Rapid industrialization advancements have grabbed worldwide attention to integrate a very large number of electronic components into a smaller space for performing multifunctional operations.To fulfill the growing com...Rapid industrialization advancements have grabbed worldwide attention to integrate a very large number of electronic components into a smaller space for performing multifunctional operations.To fulfill the growing computing demand state-of-the-art materials are required for substituting traditional silicon and metal oxide semiconductors frameworks.Two-dimensional(2D)materials have shown their tremendous potential surpassing the limitations of conventional materials for developing smart devices.Despite their ground-breaking progress over the last two decades,systematic studies providing in-depth insights into the exciting physics of 2D materials are still lacking.Therefore,in this review,we discuss the importance of 2D materials in bridging the gap between conventional and advanced technologies due to their distinct statistical and quantum physics.Moreover,the inherent properties of these materials could easily be tailored to meet the specific requirements of smart devices.Hence,we discuss the physics of various 2D materials enabling them to fabricate smart devices.We also shed light on promising opportunities in developing smart devices and identified the formidable challenges that need to be addressed.展开更多
The rapid industrial growth and increasing population have led to significant pollution and deterioration of the natural atmospheric environment.Major atmospheric pollutants include NO_(2)and CO_(2).Hence,it is impera...The rapid industrial growth and increasing population have led to significant pollution and deterioration of the natural atmospheric environment.Major atmospheric pollutants include NO_(2)and CO_(2).Hence,it is imperative to develop NO_(2)and CO_(2)sensors for ambient conditions,that can be used in indoor air quality monitoring,breath analysis,food spoilage detection,etc.In the present study,two thin film nanocomposite(nickel oxide-graphene and nickel oxide-silver nanowires)gas sensors are fabricated using direct ink writing.The nano-composites are investigated for their structural,optical,and electrical properties.Later the nano-composite is deposited on the interdigitated electrode(IDE)pattern to form NO_(2)and CO_(2)sensors.The deposited films are then exposed to NO_(2)and CO_(2)gases separately and their response and recovery times are determined using a custom-built gas sensing setup.Nickel oxide-graphene provides a good response time and recovery time of 10 and 9 s,respectively for NO_(2),due to the higher electron affinity of graphene towards NO_(2).Nickel oxide-silver nanowire nano-composite is suited for CO_(2)gas because silver is an excellent electrocatalyst for CO_(2)by giving response and recovery times of 11 s each.This is the first report showcasing NiO nano-composites for NO_(2)and CO_(2)sensing at room temperature.展开更多
Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models...Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.展开更多
Assessing the behaviour and concentration of waste pollutants deposited between two parallel plates is essential for effective environmental management.Determining the effectiveness of treatment methods in reducing po...Assessing the behaviour and concentration of waste pollutants deposited between two parallel plates is essential for effective environmental management.Determining the effectiveness of treatment methods in reducing pollution scales is made easier by analysing waste discharge concentrations.The waste discharge concentration analysis is useful for assessing how effectively wastewater treatment techniques reduce pollution levels.This study aims to explore the Casson micropolar fluid flow through two parallel plates with the influence of pollutant concentration and thermophoretic particle deposition.To explore the mass and heat transport features,thermophoretic particle deposition and thermal radiation are considered.The governing equations are transformed into ordinary differential equations with the help of suitable similarity transformations.The Runge-Kutta-Fehlberg’s fourthfifth order technique and shooting procedure are used to solve the reduced set of equations and boundary conditions.The integration of a neural network model based on the Levenberg-Marquardt algorithm serves to improve the accuracy of predictions and optimize the analysis of parameters.Graphical outcomes are displayed to analyze the characteristics of the relevant dimensionless parameters in the current problem.Results reveal that concentration upsurges as the micropolar parameter increases.The concentration reduces with an upsurge in the thermophoretic parameter.An upsurge in the external pollutant source variation and the local pollutant external source parameters enhances mass transport.The surface drag force declines for improved values of porosity and micropolar parameters.展开更多
Background:The accurate identification of cardiac abnormalities is essential for proper diagnosis and effective treatment of cardiovascular diseases.Method:This work introduces an advanced methodology for detecting ca...Background:The accurate identification of cardiac abnormalities is essential for proper diagnosis and effective treatment of cardiovascular diseases.Method:This work introduces an advanced methodology for detecting cardiac abnormalities and estimating electrocardiographic age(ECG Age)using sophisticated signal processing and deep learning techniques.This study looks at six main heart conditions found in 12-lead electrocardiogram(ECG)data.It addresses important issues like class imbalances,missing lead scenarios,and model generalizations.A modified residual neural network(ResNet)architecture was developed to enhance the detection of cardiac abnormalities.Results:The proposed ResNet demonst rated superior performance when compared with two linear models and an alternative ResNet architectures,achieving an overall classification accuracy of 91.25%and an F1 score of 93.9%,surpassing baseline models.A comprehensive lead loss analysis was conducted,evaluating model performance across 4096 combinations of missing leads.The results revealed that pulse rate-based factors remained robust with up to 75%lead loss,while block-based factors experienced significant performance declines beyond the loss of four leads.Conclusion:This analysis highlighted the importance of addressing lead loss impacts to maintain a robust model.To optimize performance,targeted training approaches were developed for different conditions.Based on these insights,a grouping strategy was implemented to train specialized models for pulse rate-based and block-based conditions.This approach resulted in notable improvements,achieving an overall classification accuracy of 95.12%and an F1 score of 95.79%.展开更多
The electromagnetic levitation system(EMLS)serves as the most important part of any magnetic levitation system.However,its characteristics are defined by its highly nonlinear dynamics and instability.Furthermore,the u...The electromagnetic levitation system(EMLS)serves as the most important part of any magnetic levitation system.However,its characteristics are defined by its highly nonlinear dynamics and instability.Furthermore,the uncertainties in the dynamics of an electromagnetic levitation system make the controller design more difficult.Therefore,it is necessary to design a robust control law that will ensure the system’s stability in the presence of these uncertainties.In this framework,the dynamics of an electromagnetic levitation system are addressed in terms of matched and unmatched uncertainties.The robust control problem is translated into the optimal control problem,where the uncertainties of the electromagnetic levitation system are directly reflected in the cost function.The optimal control method is used to solve the robust control problem.The solution to the optimal control problem for the electromagnetic levitation system is indeed a solution to the robust control problem of the electromagnetic levitation system under matched and unmatched uncertainties.The simulation and experimental results demonstrate the performance of the designed control scheme.The performance indices such as integral absolute error(IAE),integral square error(ISE),integral time absolute error(ITAE),and integral time square error(ITSE)are compared for both uncertainties to showcase the robustness of the designed control scheme.展开更多
