The present study of metabasalts was carried out to understand the mantle source and geodynamic setting of the Mahakoshal Group in the Central Indian Tectonic Zone.In this study,we present detailed field,petrography,a...The present study of metabasalts was carried out to understand the mantle source and geodynamic setting of the Mahakoshal Group in the Central Indian Tectonic Zone.In this study,we present detailed field,petrography,and whole rock geochemistry of the Mahakoshal metabasalts.The Mahakoshal metabasalts are sub-alkaline in nature and belong to the tholeiitic series of rocks.The variation in rareearth element patterns of metabasalts indicates the different degrees of partial melting at shallow as well as deeper depths.Further,Eu/Eu*varies from 0.8 to 1.1(except sample KP-144=0.3),Ce/Ce*varies from 0.97 to 1.05,showing no cerium anomaly,and Nb/Nb*ranges from 0.7 to 1.3(except KP-144=0.1).The magnesium number(Mg#)varies from 0.2 to 0.3,which is quite low,indicating the evolved nature of the metabasalts.The studied metabasalts show E-MORB to OIB-type affinities,which are placed in the trench-distal back-arc setting.The opening of the Mahakoshal Basin is due to retreating orogen in the accretionary orogen setting and is contemporaneous with the assembly of the Columbia Supercontinent(~2.1-1.8 Ga).Hence,field,petrographic,and geochemical signatures indicate that the Mahakoshal basin opened as a back-arc rift basin on the Bundelkhand Craton,and that metabasalts are derived from the mantle that underwent variable degrees of partial melting at different depths.展开更多
A grey-box modelling framework was developed for the estimation of cut point temperature of a crude distillation unit(CDU)under uncertainty in crude composition and process conditions.First principle(FP)model of CDU w...A grey-box modelling framework was developed for the estimation of cut point temperature of a crude distillation unit(CDU)under uncertainty in crude composition and process conditions.First principle(FP)model of CDU was developed for Pakistani crudes from Zamzama and Kunnar fields.A hybrid methodology based on the integration of Taguchi method and genetic algorithm(GA)was employed to estimate the optimal cut point temperature for various sets of process variables.Optimised datasets were utilised to develop an artificial neural networks(ANN)model for the prediction of optimum values of cut points.The ANN model was then used to replace the hybrid framework of the Taguchi method and the GA.The integration of the ANN and FP model makes it a grey-box(GB)model.For the case of Zamama crude,the GB model helped in the decrease of up to 38.93%in energy required per kilo barrel of diesel and an 8.2%increase in diesel production compared to the stand-alone FP model under uncertainty.Similarly,for Kunnar crude,up to 18.87%decrease in energy required per kilo barrel of diesel and a 33.96%increase in diesel production was observed in comparison to the stand-alone FP model.展开更多
In the current digital era,new technologies are becoming an essential part of our lives.Consequently,the number ofmalicious software ormalware attacks is rapidly growing.There is no doubt,themajority ofmalware attacks...In the current digital era,new technologies are becoming an essential part of our lives.Consequently,the number ofmalicious software ormalware attacks is rapidly growing.There is no doubt,themajority ofmalware attacks can be detected by most antivirus programs.However,such types of antivirus programs are one step behind malicious software.Due to these dilemmas,deep learning become popular in the detection and classification of malicious data.Therefore,researchers have significantly focused on finding solutions for malware attacks by analyzing malicious samples with the help of different techniques and models.In this research,we presented a lightweight attention-based novel deep Convolutional Neural Network(DNN-CNN)model for binary and multi-class malware classification,including benign,trojan horse,ransomware,and spyware.We applied the Principal Component Analysis(PCA)technique for feature extraction for binary classification.We used the Synthetic Minority Oversampling Technique(SMOTE)to handle the imbalanced data during multi-class classification.Our proposed attention-based malware detectionmodel is trained on the benchmarkmalware memory dataset named CIC-MalMem-2022.Theresults indicate that our model obtained high accuracy for binary and multi-class classification,99.5% and 97.9%,respectively.展开更多
In this study,a Grey-box(GB)model was developed to predict the optimum mass flow rates of inlet streams of a Shell and Tube Heat Exchanger(STHE)under varying process conditions.Aspen Exchanger Design and Rating(Aspen-...In this study,a Grey-box(GB)model was developed to predict the optimum mass flow rates of inlet streams of a Shell and Tube Heat Exchanger(STHE)under varying process conditions.Aspen Exchanger Design and Rating(Aspen-EDR)was initially used to construct a first principle model(FP)of the STHE using industrial data.The Genetic Algorithm(GA)was incorporated into the FP model to attain the minimum exit temperature for the hot kerosene process stream under varying process conditions.A dataset comprised of optimum process conditions was generated through FP-GA integration and was utilised to develop an Artificial Neural Networks(ANN)model.Subsequently,the ANN model was merged with the FP model by substituting the GA,to form a GB model.The developed GB model,that is,ANN and FP integration,achieved higher effectiveness and lower outlet temperature than those derived through the standalone FP model.Performance of the GB framework was also comparable to the FP-GA approach but it significantly reduced the computation time required for estimating the optimum process conditions.The proposed GB-based method improved the STHE's ability to extract energy from the process stream and strengthened its resilience to cope with diverse process conditions.展开更多
Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome...Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.展开更多
