Developing cost-effective and high-performance catalyst systems for dry reforming of methane(DRM)is crucial for producing hydrogen(H_(2))sustainably.Herein,we investigate using iron(Fe)as a promoter and major alumina ...Developing cost-effective and high-performance catalyst systems for dry reforming of methane(DRM)is crucial for producing hydrogen(H_(2))sustainably.Herein,we investigate using iron(Fe)as a promoter and major alumina support in Ni-based catalysts to improve their DRM performance.The addition of iron as a promotor was found to add reducible iron species along with reducible NiO species,enhance the basicity and induce the deposition of oxidizable carbon.By incorporating 1 wt.%Fe into a 5Ni/10ZrAl catalyst,a higher CO_(2) interaction and formation of reducible"NiO-species having strong interaction with support"was observed,which led to an∼80%H_(2) yield in 420 min of Time on Stream(TOS).Further increasing the Fe content to 2 wt.%led to the formation of additional reducible iron oxide species and a noticeable rise in H_(2) yield up to 84%.Despite the severe weight loss on Fe-promoted catalysts,high H_(2) yield was maintained due to the proper balance between the rate of CH_(4) decomposition and the rate of carbon deposit diffusion.Finally,incorporating 3 wt.%Fe into the 5Ni/10ZrAl catalyst resulted in the highest CO_(2) interaction,wide presence of reducible NiO-species,minimumgraphitic deposit and an 87%H_(2) yield.Our findings suggest that ironpromoted zirconia-alumina-supported Ni catalysts can be a cheap and excellent catalytic system for H_(2) production via DRM.展开更多
We introduce a dual distribution of relaxation(DRT)based approach for analyzing electrochemical impedance spectroscopy(EIS)data in perovskite solar cells(PSCs),combining regression and classification with Bayesian mod...We introduce a dual distribution of relaxation(DRT)based approach for analyzing electrochemical impedance spectroscopy(EIS)data in perovskite solar cells(PSCs),combining regression and classification with Bayesian model selection and Havriliak-Negami(HN)modeling to resolve spectra into discrete,Lorentzian-like peaks.This time-domain decomposition offers a powerful alternative for identifying underlying physical processes,such as charge transfer,trap-assisted recombination,and ionic migration by directly extracting characteristic relaxation times(τ).In contrast to traditional equivalent circuit fitting or conventional DRT methods,which often yield broad and overlapping Gaussian-like peaks,our method enables sharper resolution of individual electrochemical signatures.Furthermore,we validated the framework using simulated EIS spectra for two distinct system types,determining the optimal number of peaks(Q)through statistical model selection.Applied to experimental PSC data under varying bias conditions,the approach helps to identify the voltage-dependent relaxation processes,including fast charge transfer(τ~10^(-6)s),intermediate trap-mediated recombination(τ~10^(-2)s),and slow ionic motion(τ~1 s).Lower-Q models fail to capture low-frequency features such as polarization and charge accumulation,while optimal Q yields accurate,physically meaningful representations of device behavior.This data-driven methodology highlights time-domain DRT as a rigorous and insightful tool for dissecting the complex kinetics that govern PSC performance.展开更多
In the re-entry phase of a ballistic missile,decoys can be deployed as a mean to overburden enemy defenses.This results in a single track being split into multiple track-lets.Tracking of these track-lets is a critical...In the re-entry phase of a ballistic missile,decoys can be deployed as a mean to overburden enemy defenses.This results in a single track being split into multiple track-lets.Tracking of these track-lets is a critical task as any miss in the tracking procedure can become a cause of a major threat.The tracking process becomes more complicated in the presence of clutter.The low detection rate is one of the factors that may contribute to increasing the difficulty level in terms of tracking in the cluttered environment.This work introduces a new algorithm for the split event detection and target tracking under the framework of the joint integrated probabilistic data association(JIPDA)algorithm.The proposed algorithm is termed as split event-JIPDA(SE-JIPDA).This work establishes the mathematical foundation for the split target detection and tracking mechanism.The performance analysis is made under different simulation conditions to provide a clear insight into the merits of the proposed algorithm.The performance parameters in these simulations are the root mean square error(RMSE),confirmed true track rate(CTTR)and confirmed split true track rate(CSTTR).展开更多
In the present paper, we report on the results of various thermodynamic properties of 3C-SiC at high pressure and temperature using first principles calculations. We use the plane-wave pseudopotential density function...In the present paper, we report on the results of various thermodynamic properties of 3C-SiC at high pressure and temperature using first principles calculations. We use the plane-wave pseudopotential density functional theory as im- plemented in Quantum ESPRESSO code for calculating various cohesive properties in ambient condition. Further, ionic motion at a finite temperature is taken into account using the quasiharmonic Debye model. The calculated thermody- namic properties, phonon dispersion curves, and phonon densities of states at different temperatures and structural phase transitions at high pressures are found to be in good agreement with experimental and other theoretical results.展开更多