On-device Artificial Intelligence(AI)accelerators capable of not only inference but also training neural network models are in increasing demand in the industrial AI field,where frequent retraining is crucial due to f...On-device Artificial Intelligence(AI)accelerators capable of not only inference but also training neural network models are in increasing demand in the industrial AI field,where frequent retraining is crucial due to frequent production changes.Batch normalization(BN)is fundamental to training convolutional neural networks(CNNs),but its implementation in compact accelerator chips remains challenging due to computational complexity,particularly in calculating statistical parameters and gradients across mini-batches.Existing accelerator architectures either compromise the training accuracy of CNNs through approximations or require substantial computational resources,limiting their practical deployment.We present a hardware-optimized BN accelerator that maintains training accuracy while significantly reducing computational overhead through three novel techniques:(1)resourcesharing for efficient resource utilization across forward and backward passes,(2)interleaved buffering for reduced dynamic random-access memory(DRAM)access latencies,and(3)zero-skipping for minimal gradient computation.Implemented on a VCU118 Field Programmable Gate Array(FPGA)on 100 MHz and validated using You Only Look Once version 2-tiny(YOLOv2-tiny)on the PASCALVisualObjectClasses(VOC)dataset,our normalization accelerator achieves a 72%reduction in processing time and 83%lower power consumption compared to a 2.4 GHz Intel Central Processing Unit(CPU)software normalization implementation,while maintaining accuracy(0.51%mean Average Precision(mAP)drop at floating-point 32 bits(FP32),1.35%at brain floating-point 16 bits(bfloat16)).When integrated into a neural processing unit(NPU),the design demonstrates 63%and 97%performance improvements over AMD CPU and Reduced Instruction Set Computing-V(RISC-V)implementations,respectively.These results confirm that our proposed BN hardware design enables efficient,high-accuracy,and power-saving on-device training for modern CNNs.Our results demonstrate that efficient hardware implementation of standard batch normalization is achievable without sacrificing accuracy,enabling practical on-device CNN training with significantly reduced computational and power requirements.展开更多
In digital signal processing,image enhancement or image denoising are challenging task to preserve pixel quality.There are several approaches from conventional to deep learning that are used to resolve such issues.But...In digital signal processing,image enhancement or image denoising are challenging task to preserve pixel quality.There are several approaches from conventional to deep learning that are used to resolve such issues.But they still face challenges in terms of computational requirements,overfitting and generalization issues,etc.To resolve such issues,optimization algorithms provide greater control and transparency in designing digital filters for image enhancement and denoising.Therefore,this paper presented a novel denoising approach for medical applications using an Optimized Learning⁃based Multi⁃level discrete Wavelet Cascaded Convolutional Neural Network(OLMWCNN).In this approach,the optimal filter parameters are identified to preserve the image quality after denoising.The performance and efficiency of the OLMWCNN filter are evaluated,demonstrating significant progress in denoising medical images while overcoming the limitations of conventional methods.展开更多
A Wireless Sensor Network(WSN)comprises a series of spatially distributed autonomous devices,each equipped with sophisticated sensors.These sensors play a crucial role in monitoring diverse environmental conditions su...A Wireless Sensor Network(WSN)comprises a series of spatially distributed autonomous devices,each equipped with sophisticated sensors.These sensors play a crucial role in monitoring diverse environmental conditions such as light intensity,air pressure,temperature,humidity,wind,etc.These sensors are generally deployed in harsh and hostile conditions;hence they suffer from different kinds of faults.However,identifying faults in WSN data remains a complex task,as existing fault detection methods,including centralized,distributed,and hybrid approaches,rely on the spatio⁃temporal correlation among sensor nodes.Moreover,existing techniques predominantly leverage classification⁃based machine learning methods to discern the fault state within WSN.In this paper,we propose a regression⁃based bagging method to detect the faults in the network.The proposed bagging method is consisted of GRU(Gated Recurrent Unit)and Prophet model.Bagging allows weak learners to combine efforts to outperform a strong learner,hence it is appropriate to use in WSN.The proposed bagging method was first trained at the base station,then they were deployed at each SN(Sensor Node).Most of the common faults in WSN,such as transient,intermittent and permanent faults,were considered.The validity of the proposed scheme was tested using a trusted online published dataset.Using experimental studies,compared to the latest state⁃of⁃the⁃art machine learning models,the effectiveness of the proposed model is shown for fault detection.Performance evaluation in terms of false positive rate,accuracy,and false alarm rate shows the efficiency of the proposed algorithm.展开更多
The hybridization between oxygen 2p and transition-metal 3d states largely determines the electronic structure near the Fermi level and related functionalities of transition-metal oxides(TMOs).Considerable efforts hav...The hybridization between oxygen 2p and transition-metal 3d states largely determines the electronic structure near the Fermi level and related functionalities of transition-metal oxides(TMOs).Considerable efforts have been made to manipulate the p-d hybridization in TMOs by tailoring the spatial orbital overlap via structural engineering.Here,we demonstrate enhanced p-d hybridization in Ba^(2+)-doped LaNiO_(3)epitaxial films by simultaneously modifying both the spatial and energetic overlaps between the O-2p and Ni-3d orbitals.Combining x-ray absorption spectroscopy and firstprinciples calculations,we reveal that the enhanced hybridization stems from the synergistic effects of a reduced chargetransfer energy due to hole injection and an increased spatial orbital overlap due to straightening of Ni-O-Ni bonds.We further show that the enhanced p-d hybridization can be utilized to promote the oxygen evolution activity of LaNiO_(3).This work sheds new insights into the fine-tuning of the electronic structures of TMOs for enhanced functionalities.展开更多
Aqueous zinc(Zn)-ion batteries(AZIBs)have gained significant interest in energy storage due to several unique advantages.Utilizing waterbased electrolytes enhances environmental sustainability,while the abundance and ...Aqueous zinc(Zn)-ion batteries(AZIBs)have gained significant interest in energy storage due to several unique advantages.Utilizing waterbased electrolytes enhances environmental sustainability,while the abundance and affordability of Zn offer economic benefits.Manganese(Mn)-based materials,commonly used as cathodes in these batteries,provide high theoretical capacity,high electrical conductivity,and good structural stability.However,these materials suffer from capacity degradation over repeated cycles due to structural collapse and limited conductivity.To address this problem,we synthesized a magnesium(Mg)-and Mn-based composite,Mg^(2+)-Mn_(3)O_(4),using the hydrothermal method with an optimized amount of ammonium hydroxide(NH_(4)OH)solution.This approach effectively stabilizes the structure during cycling,enhancing both capacity retention and conductivity.The Zn^(2+)/H+intercalation/deintercalation process was confirmed by experimental results and ex-situ X-ray diffraction analysis,which demonstrates that Mg^(2+),along with optimized NH_(4)OH amount,prevents structural collapse and improves conductivity.Under optimal process conditions,the composite electrode(Mg^(2+)-Mn_(3)O_(4)–8 ml)achieved a capacity of 173.58 mA h g^(-1) at 0.5 A g^(-1),with excellent rate performance of 71.39 mA h g^(-1) at 10 A g^(-1).Remarkably,even at 5 A g^(-1),the electrode maintained a capacity of 86.87 mA h g^(-1) over 2100 cycles,underscoring the role of Mg^(2+)and NH_(4)OH in enhancing the reversible insertion/extraction stability of Zn^(2+)in Mn-based layered materials.This study presents a novel strategy for metal-ion incorporation in Mn-based AZIBs,offering insights into the optimization of cathode materials and advancing research on associated storage mechanisms.展开更多
