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
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
Heavy metal ion(HMI)contamination threatens the environment’s integrity and human health,indicating the critical need for sensitive and reliable detection methods.The present review critically discusses the most rece...Heavy metal ion(HMI)contamination threatens the environment’s integrity and human health,indicating the critical need for sensitive and reliable detection methods.The present review critically discusses the most recent flexible and rigid sensor technologies for the detection of HMIs.Flexible sensors are heterogeneous in the following types:adaptability to different shapes,various environments,and realtime monitoring,which make them uniquely appropriate for applications in wearable devices,biomedical applications,and environmental monitoring systems.The nonflexible sensors perform well with great accuracy and sensitivity,especially in laboratory and industrial environments.Recent advances are focused on materials design,fabrication methodologies,and signal processing algorithms,which significantly improve sensor performance and help deploy sensors in complex real-world scenarios.Still,there are drawbacks to the need for more sensitivity,selectivity,durability,and scalability in developing sensors.Future directions for research should involve improvement in the design of materials to use multimodal sensing techniques and develop further miniaturizations and energy efficiency,as well as development of intelligent sensor networks and collaborative work in cross-disciplinary fields.This review can be an essential reference for the scientific and engineering communities focused on developing and applying flexible and non-flexible sensors to detect HMIs.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
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
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
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
Medical image analysis has become a cornerstone of modern healthcare,driven by the exponential growth of data from imaging modalities such as MRI,CT,PET,ultrasound,and X-ray.Traditional machine learning methods have m...Medical image analysis has become a cornerstone of modern healthcare,driven by the exponential growth of data from imaging modalities such as MRI,CT,PET,ultrasound,and X-ray.Traditional machine learning methods have made early contributions;however,recent advancements in deep learning(DL)have revolutionized the field,offering state-of-the-art performance in image classification,segmentation,detection,fusion,registration,and enhancement.This comprehensive review presents an in-depth analysis of deep learning methodologies applied across medical image analysis tasks,highlighting both foundational models and recent innovations.The article begins by introducing conventional techniques and their limitations,setting the stage for DL-based solutions.Core DL architectures,including Convolutional Neural Networks(CNNs),Recurrent Neural Networks(RNNs),Generative Adversarial Networks(GANs),Vision Transformers(ViTs),and hybrid models,are discussed in detail,including their advantages and domain-specific adaptations.Advanced learning paradigms such as semi-supervised learning,selfsupervised learning,and few-shot learning are explored for their potential to mitigate data annotation challenges in clinical datasets.This review further categorizes major tasks in medical image analysis,elaborating on how DL techniques have enabled precise tumor segmentation,lesion detection,modality fusion,super-resolution,and robust classification across diverse clinical settings.Emphasis is placed on applications in oncology,cardiology,neurology,and infectious diseases,including COVID-19.Challenges such as data scarcity,label imbalance,model generalizability,interpretability,and integration into clinical workflows are critically examined.Ethical considerations,explainable AI(XAI),federated learning,and regulatory compliance are discussed as essential components of real-world deployment.Benchmark datasets,evaluation metrics,and comparative performance analyses are presented to support future research.The article concludes with a forward-looking perspective on the role of foundation models,multimodal learning,edge AI,and bio-inspired computing in the future of medical imaging.Overall,this review serves as a valuable resource for researchers,clinicians,and developers aiming to harness deep learning for intelligent,efficient,and clinically viable medical image analysis.展开更多