This paper addresses the sampled-data multi-objective active suspension control problem for an in-wheel motor driven electric vehicle subject to stochastic sampling periods and asynchronous premise variables.The focus...This paper addresses the sampled-data multi-objective active suspension control problem for an in-wheel motor driven electric vehicle subject to stochastic sampling periods and asynchronous premise variables.The focus is placed on the scenario that the dynamical state of the half-vehicle active suspension system is transmitted over an in-vehicle controller area network that only permits the transmission of sampled data packets.For this purpose,a stochastic sampling mechanism is developed such that the sampling periods can randomly switch among different values with certain mathematical probabilities.Then,an asynchronous fuzzy sampled-data controller,featuring distinct premise variables from the active suspension system,is constructed to eliminate the stringent requirement that the sampled-data controller has to share the same grades of membership.Furthermore,novel criteria for both stability analysis and controller design are derived in order to guarantee that the resultant closed-loop active suspension system is stochastically stable with simultaneous𝐻2 and𝐻∞performance requirements.Finally,the effectiveness of the proposed stochastic sampled-data multi-objective control method is verified via several numerical cases studies in both time domain and frequency domain under various road disturbance profiles.展开更多
In recent years,the integration of stochastic techniques,especially those based on artificial neural networks,has emerged as a pivotal advancement in the field of computational fluid dynamics.These techniques offer a ...In recent years,the integration of stochastic techniques,especially those based on artificial neural networks,has emerged as a pivotal advancement in the field of computational fluid dynamics.These techniques offer a powerful framework for the analysis of complex fluid flow phenomena and address the uncertainties inherent in fluid dynamics systems.Following this trend,the current investigation portrays the design and construction of an important technique named swarming optimized neuroheuristic intelligence with the competency of artificial neural networks to analyze nonlinear viscoelastic magneto-hydrodynamic Prandtl-Eyring fluid flow model,with diffusive magnetic layers effect along an extended sheet.The currently designed computational technique is established using inverse multiquadric radial basis activation function through the hybridization of a well-known global searching technique of particle swarm optimization and sequential quadratic programming,a technique capable of rapid convergence locally.The most appropriate scaling group involved transformations that are implemented on governing equations of the suggested fluidic model to convert it from a system of nonlinear partial differential equations into a dimensionless form of a third-order nonlinear ordinary differential equation.The transformed/reduced fluid flow model is solved for sundry variations of physical quantities using the designed scheme and outcomes are matched consistently with Adam's numerical technique with negligible magnitude of absolute errors and mean square errors.Moreover,it is revealed that the velocity of the fluid depreciates in the presence of a strong magnetic field effect.The efficacy of the designed solver is depicted evidently through rigorous statistical observations via exhaustive numerical experimentation of the fluidic problem.展开更多
Hardware-based sensing frameworks such as cooperative fuel research engines are conventionally used to monitor research octane number(RON)in the petroleum refining industry.Machine learning techniques are employed to ...Hardware-based sensing frameworks such as cooperative fuel research engines are conventionally used to monitor research octane number(RON)in the petroleum refining industry.Machine learning techniques are employed to predict the RON of integrated naphtha reforming and isomerisation processes.A dynamic Aspen HYSYS model was used to generate data by introducing artificial uncertainties in the range of±5%in process conditions,such as temperature,flow rates,etc.The generated data was used to train support vector machines(SVM),Gaussian process regression(GPR),artificial neural networks(ANN),regression trees(RT),and ensemble trees(ET).Hyperparameter tuning was performed to enhance the prediction capabilities of GPR,ANN,SVM,ET and RT models.Performance analysis of the models indicates that GPR,ANN,and SVM with R2 values of 0.99,0.978,and 0.979 and RMSE values of 0.108,0.262,and 0.258,respectively performed better than the remaining models and had the prediction capability to capture the RON dependence on predictor variables.ET and RT had an R2 value of 0.94 and 0.89,respectively.The GPR model was used as a surrogate model for fitness function evaluations in two optimisation frameworks based on genetic algorithm and particle swarm method.Optimal parameter values found by the optimisation methodology increased the RON value by 3.52%.The proposed methodology of surrogate-based optimisation will provide a platform for plant-level implementation to realise the concept of industry 4.0 in the refinery.展开更多
This paper addresses the co-design problem of decentralized dynamic event-triggered communication and active suspension control for an in-wheel motor driven electric vehicle equipped with a dynamic damper. The main ob...This paper addresses the co-design problem of decentralized dynamic event-triggered communication and active suspension control for an in-wheel motor driven electric vehicle equipped with a dynamic damper. The main objective is to simultaneously improve the desired suspension performance caused by various road disturbances and alleviate the network resource utilization for the concerned in-vehicle networked suspension system. First, a T-S fuzzy active suspension model of an electric vehicle under dynamic damping is established. Second,a novel decentralized dynamic event-triggered communication mechanism is developed to regulate each sensor's data transmissions such that sampled data packets on each sensor are scheduled in an independent manner. In contrast to the traditional static triggering mechanisms, a key feature of the proposed mechanism is that the threshold parameter in the event trigger is adjusted adaptively over time to reduce the network resources occupancy. Third, co-design criteria for the desired event-triggered fuzzy controller and dynamic triggering mechanisms are derived. Finally, comprehensive comparative simulation studies of a 3-degrees-of-freedom quarter suspension model are provided under both bump road disturbance and ISO-2631 classified random road disturbance to validate the effectiveness of the proposed co-design approach. It is shown that ride comfort can be greatly improved in either road disturbance case and the suspension deflection, dynamic tyre load and actuator control input are all kept below the prescribed maximum allowable limits, while simultaneously maintaining desirable communication efficiency.展开更多