High-performance aqueous zinc(Zn)-ion batteries(AZIBs)have emerged as one of the greatest favorable candidates for next-generation energy storage systems because of their low cost,sustainability,high safety,and eco-fr...High-performance aqueous zinc(Zn)-ion batteries(AZIBs)have emerged as one of the greatest favorable candidates for next-generation energy storage systems because of their low cost,sustainability,high safety,and eco-friendliness.In this report,we prepared magnesium vanadate(MgVO)-based nanostructures by a facile single-step solvothermal method with varying experimental reaction times(1,3,and 6 h)and investigated the effect of the reaction time on the morphology and layered structure for MgVO-based compounds.The newly prepared MgVO-1 h,MgVO-3 h and MgVO-6 h samples were used as cathode materials for AZIBs.Compared to the MgVO-1 h and MgVO-6 h cathodes,the MgVO-3 h cathode showed a higher specific capacity of 492.74 mA h g^(-1) at 1 A g^(-1) over 500 cycles and excellent rate behavior(291.58 mA h g^(-1) at 3.75 A g^(-1))with high cycling stability(116%)over 2000 cycles at 5 A g^(-1).Moreover,the MgVO-3 h electrode exhibited good electrochemical performance owing to its fast Zn-ion diffusion kinetics.Additionally,various ex-situ analyses confirmed that the MgVO-3 h cathode displayed excellent insertion/extraction of Zn^(2+)ions during charge and discharge processes.This study offers an efficient method for the synthesis of nanostructured MgVO-based cathode materials for high-performance AZIBs.展开更多
Subspace-based signal processing methods are fundamentally pre-trained Artificial Neural Networks(ANN)that provide the basic structure for numerous computer vision applications and explore the most promising Earth Obs...Subspace-based signal processing methods are fundamentally pre-trained Artificial Neural Networks(ANN)that provide the basic structure for numerous computer vision applications and explore the most promising Earth Observation Applications(EOA).This paper examines the fundamentals of subspacebased methods and explores the most promising algorithm for forecasting ionospheric signal delays,which was designed explicitly regarding signal and noise subspaces.The learning efficiency derived from the subspace-based components of Singular Spectrum Analysis(SSA)significantly influences the implementation of Linear Recurrent Formula(LRF)and ANN models.The proposed study introduces a novel enhancement to LRF and ANN methodologies for Global Positioning System(GPS)-Total Electron Content(TEC)forecasts based on SSA.The GPS-derived TEC at Bangalore(13.02°N and 77.57°E)location grid during sunspot cycle 25(2020)is considered for analysis.The SSA-LRF-ANN model demonstrates superior accuracy compared with the SSA-LRF,Autoregressive Moving Average(ARMA),and Holt-Winter(HW)models,achieving a correlation of 0.99,a Mean Absolute Error(MAE)of 0.55 TECU,a Mean Absolute Percentage Error(MAPE)of 7.06%,and a Root Mean Square Error(RMSE)of 0.75 TECU.Furthermore,the results and discussions section presents numerical illustrations that showcase the practical implementation of the SSA-LRF-ANN and its application.展开更多
Exploring efficient transition-metal-based electrocatalysts is critical for the wide application of electrochemical hydrogen generation technology.Although the phase displays prominent influence on their performance,i...Exploring efficient transition-metal-based electrocatalysts is critical for the wide application of electrochemical hydrogen generation technology.Although the phase displays prominent influence on their performance,it remains a major challenge to achieve phase regulation in the same synthesis method and elucidate the intrinsic relationship between the phase and activity.Herein,we developed a sulfur induced electrodeposition strategy to achieve the precise phase regulation of nickel-based materials from Ni(OH)_(2)to Ni and Ni_(3)S_(2).S atoms can be introduced into Ni and Ni(OH)_(2)due to sulfur inducement,and the S proportion is finely controlled via changing the deposition parameters.Importantly,the obtained S-Ni catalyst displays enhanced hydrogen evolution activity with an ultralow overpotential of 27 mV at 10 mA cm^(-2),which is superior to the S-Ni(OH)_(2),Ni_(3)S_(2),and even Pt/C.Density functional theory(DFT)calculations disclose the S-Ni catalyst exhibits optimal charge state and local coordination,remarkably optimizing the water adsorption and Ni-H^(*)binding energy.This work provides new insights into phase regulation in electrodeposition and an understanding of the intrinsic relationship between phase and activity.展开更多
The manuscript by Agidew et al,evaluates the critical role of family background and socioeconomic status in shaping breast cancer awareness,attitudes,and preventive behaviors,particularly in low-resource settings.Brea...The manuscript by Agidew et al,evaluates the critical role of family background and socioeconomic status in shaping breast cancer awareness,attitudes,and preventive behaviors,particularly in low-resource settings.Breast cancer continues to be a leading cause of cancer-related deaths globally,with a disproportionate impact on women in low-and middle-income countries.Recent research by Agidew et al underscores a significant association between family history of breast cancer and elevated levels of knowledge,positive attitudes,and proactive behaviors among women in Northeast Ethiopia.Building upon these findings,this editorial explores the psychological mechanisms and behavioral tendencies that drive greater awareness among women with familial exposure to the disease.Additionally,it highlights persistent socioeconomic challenges—such as limited healthcare access,education disparities,and cultural stigmas-that impede widespread preventive action,especially among women without a known family history.The editorial emphasizes the necessity of integrated public health strategies that combine culturally sensitive education,community outreach,and accessible screening services.Drawing from clinical and policy perspectives,it offers guidance on how to strengthen early detection and preventive care in under-resourced environments.Ultimately,the piece advocates for a more inclusive approach to breast cancer education and prevention that addresses both familial influence and systemic socioeconomic barriers.展开更多
The rapid expansion of Internet of Things(IoT)networks has introduced challenges in network management,primarily in maintaining energy efficiency and robust connectivity across an increasing array of devices.This pape...The rapid expansion of Internet of Things(IoT)networks has introduced challenges in network management,primarily in maintaining energy efficiency and robust connectivity across an increasing array of devices.This paper introduces the Adaptive Blended Marine Predators Algorithm(AB-MPA),a novel optimization technique designed to enhance Quality of Service(QoS)in IoT systems by dynamically optimizing network configurations for improved energy efficiency and stability.Our results represent significant improvements in network performance metrics such as energy consumption,throughput,and operational stability,indicating that AB-MPA effectively addresses the pressing needs ofmodern IoT environments.Nodes are initiated with 100 J of stored energy,and energy is consumed at 0.01 J per square meter in each node to emphasize energy-efficient networks.The algorithm also provides sufficient network lifetime extension to a resourceful 7000 cycles for up to 200 nodes with a maximum Packet Delivery Ratio(PDR)of 99% and a robust network throughput of up to 1800 kbps in more compact node configurations.This study proposes a viable solution to a critical problem and opens avenues for further research into scalable network management for diverse applications.展开更多