Alginate-based magnetic micro/millirobots have demonstrated significant potential for biomedical applications due to their flexible structures and capacity to carry various types of cargo,such as cells,enabling target...Alginate-based magnetic micro/millirobots have demonstrated significant potential for biomedical applications due to their flexible structures and capacity to carry various types of cargo,such as cells,enabling targeted therapy to specific diseased regions within the body.Their active therapy is typically achieved through magnetic actuation and magnetic heating,while monitored by medical imaging methods like CT which pose additional risks due to radiation exposure.In the last decades,a novel imaging method for superparamagnetic materials,known as magnetic particle imaging(MPI),has been under active development,offering not only positional tracking but also the ability to measure concentration and temperature.Here,we report the world's first MPI-traceable magnetic hydrogel robots,which employ a combination of iron oxide nanoflowers,NdFeB powder,and calcium alginate.Unlike previous magnetic alginate robots composed of a single magnetic material,the synergistic combination of NdFeB and nanoflowers enables these robots to exhibit triple magnetic functionalities:magnetic heating,locomotion at low magnetic fields,and tracking,all of which can be controlled using a single all-in-one electromagnetic coil system.The effects of various magnetization fields,as well as different concentrations of NdFeB and nanoflowers on the robots'magnetic properties were analyzed.This led to the development of three types of triple-function robots(spiral,droplet,and hybrid),with experimental results demonstrating biocompatibility,a magnetic heating temperature increase of over 10℃in plasma fluid under a magnetic field of 13 kA·m^(-1)at 200 kHz,locomotion speeds of up to 25 mm·s^(-1)in fields below 2 mT,and an MPI tracking error of 2.8 mm with a selection field of 0.4 mT·mm^(-1).Additionally,the robots'capacity for localized thermal therapy and selectively targeted cell delivery,as well as their locomotion within a medical phantom against a maximum flow of 50 mm·s^(-1)were demonstrated.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
Background:Long-term exposure to toxic substances increases the risk of their accumulation and triggering endocrine disruption.The ill-planned irrigation systems in Uzbekistan led to severe environmental disasters,res...Background:Long-term exposure to toxic substances increases the risk of their accumulation and triggering endocrine disruption.The ill-planned irrigation systems in Uzbekistan led to severe environmental disasters,resulting in aridization and the spread of organochlorine pesticides and toxic metals across the region.Objective:We investigate the influence of negative environmental factors on hormonal activities such as sex steroids,gonadotropic hormones,and growth factors,in addition to potential disruptions in growth rate and anthropometric indicators in prepubertal adolescents living in this region.Methods:This study included 405 prepubertal boys aged 11 to 13 years,residing in the cities of Muynak(unfavorable region)and Nukus(favorable region)of Uzbekistan.We measured 20 anthropometric parameters and assessed somatotype by the Heath-Carter method,peak height velocity(PHV),and maturity offset based on Mirwald's equation.In addition,the endocrine profile of 8 hormonal parameters was assessed,which includes growth factors,gonadotropins,sex steroids,and thyroid status.Results:We observed a positive dependence of physique,rate of maturation,and level of androgens,stimulated by gonadotropin hormones.Children living in unfavorable environmental regions show a decrease in folliclestimulating hormone,luteinizing hormone,total testosterone(the best predictor),and anthropometric signs of androgen deficiency.These resulted in an increase in maturity offset and PHV,combined with lower skeletal density and ectomorphic somatotype compared to their peers living in favorable environmental regions.Conclusion:Regional differences in stature,body weight,and anthropometric parameters of adipose tissue,growth factor axis,and thyroid hormones are the influencing indices in the regulation of growth in prepubertal children.We hypothesize that the inhibition of the gonadotropin-testosterone axis may result from endocrine disruption due to higher levels of toxic metals and pesticide exposure.展开更多
