Graphene(Gr)has unique properties including high electrical conductivity;Thus,graphene/copper(Gr/Cu)composites have attracted increasing attention to replace traditional Cu for electrical applications. However,the pro...Graphene(Gr)has unique properties including high electrical conductivity;Thus,graphene/copper(Gr/Cu)composites have attracted increasing attention to replace traditional Cu for electrical applications. However,the problem of how to control graphene to form desired Gr/Cu composite is not well solved. This paper aims at exploring the best parameters for preparing graphene with different layers on Cu foil by chemical vapor deposition(CVD)method and studying the effects of different layers graphene on Gr/Cu composite’s electrical conductivity. Graphene grown on single-sided and double-sided copper was prepared for Gr/Cu and Gr/Cu/Gr composites. The resultant electrical conductivity of Gr/Cu composites increased with decreasing graphene layers and increasing graphene volume fraction. The Gr/Cu/Gr composite with monolayer graphene owns volume fraction of less than 0.002%,producing the best electrical conductivity up to59.8 ×10^(6)S/m,equivalent to 104.5% IACS and 105.3% pure Cu foil.展开更多
Supercapacitors are gaining popularity due to their high cycling stability,power density,and fast charge and discharge rates.Researchers are ex-ploring electrode materials,electrolytes,and separat-ors for cost-effecti...Supercapacitors are gaining popularity due to their high cycling stability,power density,and fast charge and discharge rates.Researchers are ex-ploring electrode materials,electrolytes,and separat-ors for cost-effective energy storage systems.Ad-vances in materials science have led to the develop-ment of hybrid nanomaterials,such as combining fil-amentous carbon forms with inorganic nanoparticles,to create new charge and energy transfer processes.Notable materials for electrochemical energy-stor-age applications include MXenes,2D transition met-al carbides,and nitrides,carbon black,carbon aerogels,activated carbon,carbon nanotubes,conducting polymers,carbon fibers,and nanofibers,and graphene,because of their thermal,electrical,and mechanical properties.Carbon materials mixed with conducting polymers,ceramics,metal oxides,transition metal oxides,metal hydroxides,transition metal sulfides,trans-ition metal dichalcogenide,metal sulfides,carbides,nitrides,and biomass materials have received widespread attention due to their remarkable performance,eco-friendliness,cost-effectiveness,and renewability.This article explores the development of carbon-based hybrid materials for future supercapacitors,including electric double-layer capacitors,pseudocapacitors,and hy-brid supercapacitors.It investigates the difficulties that influence structural design,manufacturing(electrospinning,hydro-thermal/solvothermal,template-assisted synthesis,electrodeposition,electrospray,3D printing)techniques and the latest car-bon-based hybrid materials research offer practical solutions for producing high-performance,next-generation supercapacitors.展开更多
The connection and interaction between the eye and the brain are crucial to understanding brain disorders(Marchesi et al.,2021).Both the eye and the brain have a limited regenerative capacity as there are few progenit...The connection and interaction between the eye and the brain are crucial to understanding brain disorders(Marchesi et al.,2021).Both the eye and the brain have a limited regenerative capacity as there are few progenitor cells,and nerve cells do not replicate.Hence,neurodegeneration implicates irreversible damage to the central nervous system,as observed in several neurodegenerative diseases(Marchesi et al.,2021).展开更多
The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational per...The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ensemble methods for battery SOH estimation. The convergence of advanced machine learning with ensemble learning is anticipated to yield valuable avenues for research. Accelerated research in ensemble learning holds promising prospects for achieving more accurate and reliable battery SOH estimation under real-world conditions.展开更多
Software-related security aspects are a growing and legitimate concern,especially with 5G data available just at our palms.To conduct research in this field,periodic comparative analysis is needed with the new techniq...Software-related security aspects are a growing and legitimate concern,especially with 5G data available just at our palms.To conduct research in this field,periodic comparative analysis is needed with the new techniques coming up rapidly.The purpose of this study is to review the recent developments in the field of security integration in the software development lifecycle(SDLC)by analyzing the articles published in the last two decades and to propose a way forward.This review follows Kitchenham’s review protocol.The review has been divided into three main stages including planning,execution,and analysis.From the selected 100 articles,it becomes evident that need of a collaborative approach is necessary for addressing critical software security risks(CSSRs)through effective risk management/estimation techniques.Quantifying risks using a numeric scale enables a comprehensive understanding of their severity,facilitating focused resource allocation and mitigation efforts.Through a comprehensive understanding of potential vulnerabilities and proactive mitigation efforts facilitated by protection poker,organizations can prioritize resources effectively to ensure the successful outcome of projects and initiatives in today’s dynamic threat landscape.The review reveals that threat analysis and security testing are needed to develop automated tools for the future.Accurate estimation of effort required to prioritize potential security risks is a big challenge in software security.The accuracy of effort estimation can be further improved by exploring new techniques,particularly those involving deep learning.It is also imperative to validate these effort estimation methods to ensure all potential security threats are addressed.Another challenge is selecting the right model for each specific security threat.To achieve a comprehensive evaluation,researchers should use well-known benchmark checklists.展开更多
This study introduces a comprehensive theoretical framework for accurately calculating the electronic band-structure of strained long-wavelength InAs/GaSb type-Ⅱsuperlattices.Utilizing an eight-band k·p Hamilto⁃...This study introduces a comprehensive theoretical framework for accurately calculating the electronic band-structure of strained long-wavelength InAs/GaSb type-Ⅱsuperlattices.Utilizing an eight-band k·p Hamilto⁃nian in conjunction with a scattering matrix method,the model effectively incorporates quantum confinement,strain effects,and interface states.This robust and numerically stable approach achieves exceptional agreement with experimental data,offering a reliable tool for analyzing and engineering the band structure of complex multi⁃layer systems.展开更多