This work explains the synergistic contribution of graphene nanoplatelets(GNP)and zirconia ceramic nanoparticles(ZrO2)on the microstructure,mechanical performance and ballistic properties of the alumina(Al2O3)ceramic ...This work explains the synergistic contribution of graphene nanoplatelets(GNP)and zirconia ceramic nanoparticles(ZrO2)on the microstructure,mechanical performance and ballistic properties of the alumina(Al2O3)ceramic hybrid nanocomposites.Over the benchmarked monolithic alumina,the hotpressed hybrid nanocomposite microstructure demonstrated 68%lower grain size due to grain pinning phenomenon by the homogenously distributed reinforcing GNP(0.5 wt%)and zirconia(4 wt%)inclusions.Moreover,the hybrid nanocomposite manifested 155%better fracture toughness(KIC)and 17%higher microhardness as well as 88%superior ballistic trait over the monolithic alumina,respectively.The superior mechanical and ballistic performance of the hybrid nanocomposites was attributed to the combined role of zirconia nanoparticles and GNP nanomaterial in refining the microstructure and inducing idiosyncratic strengthening/toughening mechanisms.Extensive combined electron microscopy revealed complicated physical interlocking of the GNP into the microstructure as well as excellent bonding of the GNP with alumina at their interface in the hybrid nanocomposites.We also probed the efficiency of the pull-out and crack-bridging toughening mechanisms through proven quantitative methods.Based on the information extracted from the in-depth SEM/TEM investigation,we outlined schematic models for understating the reinforcing ability as well as toughening mechanisms in the hybrid nanocomposites and meticulously discussed.The hot-pressed hybrid nanocomposites owning high toughness and hardness may have applications in advanced armor technology.展开更多
In Pakistan,the solar analogue has been addressed but its surface geographical parameterization has given least attention.Inappropriate density of stations and their spatial coverage particularly in difficult peripher...In Pakistan,the solar analogue has been addressed but its surface geographical parameterization has given least attention.Inappropriate density of stations and their spatial coverage particularly in difficult peripheral national territories,little or no solar radiation data,non-satisfactory sunshine hours data,and low quality of ground observed cloud cover data create a situation in which the spatial modeling of Extraterrestrial Solar Radiation(ESR) and its ground parameterization got sufficient scope.The Digital Elevation Model (DEM) input into Geographic Information System (GIS) is a compatible tool to demonstrate the spatial distribution of ESR over the rugged terrains of the study domain.For the first time,distributed modeling of ESR is done over the rugged terrains of Pakistan,based on DEM and ArcGIS..Results clearly depict that the complex landforms profoundly disrupt the zonal distribution of ESR in Pakistan.The screening impact of topography is higher on spatial distribution of ESR in winter and considerably low in summer.The combined effect of topography and latitude is obvious.Hence,the model was further testified by plotting Rb (ratio of ESR quantity over rugged terrain against plane surface) against azimuth at different latitudes with different angled slopes.The results clearly support the strong screening effect of rugged terrain through out the country especially in Himalayas,Karakoram and Hindukush (HKH),western border mountains and Balochistan Plateau.This model can be instrumental as baseline geospatial information for scientific investigations in Pakistan,where substantial fraction of national population is living in mountainous regions.展开更多
Zinc telluride is a versatile wide band gap semiconductor used in many applications.But it has certain limitations like large dimensions and large band gaps.Introducing alkali metal to its bulk lattice(3D)can reduce i...Zinc telluride is a versatile wide band gap semiconductor used in many applications.But it has certain limitations like large dimensions and large band gaps.Introducing alkali metal to its bulk lattice(3D)can reduce its dimensions and lanthanide can produce a red shift in the energy gap by converting it into quaternary compounds.The alkali and lanthanide incorporated quaternary zinc tellurides CsLnZnTe_(3)(Ln=La,Pr,Nd and Sm)form layered crystal structure in which_(∞)^(2)[LnZnTe_(3)]-layers are separated by Cs+layer.The famous lanthanide contraction is experimental both from lattice constants and bond lengths.The calculated band gaps are 2.26,2.28,2.12,2.05 eV for CsLaZnTe_(3),CsPrZnTe_(3),CsNdZnTe_(3) and CsSmZnTe_(3),respectively.These compounds show direct band gap nature.The energy band gaps of these compounds have not been evaluated yet both experimentally and theoretically.Energy loss functions,refractive index and dielectric functions were also calculated to explore the potential applications of CsLnZnTe_(3) in optoelectronic devices.展开更多
An M-type Rb^(87) atomic system is proposed for one-dimensional atom microscopy under the condition of Electromagnetically Induced Transparency.Super-localization of the atom in the absorption spectrum while its deloc...An M-type Rb^(87) atomic system is proposed for one-dimensional atom microscopy under the condition of Electromagnetically Induced Transparency.Super-localization of the atom in the absorption spectrum while its delocalization in the dispersion spectrum is observed due to the dual superposition effect of the resonant Reids.The observed minimum uncertainty peaks will find important applications in Laser cooling,creating focused atom beams,atom nanolithography,and in measurement of the center-of-mass wave function of moving atoms.展开更多