Alzheimer’s disease(AD)is the most common type of dementia.The exact cause and treatment of the disease are still unknown.Different neuroimaging modalities,such as magnetic resonance imaging(MRI),positron emission to...Alzheimer’s disease(AD)is the most common type of dementia.The exact cause and treatment of the disease are still unknown.Different neuroimaging modalities,such as magnetic resonance imaging(MRI),positron emission tomography,and single-photon emission computed tomography,have played a significant role in the study of AD.However,the effective diagnosis of AD,as well as mild cognitive impairment(MCI),has recently drawn large attention.Various technological advancements,such as robots,global positioning system technology,sensors,and machine learning(ML)algorithms,have helped improve the diagnostic process of AD.This study aimed to determine the influence of implementing different ML classifiers in MRI and analyze the use of support vector machines with various multimodal scans for classifying patients with AD/MCI and healthy controls.Conclusions have been drawn in terms of employing different classifier techniques and presenting the optimal multimodal paradigm for the classification of AD.展开更多
The health monitoring has been studied to ensure integrity of design of engine structure by detection,quantification,and prediction of damages.Early detection of faults may allow the downtime of maintenance to be resc...The health monitoring has been studied to ensure integrity of design of engine structure by detection,quantification,and prediction of damages.Early detection of faults may allow the downtime of maintenance to be rescheduled,thus preventing sudden shutdown of machines.In cylinder pressure developed,vibrations and noise emissions data provide a rich source of information about condition of engines.Monitoring of vibrations and noise emissions are novel non-intrusive methodologies for which positioning of various transducers are important issue.The presented work shows applicability of these diagnosis methodologies adopted in case of diesel engines.The effects of changing various fuel injection parameters was analyzed.Scope of using non-intrusive technique has been analyzed by changing locations of microphone.Novelty of this worklies in exploring signal processing methods for various locations around the engine test set up.Various frequency ranges of contributing noise and vibration sources were identified.Time-Frequency analysis showed the onset of various cyclic.Based on the identification of various frequency bands,it is possible to device suitable filters in order to extract more information.展开更多
Agriculture plays a significant role in the economic sector.The automation in agriculture is themain concern and the emerging subject across theworld.The population is increasing tremendously and with this increase th...Agriculture plays a significant role in the economic sector.The automation in agriculture is themain concern and the emerging subject across theworld.The population is increasing tremendously and with this increase the demand of food and employment is also increasing.The traditional methodswhich were used by the farmers,were not sufficient enough to fulfill these requirements.Thus,new automated methods were introduced.These new methods satisfied the food requirements and also provided employment opportunities to billions of people.Artificial Intelligence in agriculture has brought an agriculture revolution.This technology has protected the crop yield fromvarious factors like the climate changes,population growth,employment issues and the food security problems.This main concern of this paper is to audit the various applications of Artificial intelligence in agriculture such as for irrigation,weeding,spraying with the help of sensors and other means embedded in robots and drones.These technologies saves the excess use of water,pesticides,herbicides,maintains the fertility of the soil,also helps in the efficient use of man power and elevate the productivity and improve the quality.This paper surveys the work of many researchers to get a brief overview about the current implementation of automation in agriculture,the weeding systems through the robots and drones.The various soil water sensing methods are discussed alongwith two automatedweeding techniques.The implementation of drones is discussed,the various methods used by drones for spraying and crop-monitoring is also discussed in this paper.展开更多