The human brain is asymmetrical in function, with each of its two hemispheres being somewhat responsible for distinct cognitive and motor tasks, to include writing. It stands to reason that engineering students who ha...The human brain is asymmetrical in function, with each of its two hemispheres being somewhat responsible for distinct cognitive and motor tasks, to include writing. It stands to reason that engineering students who have established entrance into their upper-division programs will have demonstrated cognitive proficiency in math and logical operations, abstract and analytical reasoning and language usage, to include writing. In this study the question was asked: is there a correlation between an upper-division electrical engineering students’ analytical reasoning ability and their descriptive writing ability? Descriptive writing is taken here to mean a students’ ability to identify key physical aspects of a mathematical model and to express—in words—a concise and well-balanced description that demonstrates a deep conceptual understanding of the model. This includes more than a description of the variables or the particular application to an engineering problem;it includes a demonstrated recognition of the basic physics that govern the model, certain limitations (idealizations) inherent in the model, and an understanding of how to make practical experimental measurements to verify the governing physics in the model. A student at this level may demonstrate proficiency in their analytical reasoning skills and hence be capable of correctly solving a given problem. However, this does not guarantee that the same student is skilled in associating equations with their physical meaning on a deep conceptual level or in understanding physical limitations of the equation. Consequently, such a student may demonstrate difficulty in mapping their comprehension of the model into written language that demonstrates a sound conceptual understanding of the governing physics. The findings represent a sample of two independent class sections of Electrical and Computer Engineering junior’s first course in Microe-lectronic Devices and Circuits during fall semesters 2012 and 2013 at a private mid-size university in NW Oregon. A total of three exams were administered to each of the 2012/2013 groups. Correlations between exam scores that students achieved on their descriptive writing of microelectronics phenomena and their analytical problem-solving abilities were examined and found to be quite significant.展开更多
In this study,we design and numerically investigate a novel all optical D flip-flop(AODFF)based on linear photonic crystal(LPhC)structure that is composed of optical waveguides using the finite difference time domain(...In this study,we design and numerically investigate a novel all optical D flip-flop(AODFF)based on linear photonic crystal(LPhC)structure that is composed of optical waveguides using the finite difference time domain(FDTD)method.The proposed structure has the hexagonal close packed of 16×20 circular rods that are suspended in the air substrate with a lattice constant of 606 nm.The plane wave expansion(PWE)method is used to obtain the band diagram for AODFF at an operating wavelength of 1550 nm.The proposed optical flip-flop achieves a low delay time of 0.2 ps and a high contrast ratio(CR)of 10.33 dB.The main advantage of this design is that the input power as low as 1 mW/μm^(2) is sufficient for its operation,since no nonlinear rods are included.In addition,the footprint of the proposed AODFF is 100μm^(2),which is smaller compared to the structures reported in the literature,and it has a fast switching frequency of 5 Tbit/s.展开更多
The global rise in energy demand, particularly in remote and sparsely populated regions, necessitates innovative and cost-effective electrical distribution solutions. Traditional Rural Electrification (RE) methods, li...The global rise in energy demand, particularly in remote and sparsely populated regions, necessitates innovative and cost-effective electrical distribution solutions. Traditional Rural Electrification (RE) methods, like Conventional Rural Electrification (CRE), have proven economically unfeasible in such areas due to high infrastructure costs and low electricity demand. Consequently, Unconventional Rural Electrification (URE) technologies, such as Capacitor Coupled Substations (CCS), are gaining attention as viable alternatives. This study presents the design and simulation of an 80 kW CCS system, which taps power directly from a 132 kV transmission line to supply low-voltage consumers. The critical components of the CCS, the capacitors are calculated, then a MATLAB/Simulink model with the attained results is executed. Mathematical representation and state-space representation for maintaining the desired tapped voltage area also developed. The research further explores the feasibility and operational performance of this CCS configuration, aiming to address the challenges of rural electrification by offering a sustainable and scalable solution. The results show that the desired value of the tapped voltage can be achieved at any level of High Voltage (HV) with the selection of capacitors that are correctly rated. With an adequately designed control strategy, the research also shows that tapped voltage can be attained under both steady-state and dynamic loads. By leveraging CCS technology, the study demonstrates the potential for delivering reliable electricity to underserved areas, highlighting the system’s practicality and effectiveness in overcoming the limitations of conventional distribution methods.展开更多
The efficiency of energy conversion from mechanical to electrical in AC generators is not entirely optimal,as power losses are converted into heat.Accurate thermal modeling and temperature measurement of advanced elec...The efficiency of energy conversion from mechanical to electrical in AC generators is not entirely optimal,as power losses are converted into heat.Accurate thermal modeling and temperature measurement of advanced electric machines with complex structures are mandatory to confirm their reliability and safe operation.In a unique axial transverse flux switching permanent magnet(ATFSPM)generator,due to its high power density,large stray loss from leakage flux,compact topology,and totally enclosed structure,thermal analysis is of paramount significance.In this paper,thermal modeling and analysis of ATFSPM are carried out in detail using a three-dimensional(3D)finite element analysis(FEA)to evaluate the thermal condition for a precise performance improvement.To begin,all loss sources are accurately derived using 3-D FEA and analytical methods,taking into account the temperature dependence of material properties,and then losses are coupled to the thermal model as heat sources.Afterward,aiming for realistic thermal modelling,the convection heat transfer in the different regions of internal and external areas as well as thin layers of interface gaps between components are all considered.In addition,the prototype of ATFSPM is supplied to validate the accuracy of 3-D FEA temperature prediction.Furthermore,a novel technique is carried out to effectively improve thermal performance,enhance the efficiency,and limit hot-spot temperatures.The steady-state and transient temperature results demonstrate the high accuracy of the thermal modeling,enhance the secure operation of the ATFSPM,and facilitate increased loading utilizing the proposed technique.(1)展开更多
This article briefly reviews the topic of complex network synchronization,with its graph-theoretic criterion,showing that the homogeneous and symmetrical network structures are essential for optimal synchronization.Fu...This article briefly reviews the topic of complex network synchronization,with its graph-theoretic criterion,showing that the homogeneous and symmetrical network structures are essential for optimal synchronization.Furthermore,it briefly reviews the notion of higher-order network topologies and shows their promising potential in application to evaluating the optimality of network synchronizability.展开更多
