Let G be a finite group and H a subgroup of G.The normal index of H in G is defined as the order of K/H_(G),where K is a normal supplement of H in G such that|K|is minimal and H_(G)≤K■G.Let p be a prime which divide...Let G be a finite group and H a subgroup of G.The normal index of H in G is defined as the order of K/H_(G),where K is a normal supplement of H in G such that|K|is minimal and H_(G)≤K■G.Let p be a prime which divides the order of a group G.In this paper,some characterizations of G being p-solvable or p-supersolvable were obtained by analyzing the normal index of certain subgroups of G.These results can be viewed as local version of recent results in the literature.展开更多
Artificial intelligence(AI)based models have been used to predict the structural,optical,mechanical,and electrochemical properties of zinc oxide/graphene oxide nanocomposites.Machine learning(ML)models such as Artific...Artificial intelligence(AI)based models have been used to predict the structural,optical,mechanical,and electrochemical properties of zinc oxide/graphene oxide nanocomposites.Machine learning(ML)models such as Artificial Neural Networks(ANN),Support Vector Regression(SVR),Multilayer Perceptron(MLP),and hybrid,along with fuzzy logic tools,were applied to predict the different properties like wavelength at maximum intensity(444 nm),crystallite size(17.50 nm),and optical bandgap(2.85 eV).While some other properties,such as energy density,power density,and charge transfer resistance,were also predicted with the help of datasets of 1000(80:20).In general,the energy parameters were predicted more accurately by hybrid models.The hydrothermal method was used to synthesize graphene oxide(GO)and zinc oxide(ZnO)nanocomposites.The increased surface area,conductivity,and stability of graphene oxide in zinc oxide nanoparticles make the composite an ideal option for energy storage.X-ray diffraction(XRD)confirmed the crystallite size of 17.41 nm for the nanocomposite and the presence of GO(12.8○)peaks.The scanning electron microscope(SEM)showed anchored wrinkled GO sheets on zinc oxide with an average particle size of 2.93μm.Energy-dispersive X-ray spectroscopy(EDX)confirmed the elemental composition,and Fouriertransform infrared spectroscopy(FTIR)revealed the impact of GO on functional groups and electrochemical behavior.Photoluminescence(PL)wavelength of(439 nm)and band gap of(2.81 eV)show that the material is suitable for energy applications in nanocomposites.Smart nanocomposite materials with improved performance in energy storage and related applications were fabricated by combining synthesis,characterization,fuzzy logic,and machine learning in this work.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
Over the years,Generative Adversarial Networks(GANs)have revolutionized the medical imaging industry for applications such as image synthesis,denoising,super resolution,data augmentation,and cross-modality translation...Over the years,Generative Adversarial Networks(GANs)have revolutionized the medical imaging industry for applications such as image synthesis,denoising,super resolution,data augmentation,and cross-modality translation.The objective of this review is to evaluate the advances,relevances,and limitations of GANs in medical imaging.An organised literature review was conducted following the guidelines of PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses).The literature considered included peer-reviewed papers published between 2020 and 2025 across databases including PubMed,IEEE Xplore,and Scopus.The studies related to applications of GAN architectures in medical imaging with reported experimental outcomes and published in English in reputable journals and conferences were considered for the review.Thesis,white papers,communication letters,and non-English articles were not included for the same.CLAIM based quality assessment criteria were applied to the included studies to assess the quality.The study classifies diverse GAN architectures,summarizing their clinical applications,technical performances,and their implementation hardships.Key findings reveal the increasing applications of GANs for enhancing diagnostic accuracy,reducing data scarcity through synthetic data generation,and supporting modality translation.However,concerns such as limited generalizability,lack of clinical validation,and regulatory constraints persist.This review provides a comprehensive study of the prevailing scenario of GANs in medical imaging and highlights crucial research gaps and future directions.Though GANs hold transformative capability for medical imaging,their integration into clinical use demands further validation,interpretability,and regulatory alignment.展开更多
