multiple authorized smart meters.This scenario is further enhanced by the integration of Reconfigurable Intelligent Surface(RIS)technology,which dynamically adjusts the direction of information transmission.Our objective is to maximize the secure rate within this UAV-RIS-aided system with multiple authorized smart meters and an eavesdropper based on physical layer security(PLS)techniques.We formulate the problem of secure rate maximization by jointly optimizing the active beamforming of the UAV,the passive beamforming of the RIS,and the UAV’s trajectory.To solve this complex optimization problem,we introduce the Twin Soft Actor-Critic(TSAC)algorithm.This algorithm employs a dual-agent framework,where Agent 1 focuses on optimizing the beamforming for both the UAV and the RIS,while Agent 2 concurrently searches for the optimal trajectory of the UAV.Simulation results demonstrate the TSAC algorithm significantly enhances the secure rate of the system,achieving faster convergence and higher rewards under the worst communication conditions.The TSAC algorithm consistently outperforms the Twin Deep Deterministic Policy Gradient(TDDPG)and Twin Delayed Deep Deterministic Policy Gradient(TTD3)algorithms.Furthermore,the TSAC algorithm exhibits robust performance when the distribution of smart meters follows a Gaussian distribution,further validating its practical applicability and effectiveness in real-world scenarios.
基金funded by the Deanship of Scientific Research and Libraries at Princess Nourah bint Abdulrahman University,through the“Nafea”Program,Grant No.(NP-45-082).
文摘Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The present article outlines the TransCarbonNet,a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory(Bi-LSTM)network to forecast the carbon intensity of the grid several days.The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data;hence,it is able to give suitable forecasts over a period of seven days.TransCarbonNet takes advantage of a multi-head self-attention element to identify significant temporal connections,which means the Bi-LSTM element calculates sequential dependencies in both directions.Massive tests on two actual data sets indicate much improved results in comparison with the existing results,with mean relative errors of 15.3 percent and 12.7 percent,respectively.The framework has given explicable weights of attention that reveal critical periods that influence carbon intensity alterations,and informed decisions on the management of carbon sustainability.The effectiveness of the proposed solution has been validated in numerous cases of operations,and TransCarbonNet is established to be an effective tool when it comes to carbon-friendly optimization of the grid.
基金the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia,for funding this research work through the project number“NBU-FFR-2025-3623-11”.
文摘Modern power systems increasingly depend on interconnected microgrids to enhance reliability and renewable energy utilization.However,the high penetration of intermittent renewable sources often causes frequency deviations,voltage fluctuations,and poor reactive power coordination,posing serious challenges to grid stability.Conventional Interconnection FlowControllers(IFCs)primarily regulate active power flowand fail to effectively handle dynamic frequency variations or reactive power sharing in multi-microgrid networks.To overcome these limitations,this study proposes an enhanced Interconnection Flow Controller(e-IFC)that integrates frequency response balancing and an Interconnection Reactive Power Flow Controller(IRFC)within a unified adaptive control structure.The proposed e-IFC is implemented and analyzed in DIgSILENT PowerFactory to evaluate its performance under various grid disturbances,including frequency drops,load changes,and reactive power fluctuations.Simulation results reveal that the e-IFC achieves 27.4% higher active power sharing accuracy,19.6% lower reactive power deviation,and 18.2% improved frequency stability compared to the conventional IFC.The adaptive controller ensures seamless transitions between grid-connected and islanded modes and maintains stable operation even under communication delays and data noise.Overall,the proposed e-IFCsignificantly enhances active-reactive power coordination and dynamic stability in renewable-integrated multi-microgrid systems.Future research will focus on coupling the e-IFC with tertiary-level optimization frameworks and conducting hardware-in-the-loop validation to enable its application in large-scale smart microgrid environments.