Recent years have seen a surge in interest in object detection on remote sensing images for applications such as surveillance andmanagement.However,challenges like small object detection,scale variation,and the presen...Recent years have seen a surge in interest in object detection on remote sensing images for applications such as surveillance andmanagement.However,challenges like small object detection,scale variation,and the presence of closely packed objects in these images hinder accurate detection.Additionally,the motion blur effect further complicates the identification of such objects.To address these issues,we propose enhanced YOLOv9 with a transformer head(YOLOv9-TH).The model introduces an additional prediction head for detecting objects of varying sizes and swaps the original prediction heads for transformer heads to leverage self-attention mechanisms.We further improve YOLOv9-TH using several strategies,including data augmentation,multi-scale testing,multi-model integration,and the introduction of an additional classifier.The cross-stage partial(CSP)method and the ghost convolution hierarchical graph(GCHG)are combined to improve detection accuracy by better utilizing feature maps,widening the receptive field,and precisely extracting multi-scale objects.Additionally,we incorporate the E-SimAM attention mechanism to address low-resolution feature loss.Extensive experiments on the VisDrone2021 and DIOR datasets demonstrate the effectiveness of YOLOv9-TH,showing good improvement in mAP compared to the best existing methods.The YOLOv9-TH-e achieved 54.2% of mAP50 on the VisDrone2021 dataset and 92.3% of mAP on the DIOR dataset.The results confirmthemodel’s robustness and suitability for real-world applications,particularly for small object detection in remote sensing images.展开更多
The integration of renewable energy sources(RESs)with inverter interfaces has fundamentally reshaped power system dynamics,challenging traditional stability analysis frameworks designed for synchronous generator-domin...The integration of renewable energy sources(RESs)with inverter interfaces has fundamentally reshaped power system dynamics,challenging traditional stability analysis frameworks designed for synchronous generator-dominated grids.Conventional classifica-tions,which decouple voltage,frequency,and rotor angle stability,fail to address the emerging strong voltage‒angle coupling effects caused by RES dynamics.This coupling introduces complex oscillation modes and undermines system robustness,neces-sitating novel stability assessment tools.Recent studies focus on eigenvalue distributions and damping redistribution but lack quantitative criteria and interpretative clarity for coupled stability.This work proposes a transient energy-based framework to resolve these gaps.By decomposing transient energy into subsystem-dissipated components and coupling-induced energy exchange,the method establishes stability criteria compatible with a broad variety of inverter-interfaced devices while offering an intuitive energy-based interpretation for engineers.The coupling strength is also quantified by defining the relative coupling strength index,which is directly related to the transient energy interpretation of the coupled stability.Angle‒voltage coupling may induce instability by injecting transient energy into the system,even if the individual phase angle and voltage dynamics themselves are stable.The main contributions include a systematic stability evaluation framework and an energy decomposition approach that bridges theoretical analysis with practical applicability,addressing the urgent need for tools for managing modern power system evolving stability challenges.展开更多
Skin cancer is the most prevalent cancer globally,primarily due to extensive exposure to Ultraviolet(UV)radiation.Early identification of skin cancer enhances the likelihood of effective treatment,as delays may lead t...Skin cancer is the most prevalent cancer globally,primarily due to extensive exposure to Ultraviolet(UV)radiation.Early identification of skin cancer enhances the likelihood of effective treatment,as delays may lead to severe tumor advancement.This study proposes a novel hybrid deep learning strategy to address the complex issue of skin cancer diagnosis,with an architecture that integrates a Vision Transformer,a bespoke convolutional neural network(CNN),and an Xception module.They were evaluated using two benchmark datasets,HAM10000 and Skin Cancer ISIC.On the HAM10000,the model achieves a precision of 95.46%,an accuracy of 96.74%,a recall of 96.27%,specificity of 96.00%and an F1-Score of 95.86%.It obtains an accuracy of 93.19%,a precision of 93.25%,a recall of 92.80%,a specificity of 92.89%and an F1-Score of 93.19%on the Skin Cancer ISIC dataset.The findings demonstrate that the model that was proposed is robust and trustworthy when it comes to the classification of skin lesions.In addition,the utilization of Explainable AI techniques,such as Grad-CAM visualizations,assists in highlighting the most significant lesion areas that have an impact on the decisions that are made by the model.展开更多
Flexible surface micro-discharge plasma is a non-thermal plasma technique used for treating wounds in a painless way, with significant efficacy for chronic or hard-to-heal wounds. In this study, a confined space was d...Flexible surface micro-discharge plasma is a non-thermal plasma technique used for treating wounds in a painless way, with significant efficacy for chronic or hard-to-heal wounds. In this study, a confined space was designed to simulate wound conditions, with gelatin used to simulate wound tissue. The distinction between open and confined spaces was explored, and the effects of temperature, humidity, discharge power and the gap size within the confined space on the plasma characteristics were analyzed. It was found that temperature, humidity and discharge power are important factors that affect the concentration distribution of active components and the mode transition between ozone and nitrogen oxides. Compared to open space, the concentration of ozone in confined space was relatively lower, which facilitated the formation of nitrogen oxides. In open space, the discharge was dominated by ozone initially. As the temperature,humidity and discharge power increased, nitrogen oxides in the gas-phase products were gradually detected. In confined space, nitrogen oxides can be detected at an early stage and at much higher concentrations than ozone concentration. Furthermore, as the gap of the confined space decreased, the concentration of ozone was observed to decrease while that of nitrate increased, and the rate of this concentration change was further accelerated at higher temperature and higher power. It was shown that ozone concentration decreased from 0.11 to 0.03 μmol and the nitrate concentration increased from 20.5 to 24.5 μmol when the spacing in the confined space was reduced from 5 to 1 mm, the temperature of the external discharge was controlled at 40 ℃, and the discharge power was 12 W. In summary, this study reveals the formation and transformation mechanisms of active substances in air surface micro-discharge plasma within confined space, providing foundational data for its medical applications.展开更多