Based on the density functional theory,the double half-Heusler alloys LuXCo_(2)Bi_(2)(X=V,Nb,and Ta)were studied to predict their structural,thermodynamic,thermoelectric,and optical characteristics.All the considered ...Based on the density functional theory,the double half-Heusler alloys LuXCo_(2)Bi_(2)(X=V,Nb,and Ta)were studied to predict their structural,thermodynamic,thermoelectric,and optical characteristics.All the considered alloys are thermodynamically stable and have semiconductor behavior with indirect band gaps of 0.62,0.75,and 0.8 eV for LuVCo_(2)Bi_(2),LuNbCo_(2)Bi_(2),and LuTaCu_(2)Bi_(2),respectively.The investigated compounds exhibit semiconducting behavior with energy gaps below 0.8 eV.The impact of heat and pressure on thermodynamic coefficients was evaluated,and the influence of charge carriers on the temperature-dependent properties was studied using the semi-classical Boltzmann model.The studied compounds were characterized by their low lattice thermal conductivity at room temperature and low thermal expansion coefficient.These alloys exhibit substantial absorption coefficients in the ultraviolet(UV)light region,high optical conductivity,and high reflectivity in the visible light region,making them highly appealing materials for applications in the energy and electronics sectors.展开更多
The combining microelectronic devices and associated technologies onto a single silicon chip poses a substantial challenge.However,in recent years,the area of silicon photonics has experienced remarkable advancements ...The combining microelectronic devices and associated technologies onto a single silicon chip poses a substantial challenge.However,in recent years,the area of silicon photonics has experienced remarkable advancements and notable leaps in performance.The performance of silicon on insulator(SOI)based photonic devices,such as fast silicon optical modulators,photonic transceivers,optical filters,etc.,have been discussed.This would be a step forward in creating standalone silicon photonic devices,strengthening the possibility of single on-chip nanophotonic integrated circuits.Suppose an integrated silicon photonic chip is designed and fabricated.In that case,it might drastically modify these combined photonic component costs,power consumption,and size,bringing substantial,perhaps revolutionary,changes to the next-generation communications sector.Yet,the monolithic integration of photonic and electrical circuitry is a significant technological difficulty.A complicated set of factors must be carefully considered to determine which application will have the best chance of success employing silicon-based integrated product solutions.The processing limitations connected to the current process flow,the process generation(sometimes referred to as lithography node generation),and packaging requirements are a few of these factors to consider.This review highlights recent developments in integrated silicon photonic devices and their proven applications,including but not limited to photonic waveguides,photonic amplifiers and filters,onchip photonic transceivers,and the state-of-the-art of silicon photonic in multidimensional quantum systems.The investigated devices aim to expedite the transfer of silicon photonics from academia to industry by opening the next phase in on-chip silicon photonics and enabling the application of silicon photonic-based devices in various optical systems.展开更多
A four-element compact dual-band patch antenna having a common ground plane operating at 28/38 GHz is proposed formillimeter-wave communication systems in this paper.Themultiple-input-multiple-output(MIMO)antenna geom...A four-element compact dual-band patch antenna having a common ground plane operating at 28/38 GHz is proposed formillimeter-wave communication systems in this paper.Themultiple-input-multiple-output(MIMO)antenna geometry consists of a slotted ellipse enclosed within a hollow circle which is orthogonally rotated with a connected partial ground at the back.The overall size of the four elements MIMO antenna is 2.24λ×2.24λ(at 27.12GHz).The prototype of four-element MIMOresonator is designed and printed using Rogers RTDuroid 5880 withε_(r)=2.2 and loss tangent=0.0009 and having a thickness of 0.8 mm.It covers dual-band having a fractional bandwidth of 15.7%(27.12-31.34 GHz)and 4.2%(37.21-38.81 GHz)for millimeter-wave applications with a gain of more than 4 dBi at both bands.The proposed antenna analysis in terms ofMIMOdiversity parameters(Envelope Correlation Coefficient(ECC)and Diversity Gain(DG))is also carried out.The experimental result in terms of reflection coefficient,radiation pattern,gain and MIMOdiversity parameter correlates very well with the simulated ones that show the potential of the proposed design for MIMO applications at millimeter-wave frequencies.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