Colorectal cancer is the third most diagnosed cancer worldwide,and immune checkpoint inhibitors have shown promising therapeutic outcomes in selected patient groups.This study performed a comprehensive analysis of mul...Colorectal cancer is the third most diagnosed cancer worldwide,and immune checkpoint inhibitors have shown promising therapeutic outcomes in selected patient groups.This study performed a comprehensive analysis of multi-omics data from The Cancer Genome Atlas colorectal adenocarcinoma cohort(TCGA-COADREAD),accessed through cBioPortal,to develop machine learning models for predicting progression-free survival(PFS)following immunotherapy.The dataset included clinical variables,genomic alterations in Kirsten Rat Sarcoma Viral Oncogene Homolog(KRAS),B-Raf Proto-Oncogene(BRAF),and Neuroblastoma RAS Viral Oncogene Homolog(NRAS),microsatellite instability(MSI)status,tumor mutation burden(TMB),and expression of immune checkpoint genes.Kaplan–Meier analysis showed that KRAS mutations were significantly associated with reduced PFS,while BRAF and NRAS mutations had no significant impact.MSI-high tumors exhibited elevated TMB and increased immune checkpoint expression,reflecting their immunologically active phenotype.We developed both survival and classification models,with the Extra Trees classifier achieving the best performance(accuracy=0.86,precision=0.67,recall=0.70,F1-score=0.68,AUC=0.84).These findings highlight the potential of combining genomic and immune biomarkers with machine learning to improve patient stratification and guide personalized immunotherapy decisions.An interactive web application was also developed to enable clinicians to input patient-specific molecular and clinical data and visualize individualized PFS predictions,supporting timely,data-driven treatment planning.展开更多
The innovative development of Wearable Electronics (WE) is creating exciting opportunities for application across many industries. Two sectors with high potential are healthcare and childcare. However, it is in these ...The innovative development of Wearable Electronics (WE) is creating exciting opportunities for application across many industries. Two sectors with high potential are healthcare and childcare. However, it is in these two sectors where the challenges of privacy are presumed to be of the highest. In order to ascertain the personal views of people about potential privacy problems in WE application in these two sectors, interviews with questionnaires were conducted in two different countries: Finland and the United Kingdom (UK). The results indicated that the majority of people in both countries are positive about the use of WE in healthcare and childcare environments. However, when more information is added to be read wirelessly, the attitudes become more negative. In general, the application of WE is more favorable in the UK and the reason as to the difference will make for interesting further research. Several interesting viewpoints and concerns were presented in the interviews. It can be concluded that the implementation of WE in these two sectors will require the collaboration of work on several areas and the development of versatile user studies.展开更多
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
This paper presents two four-quadrant topologies for Permanent Magnet Direct Current （PMDC） motor drives, built using combination of power electronics switches. The issue is the increased efficiency of the topology ...This paper presents two four-quadrant topologies for Permanent Magnet Direct Current （PMDC） motor drives, built using combination of power electronics switches. The issue is the increased efficiency of the topology compared to conventional ones built using only one type of power electronic switches. The suggested combinations-MOSFET-IGBT and MOSFET-SCR improve the current bridge topologies by uniting the advantages of the different switches-the high switching capabilities of the MOSFET and the better antiparallel diodes of IGBTs and SCRs. The total efficiency of the motor control is improved by several percents. This reduces the overall consumption of the converter circuitry, which can be very beneficial-especially at high power motors, as the ones presented in the paper. Statistical and experimental data is presented proving the efficiency of the suggest topologies.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
