从一块腐烂的鲸鱼骨头分离鉴定了一株真菌Penicillium sp. S2014503。利用大米固体发酵培养技术从该菌产物中获得10个化合物,通过核磁共振波谱数据比对分析结合质谱数据分析,手性化合物测定旋光值或者进行X-射线单晶衍射分析后将这10个...从一块腐烂的鲸鱼骨头分离鉴定了一株真菌Penicillium sp. S2014503。利用大米固体发酵培养技术从该菌产物中获得10个化合物,通过核磁共振波谱数据比对分析结合质谱数据分析,手性化合物测定旋光值或者进行X-射线单晶衍射分析后将这10个化合物分别鉴定为emodin(1)、citreorosein(2)、tetrahydroaltersolanolB(3)、conioxanthoneA(4)、chrysogine(5)、pyramidamycin B (6)、germicidin O (7)、2-(6-hydroxy-5,7-dimethylbenzefuranone-4-yl) acetaldehyde (8)、astrophenone (9)和chenopodolans A (10)。经生物活性测试发现,化合物1和2对藤黄微球菌具有弱的抑制活性,化合物1、2和4对卤虫幼虫具有显著的致死毒性。此外,化合物1是中药大黄和虎杖的主要致泻成分。文章首次报道了来源于海洋动物样品鲸鱼骨的真菌Penicillium sp.能够生产大黄素类等活性天然产物。展开更多
Algorithms for steganography are methods of hiding data transfers in media files.Several machine learning architectures have been presented recently to improve stego image identification performance by using spatial i...Algorithms for steganography are methods of hiding data transfers in media files.Several machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image analysis.Images with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for classification.To address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of images.Support Vector Machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or cover.The Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the proposedmethod.Using WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods.展开更多
The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments,soil conditions and higher human consumption.It is cultivated in vast areas of Asian and Non...The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments,soil conditions and higher human consumption.It is cultivated in vast areas of Asian and Non-Asian countries,including Pakistan.The guava plant is vulnerable to diseases,specifically the leaves and fruit,which result in massive crop and profitability losses.The existing plant leaf disease detection techniques can detect only one disease from a leaf.However,a single leaf may contain symptoms of multiple diseases.This study has proposed a hybrid deep learning-based framework for the real-time detection of multiple diseases from a single guava leaf in several steps.Firstly,Guava Infected Patches Modified MobileNetV2 and U-Net(GIP-MU-NET)has been proposed to segment the infected guava patches.The proposed model consists of modified MobileNetv2 as an encoder,and the U-Net model’s up-sampling layers are used as a decoder part.Secondly,the Guava Leaf SegmentationModel(GLSM)is proposed to segment the healthy and infected leaves.In the final step,the Guava Multiple Leaf Diseases Detection(GMLDD)model based on the YOLOv5 model detects various diseases from a guava leaf.Two self-collected datasets(the Guava Patches Dataset and the Guava Leaf Diseases Dataset)are used for training and validation.The proposed method detected the various defects,including five distinct classes,i.e.,anthracnose,insect attack,nutrition deficiency,wilt,and healthy.On average,the GIP-MU-Net model achieved 92.41%accuracy,the GLSM gained 83.40%accuracy,whereas the proposed GMLDD technique achieved 73.3%precision,73.1%recall,71.0%mAP@0.5 and 50.3 mAP@0.5:0.95 scores for all the aforesaid classes.展开更多
Many plant species have a startling degree of morphological similarity,making it difficult to split and categorize them reliably.Unknown plant species can be challenging to classify and segment using deep learning.Whi...Many plant species have a startling degree of morphological similarity,making it difficult to split and categorize them reliably.Unknown plant species can be challenging to classify and segment using deep learning.While using deep learning architectures has helped improve classification accuracy,the resulting models often need to be more flexible and require a large dataset to train.For the sake of taxonomy,this research proposes a hybrid method for categorizing guava,potato,and java plumleaves.Two new approaches are used to formthe hybridmodel suggested here.The guava,potato,and java plum plant species have been successfully segmented using the first model built on the MobileNetV2-UNET architecture.As a second model,we use a Plant Species Detection Stacking Ensemble Deep Learning Model(PSD-SE-DLM)to identify potatoes,java plums,and guava.The proposed models were trained using data collected in Punjab,Pakistan,consisting of images of healthy and sick leaves from guava,java plum,and potatoes.These datasets are known as PLSD and PLSSD.Accuracy levels of 99.84%and 96.38%were achieved for the suggested PSD-SE-DLM and MobileNetV2-UNET models,respectively.展开更多