With the increasing growth of online news,fake electronic news detection has become one of the most important paradigms of modern research.Traditional electronic news detection techniques are generally based on contex...With the increasing growth of online news,fake electronic news detection has become one of the most important paradigms of modern research.Traditional electronic news detection techniques are generally based on contextual understanding,sequential dependencies,and/or data imbalance.This makes distinction between genuine and fabricated news a challenging task.To address this problem,we propose a novel hybrid architecture,T5-SA-LSTM,which synergistically integrates the T5 Transformer for semantically rich contextual embedding with the Self-Attentionenhanced(SA)Long Short-Term Memory(LSTM).The LSTM is trained using the Adam optimizer,which provides faster and more stable convergence compared to the Stochastic Gradient Descend(SGD)and Root Mean Square Propagation(RMSProp).The WELFake and FakeNewsPrediction datasets are used,which consist of labeled news articles having fake and real news samples.Tokenization and Synthetic Minority Over-sampling Technique(SMOTE)methods are used for data preprocessing to ensure linguistic normalization and class imbalance.The incorporation of the Self-Attention(SA)mechanism enables the model to highlight critical words and phrases,thereby enhancing predictive accuracy.The proposed model is evaluated using accuracy,precision,recall(sensitivity),and F1-score as performance metrics.The model achieved 99%accuracy on the WELFake dataset and 96.5%accuracy on the FakeNewsPrediction dataset.It outperformed the competitive schemes such as T5-SA-LSTM(RMSProp),T5-SA-LSTM(SGD)and some other models.展开更多
An important concern in gear design is to reduce the dynamic load and noise of gear systems. It has been found that the noise generated from gearing is basically due to gearbox vibration excited by the dynamic load. S...An important concern in gear design is to reduce the dynamic load and noise of gear systems. It has been found that the noise generated from gearing is basically due to gearbox vibration excited by the dynamic load. Since one of the situations that demand high performance is the high rotational speeds, there is a need to understand the dynamic behavior of the gears at such speeds. Such knowledge would shed light on detrimental characteristics like dynamic loads and vibrations. An efficient way in performing studies on the dynamic behavior of gears is using computer aided analysis on numerical models. In this paper, a developed computer program is introduced to analyze dynamic behavior of spur gears with asymmetric teeth that have a potential use for higher performance in wind turbine gearboxes. This program can be used to compare conventional spur gears with symmetric teeth and spur gears with asymmetric teeth. By using this program, gear designers can design a gear pair and obtain results, e.g dynamic Load, transmitted torque, static transmission error, and frequency spectra of static transmission error etc., just by pressing a command button.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
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supporting the project titled Design and Development of MEMS-based Portable Platform with Ion-Selective Self-Assembled Monolayer(SAMs)for the Detection of Toxic Heavy Metal Ions(HMIs)in Ground Water"(DST/TDT/DDP-60/2021).
文摘Heavy metal ion(HMI)contamination threatens the environment’s integrity and human health,indicating the critical need for sensitive and reliable detection methods.The present review critically discusses the most recent flexible and rigid sensor technologies for the detection of HMIs.Flexible sensors are heterogeneous in the following types:adaptability to different shapes,various environments,and realtime monitoring,which make them uniquely appropriate for applications in wearable devices,biomedical applications,and environmental monitoring systems.The nonflexible sensors perform well with great accuracy and sensitivity,especially in laboratory and industrial environments.Recent advances are focused on materials design,fabrication methodologies,and signal processing algorithms,which significantly improve sensor performance and help deploy sensors in complex real-world scenarios.Still,there are drawbacks to the need for more sensitivity,selectivity,durability,and scalability in developing sensors.Future directions for research should involve improvement in the design of materials to use multimodal sensing techniques and develop further miniaturizations and energy efficiency,as well as development of intelligent sensor networks and collaborative work in cross-disciplinary fields.This review can be an essential reference for the scientific and engineering communities focused on developing and applying flexible and non-flexible sensors to detect HMIs.
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.
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.