This paper proposes a linear companding transform(CT)using either a single inflection point or two inflection points to reduce the peakto-average power ratio(PAPR)in orthogonal timefrequency space(OTFS)signals.The CT ...This paper proposes a linear companding transform(CT)using either a single inflection point or two inflection points to reduce the peakto-average power ratio(PAPR)in orthogonal timefrequency space(OTFS)signals.The CT strategically compresses higher amplitudes and enhances lower amplitudes based on carefully chosen scaling factors and points of inflection.With these selected parameters,the CT effectively reduces peak power while maintaining average power,leading to a substantial decrease in PAPR.We analyze noise changes in the inverse companding transform(ICT)process.The analysis reveals that the ICT amplifies less than 20%of the total noise.A convolutional encoder and soft decision Viterbi decoding algorithm are utilized in the OTFS system to improve the detection performance.We present simulation results focusing on PAPR reduction and bit error rate(BER)performance.These results demonstrate that the CT with two inflection points outperforms both the single inflection point case and the existingμ-law companding,clipping,peak windowing,unique OTFS frame structure,selected mapping,and partial transmit sequence methods,achieving significant PAPR reduction and BER performance.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
The effects of climate change are becoming more evident nowadays,and the environmental stress imposed on crops has become more severe.Farmers around the globe continually seek ways to gain insights into crop health an...The effects of climate change are becoming more evident nowadays,and the environmental stress imposed on crops has become more severe.Farmers around the globe continually seek ways to gain insights into crop health and provide mitigation as early as possible.Phenotyping is a non-destructive method for assessing crop responses to environmental stresses and can be performed using airborne systems.Unmanned Aerial Systems(UAS)have significantly contributed to high-throughput phenotyping andmade the process rapid,efficient,and non-invasive for collecting large-scale agronomic data.Because of the high complexity and cost of specialized equipment used in aerial phenotyping,such as multispectral and hyperspectral cameras as well as lidar,this study proposes a framework for implementing aerial phenotyping where chlorophyll estimation,leaf count,and coverage are determined using the RGB(Red,Green and Blue)camera native to a UAS.Thestudy proposes the Dynamic Coefficient Triangular Greenness Index(DCTGI)for aerial phenotyping.Evaluation of the proposed DCTGI includes the correlation with chlorophyll content estimated using a Soil Plant Analysis Development(SPAD)chlorophyll meter on randomly sampled Liberica coffee seedlings.Analysis revealed a strong relationship between DCTGI values and chlorophyll estimates derived from SPAD measurements,with a Pearson’s correlation coefficient of 0.912.However,the study didn’t implement tissue-level validation and field-scale temporal analysis to assess seasonal variability.In addition,the SPAD meter provided the approximate nitrogen content together with the chlorohyll estimate.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
基金Supported by the National Natural Science Foundation of China(Grant No.12071092)Guangdong Basic and Applied Basic Research Foundation(Grant No.2025A1515012072)+1 种基金the Natural Science Research Project of Anhui Educational Committee(Grant No.2024AH051298)the Scientific Research Foundation of Bozhou University(Grant No.BYKQ202419).
文摘Let G be a finite group and H a subgroup of G.The normal index of H in G is defined as the order of K/H_(G),where K is a normal supplement of H in G such that|K|is minimal and H_(G)≤K■G.Let p be a prime which divides the order of a group G.In this paper,some characterizations of G being p-solvable or p-supersolvable were obtained by analyzing the normal index of certain subgroups of G.These results can be viewed as local version of recent results in the literature.
基金extend their gratitude to the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia,for funding the publication of this work under the Ambitious Researcher program(Project No.KFU253806).