The demand for sustainable and stretchable thin-film printed batteries for bioelectronics,wearables,and e-textiles is rapidly increasing.Recently,we developed a fully 3D-printed soft-matter thin-film Ga-Ag_(2)O batter...The demand for sustainable and stretchable thin-film printed batteries for bioelectronics,wearables,and e-textiles is rapidly increasing.Recently,we developed a fully 3D-printed soft-matter thin-film Ga-Ag_(2)O battery with 3R characteristics:resilient to mechanical strain,repairable after damage,and recyclable.This battery achieved a record-breaking areal capacity of 26.37 mAh cm-2,increasing to 30.32 mAh cm^(-2) after 10 cycles under 100%strain.This performance stems from the synergistic effects of gallium’s liquid metal properties and the styrene-isoprene-styrene polymer in the anode.Gallium’s high specific capacity(1153.2 mAh g^(-1)),deformability,and self-healing abilities,supported by its supercooled liquid phase,significantly enhance the battery’s resilience and efficiency.However,the cathode’s lower theoretical capacity,due to Ag_(2)O(231.31 mAh g^(-1)),remains a limitation.Traditional Ag_(2)O-carbon black-styrene-isoprene-styrene cathodes experience rapid capacity decay as only the surface area of the active materials interacts with the electrolyte.To overcome this,we designed a carbon-filled Ag_(2)O foam electrode using a sacrificial sugar template,increasing the effective surface area.This optimization enhanced ion-exchange efficiency,specific capacity,and cyclability,achieving a specific capacity of 221.16 mAh g^(-1).Consequently,the Ga-Ag_(2)O stretchable battery attained a record areal capacity of 40.91 mAh cm^(-2)—double that of nonfoam electrodes—and exhibited fivefold improved charge-discharge cycles.Using ultrastretchable Ag-EGaIn-styrene-isoprene-styrene and carbon black-styrene-isoprene-styrene current collectors,the battery’s specific capacity increased by 33%under 50%strain.Integrated into a soft-matter smart wristband for temperature monitoring,the battery demonstrated its promise for wearable electronics.展开更多
Against the backdrop of active global responses to climate change and the accelerated green and low-carbon energy transition,the co-optimization and innovative mechanism design of multimodal energy systems have become...Against the backdrop of active global responses to climate change and the accelerated green and low-carbon energy transition,the co-optimization and innovative mechanism design of multimodal energy systems have become a significant instrument for propelling the energy revolution and ensuring energy security.Under increasingly stringent carbon emission constraints,how to achieve multi-dimensional improvements in energy utilization efficiency,renewable energy accommodation levels,and system economics-through the intelligent coupling of diverse energy carriers such as electricity,heat,natural gas,and hydrogen,and the effective application of market-based instruments like carbon trading and demand response-constitutes a critical scientific and engineering challenge demanding urgent solutions.展开更多
To overcome the limited electronic conductivity and capacity of single and binary transition metal phos-phates(TMPs),highly electrochemical active materials and rational structural design of ternary TMPs composite are...To overcome the limited electronic conductivity and capacity of single and binary transition metal phos-phates(TMPs),highly electrochemical active materials and rational structural design of ternary TMPs composite are urgently required.In this study,we successfully synthesized an amorphous 3D Ni-Co-Mn phosphate@2D Ti_(3)C_(2)T_(x)(MXene)nanocomposite(NCMP series)through the electrodeposition method.The amorphous Ni-Co-Mn phosphate effectively restricts the self-accumulation of MXene nanosheets,result-ing in the development of a porous nanostructure.This structure exposes more active sites,expands the ion transport path,and enhances the conductivity of the Ni-Co-Mn phosphate@Ti_(3)C_(2)T_(x) material.Owing to the synergistic effect offered by Ni-Co-Mn phosphate and MXene nanocomposite,the anchored Ni-Co-Mn phosphate@Ti_(3)C_(2)T_(x)(NCMP-5)electrode delivers an elevated capacity of 342 mAh/g(1230 C/g)at 5.0 A/g,surpassing the pristine Ni-Co-Mn phosphate(NCMP-4,260 mAh/g)and MXene(33.3 mAh/g).Moreover,a hybrid solid-state supercapacitor(HSSC)device is assembled with NCMP-5 as a cathode and reduced graphene oxide(rGO)as an anode within a polymer gel(PVA-KOH)electrolyte.Notably,the fabricated HSSC device displays a supreme specific capacity of 27.5 mAh/g(99 C/g)and a high(volumetric)energy density of 22 Wh/kg(3.6 Wh/cm^(3))at a power density of 0.80 kW/kg(0.13 kW/cm^(3))for 1.0 A/g.Moreover,the HSSC device retains 95.4%of its initial capacity even after 10,000 cycles.Importantly,the operational potential window of two serially connected HSSC devices approaches+3.2 V,enabling different colored commercial light-emitting diodes(LEDs)to be efficiently illuminated.Eventually,the remarkable super-capacitive characteristics of the 3D@2D amorphous Ni-Co-Mn phosphate@MXene nanocomposite make it an attractive choice for advanced electroactive materials in upcoming hybrid energy storage technologies.展开更多
Breast Cancer(BC)remains a leadingmalignancy among women,resulting in highmortality rates.Early and accurate detection is crucial for improving patient outcomes.Traditional diagnostic tools,while effective,have limita...Breast Cancer(BC)remains a leadingmalignancy among women,resulting in highmortality rates.Early and accurate detection is crucial for improving patient outcomes.Traditional diagnostic tools,while effective,have limitations that reduce their accessibility and accuracy.This study investigates the use ofConvolutionalNeuralNetworks(CNNs)to enhance the diagnostic process of BC histopathology.Utilizing the BreakHis dataset,which contains thousands of histopathological images,we developed a CNN model designed to improve the speed and accuracy of image analysis.Our CNN architecture was designed with multiple convolutional layers,max-pooling layers,and a fully connected network optimized for feature extraction and classification.Hyperparameter tuning was conducted to identify the optimal learning rate,batch size,and number of epochs,ensuring robust model performance.The dataset was divided into training(80%),validation(10%),and testing(10%)subsets,with performance evaluated using accuracy,precision,recall,and F1-score metrics.Our CNN model achieved a magnification-independent accuracy of 97.72%,with specific accuracies of 97.50%at 40×,97.61%at 100×,99.06%at 200×,and 97.25%at 400×magnification levels.These results demonstrate the model’s superior performance relative to existing methods.The integration of CNNs in diagnostic workflows can potentially reduce pathologist workload,minimize interpretation errors,and increase the availability of diagnostic testing,thereby improving BC management and patient survival rates.This study highlights the effectiveness of deep learning in automating BC histopathological classification and underscores the potential for AI-driven diagnostic solutions to improve patient care.展开更多