In this paper, we explored the structural, elastic and mechanical properties of the strongly correlated electron systems, intermetallic Ln-Au（Ln = Ce, Pr, Nd, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu） in cubic structure,using...In this paper, we explored the structural, elastic and mechanical properties of the strongly correlated electron systems, intermetallic Ln-Au（Ln = Ce, Pr, Nd, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu） in cubic structure,using PF-LAPW method within the density functional theory. Structural properties of these intermetallics were investigated by treating the exchange-correlation potential with the GGA-PBE, GGA-PBEsol and GGA ＋ U. The effectiveness of the U for the structural properties as compared to other methods confirms the strong correlated nature of these compounds and the calculated lattice constants endorse the divalency of Yb. The results demonstrate the stable cubic CsCl structure of these compounds. Bulk modulus, Young＇s modulus, shear modulus, B/G ratio, Cauchy pressure, Poisson＇s ratio, anisotropic ratio,Kleinman parameters and Lame＇s coefficients were studied using the PBEsol to evaluate their importance in various types of engineering applications. The most prominent features of these compounds are their ductility, very high melting points, resistance to corrosion, and anisotropic nature.展开更多
Over the last decade,a significant increase has been observed in the use of web-based Information systems that process sensitive information,e.g.,personal,financial,medical.With this increased use,the security of such...Over the last decade,a significant increase has been observed in the use of web-based Information systems that process sensitive information,e.g.,personal,financial,medical.With this increased use,the security of such systems became a crucial aspect to ensure safety,integrity and authenticity of the data.To achieve the objectives of data safety,security testing is performed.However,with growth and diversity of information systems,it is challenging to apply security testing for each and every system.Therefore,it is important to classify the assets based on their required level of security using an appropriate technique.In this paper,we propose an asset security classification technique to classify the System Under Test(SUT)based on various factors such as system exposure,data criticality and security requirements.We perform an extensive evaluation of our technique on a sample of 451 information systems.Further,we use security testing on a sample extracted from the resulting prioritized systems to investigate the presence of vulnerabilities.Our technique achieved promising results of successfully assigning security levels to various assets in the tested environments and also found several vulnerabilities in them.展开更多
Blockchain technology is one of the key technological breakthroughs of the last decade.It has the ability to revolutionize numerous aspects of society,including financial systems,healthcare,e-government and many other...Blockchain technology is one of the key technological breakthroughs of the last decade.It has the ability to revolutionize numerous aspects of society,including financial systems,healthcare,e-government and many others.One such area that is able to reap the benefits of blockchain technology is the real estate industry.Like many other industries,real estate faces major administrative problems such as high transaction fees,a lack of transparency,fraud and the effects of a middleman including undue influence and commissions.Blockchain enables supporting technologies to overcome the obstacles inherent within the real estate investment market.These technologies include smart contracts,immutable record management and time-stamped storage.We utilize these key properties of blockchain technology in our work by proposing a system that has the ability to record real estate transactions in a private blockchain,using smart contracts.The immutability of the blockchain ledger and transactions can provide a safe space for the real estate business.Blockchain technology can also assist the authentication process by hastening background checks.Personal digital keys are provided to parties that are involved in a contract,thus minimizing the risk of fraud.We also discuss the rationale behind the advantages of using a blockchain in this manner,and how we selected the consensus mechanism for our proposed system.展开更多
Brain tumors are life-threatening for adults and children.However,accurate and timely detection can save lives.This study focuses on three different types of brain tumors:Glioma,meningioma,and pituitary tumors.Many st...Brain tumors are life-threatening for adults and children.However,accurate and timely detection can save lives.This study focuses on three different types of brain tumors:Glioma,meningioma,and pituitary tumors.Many studies describe the analysis and classification of brain tumors,but few have looked at the problem of feature engineering.Methods are needed to overcome the drawbacks of manual diagnosis and conventional featureengineering techniques.An automatic diagnostic system is thus necessary to extract features and classify brain tumors accurately.While progress continues to be made,the automatic diagnoses of brain tumors still face challenges of low accuracy and high false-positive results.The model presented in this study,which offers improvements for feature extraction and classification,uses deep learning and machine learning for the assessment of brain tumors.Deep learning is used for feature extraction and encompasses the application of different models such as fine-tuned Inception-v3 and fine-tuned Xception.The classification of brain tumors is explored through deep-and machine-learning algorithms including softmax,Random Forest,Support Vector Machine,K-Nearest Neighbors,and the ensemble technique.The results of these approaches are compared with existing methods.The Inception-v3 model has a test accuracy of 94.34%and achieves the highest performance compared with other recently reported methods.This improvement may be sufficient to support a significant role in clinical applications for brain tumor analysis.Furthermore,this type of approach can be used as an effective decisionsupport tool for radiologists in medical diagnostics as a second opinion based on the magnetic resonance imaging(MRI)analysis.It may also save valuable time for radiologists who have to manually review numerous MRI images of patients.展开更多