Ensuring a sustainable and eco-friendly environment is essential for promoting a healthy and balanced social life.However,decision-making in such contexts often involves handling vague,imprecise,and uncertain informat...Ensuring a sustainable and eco-friendly environment is essential for promoting a healthy and balanced social life.However,decision-making in such contexts often involves handling vague,imprecise,and uncertain information.To address this challenge,this study presents a novel multi-criteria decision-making(MCDM)approach based on picture fuzzy hypersoft sets(PFHSS),integrating the flexibility of Schweizer-Sklar triangular norm-based aggregation operators.The proposed aggregation mechanisms—weighted average and weighted geometric operators—are formulated using newly defined operational laws under the PFHSS framework and are proven to satisfy essential mathematical properties,such as idempotency,monotonicity,and boundedness.The decision-making model system-atically incorporates both benefit and cost-type criteria,enabling more nuanced evaluations in complex social or environmental decision problems.To enhance interpretability and practical relevance,the study conducts a sensitivity analysis on the Schweizer-Sklar parameter(Δ).The results show that varyingΔaffects the strictness of aggregation,thereby influencing the ranking stability of alternatives.A comparative analysis with existing fuzzy and hypersoft-based MCDM methods confirms the robustness,expressiveness,and adaptability of the proposed approach.Notably,the use of picture fuzzy sets allows for the inclusion of positive,neutral,and negative memberships,offering a richer representation of expert opinions compared to traditional models.A case study focused on green technology adoption for environmental sustainability illustrates the real-world applicability of the proposed method.The analysis confirms that the approach yields consistent and interpretable results,even under varying degrees of decision uncertainty.Overall,this work contributes an efficient and flexible MCDM tool that can support decision-makers in formulating policies aligned with sustainable and socially responsible outcomes.展开更多
Innovative use of HfO_(2)-based high-dielectric-permittivity materials could enable their integration into few-nanometre-scale devices for storing substantial quantities of electrical charges,which have received wides...Innovative use of HfO_(2)-based high-dielectric-permittivity materials could enable their integration into few-nanometre-scale devices for storing substantial quantities of electrical charges,which have received widespread applications in high-storage-density dynamic random access memory and energy-efficient complementary metal-oxide-semiconductor devices.During bipolar high electric-field cycling in numbers close to dielectric breakdown,the dielectric permittivity suddenly increases by 30 times after oxygen-vacancy ordering and ferroelectric-to-nonferroelectric phase transition of near-edge plasma-treated Hf_(0.5)Zr_(0.5)O_(2) thin-film capacitors.Here we report a much higher dielectric permittivity of 1466 during downscaling of the capacitor into the diameter of 3.85μm when the ferroelectricity suddenly disappears without high-field cycling.The stored charge density is as high as 183μC cm^(−2) at an operating voltage/time of 1.2 V/50 ns at cycle numbers of more than 10^(12) without inducing dielectric breakdown.The study of synchrotron X-ray micro-diffraction patterns show missing of a mixed tetragonal phase.The image of electron energy loss spectroscopy shows the preferred oxygen-vacancy accumulation at the regions near top/bottom electrodes as well as grain boundaries.The ultrahigh dielectric-permittivity material enables high-density integration of extremely scaled logic and memory devices in the future.展开更多
Traditionally,the construction of stable interphases relies on solvent structures dominated by aggregated anionic structures(AGG/AGG+).Nonetheless,we find that the construction of stable interphases in hightemperature...Traditionally,the construction of stable interphases relies on solvent structures dominated by aggregated anionic structures(AGG/AGG+).Nonetheless,we find that the construction of stable interphases in hightemperature environments is based on contact ion pairs(CIPs)dominated solvation structure here.In detail,in the long-chain phosphate ester-based electrolyte,the spatial site-blocking effect enables the strong solvation co-solvent ether(diethylene glycol dimethyl ether,G2)to exhibit strong ion-dipole interactions,further multicomponent competitive coordination maintaining the CIP,balancing electrode kinetics,and optimizing the high-temperature interphases.High-temperature in-situ Raman spectroscopy monitors the changes in the stable solvent structure during charge/discharge processes for the first time,and time of flight secondary ion mass spectrometry(TOF-SIMS)reveals the stable solid electrolyte interphase(SEI)with full-depth enrichment of the inorganic component.Benefiting from the high-temperature interfacial chemistry-dependent solvent structure,the advanced electrolyte enables stable cycling of 1.6 Ah 18650 batterie at 100-125℃and discharging with high current pulses(~1.83 A)at 150℃,which has rarely been reported so far.In addition,pin-pricking of 18650 batteries at100%state of charge(SoC)without fire or smoke and the moderate thermal runaway temperature(187℃)tested via the accelerating rate calorimetry(ARC)demonstrate the excellent safety of the optimized electrolyte.展开更多
The growing spectrum of Generative Adversarial Network (GAN) applications in medical imaging, cyber security, data augmentation, and the field of remote sensing tasks necessitate a sharp spike in the criticality of re...The growing spectrum of Generative Adversarial Network (GAN) applications in medical imaging, cyber security, data augmentation, and the field of remote sensing tasks necessitate a sharp spike in the criticality of review of Generative Adversarial Networks. Earlier reviews that targeted reviewing certain architecture of the GAN or emphasizing a specific application-oriented area have done so in a narrow spirit and lacked the systematic comparative analysis of the models’ performance metrics. Numerous reviews do not apply standardized frameworks, showing gaps in the efficiency evaluation of GANs, training stability, and suitability for specific tasks. In this work, a systemic review of GAN models using the PRISMA framework is developed in detail to fill the gap by structurally evaluating GAN architectures. A wide variety of GAN models have been discussed in this review, starting from the basic Conditional GAN, Wasserstein GAN, and Deep Convolutional GAN, and have gone down to many specialized models, such as EVAGAN, FCGAN, and SIF-GAN, for different applications across various domains like fault diagnosis, network security, medical imaging, and image segmentation. The PRISMA methodology systematically filters relevant studies by inclusion and exclusion criteria to ensure transparency and replicability in the review process. Hence, all models are assessed relative to specific performance metrics such as accuracy, stability, and computational efficiency. There are multiple benefits to using the PRISMA approach in this setup. Not only does this help in finding optimal models suitable for various applications, but it also provides an explicit framework for comparing GAN performance. In addition to this, diverse types of GAN are included to ensure a comprehensive view of the state-of-the-art techniques. This work is essential not only in terms of its result but also because it guides the direction of future research by pinpointing which types of applications require some GAN architectures, works to improve specific task model selection, and points out areas for further research on the development and application of GANs.展开更多
文摘Rapid industrialization advancements have grabbed worldwide attention to integrate a very large number of electronic components into a smaller space for performing multifunctional operations.To fulfill the growing computing demand state-of-the-art materials are required for substituting traditional silicon and metal oxide semiconductors frameworks.Two-dimensional(2D)materials have shown their tremendous potential surpassing the limitations of conventional materials for developing smart devices.Despite their ground-breaking progress over the last two decades,systematic studies providing in-depth insights into the exciting physics of 2D materials are still lacking.Therefore,in this review,we discuss the importance of 2D materials in bridging the gap between conventional and advanced technologies due to their distinct statistical and quantum physics.Moreover,the inherent properties of these materials could easily be tailored to meet the specific requirements of smart devices.Hence,we discuss the physics of various 2D materials enabling them to fabricate smart devices.We also shed light on promising opportunities in developing smart devices and identified the formidable challenges that need to be addressed.