In recent years,the increased application of inverter-based resources in power grids,along with the gradual replacement of synchronous generators,has made the grid support capability of inverters essential for maintai...In recent years,the increased application of inverter-based resources in power grids,along with the gradual replacement of synchronous generators,has made the grid support capability of inverters essential for maintaining system stability under large disturbances.Critical clearing time provides a quantitative measure of fault severity and system stability,and its sensitivity can help guide parameter adjustments to enhance the grid support capability of inverters.Building on previous researches,this paper proposes a method for calculating critical clearing time sensitivity in power systems with a high proportion of power electronic devices,accounting for the new dynamic characteristics introduced by these devices.The current limit and switching control of inverterbased resources are considered,and the critical clearing time sensitivity under controlling periodic orbits is derived.The proposed critical clearing time sensitivity calculation method is then validated using a double generator single load system and a modified 39-bus system.展开更多
基金supported substantially by the Southwest Jiaotong University for Material and Financial Support。
文摘Graphene(Gr)has unique properties including high electrical conductivity;Thus,graphene/copper(Gr/Cu)composites have attracted increasing attention to replace traditional Cu for electrical applications. However,the problem of how to control graphene to form desired Gr/Cu composite is not well solved. This paper aims at exploring the best parameters for preparing graphene with different layers on Cu foil by chemical vapor deposition(CVD)method and studying the effects of different layers graphene on Gr/Cu composite’s electrical conductivity. Graphene grown on single-sided and double-sided copper was prepared for Gr/Cu and Gr/Cu/Gr composites. The resultant electrical conductivity of Gr/Cu composites increased with decreasing graphene layers and increasing graphene volume fraction. The Gr/Cu/Gr composite with monolayer graphene owns volume fraction of less than 0.002%,producing the best electrical conductivity up to59.8 ×10^(6)S/m,equivalent to 104.5% IACS and 105.3% pure Cu foil.
文摘Supercapacitors are gaining popularity due to their high cycling stability,power density,and fast charge and discharge rates.Researchers are ex-ploring electrode materials,electrolytes,and separat-ors for cost-effective energy storage systems.Ad-vances in materials science have led to the develop-ment of hybrid nanomaterials,such as combining fil-amentous carbon forms with inorganic nanoparticles,to create new charge and energy transfer processes.Notable materials for electrochemical energy-stor-age applications include MXenes,2D transition met-al carbides,and nitrides,carbon black,carbon aerogels,activated carbon,carbon nanotubes,conducting polymers,carbon fibers,and nanofibers,and graphene,because of their thermal,electrical,and mechanical properties.Carbon materials mixed with conducting polymers,ceramics,metal oxides,transition metal oxides,metal hydroxides,transition metal sulfides,trans-ition metal dichalcogenide,metal sulfides,carbides,nitrides,and biomass materials have received widespread attention due to their remarkable performance,eco-friendliness,cost-effectiveness,and renewability.This article explores the development of carbon-based hybrid materials for future supercapacitors,including electric double-layer capacitors,pseudocapacitors,and hy-brid supercapacitors.It investigates the difficulties that influence structural design,manufacturing(electrospinning,hydro-thermal/solvothermal,template-assisted synthesis,electrodeposition,electrospray,3D printing)techniques and the latest car-bon-based hybrid materials research offer practical solutions for producing high-performance,next-generation supercapacitors.
基金supported by grants from City University of Hong Kong,China (Project No.SRG-Fd7005632,SRG-Fd 7005854SIRG 7020058)(to LLHC)。
文摘The connection and interaction between the eye and the brain are crucial to understanding brain disorders(Marchesi et al.,2021).Both the eye and the brain have a limited regenerative capacity as there are few progenitor cells,and nerve cells do not replicate.Hence,neurodegeneration implicates irreversible damage to the central nervous system,as observed in several neurodegenerative diseases(Marchesi et al.,2021).
基金National Natural Science Foundation of China (52075420)Fundamental Research Funds for the Central Universities (xzy022023049)National Key Research and Development Program of China (2023YFB3408600)。
文摘The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ensemble methods for battery SOH estimation. The convergence of advanced machine learning with ensemble learning is anticipated to yield valuable avenues for research. Accelerated research in ensemble learning holds promising prospects for achieving more accurate and reliable battery SOH estimation under real-world conditions.
文摘Software-related security aspects are a growing and legitimate concern,especially with 5G data available just at our palms.To conduct research in this field,periodic comparative analysis is needed with the new techniques coming up rapidly.The purpose of this study is to review the recent developments in the field of security integration in the software development lifecycle(SDLC)by analyzing the articles published in the last two decades and to propose a way forward.This review follows Kitchenham’s review protocol.The review has been divided into three main stages including planning,execution,and analysis.From the selected 100 articles,it becomes evident that need of a collaborative approach is necessary for addressing critical software security risks(CSSRs)through effective risk management/estimation techniques.Quantifying risks using a numeric scale enables a comprehensive understanding of their severity,facilitating focused resource allocation and mitigation efforts.Through a comprehensive understanding of potential vulnerabilities and proactive mitigation efforts facilitated by protection poker,organizations can prioritize resources effectively to ensure the successful outcome of projects and initiatives in today’s dynamic threat landscape.The review reveals that threat analysis and security testing are needed to develop automated tools for the future.Accurate estimation of effort required to prioritize potential security risks is a big challenge in software security.The accuracy of effort estimation can be further improved by exploring new techniques,particularly those involving deep learning.It is also imperative to validate these effort estimation methods to ensure all potential security threats are addressed.Another challenge is selecting the right model for each specific security threat.To achieve a comprehensive evaluation,researchers should use well-known benchmark checklists.
文摘This study introduces a comprehensive theoretical framework for accurately calculating the electronic band-structure of strained long-wavelength InAs/GaSb type-Ⅱsuperlattices.Utilizing an eight-band k·p Hamilto⁃nian in conjunction with a scattering matrix method,the model effectively incorporates quantum confinement,strain effects,and interface states.This robust and numerically stable approach achieves exceptional agreement with experimental data,offering a reliable tool for analyzing and engineering the band structure of complex multi⁃layer systems.