Bronchial asthma may result in oxidant/antioxidant imbalance. Antioxidant vitamins E and C concentrations were estimated in plasma of asthmatics that were also simultaneously subjected to spirometry and matched with h...Bronchial asthma may result in oxidant/antioxidant imbalance. Antioxidant vitamins E and C concentrations were estimated in plasma of asthmatics that were also simultaneously subjected to spirometry and matched with healthy controls showing significant changes in both the vitamin concentrations. Vitamin C showed strong correlation whereas vitamin E was not correlated with spirometry.展开更多
文摘The present study of metabasalts was carried out to understand the mantle source and geodynamic setting of the Mahakoshal Group in the Central Indian Tectonic Zone.In this study,we present detailed field,petrography,and whole rock geochemistry of the Mahakoshal metabasalts.The Mahakoshal metabasalts are sub-alkaline in nature and belong to the tholeiitic series of rocks.The variation in rareearth element patterns of metabasalts indicates the different degrees of partial melting at shallow as well as deeper depths.Further,Eu/Eu*varies from 0.8 to 1.1(except sample KP-144=0.3),Ce/Ce*varies from 0.97 to 1.05,showing no cerium anomaly,and Nb/Nb*ranges from 0.7 to 1.3(except KP-144=0.1).The magnesium number(Mg#)varies from 0.2 to 0.3,which is quite low,indicating the evolved nature of the metabasalts.The studied metabasalts show E-MORB to OIB-type affinities,which are placed in the trench-distal back-arc setting.The opening of the Mahakoshal Basin is due to retreating orogen in the accretionary orogen setting and is contemporaneous with the assembly of the Columbia Supercontinent(~2.1-1.8 Ga).Hence,field,petrographic,and geochemical signatures indicate that the Mahakoshal basin opened as a back-arc rift basin on the Bundelkhand Craton,and that metabasalts are derived from the mantle that underwent variable degrees of partial melting at different depths.
基金Higher Education Commission,Pakistan,under the National Research Program for Universities Project,Grant/Award Number:NBU-FPEJ-2024-1243-02。
文摘A grey-box modelling framework was developed for the estimation of cut point temperature of a crude distillation unit(CDU)under uncertainty in crude composition and process conditions.First principle(FP)model of CDU was developed for Pakistani crudes from Zamzama and Kunnar fields.A hybrid methodology based on the integration of Taguchi method and genetic algorithm(GA)was employed to estimate the optimal cut point temperature for various sets of process variables.Optimised datasets were utilised to develop an artificial neural networks(ANN)model for the prediction of optimum values of cut points.The ANN model was then used to replace the hybrid framework of the Taguchi method and the GA.The integration of the ANN and FP model makes it a grey-box(GB)model.For the case of Zamama crude,the GB model helped in the decrease of up to 38.93%in energy required per kilo barrel of diesel and an 8.2%increase in diesel production compared to the stand-alone FP model under uncertainty.Similarly,for Kunnar crude,up to 18.87%decrease in energy required per kilo barrel of diesel and a 33.96%increase in diesel production was observed in comparison to the stand-alone FP model.
基金funded by Naif Arab University for Security Sciences under grant No.NAUSS-23-R11.
文摘In the current digital era,new technologies are becoming an essential part of our lives.Consequently,the number ofmalicious software ormalware attacks is rapidly growing.There is no doubt,themajority ofmalware attacks can be detected by most antivirus programs.However,such types of antivirus programs are one step behind malicious software.Due to these dilemmas,deep learning become popular in the detection and classification of malicious data.Therefore,researchers have significantly focused on finding solutions for malware attacks by analyzing malicious samples with the help of different techniques and models.In this research,we presented a lightweight attention-based novel deep Convolutional Neural Network(DNN-CNN)model for binary and multi-class malware classification,including benign,trojan horse,ransomware,and spyware.We applied the Principal Component Analysis(PCA)technique for feature extraction for binary classification.We used the Synthetic Minority Oversampling Technique(SMOTE)to handle the imbalanced data during multi-class classification.Our proposed attention-based malware detectionmodel is trained on the benchmarkmalware memory dataset named CIC-MalMem-2022.Theresults indicate that our model obtained high accuracy for binary and multi-class classification,99.5% and 97.9%,respectively.
基金National Research Program for Universities,Grant/Award Number:10215/FederalNorthern Border University,Arar,KSA,Grant/Award Number:NBU-FPEJ-2024-1243-01。
文摘In this study,a Grey-box(GB)model was developed to predict the optimum mass flow rates of inlet streams of a Shell and Tube Heat Exchanger(STHE)under varying process conditions.Aspen Exchanger Design and Rating(Aspen-EDR)was initially used to construct a first principle model(FP)of the STHE using industrial data.The Genetic Algorithm(GA)was incorporated into the FP model to attain the minimum exit temperature for the hot kerosene process stream under varying process conditions.A dataset comprised of optimum process conditions was generated through FP-GA integration and was utilised to develop an Artificial Neural Networks(ANN)model.Subsequently,the ANN model was merged with the FP model by substituting the GA,to form a GB model.The developed GB model,that is,ANN and FP integration,achieved higher effectiveness and lower outlet temperature than those derived through the standalone FP model.Performance of the GB framework was also comparable to the FP-GA approach but it significantly reduced the computation time required for estimating the optimum process conditions.The proposed GB-based method improved the STHE's ability to extract energy from the process stream and strengthened its resilience to cope with diverse process conditions.