文摘The rapid industrial growth and increasing population have led to significant pollution and deterioration of the natural atmospheric environment.Major atmospheric pollutants include NO_(2)and CO_(2).Hence,it is imperative to develop NO_(2)and CO_(2)sensors for ambient conditions,that can be used in indoor air quality monitoring,breath analysis,food spoilage detection,etc.In the present study,two thin film nanocomposite(nickel oxide-graphene and nickel oxide-silver nanowires)gas sensors are fabricated using direct ink writing.The nano-composites are investigated for their structural,optical,and electrical properties.Later the nano-composite is deposited on the interdigitated electrode(IDE)pattern to form NO_(2)and CO_(2)sensors.The deposited films are then exposed to NO_(2)and CO_(2)gases separately and their response and recovery times are determined using a custom-built gas sensing setup.Nickel oxide-graphene provides a good response time and recovery time of 10 and 9 s,respectively for NO_(2),due to the higher electron affinity of graphene towards NO_(2).Nickel oxide-silver nanowire nano-composite is suited for CO_(2)gas because silver is an excellent electrocatalyst for CO_(2)by giving response and recovery times of 11 s each.This is the first report showcasing NiO nano-composites for NO_(2)and CO_(2)sensing at room temperature.
文摘Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.
文摘Assessing the behaviour and concentration of waste pollutants deposited between two parallel plates is essential for effective environmental management.Determining the effectiveness of treatment methods in reducing pollution scales is made easier by analysing waste discharge concentrations.The waste discharge concentration analysis is useful for assessing how effectively wastewater treatment techniques reduce pollution levels.This study aims to explore the Casson micropolar fluid flow through two parallel plates with the influence of pollutant concentration and thermophoretic particle deposition.To explore the mass and heat transport features,thermophoretic particle deposition and thermal radiation are considered.The governing equations are transformed into ordinary differential equations with the help of suitable similarity transformations.The Runge-Kutta-Fehlberg’s fourthfifth order technique and shooting procedure are used to solve the reduced set of equations and boundary conditions.The integration of a neural network model based on the Levenberg-Marquardt algorithm serves to improve the accuracy of predictions and optimize the analysis of parameters.Graphical outcomes are displayed to analyze the characteristics of the relevant dimensionless parameters in the current problem.Results reveal that concentration upsurges as the micropolar parameter increases.The concentration reduces with an upsurge in the thermophoretic parameter.An upsurge in the external pollutant source variation and the local pollutant external source parameters enhances mass transport.The surface drag force declines for improved values of porosity and micropolar parameters.
文摘Background:The accurate identification of cardiac abnormalities is essential for proper diagnosis and effective treatment of cardiovascular diseases.Method:This work introduces an advanced methodology for detecting cardiac abnormalities and estimating electrocardiographic age(ECG Age)using sophisticated signal processing and deep learning techniques.This study looks at six main heart conditions found in 12-lead electrocardiogram(ECG)data.It addresses important issues like class imbalances,missing lead scenarios,and model generalizations.A modified residual neural network(ResNet)architecture was developed to enhance the detection of cardiac abnormalities.Results:The proposed ResNet demonst rated superior performance when compared with two linear models and an alternative ResNet architectures,achieving an overall classification accuracy of 91.25%and an F1 score of 93.9%,surpassing baseline models.A comprehensive lead loss analysis was conducted,evaluating model performance across 4096 combinations of missing leads.The results revealed that pulse rate-based factors remained robust with up to 75%lead loss,while block-based factors experienced significant performance declines beyond the loss of four leads.Conclusion:This analysis highlighted the importance of addressing lead loss impacts to maintain a robust model.To optimize performance,targeted training approaches were developed for different conditions.Based on these insights,a grouping strategy was implemented to train specialized models for pulse rate-based and block-based conditions.This approach resulted in notable improvements,achieving an overall classification accuracy of 95.12%and an F1 score of 95.79%.
文摘The electromagnetic levitation system(EMLS)serves as the most important part of any magnetic levitation system.However,its characteristics are defined by its highly nonlinear dynamics and instability.Furthermore,the uncertainties in the dynamics of an electromagnetic levitation system make the controller design more difficult.Therefore,it is necessary to design a robust control law that will ensure the system’s stability in the presence of these uncertainties.In this framework,the dynamics of an electromagnetic levitation system are addressed in terms of matched and unmatched uncertainties.The robust control problem is translated into the optimal control problem,where the uncertainties of the electromagnetic levitation system are directly reflected in the cost function.The optimal control method is used to solve the robust control problem.The solution to the optimal control problem for the electromagnetic levitation system is indeed a solution to the robust control problem of the electromagnetic levitation system under matched and unmatched uncertainties.The simulation and experimental results demonstrate the performance of the designed control scheme.The performance indices such as integral absolute error(IAE),integral square error(ISE),integral time absolute error(ITAE),and integral time square error(ITSE)are compared for both uncertainties to showcase the robustness of the designed control scheme.