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Medical image analysis has become a cornerstone of modern healthcare,driven by the exponential growth of data from imaging modalities such as MRI,CT,PET,ultrasound,and X-ray.Traditional machine learning methods have made early contributions;however,recent advancements in deep learning(DL)have revolutionized the field,offering state-of-the-art performance in image classification,segmentation,detection,fusion,registration,and enhancement.This comprehensive review presents an in-depth analysis of deep learning methodologies applied across medical image analysis tasks,highlighting both foundational models and recent innovations.The article begins by introducing conventional techniques and their limitations,setting the stage for DL-based solutions.Core DL architectures,including Convolutional Neural Networks(CNNs),Recurrent Neural Networks(RNNs),Generative Adversarial Networks(GANs),Vision Transformers(ViTs),and hybrid models,are discussed in detail,including their advantages and domain-specific adaptations.Advanced learning paradigms such as semi-supervised learning,selfsupervised learning,and few-shot learning are explored for their potential to mitigate data annotation challenges in clinical datasets.This review further categorizes major tasks in medical image analysis,elaborating on how DL techniques have enabled precise tumor segmentation,lesion detection,modality fusion,super-resolution,and robust classification across diverse clinical settings.Emphasis is placed on applications in oncology,cardiology,neurology,and infectious diseases,including COVID-19.Challenges such as data scarcity,label imbalance,model generalizability,interpretability,and integration into clinical workflows are critically examined.Ethical considerations,explainable AI(XAI),federated learning,and regulatory compliance are discussed as essential components of real-world deployment.Benchmark datasets,evaluation metrics,and comparative performance analyses are presented to support future research.The article concludes with a forward-looking perspective on the role of foundation models,multimodal learning,edge AI,and bio-inspired computing in the future of medical imaging.Overall,this review serves as a valuable resource for researchers,clinicians,and developers aiming to harness deep learning for intelligent,efficient,and clinically viable medical image analysis.
基金supported by a Korea University Grantby the National Research Foundation of Korea(NRF)funded by the Korean government(MSIT)with Grant Number 2022R1A2C1003381。
文摘Alginate-based magnetic micro/millirobots have demonstrated significant potential for biomedical applications due to their flexible structures and capacity to carry various types of cargo,such as cells,enabling targeted therapy to specific diseased regions within the body.Their active therapy is typically achieved through magnetic actuation and magnetic heating,while monitored by medical imaging methods like CT which pose additional risks due to radiation exposure.In the last decades,a novel imaging method for superparamagnetic materials,known as magnetic particle imaging(MPI),has been under active development,offering not only positional tracking but also the ability to measure concentration and temperature.Here,we report the world's first MPI-traceable magnetic hydrogel robots,which employ a combination of iron oxide nanoflowers,NdFeB powder,and calcium alginate.Unlike previous magnetic alginate robots composed of a single magnetic material,the synergistic combination of NdFeB and nanoflowers enables these robots to exhibit triple magnetic functionalities:magnetic heating,locomotion at low magnetic fields,and tracking,all of which can be controlled using a single all-in-one electromagnetic coil system.The effects of various magnetization fields,as well as different concentrations of NdFeB and nanoflowers on the robots'magnetic properties were analyzed.This led to the development of three types of triple-function robots(spiral,droplet,and hybrid),with experimental results demonstrating biocompatibility,a magnetic heating temperature increase of over 10℃in plasma fluid under a magnetic field of 13 kA·m^(-1)at 200 kHz,locomotion speeds of up to 25 mm·s^(-1)in fields below 2 mT,and an MPI tracking error of 2.8 mm with a selection field of 0.4 mT·mm^(-1).Additionally,the robots'capacity for localized thermal therapy and selectively targeted cell delivery,as well as their locomotion within a medical phantom against a maximum flow of 50 mm·s^(-1)were demonstrated.
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.