基金supported by King Saud University,Riyadh,Saudi Arabia,through the Researchers Supporting Project under Grant RSPD2025R697.
文摘Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.
文摘This paper addresses the sampled-data multi-objective active suspension control problem for an in-wheel motor driven electric vehicle subject to stochastic sampling periods and asynchronous premise variables.The focus is placed on the scenario that the dynamical state of the half-vehicle active suspension system is transmitted over an in-vehicle controller area network that only permits the transmission of sampled data packets.For this purpose,a stochastic sampling mechanism is developed such that the sampling periods can randomly switch among different values with certain mathematical probabilities.Then,an asynchronous fuzzy sampled-data controller,featuring distinct premise variables from the active suspension system,is constructed to eliminate the stringent requirement that the sampled-data controller has to share the same grades of membership.Furthermore,novel criteria for both stability analysis and controller design are derived in order to guarantee that the resultant closed-loop active suspension system is stochastically stable with simultaneous𝐻2 and𝐻∞performance requirements.Finally,the effectiveness of the proposed stochastic sampled-data multi-objective control method is verified via several numerical cases studies in both time domain and frequency domain under various road disturbance profiles.
文摘In recent years,the integration of stochastic techniques,especially those based on artificial neural networks,has emerged as a pivotal advancement in the field of computational fluid dynamics.These techniques offer a powerful framework for the analysis of complex fluid flow phenomena and address the uncertainties inherent in fluid dynamics systems.Following this trend,the current investigation portrays the design and construction of an important technique named swarming optimized neuroheuristic intelligence with the competency of artificial neural networks to analyze nonlinear viscoelastic magneto-hydrodynamic Prandtl-Eyring fluid flow model,with diffusive magnetic layers effect along an extended sheet.The currently designed computational technique is established using inverse multiquadric radial basis activation function through the hybridization of a well-known global searching technique of particle swarm optimization and sequential quadratic programming,a technique capable of rapid convergence locally.The most appropriate scaling group involved transformations that are implemented on governing equations of the suggested fluidic model to convert it from a system of nonlinear partial differential equations into a dimensionless form of a third-order nonlinear ordinary differential equation.The transformed/reduced fluid flow model is solved for sundry variations of physical quantities using the designed scheme and outcomes are matched consistently with Adam's numerical technique with negligible magnitude of absolute errors and mean square errors.Moreover,it is revealed that the velocity of the fluid depreciates in the presence of a strong magnetic field effect.The efficacy of the designed solver is depicted evidently through rigorous statistical observations via exhaustive numerical experimentation of the fluidic problem.
基金Higher Education Commission(HEC),Pakistan,under the National Research Program for Universities(NRPU)Project No.10,215/Federal.
文摘Hardware-based sensing frameworks such as cooperative fuel research engines are conventionally used to monitor research octane number(RON)in the petroleum refining industry.Machine learning techniques are employed to predict the RON of integrated naphtha reforming and isomerisation processes.A dynamic Aspen HYSYS model was used to generate data by introducing artificial uncertainties in the range of±5%in process conditions,such as temperature,flow rates,etc.The generated data was used to train support vector machines(SVM),Gaussian process regression(GPR),artificial neural networks(ANN),regression trees(RT),and ensemble trees(ET).Hyperparameter tuning was performed to enhance the prediction capabilities of GPR,ANN,SVM,ET and RT models.Performance analysis of the models indicates that GPR,ANN,and SVM with R2 values of 0.99,0.978,and 0.979 and RMSE values of 0.108,0.262,and 0.258,respectively performed better than the remaining models and had the prediction capability to capture the RON dependence on predictor variables.ET and RT had an R2 value of 0.94 and 0.89,respectively.The GPR model was used as a surrogate model for fitness function evaluations in two optimisation frameworks based on genetic algorithm and particle swarm method.Optimal parameter values found by the optimisation methodology increased the RON value by 3.52%.The proposed methodology of surrogate-based optimisation will provide a platform for plant-level implementation to realise the concept of industry 4.0 in the refinery.