文摘Artificial intelligence(AI)based models have been used to predict the structural,optical,mechanical,and electrochemical properties of zinc oxide/graphene oxide nanocomposites.Machine learning(ML)models such as Artificial Neural Networks(ANN),Support Vector Regression(SVR),Multilayer Perceptron(MLP),and hybrid,along with fuzzy logic tools,were applied to predict the different properties like wavelength at maximum intensity(444 nm),crystallite size(17.50 nm),and optical bandgap(2.85 eV).While some other properties,such as energy density,power density,and charge transfer resistance,were also predicted with the help of datasets of 1000(80:20).In general,the energy parameters were predicted more accurately by hybrid models.The hydrothermal method was used to synthesize graphene oxide(GO)and zinc oxide(ZnO)nanocomposites.The increased surface area,conductivity,and stability of graphene oxide in zinc oxide nanoparticles make the composite an ideal option for energy storage.X-ray diffraction(XRD)confirmed the crystallite size of 17.41 nm for the nanocomposite and the presence of GO(12.8○)peaks.The scanning electron microscope(SEM)showed anchored wrinkled GO sheets on zinc oxide with an average particle size of 2.93μm.Energy-dispersive X-ray spectroscopy(EDX)confirmed the elemental composition,and Fouriertransform infrared spectroscopy(FTIR)revealed the impact of GO on functional groups and electrochemical behavior.Photoluminescence(PL)wavelength of(439 nm)and band gap of(2.81 eV)show that the material is suitable for energy applications in nanocomposites.Smart nanocomposite materials with improved performance in energy storage and related applications were fabricated by combining synthesis,characterization,fuzzy logic,and machine learning in this work.
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 Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/540/46.
文摘Over the years,Generative Adversarial Networks(GANs)have revolutionized the medical imaging industry for applications such as image synthesis,denoising,super resolution,data augmentation,and cross-modality translation.The objective of this review is to evaluate the advances,relevances,and limitations of GANs in medical imaging.An organised literature review was conducted following the guidelines of PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses).The literature considered included peer-reviewed papers published between 2020 and 2025 across databases including PubMed,IEEE Xplore,and Scopus.The studies related to applications of GAN architectures in medical imaging with reported experimental outcomes and published in English in reputable journals and conferences were considered for the review.Thesis,white papers,communication letters,and non-English articles were not included for the same.CLAIM based quality assessment criteria were applied to the included studies to assess the quality.The study classifies diverse GAN architectures,summarizing their clinical applications,technical performances,and their implementation hardships.Key findings reveal the increasing applications of GANs for enhancing diagnostic accuracy,reducing data scarcity through synthetic data generation,and supporting modality translation.However,concerns such as limited generalizability,lack of clinical validation,and regulatory constraints persist.This review provides a comprehensive study of the prevailing scenario of GANs in medical imaging and highlights crucial research gaps and future directions.Though GANs hold transformative capability for medical imaging,their integration into clinical use demands further validation,interpretability,and regulatory alignment.
基金funded by the Research,Development,and Innovation Authority(RDIA)—Kingdom of Saudi Arabia(Grant No.13292-psu-2023-PSNU-R-3-1-EF-).
文摘Colorectal cancer is the third most diagnosed cancer worldwide,and immune checkpoint inhibitors have shown promising therapeutic outcomes in selected patient groups.This study performed a comprehensive analysis of multi-omics data from The Cancer Genome Atlas colorectal adenocarcinoma cohort(TCGA-COADREAD),accessed through cBioPortal,to develop machine learning models for predicting progression-free survival(PFS)following immunotherapy.The dataset included clinical variables,genomic alterations in Kirsten Rat Sarcoma Viral Oncogene Homolog(KRAS),B-Raf Proto-Oncogene(BRAF),and Neuroblastoma RAS Viral Oncogene Homolog(NRAS),microsatellite instability(MSI)status,tumor mutation burden(TMB),and expression of immune checkpoint genes.Kaplan–Meier analysis showed that KRAS mutations were significantly associated with reduced PFS,while BRAF and NRAS mutations had no significant impact.MSI-high tumors exhibited elevated TMB and increased immune checkpoint expression,reflecting their immunologically active phenotype.We developed both survival and classification models,with the Extra Trees classifier achieving the best performance(accuracy=0.86,precision=0.67,recall=0.70,F1-score=0.68,AUC=0.84).These findings highlight the potential of combining genomic and immune biomarkers with machine learning to improve patient stratification and guide personalized immunotherapy decisions.An interactive web application was also developed to enable clinicians to input patient-specific molecular and clinical data and visualize individualized PFS predictions,supporting timely,data-driven treatment planning.