基金supported by the National Research Foundation of Korea(NRF)grant for RLRC funded by the Korea government(MSIT)(No.2022R1A5A8026986,RLRC)supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2020-0-01304,Development of Self-Learnable Mobile Recursive Neural Network Processor Technology)+3 种基金supported by the MSIT(Ministry of Science and ICT),Republic of Korea,under the Grand Information Technology Research Center support program(IITP-2024-2020-0-01462,Grand-ICT)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation)supported by the Korea Technology and Information Promotion Agency for SMEs(TIPA)supported by the Korean government(Ministry of SMEs and Startups)’s Smart Manufacturing Innovation R&D(RS-2024-00434259).
文摘On-device Artificial Intelligence(AI)accelerators capable of not only inference but also training neural network models are in increasing demand in the industrial AI field,where frequent retraining is crucial due to frequent production changes.Batch normalization(BN)is fundamental to training convolutional neural networks(CNNs),but its implementation in compact accelerator chips remains challenging due to computational complexity,particularly in calculating statistical parameters and gradients across mini-batches.Existing accelerator architectures either compromise the training accuracy of CNNs through approximations or require substantial computational resources,limiting their practical deployment.We present a hardware-optimized BN accelerator that maintains training accuracy while significantly reducing computational overhead through three novel techniques:(1)resourcesharing for efficient resource utilization across forward and backward passes,(2)interleaved buffering for reduced dynamic random-access memory(DRAM)access latencies,and(3)zero-skipping for minimal gradient computation.Implemented on a VCU118 Field Programmable Gate Array(FPGA)on 100 MHz and validated using You Only Look Once version 2-tiny(YOLOv2-tiny)on the PASCALVisualObjectClasses(VOC)dataset,our normalization accelerator achieves a 72%reduction in processing time and 83%lower power consumption compared to a 2.4 GHz Intel Central Processing Unit(CPU)software normalization implementation,while maintaining accuracy(0.51%mean Average Precision(mAP)drop at floating-point 32 bits(FP32),1.35%at brain floating-point 16 bits(bfloat16)).When integrated into a neural processing unit(NPU),the design demonstrates 63%and 97%performance improvements over AMD CPU and Reduced Instruction Set Computing-V(RISC-V)implementations,respectively.These results confirm that our proposed BN hardware design enables efficient,high-accuracy,and power-saving on-device training for modern CNNs.Our results demonstrate that efficient hardware implementation of standard batch normalization is achievable without sacrificing accuracy,enabling practical on-device CNN training with significantly reduced computational and power requirements.
文摘In digital signal processing,image enhancement or image denoising are challenging task to preserve pixel quality.There are several approaches from conventional to deep learning that are used to resolve such issues.But they still face challenges in terms of computational requirements,overfitting and generalization issues,etc.To resolve such issues,optimization algorithms provide greater control and transparency in designing digital filters for image enhancement and denoising.Therefore,this paper presented a novel denoising approach for medical applications using an Optimized Learning⁃based Multi⁃level discrete Wavelet Cascaded Convolutional Neural Network(OLMWCNN).In this approach,the optimal filter parameters are identified to preserve the image quality after denoising.The performance and efficiency of the OLMWCNN filter are evaluated,demonstrating significant progress in denoising medical images while overcoming the limitations of conventional methods.
文摘A Wireless Sensor Network(WSN)comprises a series of spatially distributed autonomous devices,each equipped with sophisticated sensors.These sensors play a crucial role in monitoring diverse environmental conditions such as light intensity,air pressure,temperature,humidity,wind,etc.These sensors are generally deployed in harsh and hostile conditions;hence they suffer from different kinds of faults.However,identifying faults in WSN data remains a complex task,as existing fault detection methods,including centralized,distributed,and hybrid approaches,rely on the spatio⁃temporal correlation among sensor nodes.Moreover,existing techniques predominantly leverage classification⁃based machine learning methods to discern the fault state within WSN.In this paper,we propose a regression⁃based bagging method to detect the faults in the network.The proposed bagging method is consisted of GRU(Gated Recurrent Unit)and Prophet model.Bagging allows weak learners to combine efforts to outperform a strong learner,hence it is appropriate to use in WSN.The proposed bagging method was first trained at the base station,then they were deployed at each SN(Sensor Node).Most of the common faults in WSN,such as transient,intermittent and permanent faults,were considered.The validity of the proposed scheme was tested using a trusted online published dataset.Using experimental studies,compared to the latest state⁃of⁃the⁃art machine learning models,the effectiveness of the proposed model is shown for fault detection.Performance evaluation in terms of false positive rate,accuracy,and false alarm rate shows the efficiency of the proposed algorithm.
基金supported by the National Key R&D Program of China(Grant No.2022YFA1402902)the National Natural Science Foundation of China(Grant Nos.12374179,12074119,12374145,051B22001,12104157,12134003,and 12304218)the Shanghai Pujiang Program(Grant No.23PJ1402200).
文摘The hybridization between oxygen 2p and transition-metal 3d states largely determines the electronic structure near the Fermi level and related functionalities of transition-metal oxides(TMOs).Considerable efforts have been made to manipulate the p-d hybridization in TMOs by tailoring the spatial orbital overlap via structural engineering.Here,we demonstrate enhanced p-d hybridization in Ba^(2+)-doped LaNiO_(3)epitaxial films by simultaneously modifying both the spatial and energetic overlaps between the O-2p and Ni-3d orbitals.Combining x-ray absorption spectroscopy and firstprinciples calculations,we reveal that the enhanced hybridization stems from the synergistic effects of a reduced chargetransfer energy due to hole injection and an increased spatial orbital overlap due to straightening of Ni-O-Ni bonds.We further show that the enhanced p-d hybridization can be utilized to promote the oxygen evolution activity of LaNiO_(3).This work sheds new insights into the fine-tuning of the electronic structures of TMOs for enhanced functionalities.
基金supported by a National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(No.2018R1A6A1A03025708).