Software Product Line(SPL)is a group of software-intensive systems that share common and variable resources for developing a particular system.The feature model is a tree-type structure used to manage SPL’s common an...Software Product Line(SPL)is a group of software-intensive systems that share common and variable resources for developing a particular system.The feature model is a tree-type structure used to manage SPL’s common and variable features with their different relations and problem of Crosstree Constraints(CTC).CTC problems exist in groups of common and variable features among the sub-tree of feature models more diverse in Internet of Things(IoT)devices because different Internet devices and protocols are communicated.Therefore,managing the CTC problem to achieve valid product configuration in IoT-based SPL is more complex,time-consuming,and hard.However,the CTC problem needs to be considered in previously proposed approaches such as Commonality VariabilityModeling of Features(COVAMOF)andGenarch+tool;therefore,invalid products are generated.This research has proposed a novel approach Binary Oriented Feature Selection Crosstree Constraints(BOFS-CTC),to find all possible valid products by selecting the features according to cardinality constraints and cross-tree constraint problems in the featuremodel of SPL.BOFS-CTC removes the invalid products at the early stage of feature selection for the product configuration.Furthermore,this research developed the BOFS-CTC algorithm and applied it to,IoT-based feature models.The findings of this research are that no relationship constraints and CTC violations occur and drive the valid feature product configurations for the application development by removing the invalid product configurations.The accuracy of BOFS-CTC is measured by the integration sampling technique,where different valid product configurations are compared with the product configurations derived by BOFS-CTC and found 100%correct.Using BOFS-CTC eliminates the testing cost and development effort of invalid SPL products.展开更多
With the capacities of self-learning,acquainted capacities,high-speed looking for ideal arrangements,solid nonlin-ear fitting,and mapping self-assertively complex nonlinear relations,neural systems have made incredibl...With the capacities of self-learning,acquainted capacities,high-speed looking for ideal arrangements,solid nonlin-ear fitting,and mapping self-assertively complex nonlinear relations,neural systems have made incredible advances and accomplished broad application over the final half-century.As one of the foremost conspicuous methods for fake insights,neural systems are growing toward high computational speed and moo control utilization.Due to the inborn impediments of electronic gadgets,it may be troublesome for electronic-implemented neural systems to make the strides these two exhibitions encourage.Optical neural systems can combine optoelectronic procedures and neural organization models to provide ways to break the bottleneck.This paper outlines optical neural networks of feedforward repetitive and spiking models to give a clearer picture of history,wildernesses,and future optical neural systems.The framework demonstrates neural systems in optic communication with the serial and parallel setup.The graphene-based laser structure for fiber optic communication is discussed.The comparison of different balance plans for photonic neural systems is made within the setting of hereditary calculation and molecule swarm optimization.In expansion,the execution comparison of routine photonic neural,time-domain with and without extending commotion is additionally expounded.The challenges and future patterns of optical neural systems on the growing scale and applications of in situ preparing nonlinear computing will hence be uncovered.展开更多
The growing number of decarbonization standards in the transportation sector has resulted in an increase in demand for electric cars.Renewable energy sources have the ability to bring the fossil fuel age to an end.Ele...The growing number of decarbonization standards in the transportation sector has resulted in an increase in demand for electric cars.Renewable energy sources have the ability to bring the fossil fuel age to an end.Electrochemical storage devices,particularly lithium-ion batteries,are critical for this transition’s success.This is owing to a combination of favorable characteristics such as high energy density and minimal self-discharge.Given the environmental degradation caused by hazardous wastes and the scarcity of some resources,recycling used lithium-ion batteries has significant economic and practical importance.Many efforts have been undertaken in recent years to recover cathode materials(such as high-value metals like cobalt,nickel,and lithium).Regrettably,the regeneration of lower-value-added anode materials(mostly graphite)has received little attention.However,given the widespread use of carbon-based materials and the higher concentration of lithium in the anode than in the environment,anode recycling has gotten a lot of attention.As a result,this article provides the most recent research progress in the recovery of graphite anode materials from spent lithium ion batteries,analyzing the strengths and weaknesses of various recovery routes such as direct physical recovery,heat treatment recovery,hydrometallurgy recovery,heat treatment-hydrometallurgy recovery,extraction,and electrochemical methods from the perspectives of energy,environment,and economy;additionally,the reuse of recycled anode mats is discussed.Finally,the problems and future possibilities of anode recycling are discussed.To enable the green recycling of wasted lithium ion batteries,a low energy-consuming and ecologically friendly solution should be investigated.展开更多