文摘The innovative development of Wearable Electronics (WE) is creating exciting opportunities for application across many industries. Two sectors with high potential are healthcare and childcare. However, it is in these two sectors where the challenges of privacy are presumed to be of the highest. In order to ascertain the personal views of people about potential privacy problems in WE application in these two sectors, interviews with questionnaires were conducted in two different countries: Finland and the United Kingdom (UK). The results indicated that the majority of people in both countries are positive about the use of WE in healthcare and childcare environments. However, when more information is added to be read wirelessly, the attitudes become more negative. In general, the application of WE is more favorable in the UK and the reason as to the difference will make for interesting further research. Several interesting viewpoints and concerns were presented in the interviews. It can be concluded that the implementation of WE in these two sectors will require the collaboration of work on several areas and the development of versatile user studies.
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This paper presents two four-quadrant topologies for Permanent Magnet Direct Current （PMDC） motor drives, built using combination of power electronics switches. The issue is the increased efficiency of the topology compared to conventional ones built using only one type of power electronic switches. The suggested combinations-MOSFET-IGBT and MOSFET-SCR improve the current bridge topologies by uniting the advantages of the different switches-the high switching capabilities of the MOSFET and the better antiparallel diodes of IGBTs and SCRs. The total efficiency of the motor control is improved by several percents. This reduces the overall consumption of the converter circuitry, which can be very beneficial-especially at high power motors, as the ones presented in the paper. Statistical and experimental data is presented proving the efficiency of the suggest topologies.
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This paper proposes a linear companding transform(CT)using either a single inflection point or two inflection points to reduce the peakto-average power ratio(PAPR)in orthogonal timefrequency space(OTFS)signals.The CT strategically compresses higher amplitudes and enhances lower amplitudes based on carefully chosen scaling factors and points of inflection.With these selected parameters,the CT effectively reduces peak power while maintaining average power,leading to a substantial decrease in PAPR.We analyze noise changes in the inverse companding transform(ICT)process.The analysis reveals that the ICT amplifies less than 20%of the total noise.A convolutional encoder and soft decision Viterbi decoding algorithm are utilized in the OTFS system to improve the detection performance.We present simulation results focusing on PAPR reduction and bit error rate(BER)performance.These results demonstrate that the CT with two inflection points outperforms both the single inflection point case and the existingμ-law companding,clipping,peak windowing,unique OTFS frame structure,selected mapping,and partial transmit sequence methods,achieving significant PAPR reduction and BER performance.
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.
文摘The effects of climate change are becoming more evident nowadays,and the environmental stress imposed on crops has become more severe.Farmers around the globe continually seek ways to gain insights into crop health and provide mitigation as early as possible.Phenotyping is a non-destructive method for assessing crop responses to environmental stresses and can be performed using airborne systems.Unmanned Aerial Systems(UAS)have significantly contributed to high-throughput phenotyping andmade the process rapid,efficient,and non-invasive for collecting large-scale agronomic data.Because of the high complexity and cost of specialized equipment used in aerial phenotyping,such as multispectral and hyperspectral cameras as well as lidar,this study proposes a framework for implementing aerial phenotyping where chlorophyll estimation,leaf count,and coverage are determined using the RGB(Red,Green and Blue)camera native to a UAS.Thestudy proposes the Dynamic Coefficient Triangular Greenness Index(DCTGI)for aerial phenotyping.Evaluation of the proposed DCTGI includes the correlation with chlorophyll content estimated using a Soil Plant Analysis Development(SPAD)chlorophyll meter on randomly sampled Liberica coffee seedlings.Analysis revealed a strong relationship between DCTGI values and chlorophyll estimates derived from SPAD measurements,with a Pearson’s correlation coefficient of 0.912.However,the study didn’t implement tissue-level validation and field-scale temporal analysis to assess seasonal variability.In addition,the SPAD meter provided the approximate nitrogen content together with the chlorohyll estimate.
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.