文摘Aqueous zinc(Zn)-ion batteries(AZIBs)have gained significant interest in energy storage due to several unique advantages.Utilizing waterbased electrolytes enhances environmental sustainability,while the abundance and affordability of Zn offer economic benefits.Manganese(Mn)-based materials,commonly used as cathodes in these batteries,provide high theoretical capacity,high electrical conductivity,and good structural stability.However,these materials suffer from capacity degradation over repeated cycles due to structural collapse and limited conductivity.To address this problem,we synthesized a magnesium(Mg)-and Mn-based composite,Mg^(2+)-Mn_(3)O_(4),using the hydrothermal method with an optimized amount of ammonium hydroxide(NH_(4)OH)solution.This approach effectively stabilizes the structure during cycling,enhancing both capacity retention and conductivity.The Zn^(2+)/H+intercalation/deintercalation process was confirmed by experimental results and ex-situ X-ray diffraction analysis,which demonstrates that Mg^(2+),along with optimized NH_(4)OH amount,prevents structural collapse and improves conductivity.Under optimal process conditions,the composite electrode(Mg^(2+)-Mn_(3)O_(4)–8 ml)achieved a capacity of 173.58 mA h g^(-1) at 0.5 A g^(-1),with excellent rate performance of 71.39 mA h g^(-1) at 10 A g^(-1).Remarkably,even at 5 A g^(-1),the electrode maintained a capacity of 86.87 mA h g^(-1) over 2100 cycles,underscoring the role of Mg^(2+)and NH_(4)OH in enhancing the reversible insertion/extraction stability of Zn^(2+)in Mn-based layered materials.This study presents a novel strategy for metal-ion incorporation in Mn-based AZIBs,offering insights into the optimization of cathode materials and advancing research on associated storage mechanisms.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIP)(No.2018R1A6A1A03025708).
文摘High-performance aqueous zinc(Zn)-ion batteries(AZIBs)have emerged as one of the greatest favorable candidates for next-generation energy storage systems because of their low cost,sustainability,high safety,and eco-friendliness.In this report,we prepared magnesium vanadate(MgVO)-based nanostructures by a facile single-step solvothermal method with varying experimental reaction times(1,3,and 6 h)and investigated the effect of the reaction time on the morphology and layered structure for MgVO-based compounds.The newly prepared MgVO-1 h,MgVO-3 h and MgVO-6 h samples were used as cathode materials for AZIBs.Compared to the MgVO-1 h and MgVO-6 h cathodes,the MgVO-3 h cathode showed a higher specific capacity of 492.74 mA h g^(-1) at 1 A g^(-1) over 500 cycles and excellent rate behavior(291.58 mA h g^(-1) at 3.75 A g^(-1))with high cycling stability(116%)over 2000 cycles at 5 A g^(-1).Moreover,the MgVO-3 h electrode exhibited good electrochemical performance owing to its fast Zn-ion diffusion kinetics.Additionally,various ex-situ analyses confirmed that the MgVO-3 h cathode displayed excellent insertion/extraction of Zn^(2+)ions during charge and discharge processes.This study offers an efficient method for the synthesis of nanostructured MgVO-based cathode materials for high-performance AZIBs.
文摘Subspace-based signal processing methods are fundamentally pre-trained Artificial Neural Networks(ANN)that provide the basic structure for numerous computer vision applications and explore the most promising Earth Observation Applications(EOA).This paper examines the fundamentals of subspacebased methods and explores the most promising algorithm for forecasting ionospheric signal delays,which was designed explicitly regarding signal and noise subspaces.The learning efficiency derived from the subspace-based components of Singular Spectrum Analysis(SSA)significantly influences the implementation of Linear Recurrent Formula(LRF)and ANN models.The proposed study introduces a novel enhancement to LRF and ANN methodologies for Global Positioning System(GPS)-Total Electron Content(TEC)forecasts based on SSA.The GPS-derived TEC at Bangalore(13.02°N and 77.57°E)location grid during sunspot cycle 25(2020)is considered for analysis.The SSA-LRF-ANN model demonstrates superior accuracy compared with the SSA-LRF,Autoregressive Moving Average(ARMA),and Holt-Winter(HW)models,achieving a correlation of 0.99,a Mean Absolute Error(MAE)of 0.55 TECU,a Mean Absolute Percentage Error(MAPE)of 7.06%,and a Root Mean Square Error(RMSE)of 0.75 TECU.Furthermore,the results and discussions section presents numerical illustrations that showcase the practical implementation of the SSA-LRF-ANN and its application.
基金supported by the National Natural Science Foundation of China(52271210,22179032,52171176)。
文摘Exploring efficient transition-metal-based electrocatalysts is critical for the wide application of electrochemical hydrogen generation technology.Although the phase displays prominent influence on their performance,it remains a major challenge to achieve phase regulation in the same synthesis method and elucidate the intrinsic relationship between the phase and activity.Herein,we developed a sulfur induced electrodeposition strategy to achieve the precise phase regulation of nickel-based materials from Ni(OH)_(2)to Ni and Ni_(3)S_(2).S atoms can be introduced into Ni and Ni(OH)_(2)due to sulfur inducement,and the S proportion is finely controlled via changing the deposition parameters.Importantly,the obtained S-Ni catalyst displays enhanced hydrogen evolution activity with an ultralow overpotential of 27 mV at 10 mA cm^(-2),which is superior to the S-Ni(OH)_(2),Ni_(3)S_(2),and even Pt/C.Density functional theory(DFT)calculations disclose the S-Ni catalyst exhibits optimal charge state and local coordination,remarkably optimizing the water adsorption and Ni-H^(*)binding energy.This work provides new insights into phase regulation in electrodeposition and an understanding of the intrinsic relationship between phase and activity.
文摘The manuscript by Agidew et al,evaluates the critical role of family background and socioeconomic status in shaping breast cancer awareness,attitudes,and preventive behaviors,particularly in low-resource settings.Breast cancer continues to be a leading cause of cancer-related deaths globally,with a disproportionate impact on women in low-and middle-income countries.Recent research by Agidew et al underscores a significant association between family history of breast cancer and elevated levels of knowledge,positive attitudes,and proactive behaviors among women in Northeast Ethiopia.Building upon these findings,this editorial explores the psychological mechanisms and behavioral tendencies that drive greater awareness among women with familial exposure to the disease.Additionally,it highlights persistent socioeconomic challenges—such as limited healthcare access,education disparities,and cultural stigmas-that impede widespread preventive action,especially among women without a known family history.The editorial emphasizes the necessity of integrated public health strategies that combine culturally sensitive education,community outreach,and accessible screening services.Drawing from clinical and policy perspectives,it offers guidance on how to strengthen early detection and preventive care in under-resourced environments.Ultimately,the piece advocates for a more inclusive approach to breast cancer education and prevention that addresses both familial influence and systemic socioeconomic barriers.
文摘The rapid expansion of Internet of Things(IoT)networks has introduced challenges in network management,primarily in maintaining energy efficiency and robust connectivity across an increasing array of devices.This paper introduces the Adaptive Blended Marine Predators Algorithm(AB-MPA),a novel optimization technique designed to enhance Quality of Service(QoS)in IoT systems by dynamically optimizing network configurations for improved energy efficiency and stability.Our results represent significant improvements in network performance metrics such as energy consumption,throughput,and operational stability,indicating that AB-MPA effectively addresses the pressing needs ofmodern IoT environments.Nodes are initiated with 100 J of stored energy,and energy is consumed at 0.01 J per square meter in each node to emphasize energy-efficient networks.The algorithm also provides sufficient network lifetime extension to a resourceful 7000 cycles for up to 200 nodes with a maximum Packet Delivery Ratio(PDR)of 99% and a robust network throughput of up to 1800 kbps in more compact node configurations.This study proposes a viable solution to a critical problem and opens avenues for further research into scalable network management for diverse applications.