文摘Background:Long-term exposure to toxic substances increases the risk of their accumulation and triggering endocrine disruption.The ill-planned irrigation systems in Uzbekistan led to severe environmental disasters,resulting in aridization and the spread of organochlorine pesticides and toxic metals across the region.Objective:We investigate the influence of negative environmental factors on hormonal activities such as sex steroids,gonadotropic hormones,and growth factors,in addition to potential disruptions in growth rate and anthropometric indicators in prepubertal adolescents living in this region.Methods:This study included 405 prepubertal boys aged 11 to 13 years,residing in the cities of Muynak(unfavorable region)and Nukus(favorable region)of Uzbekistan.We measured 20 anthropometric parameters and assessed somatotype by the Heath-Carter method,peak height velocity(PHV),and maturity offset based on Mirwald's equation.In addition,the endocrine profile of 8 hormonal parameters was assessed,which includes growth factors,gonadotropins,sex steroids,and thyroid status.Results:We observed a positive dependence of physique,rate of maturation,and level of androgens,stimulated by gonadotropin hormones.Children living in unfavorable environmental regions show a decrease in folliclestimulating hormone,luteinizing hormone,total testosterone(the best predictor),and anthropometric signs of androgen deficiency.These resulted in an increase in maturity offset and PHV,combined with lower skeletal density and ectomorphic somatotype compared to their peers living in favorable environmental regions.Conclusion:Regional differences in stature,body weight,and anthropometric parameters of adipose tissue,growth factor axis,and thyroid hormones are the influencing indices in the regulation of growth in prepubertal children.We hypothesize that the inhibition of the gonadotropin-testosterone axis may result from endocrine disruption due to higher levels of toxic metals and pesticide exposure.
文摘Based on the density functional theory,the double half-Heusler alloys LuXCo_(2)Bi_(2)(X=V,Nb,and Ta)were studied to predict their structural,thermodynamic,thermoelectric,and optical characteristics.All the considered alloys are thermodynamically stable and have semiconductor behavior with indirect band gaps of 0.62,0.75,and 0.8 eV for LuVCo_(2)Bi_(2),LuNbCo_(2)Bi_(2),and LuTaCu_(2)Bi_(2),respectively.The investigated compounds exhibit semiconducting behavior with energy gaps below 0.8 eV.The impact of heat and pressure on thermodynamic coefficients was evaluated,and the influence of charge carriers on the temperature-dependent properties was studied using the semi-classical Boltzmann model.The studied compounds were characterized by their low lattice thermal conductivity at room temperature and low thermal expansion coefficient.These alloys exhibit substantial absorption coefficients in the ultraviolet(UV)light region,high optical conductivity,and high reflectivity in the visible light region,making them highly appealing materials for applications in the energy and electronics sectors.
文摘The combining microelectronic devices and associated technologies onto a single silicon chip poses a substantial challenge.However,in recent years,the area of silicon photonics has experienced remarkable advancements and notable leaps in performance.The performance of silicon on insulator(SOI)based photonic devices,such as fast silicon optical modulators,photonic transceivers,optical filters,etc.,have been discussed.This would be a step forward in creating standalone silicon photonic devices,strengthening the possibility of single on-chip nanophotonic integrated circuits.Suppose an integrated silicon photonic chip is designed and fabricated.In that case,it might drastically modify these combined photonic component costs,power consumption,and size,bringing substantial,perhaps revolutionary,changes to the next-generation communications sector.Yet,the monolithic integration of photonic and electrical circuitry is a significant technological difficulty.A complicated set of factors must be carefully considered to determine which application will have the best chance of success employing silicon-based integrated product solutions.The processing limitations connected to the current process flow,the process generation(sometimes referred to as lithography node generation),and packaging requirements are a few of these factors to consider.This review highlights recent developments in integrated silicon photonic devices and their proven applications,including but not limited to photonic waveguides,photonic amplifiers and filters,onchip photonic transceivers,and the state-of-the-art of silicon photonic in multidimensional quantum systems.The investigated devices aim to expedite the transfer of silicon photonics from academia to industry by opening the next phase in on-chip silicon photonics and enabling the application of silicon photonic-based devices in various optical systems.