文摘This paper addresses the co-design problem of decentralized dynamic event-triggered communication and active suspension control for an in-wheel motor driven electric vehicle equipped with a dynamic damper. The main objective is to simultaneously improve the desired suspension performance caused by various road disturbances and alleviate the network resource utilization for the concerned in-vehicle networked suspension system. First, a T-S fuzzy active suspension model of an electric vehicle under dynamic damping is established. Second,a novel decentralized dynamic event-triggered communication mechanism is developed to regulate each sensor's data transmissions such that sampled data packets on each sensor are scheduled in an independent manner. In contrast to the traditional static triggering mechanisms, a key feature of the proposed mechanism is that the threshold parameter in the event trigger is adjusted adaptively over time to reduce the network resources occupancy. Third, co-design criteria for the desired event-triggered fuzzy controller and dynamic triggering mechanisms are derived. Finally, comprehensive comparative simulation studies of a 3-degrees-of-freedom quarter suspension model are provided under both bump road disturbance and ISO-2631 classified random road disturbance to validate the effectiveness of the proposed co-design approach. It is shown that ride comfort can be greatly improved in either road disturbance case and the suspension deflection, dynamic tyre load and actuator control input are all kept below the prescribed maximum allowable limits, while simultaneously maintaining desirable communication efficiency.
基金extend their sincere appreciation to the Deanship of Scientific Research at King Saud University for funding this research through the Research Group Project No.RGP283.
文摘This work explains the synergistic contribution of graphene nanoplatelets(GNP)and zirconia ceramic nanoparticles(ZrO2)on the microstructure,mechanical performance and ballistic properties of the alumina(Al2O3)ceramic hybrid nanocomposites.Over the benchmarked monolithic alumina,the hotpressed hybrid nanocomposite microstructure demonstrated 68%lower grain size due to grain pinning phenomenon by the homogenously distributed reinforcing GNP(0.5 wt%)and zirconia(4 wt%)inclusions.Moreover,the hybrid nanocomposite manifested 155%better fracture toughness(KIC)and 17%higher microhardness as well as 88%superior ballistic trait over the monolithic alumina,respectively.The superior mechanical and ballistic performance of the hybrid nanocomposites was attributed to the combined role of zirconia nanoparticles and GNP nanomaterial in refining the microstructure and inducing idiosyncratic strengthening/toughening mechanisms.Extensive combined electron microscopy revealed complicated physical interlocking of the GNP into the microstructure as well as excellent bonding of the GNP with alumina at their interface in the hybrid nanocomposites.We also probed the efficiency of the pull-out and crack-bridging toughening mechanisms through proven quantitative methods.Based on the information extracted from the in-depth SEM/TEM investigation,we outlined schematic models for understating the reinforcing ability as well as toughening mechanisms in the hybrid nanocomposites and meticulously discussed.The hot-pressed hybrid nanocomposites owning high toughness and hardness may have applications in advanced armor technology.
文摘In Pakistan,the solar analogue has been addressed but its surface geographical parameterization has given least attention.Inappropriate density of stations and their spatial coverage particularly in difficult peripheral national territories,little or no solar radiation data,non-satisfactory sunshine hours data,and low quality of ground observed cloud cover data create a situation in which the spatial modeling of Extraterrestrial Solar Radiation(ESR) and its ground parameterization got sufficient scope.The Digital Elevation Model (DEM) input into Geographic Information System (GIS) is a compatible tool to demonstrate the spatial distribution of ESR over the rugged terrains of the study domain.For the first time,distributed modeling of ESR is done over the rugged terrains of Pakistan,based on DEM and ArcGIS..Results clearly depict that the complex landforms profoundly disrupt the zonal distribution of ESR in Pakistan.The screening impact of topography is higher on spatial distribution of ESR in winter and considerably low in summer.The combined effect of topography and latitude is obvious.Hence,the model was further testified by plotting Rb (ratio of ESR quantity over rugged terrain against plane surface) against azimuth at different latitudes with different angled slopes.The results clearly support the strong screening effect of rugged terrain through out the country especially in Himalayas,Karakoram and Hindukush (HKH),western border mountains and Balochistan Plateau.This model can be instrumental as baseline geospatial information for scientific investigations in Pakistan,where substantial fraction of national population is living in mountainous regions.
基金the Deanship of Scientific Research at King Khalid University for funding this work through research groups program under grant number(RGP.2/141/43)。
文摘Zinc telluride is a versatile wide band gap semiconductor used in many applications.But it has certain limitations like large dimensions and large band gaps.Introducing alkali metal to its bulk lattice(3D)can reduce its dimensions and lanthanide can produce a red shift in the energy gap by converting it into quaternary compounds.The alkali and lanthanide incorporated quaternary zinc tellurides CsLnZnTe_(3)(Ln=La,Pr,Nd and Sm)form layered crystal structure in which_(∞)^(2)[LnZnTe_(3)]-layers are separated by Cs+layer.The famous lanthanide contraction is experimental both from lattice constants and bond lengths.The calculated band gaps are 2.26,2.28,2.12,2.05 eV for CsLaZnTe_(3),CsPrZnTe_(3),CsNdZnTe_(3) and CsSmZnTe_(3),respectively.These compounds show direct band gap nature.The energy band gaps of these compounds have not been evaluated yet both experimentally and theoretically.Energy loss functions,refractive index and dielectric functions were also calculated to explore the potential applications of CsLnZnTe_(3) in optoelectronic devices.
基金supported by the Higher Education Commission (HEC) of Pakistan
文摘An M-type Rb^(87) atomic system is proposed for one-dimensional atom microscopy under the condition of Electromagnetically Induced Transparency.Super-localization of the atom in the absorption spectrum while its delocalization in the dispersion spectrum is observed due to the dual superposition effect of the resonant Reids.The observed minimum uncertainty peaks will find important applications in Laser cooling,creating focused atom beams,atom nanolithography,and in measurement of the center-of-mass wave function of moving atoms.