文摘The human brain is asymmetrical in function, with each of its two hemispheres being somewhat responsible for distinct cognitive and motor tasks, to include writing. It stands to reason that engineering students who have established entrance into their upper-division programs will have demonstrated cognitive proficiency in math and logical operations, abstract and analytical reasoning and language usage, to include writing. In this study the question was asked: is there a correlation between an upper-division electrical engineering students’ analytical reasoning ability and their descriptive writing ability? Descriptive writing is taken here to mean a students’ ability to identify key physical aspects of a mathematical model and to express—in words—a concise and well-balanced description that demonstrates a deep conceptual understanding of the model. This includes more than a description of the variables or the particular application to an engineering problem;it includes a demonstrated recognition of the basic physics that govern the model, certain limitations (idealizations) inherent in the model, and an understanding of how to make practical experimental measurements to verify the governing physics in the model. A student at this level may demonstrate proficiency in their analytical reasoning skills and hence be capable of correctly solving a given problem. However, this does not guarantee that the same student is skilled in associating equations with their physical meaning on a deep conceptual level or in understanding physical limitations of the equation. Consequently, such a student may demonstrate difficulty in mapping their comprehension of the model into written language that demonstrates a sound conceptual understanding of the governing physics. The findings represent a sample of two independent class sections of Electrical and Computer Engineering junior’s first course in Microe-lectronic Devices and Circuits during fall semesters 2012 and 2013 at a private mid-size university in NW Oregon. A total of three exams were administered to each of the 2012/2013 groups. Correlations between exam scores that students achieved on their descriptive writing of microelectronics phenomena and their analytical problem-solving abilities were examined and found to be quite significant.
文摘In this study,we design and numerically investigate a novel all optical D flip-flop(AODFF)based on linear photonic crystal(LPhC)structure that is composed of optical waveguides using the finite difference time domain(FDTD)method.The proposed structure has the hexagonal close packed of 16×20 circular rods that are suspended in the air substrate with a lattice constant of 606 nm.The plane wave expansion(PWE)method is used to obtain the band diagram for AODFF at an operating wavelength of 1550 nm.The proposed optical flip-flop achieves a low delay time of 0.2 ps and a high contrast ratio(CR)of 10.33 dB.The main advantage of this design is that the input power as low as 1 mW/μm^(2) is sufficient for its operation,since no nonlinear rods are included.In addition,the footprint of the proposed AODFF is 100μm^(2),which is smaller compared to the structures reported in the literature,and it has a fast switching frequency of 5 Tbit/s.
文摘The global rise in energy demand, particularly in remote and sparsely populated regions, necessitates innovative and cost-effective electrical distribution solutions. Traditional Rural Electrification (RE) methods, like Conventional Rural Electrification (CRE), have proven economically unfeasible in such areas due to high infrastructure costs and low electricity demand. Consequently, Unconventional Rural Electrification (URE) technologies, such as Capacitor Coupled Substations (CCS), are gaining attention as viable alternatives. This study presents the design and simulation of an 80 kW CCS system, which taps power directly from a 132 kV transmission line to supply low-voltage consumers. The critical components of the CCS, the capacitors are calculated, then a MATLAB/Simulink model with the attained results is executed. Mathematical representation and state-space representation for maintaining the desired tapped voltage area also developed. The research further explores the feasibility and operational performance of this CCS configuration, aiming to address the challenges of rural electrification by offering a sustainable and scalable solution. The results show that the desired value of the tapped voltage can be achieved at any level of High Voltage (HV) with the selection of capacitors that are correctly rated. With an adequately designed control strategy, the research also shows that tapped voltage can be attained under both steady-state and dynamic loads. By leveraging CCS technology, the study demonstrates the potential for delivering reliable electricity to underserved areas, highlighting the system’s practicality and effectiveness in overcoming the limitations of conventional distribution methods.
基金supported by research grants of the Iran National Science Foundation(INSF)under grant No.98002866。
文摘The efficiency of energy conversion from mechanical to electrical in AC generators is not entirely optimal,as power losses are converted into heat.Accurate thermal modeling and temperature measurement of advanced electric machines with complex structures are mandatory to confirm their reliability and safe operation.In a unique axial transverse flux switching permanent magnet(ATFSPM)generator,due to its high power density,large stray loss from leakage flux,compact topology,and totally enclosed structure,thermal analysis is of paramount significance.In this paper,thermal modeling and analysis of ATFSPM are carried out in detail using a three-dimensional(3D)finite element analysis(FEA)to evaluate the thermal condition for a precise performance improvement.To begin,all loss sources are accurately derived using 3-D FEA and analytical methods,taking into account the temperature dependence of material properties,and then losses are coupled to the thermal model as heat sources.Afterward,aiming for realistic thermal modelling,the convection heat transfer in the different regions of internal and external areas as well as thin layers of interface gaps between components are all considered.In addition,the prototype of ATFSPM is supplied to validate the accuracy of 3-D FEA temperature prediction.Furthermore,a novel technique is carried out to effectively improve thermal performance,enhance the efficiency,and limit hot-spot temperatures.The steady-state and transient temperature results demonstrate the high accuracy of the thermal modeling,enhance the secure operation of the ATFSPM,and facilitate increased loading utilizing the proposed technique.(1)
基金Hong Kong Research Grants Council under the GRF(9043664).
文摘This article briefly reviews the topic of complex network synchronization,with its graph-theoretic criterion,showing that the homogeneous and symmetrical network structures are essential for optimal synchronization.Furthermore,it briefly reviews the notion of higher-order network topologies and shows their promising potential in application to evaluating the optimality of network synchronizability.