基金This work is supported by the Moore4Medical Project,funded within ECSEL JU in collaboration with the EU H2020 Framework Programme(H2020/2014-2020)under Grant Agreement H2020-ECSEL-2019-IA-876190Fundacao para a Ciência eTecnologia(ECSEL/0006/2019)This work is also funded by the FCT/MEC through national funds and when applicable co-financed by the ERDF,under the PT2020 Partnership Agreement under the UID/EEA/50008/2020 Project.
文摘A four-element compact dual-band patch antenna having a common ground plane operating at 28/38 GHz is proposed formillimeter-wave communication systems in this paper.Themultiple-input-multiple-output(MIMO)antenna geometry consists of a slotted ellipse enclosed within a hollow circle which is orthogonally rotated with a connected partial ground at the back.The overall size of the four elements MIMO antenna is 2.24λ×2.24λ(at 27.12GHz).The prototype of four-element MIMOresonator is designed and printed using Rogers RTDuroid 5880 withε_(r)=2.2 and loss tangent=0.0009 and having a thickness of 0.8 mm.It covers dual-band having a fractional bandwidth of 15.7%(27.12-31.34 GHz)and 4.2%(37.21-38.81 GHz)for millimeter-wave applications with a gain of more than 4 dBi at both bands.The proposed antenna analysis in terms ofMIMOdiversity parameters(Envelope Correlation Coefficient(ECC)and Diversity Gain(DG))is also carried out.The experimental result in terms of reflection coefficient,radiation pattern,gain and MIMOdiversity parameter correlates very well with the simulated ones that show the potential of the proposed design for MIMO applications at millimeter-wave frequencies.
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.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R195)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘With the increasing growth of online news,fake electronic news detection has become one of the most important paradigms of modern research.Traditional electronic news detection techniques are generally based on contextual understanding,sequential dependencies,and/or data imbalance.This makes distinction between genuine and fabricated news a challenging task.To address this problem,we propose a novel hybrid architecture,T5-SA-LSTM,which synergistically integrates the T5 Transformer for semantically rich contextual embedding with the Self-Attentionenhanced(SA)Long Short-Term Memory(LSTM).The LSTM is trained using the Adam optimizer,which provides faster and more stable convergence compared to the Stochastic Gradient Descend(SGD)and Root Mean Square Propagation(RMSProp).The WELFake and FakeNewsPrediction datasets are used,which consist of labeled news articles having fake and real news samples.Tokenization and Synthetic Minority Over-sampling Technique(SMOTE)methods are used for data preprocessing to ensure linguistic normalization and class imbalance.The incorporation of the Self-Attention(SA)mechanism enables the model to highlight critical words and phrases,thereby enhancing predictive accuracy.The proposed model is evaluated using accuracy,precision,recall(sensitivity),and F1-score as performance metrics.The model achieved 99%accuracy on the WELFake dataset and 96.5%accuracy on the FakeNewsPrediction dataset.It outperformed the competitive schemes such as T5-SA-LSTM(RMSProp),T5-SA-LSTM(SGD)and some other models.
文摘An important concern in gear design is to reduce the dynamic load and noise of gear systems. It has been found that the noise generated from gearing is basically due to gearbox vibration excited by the dynamic load. Since one of the situations that demand high performance is the high rotational speeds, there is a need to understand the dynamic behavior of the gears at such speeds. Such knowledge would shed light on detrimental characteristics like dynamic loads and vibrations. An efficient way in performing studies on the dynamic behavior of gears is using computer aided analysis on numerical models. In this paper, a developed computer program is introduced to analyze dynamic behavior of spur gears with asymmetric teeth that have a potential use for higher performance in wind turbine gearboxes. This program can be used to compare conventional spur gears with symmetric teeth and spur gears with asymmetric teeth. By using this program, gear designers can design a gear pair and obtain results, e.g dynamic Load, transmitted torque, static transmission error, and frequency spectra of static transmission error etc., just by pressing a command button.
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.