基金Project supported by the Higher Education Commission of Pakistan(HEC)(20-3959/NRPU/R&D/HEC2014/119)
文摘In this paper, we explored the structural, elastic and mechanical properties of the strongly correlated electron systems, intermetallic Ln-Au（Ln = Ce, Pr, Nd, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu） in cubic structure,using PF-LAPW method within the density functional theory. Structural properties of these intermetallics were investigated by treating the exchange-correlation potential with the GGA-PBE, GGA-PBEsol and GGA ＋ U. The effectiveness of the U for the structural properties as compared to other methods confirms the strong correlated nature of these compounds and the calculated lattice constants endorse the divalency of Yb. The results demonstrate the stable cubic CsCl structure of these compounds. Bulk modulus, Young＇s modulus, shear modulus, B/G ratio, Cauchy pressure, Poisson＇s ratio, anisotropic ratio,Kleinman parameters and Lame＇s coefficients were studied using the PBEsol to evaluate their importance in various types of engineering applications. The most prominent features of these compounds are their ductility, very high melting points, resistance to corrosion, and anisotropic nature.
基金the Higher Education Commission(HEC),Pakistan throughits initiative of National Center for Cyber Security for the affiliated Security Testing-Innovative SecuredSystems Lab(ISSL)established at University of Engineering&Technology(UET)Peshawar,Grant No.2(1078)/HEC/M&E/2018/707.
文摘Over the last decade,a significant increase has been observed in the use of web-based Information systems that process sensitive information,e.g.,personal,financial,medical.With this increased use,the security of such systems became a crucial aspect to ensure safety,integrity and authenticity of the data.To achieve the objectives of data safety,security testing is performed.However,with growth and diversity of information systems,it is challenging to apply security testing for each and every system.Therefore,it is important to classify the assets based on their required level of security using an appropriate technique.In this paper,we propose an asset security classification technique to classify the System Under Test(SUT)based on various factors such as system exposure,data criticality and security requirements.We perform an extensive evaluation of our technique on a sample of 451 information systems.Further,we use security testing on a sample extracted from the resulting prioritized systems to investigate the presence of vulnerabilities.Our technique achieved promising results of successfully assigning security levels to various assets in the tested environments and also found several vulnerabilities in them.
文摘Blockchain technology is one of the key technological breakthroughs of the last decade.It has the ability to revolutionize numerous aspects of society,including financial systems,healthcare,e-government and many others.One such area that is able to reap the benefits of blockchain technology is the real estate industry.Like many other industries,real estate faces major administrative problems such as high transaction fees,a lack of transparency,fraud and the effects of a middleman including undue influence and commissions.Blockchain enables supporting technologies to overcome the obstacles inherent within the real estate investment market.These technologies include smart contracts,immutable record management and time-stamped storage.We utilize these key properties of blockchain technology in our work by proposing a system that has the ability to record real estate transactions in a private blockchain,using smart contracts.The immutability of the blockchain ledger and transactions can provide a safe space for the real estate business.Blockchain technology can also assist the authentication process by hastening background checks.Personal digital keys are provided to parties that are involved in a contract,thus minimizing the risk of fraud.We also discuss the rationale behind the advantages of using a blockchain in this manner,and how we selected the consensus mechanism for our proposed system.
文摘Brain tumors are life-threatening for adults and children.However,accurate and timely detection can save lives.This study focuses on three different types of brain tumors:Glioma,meningioma,and pituitary tumors.Many studies describe the analysis and classification of brain tumors,but few have looked at the problem of feature engineering.Methods are needed to overcome the drawbacks of manual diagnosis and conventional featureengineering techniques.An automatic diagnostic system is thus necessary to extract features and classify brain tumors accurately.While progress continues to be made,the automatic diagnoses of brain tumors still face challenges of low accuracy and high false-positive results.The model presented in this study,which offers improvements for feature extraction and classification,uses deep learning and machine learning for the assessment of brain tumors.Deep learning is used for feature extraction and encompasses the application of different models such as fine-tuned Inception-v3 and fine-tuned Xception.The classification of brain tumors is explored through deep-and machine-learning algorithms including softmax,Random Forest,Support Vector Machine,K-Nearest Neighbors,and the ensemble technique.The results of these approaches are compared with existing methods.The Inception-v3 model has a test accuracy of 94.34%and achieves the highest performance compared with other recently reported methods.This improvement may be sufficient to support a significant role in clinical applications for brain tumor analysis.Furthermore,this type of approach can be used as an effective decisionsupport tool for radiologists in medical diagnostics as a second opinion based on the magnetic resonance imaging(MRI)analysis.It may also save valuable time for radiologists who have to manually review numerous MRI images of patients.
文摘Bronchial asthma may result in oxidant/antioxidant imbalance. Antioxidant vitamins E and C concentrations were estimated in plasma of asthmatics that were also simultaneously subjected to spirometry and matched with healthy controls showing significant changes in both the vitamin concentrations. Vitamin C showed strong correlation whereas vitamin E was not correlated with spirometry.