In this paper, finite element approach using two-dimensional unsteady state problem has been developed to study radial and angular calcium diffusion problem in neurons. Calcium is responsible messenger for transmittin...In this paper, finite element approach using two-dimensional unsteady state problem has been developed to study radial and angular calcium diffusion problem in neurons. Calcium is responsible messenger for transmitting information in communication process between neurons. The most important Ca^2＋ binding proteins for the dynamics of Ca^2＋ is itself buffer and other physiological parameters are located in Ca^2＋ stores. In this study, the model incorporates the physiological parameters like diffusion coefficient, receptors, exogenous buffers etc. Appropriate boundary conditions have been framed in view of the physiological conditions. Computer simulations in MATLAB 7.11 are employed to investigate mathematical models of reaction-diffusion equation, the details of the implementation can heavily affect the numerical solutions and, thus, the outcome simulated on Core（TM） i3 CPU M 330 @ 2.13GHz processing speed and 3GB memory.展开更多
基金The authors would like to extend their sincere appreciation to Researchers Supporting Project number (RSP2023R368)King Saud University,Riyadh,Saudi Arabia.RK,NP,VKS acknowledge Indus University,Ahmedabad,for supporting research.Dr.Ahmed I.Osman and Prof.David W.Rooney wish to acknowledge the support of The Bryden Centre project (Project ID VA5048)。
文摘Developing cost-effective and high-performance catalyst systems for dry reforming of methane(DRM)is crucial for producing hydrogen(H_(2))sustainably.Herein,we investigate using iron(Fe)as a promoter and major alumina support in Ni-based catalysts to improve their DRM performance.The addition of iron as a promotor was found to add reducible iron species along with reducible NiO species,enhance the basicity and induce the deposition of oxidizable carbon.By incorporating 1 wt.%Fe into a 5Ni/10ZrAl catalyst,a higher CO_(2) interaction and formation of reducible"NiO-species having strong interaction with support"was observed,which led to an∼80%H_(2) yield in 420 min of Time on Stream(TOS).Further increasing the Fe content to 2 wt.%led to the formation of additional reducible iron oxide species and a noticeable rise in H_(2) yield up to 84%.Despite the severe weight loss on Fe-promoted catalysts,high H_(2) yield was maintained due to the proper balance between the rate of CH_(4) decomposition and the rate of carbon deposit diffusion.Finally,incorporating 3 wt.%Fe into the 5Ni/10ZrAl catalyst resulted in the highest CO_(2) interaction,wide presence of reducible NiO-species,minimumgraphitic deposit and an 87%H_(2) yield.Our findings suggest that ironpromoted zirconia-alumina-supported Ni catalysts can be a cheap and excellent catalytic system for H_(2) production via DRM.
基金the ORSP of Pandit Deendayal Energy University and DST SERB(IPA/2021/96)for the financial supportthe Deanship of Research and Graduate Studies at King Khalid University for funding this work through the Large Research Project under grant number RGP 2/345/45。
文摘We introduce a dual distribution of relaxation(DRT)based approach for analyzing electrochemical impedance spectroscopy(EIS)data in perovskite solar cells(PSCs),combining regression and classification with Bayesian model selection and Havriliak-Negami(HN)modeling to resolve spectra into discrete,Lorentzian-like peaks.This time-domain decomposition offers a powerful alternative for identifying underlying physical processes,such as charge transfer,trap-assisted recombination,and ionic migration by directly extracting characteristic relaxation times(τ).In contrast to traditional equivalent circuit fitting or conventional DRT methods,which often yield broad and overlapping Gaussian-like peaks,our method enables sharper resolution of individual electrochemical signatures.Furthermore,we validated the framework using simulated EIS spectra for two distinct system types,determining the optimal number of peaks(Q)through statistical model selection.Applied to experimental PSC data under varying bias conditions,the approach helps to identify the voltage-dependent relaxation processes,including fast charge transfer(τ~10^(-6)s),intermediate trap-mediated recombination(τ~10^(-2)s),and slow ionic motion(τ~1 s).Lower-Q models fail to capture low-frequency features such as polarization and charge accumulation,while optimal Q yields accurate,physically meaningful representations of device behavior.This data-driven methodology highlights time-domain DRT as a rigorous and insightful tool for dissecting the complex kinetics that govern PSC performance.
文摘In the re-entry phase of a ballistic missile,decoys can be deployed as a mean to overburden enemy defenses.This results in a single track being split into multiple track-lets.Tracking of these track-lets is a critical task as any miss in the tracking procedure can become a cause of a major threat.The tracking process becomes more complicated in the presence of clutter.The low detection rate is one of the factors that may contribute to increasing the difficulty level in terms of tracking in the cluttered environment.This work introduces a new algorithm for the split event detection and target tracking under the framework of the joint integrated probabilistic data association(JIPDA)algorithm.The proposed algorithm is termed as split event-JIPDA(SE-JIPDA).This work establishes the mathematical foundation for the split target detection and tracking mechanism.The performance analysis is made under different simulation conditions to provide a clear insight into the merits of the proposed algorithm.The performance parameters in these simulations are the root mean square error(RMSE),confirmed true track rate(CTTR)and confirmed split true track rate(CSTTR).