文摘Recent years have seen a surge in interest in object detection on remote sensing images for applications such as surveillance andmanagement.However,challenges like small object detection,scale variation,and the presence of closely packed objects in these images hinder accurate detection.Additionally,the motion blur effect further complicates the identification of such objects.To address these issues,we propose enhanced YOLOv9 with a transformer head(YOLOv9-TH).The model introduces an additional prediction head for detecting objects of varying sizes and swaps the original prediction heads for transformer heads to leverage self-attention mechanisms.We further improve YOLOv9-TH using several strategies,including data augmentation,multi-scale testing,multi-model integration,and the introduction of an additional classifier.The cross-stage partial(CSP)method and the ghost convolution hierarchical graph(GCHG)are combined to improve detection accuracy by better utilizing feature maps,widening the receptive field,and precisely extracting multi-scale objects.Additionally,we incorporate the E-SimAM attention mechanism to address low-resolution feature loss.Extensive experiments on the VisDrone2021 and DIOR datasets demonstrate the effectiveness of YOLOv9-TH,showing good improvement in mAP compared to the best existing methods.The YOLOv9-TH-e achieved 54.2% of mAP50 on the VisDrone2021 dataset and 92.3% of mAP on the DIOR dataset.The results confirmthemodel’s robustness and suitability for real-world applications,particularly for small object detection in remote sensing images.
基金supported by the Science and Technology Project of China Southern Power Grid Co.,Ltd under Grant 036000KC23090004(GDKJXM20231026).
文摘The integration of renewable energy sources(RESs)with inverter interfaces has fundamentally reshaped power system dynamics,challenging traditional stability analysis frameworks designed for synchronous generator-dominated grids.Conventional classifica-tions,which decouple voltage,frequency,and rotor angle stability,fail to address the emerging strong voltage‒angle coupling effects caused by RES dynamics.This coupling introduces complex oscillation modes and undermines system robustness,neces-sitating novel stability assessment tools.Recent studies focus on eigenvalue distributions and damping redistribution but lack quantitative criteria and interpretative clarity for coupled stability.This work proposes a transient energy-based framework to resolve these gaps.By decomposing transient energy into subsystem-dissipated components and coupling-induced energy exchange,the method establishes stability criteria compatible with a broad variety of inverter-interfaced devices while offering an intuitive energy-based interpretation for engineers.The coupling strength is also quantified by defining the relative coupling strength index,which is directly related to the transient energy interpretation of the coupled stability.Angle‒voltage coupling may induce instability by injecting transient energy into the system,even if the individual phase angle and voltage dynamics themselves are stable.The main contributions include a systematic stability evaluation framework and an energy decomposition approach that bridges theoretical analysis with practical applicability,addressing the urgent need for tools for managing modern power system evolving stability challenges.
文摘Skin cancer is the most prevalent cancer globally,primarily due to extensive exposure to Ultraviolet(UV)radiation.Early identification of skin cancer enhances the likelihood of effective treatment,as delays may lead to severe tumor advancement.This study proposes a novel hybrid deep learning strategy to address the complex issue of skin cancer diagnosis,with an architecture that integrates a Vision Transformer,a bespoke convolutional neural network(CNN),and an Xception module.They were evaluated using two benchmark datasets,HAM10000 and Skin Cancer ISIC.On the HAM10000,the model achieves a precision of 95.46%,an accuracy of 96.74%,a recall of 96.27%,specificity of 96.00%and an F1-Score of 95.86%.It obtains an accuracy of 93.19%,a precision of 93.25%,a recall of 92.80%,a specificity of 92.89%and an F1-Score of 93.19%on the Skin Cancer ISIC dataset.The findings demonstrate that the model that was proposed is robust and trustworthy when it comes to the classification of skin lesions.In addition,the utilization of Explainable AI techniques,such as Grad-CAM visualizations,assists in highlighting the most significant lesion areas that have an impact on the decisions that are made by the model.
基金supported by Postgraduate Research&Practice Innovation Program of Jiangsu Province (No. 1003016001)。
文摘Flexible surface micro-discharge plasma is a non-thermal plasma technique used for treating wounds in a painless way, with significant efficacy for chronic or hard-to-heal wounds. In this study, a confined space was designed to simulate wound conditions, with gelatin used to simulate wound tissue. The distinction between open and confined spaces was explored, and the effects of temperature, humidity, discharge power and the gap size within the confined space on the plasma characteristics were analyzed. It was found that temperature, humidity and discharge power are important factors that affect the concentration distribution of active components and the mode transition between ozone and nitrogen oxides. Compared to open space, the concentration of ozone in confined space was relatively lower, which facilitated the formation of nitrogen oxides. In open space, the discharge was dominated by ozone initially. As the temperature,humidity and discharge power increased, nitrogen oxides in the gas-phase products were gradually detected. In confined space, nitrogen oxides can be detected at an early stage and at much higher concentrations than ozone concentration. Furthermore, as the gap of the confined space decreased, the concentration of ozone was observed to decrease while that of nitrate increased, and the rate of this concentration change was further accelerated at higher temperature and higher power. It was shown that ozone concentration decreased from 0.11 to 0.03 μmol and the nitrate concentration increased from 20.5 to 24.5 μmol when the spacing in the confined space was reduced from 5 to 1 mm, the temperature of the external discharge was controlled at 40 ℃, and the discharge power was 12 W. In summary, this study reveals the formation and transformation mechanisms of active substances in air surface micro-discharge plasma within confined space, providing foundational data for its medical applications.
基金supported by the European Research Council,ERC project Liquid3D,grant number 101045072supported by the Foundation of Science and Technology(FCT)of Portugal through the CMU-Portugal project WoW(Reference No:45913).