The non-orthogonal multiple access(NOMA)method is a novel multiple access technique that aims to increase spectral efficiency(SE)and accommodate enormous user accesses.Multi-user signals are superimposed and transmitt...The non-orthogonal multiple access(NOMA)method is a novel multiple access technique that aims to increase spectral efficiency(SE)and accommodate enormous user accesses.Multi-user signals are superimposed and transmitted in the power domain at the transmitting end by actively implementing controllable interference information,and multi-user detection algorithms,such as successive interference cancellation(SIC),are performed at the receiving end to demodulate the necessary user signals.Although its basic signal waveform,like LTE baseline,could be based on orthogonal frequency division multiple access(OFDMA)or discrete Fourier transform(DFT)-spread OFDM,NOMA superimposes numerous users in the power domain.In contrast to the orthogonal transmission method,the nonorthogonal method can achieve higher spectrum utilization.However,it will increase the complexity of its receiver.Different power allocation techniques will have a direct impact on the system’s throughput.As a result,in order to boost the system capacity,an efficient power allocation mechanism must be investigated.This research developed an efficient technique based on conjugate gradient to solve the problem of downlink power distribution.The major goal is to maximize the users’maximum weighted sum rate.The suggested algorithm’s most notable feature is that it converges to the global optimal solution.When compared to existing methods,simulation results reveal that the suggested technique has a better power allocation capability.展开更多
Physical sensors,intelligent sensors,and output recommenda-tions are all examples of smart health technology that can be used to monitor patients’health and change their behavior.Smart health is an Internet-of-Things...Physical sensors,intelligent sensors,and output recommenda-tions are all examples of smart health technology that can be used to monitor patients’health and change their behavior.Smart health is an Internet-of-Things(IoT)-aware network and sensing infrastructure that provides real-time,intelligent,and ubiquitous healthcare services.Because of the rapid development of cloud computing,as well as related technologies such as fog computing,smart health research is progressively moving in the right direction.Cloud,fog computing,IoT sensors,blockchain,privacy and security,and other related technologies have been the focus of smart health research in recent years.At the moment,the focus in cloud and smart health research is on how to use the cloud to solve the problem of enormous health data and enhance service performance,including cloud storage,retrieval,and calculation of health big data.This article reviews state-of-the-art edge computing methods that has shifted to the collection,transmission,and calculation of health data,which includes various sensors and wearable devices used to collect health data,various wireless sensor technologies,and how to process health data and improve edge performance,among other things.Finally,the typical smart health application cases,blockchain’s application in smart health,and related privacy and security issues were reviewed,as well as future difficulties and potential for smart health services.The comparative analysis provides a reference for the the mobile edge computing in healthcare systems.展开更多
Energy sustainability is a hot topic in both scientific and political cir-cles.To date,two alternative approaches to this issue are being taken.Some peo-ple believe that increasing power consumption is necessary for co...Energy sustainability is a hot topic in both scientific and political cir-cles.To date,two alternative approaches to this issue are being taken.Some peo-ple believe that increasing power consumption is necessary for countries’economic and social progress,while others are more concerned with maintaining carbon consumption under set limitations.To establish a secure,sustainable,and economical energy system while mitigating the consequences of climate change,most governments are currently pushing renewable growth policies.Energy mar-kets are meant to provide consumers with dependable electricity at the lowest pos-sible cost.A profit-maximization optimal decision model is created in the electric power market with the combined wind,solar units,loads,and energy storage sys-tems,based on the bidding mechanism in the electricity market and operational principles.This model utterly considers the technological limits of new energy units and storages,as well as the involvement of new energy and electric vehicles in market bidding through power generation strategy and the output arrangement of the virtual power plant’s coordinated operation.The accuracy and validity of the optimal decision-making model of combined wind,solar units,loads,and energy storage systems are validated using numerical examples.Under multi-operating scenarios,the effects of renewable energy output changes on joint sys-tem bidding techniques are compared.展开更多