文摘In the present paper, we report on the results of various thermodynamic properties of 3C-SiC at high pressure and temperature using first principles calculations. We use the plane-wave pseudopotential density functional theory as im- plemented in Quantum ESPRESSO code for calculating various cohesive properties in ambient condition. Further, ionic motion at a finite temperature is taken into account using the quasiharmonic Debye model. The calculated thermody- namic properties, phonon dispersion curves, and phonon densities of states at different temperatures and structural phase transitions at high pressures are found to be in good agreement with experimental and other theoretical results.
文摘Alzheimer’s disease(AD)is the most common type of dementia.The exact cause and treatment of the disease are still unknown.Different neuroimaging modalities,such as magnetic resonance imaging(MRI),positron emission tomography,and single-photon emission computed tomography,have played a significant role in the study of AD.However,the effective diagnosis of AD,as well as mild cognitive impairment(MCI),has recently drawn large attention.Various technological advancements,such as robots,global positioning system technology,sensors,and machine learning(ML)algorithms,have helped improve the diagnostic process of AD.This study aimed to determine the influence of implementing different ML classifiers in MRI and analyze the use of support vector machines with various multimodal scans for classifying patients with AD/MCI and healthy controls.Conclusions have been drawn in terms of employing different classifier techniques and presenting the optimal multimodal paradigm for the classification of AD.
文摘The health monitoring has been studied to ensure integrity of design of engine structure by detection,quantification,and prediction of damages.Early detection of faults may allow the downtime of maintenance to be rescheduled,thus preventing sudden shutdown of machines.In cylinder pressure developed,vibrations and noise emissions data provide a rich source of information about condition of engines.Monitoring of vibrations and noise emissions are novel non-intrusive methodologies for which positioning of various transducers are important issue.The presented work shows applicability of these diagnosis methodologies adopted in case of diesel engines.The effects of changing various fuel injection parameters was analyzed.Scope of using non-intrusive technique has been analyzed by changing locations of microphone.Novelty of this worklies in exploring signal processing methods for various locations around the engine test set up.Various frequency ranges of contributing noise and vibration sources were identified.Time-Frequency analysis showed the onset of various cyclic.Based on the identification of various frequency bands,it is possible to device suitable filters in order to extract more information.
文摘Agriculture plays a significant role in the economic sector.The automation in agriculture is themain concern and the emerging subject across theworld.The population is increasing tremendously and with this increase the demand of food and employment is also increasing.The traditional methodswhich were used by the farmers,were not sufficient enough to fulfill these requirements.Thus,new automated methods were introduced.These new methods satisfied the food requirements and also provided employment opportunities to billions of people.Artificial Intelligence in agriculture has brought an agriculture revolution.This technology has protected the crop yield fromvarious factors like the climate changes,population growth,employment issues and the food security problems.This main concern of this paper is to audit the various applications of Artificial intelligence in agriculture such as for irrigation,weeding,spraying with the help of sensors and other means embedded in robots and drones.These technologies saves the excess use of water,pesticides,herbicides,maintains the fertility of the soil,also helps in the efficient use of man power and elevate the productivity and improve the quality.This paper surveys the work of many researchers to get a brief overview about the current implementation of automation in agriculture,the weeding systems through the robots and drones.The various soil water sensing methods are discussed alongwith two automatedweeding techniques.The implementation of drones is discussed,the various methods used by drones for spraying and crop-monitoring is also discussed in this paper.
文摘In this paper, finite element approach using two-dimensional unsteady state problem has been developed to study radial and angular calcium diffusion problem in neurons. Calcium is responsible messenger for transmitting information in communication process between neurons. The most important Ca^2＋ binding proteins for the dynamics of Ca^2＋ is itself buffer and other physiological parameters are located in Ca^2＋ stores. In this study, the model incorporates the physiological parameters like diffusion coefficient, receptors, exogenous buffers etc. Appropriate boundary conditions have been framed in view of the physiological conditions. Computer simulations in MATLAB 7.11 are employed to investigate mathematical models of reaction-diffusion equation, the details of the implementation can heavily affect the numerical solutions and, thus, the outcome simulated on Core（TM） i3 CPU M 330 @ 2.13GHz processing speed and 3GB memory.