基金supported by the National Natural Science Foundation of China(No.62172095).
文摘Ensuring a sustainable and eco-friendly environment is essential for promoting a healthy and balanced social life.However,decision-making in such contexts often involves handling vague,imprecise,and uncertain information.To address this challenge,this study presents a novel multi-criteria decision-making(MCDM)approach based on picture fuzzy hypersoft sets(PFHSS),integrating the flexibility of Schweizer-Sklar triangular norm-based aggregation operators.The proposed aggregation mechanisms—weighted average and weighted geometric operators—are formulated using newly defined operational laws under the PFHSS framework and are proven to satisfy essential mathematical properties,such as idempotency,monotonicity,and boundedness.The decision-making model system-atically incorporates both benefit and cost-type criteria,enabling more nuanced evaluations in complex social or environmental decision problems.To enhance interpretability and practical relevance,the study conducts a sensitivity analysis on the Schweizer-Sklar parameter(Δ).The results show that varyingΔaffects the strictness of aggregation,thereby influencing the ranking stability of alternatives.A comparative analysis with existing fuzzy and hypersoft-based MCDM methods confirms the robustness,expressiveness,and adaptability of the proposed approach.Notably,the use of picture fuzzy sets allows for the inclusion of positive,neutral,and negative memberships,offering a richer representation of expert opinions compared to traditional models.A case study focused on green technology adoption for environmental sustainability illustrates the real-world applicability of the proposed method.The analysis confirms that the approach yields consistent and interpretable results,even under varying degrees of decision uncertainty.Overall,this work contributes an efficient and flexible MCDM tool that can support decision-makers in formulating policies aligned with sustainable and socially responsible outcomes.
基金supported by the National Key Basic Research Program of China (2022YFA1402904)Basic Research Project of Shanghai Science and Technology Innovation Action (grant number 24CL2900900)the National Natural Science Foundation of China (grant number 61904034)
文摘Innovative use of HfO_(2)-based high-dielectric-permittivity materials could enable their integration into few-nanometre-scale devices for storing substantial quantities of electrical charges,which have received widespread applications in high-storage-density dynamic random access memory and energy-efficient complementary metal-oxide-semiconductor devices.During bipolar high electric-field cycling in numbers close to dielectric breakdown,the dielectric permittivity suddenly increases by 30 times after oxygen-vacancy ordering and ferroelectric-to-nonferroelectric phase transition of near-edge plasma-treated Hf_(0.5)Zr_(0.5)O_(2) thin-film capacitors.Here we report a much higher dielectric permittivity of 1466 during downscaling of the capacitor into the diameter of 3.85μm when the ferroelectricity suddenly disappears without high-field cycling.The stored charge density is as high as 183μC cm^(−2) at an operating voltage/time of 1.2 V/50 ns at cycle numbers of more than 10^(12) without inducing dielectric breakdown.The study of synchrotron X-ray micro-diffraction patterns show missing of a mixed tetragonal phase.The image of electron energy loss spectroscopy shows the preferred oxygen-vacancy accumulation at the regions near top/bottom electrodes as well as grain boundaries.The ultrahigh dielectric-permittivity material enables high-density integration of extremely scaled logic and memory devices in the future.
基金supported by the National Natural Science Foundation of China(grant no.52072322,52202235)the Department of Science and Technology of Sichuan Province(CN)(grant no.23GJHZ0147)the Research and Innovation Fund for Graduate Students of Southwest Petroleum University(No.:2022KYCX111)。
文摘Traditionally,the construction of stable interphases relies on solvent structures dominated by aggregated anionic structures(AGG/AGG+).Nonetheless,we find that the construction of stable interphases in hightemperature environments is based on contact ion pairs(CIPs)dominated solvation structure here.In detail,in the long-chain phosphate ester-based electrolyte,the spatial site-blocking effect enables the strong solvation co-solvent ether(diethylene glycol dimethyl ether,G2)to exhibit strong ion-dipole interactions,further multicomponent competitive coordination maintaining the CIP,balancing electrode kinetics,and optimizing the high-temperature interphases.High-temperature in-situ Raman spectroscopy monitors the changes in the stable solvent structure during charge/discharge processes for the first time,and time of flight secondary ion mass spectrometry(TOF-SIMS)reveals the stable solid electrolyte interphase(SEI)with full-depth enrichment of the inorganic component.Benefiting from the high-temperature interfacial chemistry-dependent solvent structure,the advanced electrolyte enables stable cycling of 1.6 Ah 18650 batterie at 100-125℃and discharging with high current pulses(~1.83 A)at 150℃,which has rarely been reported so far.In addition,pin-pricking of 18650 batteries at100%state of charge(SoC)without fire or smoke and the moderate thermal runaway temperature(187℃)tested via the accelerating rate calorimetry(ARC)demonstrate the excellent safety of the optimized electrolyte.
文摘The growing spectrum of Generative Adversarial Network (GAN) applications in medical imaging, cyber security, data augmentation, and the field of remote sensing tasks necessitate a sharp spike in the criticality of review of Generative Adversarial Networks. Earlier reviews that targeted reviewing certain architecture of the GAN or emphasizing a specific application-oriented area have done so in a narrow spirit and lacked the systematic comparative analysis of the models’ performance metrics. Numerous reviews do not apply standardized frameworks, showing gaps in the efficiency evaluation of GANs, training stability, and suitability for specific tasks. In this work, a systemic review of GAN models using the PRISMA framework is developed in detail to fill the gap by structurally evaluating GAN architectures. A wide variety of GAN models have been discussed in this review, starting from the basic Conditional GAN, Wasserstein GAN, and Deep Convolutional GAN, and have gone down to many specialized models, such as EVAGAN, FCGAN, and SIF-GAN, for different applications across various domains like fault diagnosis, network security, medical imaging, and image segmentation. The PRISMA methodology systematically filters relevant studies by inclusion and exclusion criteria to ensure transparency and replicability in the review process. Hence, all models are assessed relative to specific performance metrics such as accuracy, stability, and computational efficiency. There are multiple benefits to using the PRISMA approach in this setup. Not only does this help in finding optimal models suitable for various applications, but it also provides an explicit framework for comparing GAN performance. In addition to this, diverse types of GAN are included to ensure a comprehensive view of the state-of-the-art techniques. This work is essential not only in terms of its result but also because it guides the direction of future research by pinpointing which types of applications require some GAN architectures, works to improve specific task model selection, and points out areas for further research on the development and application of GANs.