文摘The demand for sustainable and stretchable thin-film printed batteries for bioelectronics,wearables,and e-textiles is rapidly increasing.Recently,we developed a fully 3D-printed soft-matter thin-film Ga-Ag_(2)O battery with 3R characteristics:resilient to mechanical strain,repairable after damage,and recyclable.This battery achieved a record-breaking areal capacity of 26.37 mAh cm-2,increasing to 30.32 mAh cm^(-2) after 10 cycles under 100%strain.This performance stems from the synergistic effects of gallium’s liquid metal properties and the styrene-isoprene-styrene polymer in the anode.Gallium’s high specific capacity(1153.2 mAh g^(-1)),deformability,and self-healing abilities,supported by its supercooled liquid phase,significantly enhance the battery’s resilience and efficiency.However,the cathode’s lower theoretical capacity,due to Ag_(2)O(231.31 mAh g^(-1)),remains a limitation.Traditional Ag_(2)O-carbon black-styrene-isoprene-styrene cathodes experience rapid capacity decay as only the surface area of the active materials interacts with the electrolyte.To overcome this,we designed a carbon-filled Ag_(2)O foam electrode using a sacrificial sugar template,increasing the effective surface area.This optimization enhanced ion-exchange efficiency,specific capacity,and cyclability,achieving a specific capacity of 221.16 mAh g^(-1).Consequently,the Ga-Ag_(2)O stretchable battery attained a record areal capacity of 40.91 mAh cm^(-2)—double that of nonfoam electrodes—and exhibited fivefold improved charge-discharge cycles.Using ultrastretchable Ag-EGaIn-styrene-isoprene-styrene and carbon black-styrene-isoprene-styrene current collectors,the battery’s specific capacity increased by 33%under 50%strain.Integrated into a soft-matter smart wristband for temperature monitoring,the battery demonstrated its promise for wearable electronics.
文摘Against the backdrop of active global responses to climate change and the accelerated green and low-carbon energy transition,the co-optimization and innovative mechanism design of multimodal energy systems have become a significant instrument for propelling the energy revolution and ensuring energy security.Under increasingly stringent carbon emission constraints,how to achieve multi-dimensional improvements in energy utilization efficiency,renewable energy accommodation levels,and system economics-through the intelligent coupling of diverse energy carriers such as electricity,heat,natural gas,and hydrogen,and the effective application of market-based instruments like carbon trading and demand response-constitutes a critical scientific and engineering challenge demanding urgent solutions.
基金supported by the National Research Foundation of Korea(NRF)(NRF-2021R1A2C1005867)supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2021R1A6A1A03038996).
文摘To overcome the limited electronic conductivity and capacity of single and binary transition metal phos-phates(TMPs),highly electrochemical active materials and rational structural design of ternary TMPs composite are urgently required.In this study,we successfully synthesized an amorphous 3D Ni-Co-Mn phosphate@2D Ti_(3)C_(2)T_(x)(MXene)nanocomposite(NCMP series)through the electrodeposition method.The amorphous Ni-Co-Mn phosphate effectively restricts the self-accumulation of MXene nanosheets,result-ing in the development of a porous nanostructure.This structure exposes more active sites,expands the ion transport path,and enhances the conductivity of the Ni-Co-Mn phosphate@Ti_(3)C_(2)T_(x) material.Owing to the synergistic effect offered by Ni-Co-Mn phosphate and MXene nanocomposite,the anchored Ni-Co-Mn phosphate@Ti_(3)C_(2)T_(x)(NCMP-5)electrode delivers an elevated capacity of 342 mAh/g(1230 C/g)at 5.0 A/g,surpassing the pristine Ni-Co-Mn phosphate(NCMP-4,260 mAh/g)and MXene(33.3 mAh/g).Moreover,a hybrid solid-state supercapacitor(HSSC)device is assembled with NCMP-5 as a cathode and reduced graphene oxide(rGO)as an anode within a polymer gel(PVA-KOH)electrolyte.Notably,the fabricated HSSC device displays a supreme specific capacity of 27.5 mAh/g(99 C/g)and a high(volumetric)energy density of 22 Wh/kg(3.6 Wh/cm^(3))at a power density of 0.80 kW/kg(0.13 kW/cm^(3))for 1.0 A/g.Moreover,the HSSC device retains 95.4%of its initial capacity even after 10,000 cycles.Importantly,the operational potential window of two serially connected HSSC devices approaches+3.2 V,enabling different colored commercial light-emitting diodes(LEDs)to be efficiently illuminated.Eventually,the remarkable super-capacitive characteristics of the 3D@2D amorphous Ni-Co-Mn phosphate@MXene nanocomposite make it an attractive choice for advanced electroactive materials in upcoming hybrid energy storage technologies.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2024-02-01096).
文摘Breast Cancer(BC)remains a leadingmalignancy among women,resulting in highmortality rates.Early and accurate detection is crucial for improving patient outcomes.Traditional diagnostic tools,while effective,have limitations that reduce their accessibility and accuracy.This study investigates the use ofConvolutionalNeuralNetworks(CNNs)to enhance the diagnostic process of BC histopathology.Utilizing the BreakHis dataset,which contains thousands of histopathological images,we developed a CNN model designed to improve the speed and accuracy of image analysis.Our CNN architecture was designed with multiple convolutional layers,max-pooling layers,and a fully connected network optimized for feature extraction and classification.Hyperparameter tuning was conducted to identify the optimal learning rate,batch size,and number of epochs,ensuring robust model performance.The dataset was divided into training(80%),validation(10%),and testing(10%)subsets,with performance evaluated using accuracy,precision,recall,and F1-score metrics.Our CNN model achieved a magnification-independent accuracy of 97.72%,with specific accuracies of 97.50%at 40×,97.61%at 100×,99.06%at 200×,and 97.25%at 400×magnification levels.These results demonstrate the model’s superior performance relative to existing methods.The integration of CNNs in diagnostic workflows can potentially reduce pathologist workload,minimize interpretation errors,and increase the availability of diagnostic testing,thereby improving BC management and patient survival rates.This study highlights the effectiveness of deep learning in automating BC histopathological classification and underscores the potential for AI-driven diagnostic solutions to improve patient care.
文摘In recent years,the increased application of inverter-based resources in power grids,along with the gradual replacement of synchronous generators,has made the grid support capability of inverters essential for maintaining system stability under large disturbances.Critical clearing time provides a quantitative measure of fault severity and system stability,and its sensitivity can help guide parameter adjustments to enhance the grid support capability of inverters.Building on previous researches,this paper proposes a method for calculating critical clearing time sensitivity in power systems with a high proportion of power electronic devices,accounting for the new dynamic characteristics introduced by these devices.The current limit and switching control of inverterbased resources are considered,and the critical clearing time sensitivity under controlling periodic orbits is derived.The proposed critical clearing time sensitivity calculation method is then validated using a double generator single load system and a modified 39-bus system.