The economics,infrastructure,transportation,and level of living of a country are all influenced by energy.The gap between energy usage and availabil-ity is a global issue.Currently,all countries rely on fossil fuels fo...The economics,infrastructure,transportation,and level of living of a country are all influenced by energy.The gap between energy usage and availabil-ity is a global issue.Currently,all countries rely on fossil fuels for energy genera-tion,and these fossil fuels are not sustainable.The hydrogen proton exchange membrane fuel cell(PEMFC)power system is both clean and efficient.The fuel delivery system and the PEMFC make up the majority of the PEMFC power sys-tem.The lack of an efficient,safe,and cost-effective hydrogen storage system is still a major barrier to its widespread use.Solid hydrogen storage has the large capacity,safety and good reversibility.As a hydrogen source system,the hydro-gen supply characteristics affect the characteristics of the PEMFC at the output.In this paper,a mathematical model of a hydrogen source reactor and PEMFC based on chemical absorption/desorption of solid hydrogen storage is established,and a simulation model of a PEMFC power system coupled with solid hydrogen storage is established using MATLAB/SIMULINK software,and the hydrogen supply of the reactor is analyzed in detail.The influence of prominent factors is evaluated.The research results show that the proposed method improved the system perfor-mance.At the same time,increasing the PEMFC temperature,increasing the area of the proton exchange membrane and the oxygen supply pressure can increase the output power of the power system.展开更多
The large-scale application of renewable energy power generation technology brings new challenges to the operation of traditional power grids andenergy management on the load side. Microgrid can effectively solve this...The large-scale application of renewable energy power generation technology brings new challenges to the operation of traditional power grids andenergy management on the load side. Microgrid can effectively solve this problemby using its regulation and flexibility, and is considered to be an ideal platform.The traditional method of computing total transfer capability is difficult due tothe central integration of wind farms. As a result, the differential evolutionextreme learning machine is offered as a data mining approach for extractingoperating rules for the total transfer capability of tie-lines in wind-integratedpower systems. K-medoids clustering under the two-dimensional “wind power-load consumption” feature space is used to define representative operational scenarios initially. Then, using stochastic sampling and repetitive power flow, aknowledge base for total transfer capability operating rule mining is created.Then, a novel method is used to filter redundant characteristics and find featuresthat are closely associated to the total transfer capability in order to decrease theultra-high dimensionality of operational features. Finally, by feeding the trainingdata into the proposed algorithm, the total transfer capability operation rules arederived from the knowledge base. It can be seen that, the proposed algorithmcan optimize the system performance with good accuracy and generality, according to numerical data.展开更多
An effective and simple screening technique for identification of salt tolerant and salt sensitive radish genotypes was observed. Sand is used as potting media. Six genotypes of radish were used for screening against ...An effective and simple screening technique for identification of salt tolerant and salt sensitive radish genotypes was observed. Sand is used as potting media. Six genotypes of radish were used for screening against four salinity levels (0, 1, 3, 5 and 7 dS/m<sup>-1</sup>). Twenty days old seedlings of radish were salinized with salt solution (NaCl). Morphological, physiological and ionic parameters were studied. Radish genotypes Laal-Pari and 40 Days executed the best performance in all the measured attributes and categorized as salt tolerant genotype while Green Neck was the poorest in retaining normal functioning at higher salinity levels thus grouped under salt sensitive cultivar.展开更多
Objective: To identify the chemical constituents of leaf essential oil of Forsythia koreana(F. koreana) and evaluate its ef ects on bacterial strains. Methods: The essential oil of leaf of F. koreana was extracted by ...Objective: To identify the chemical constituents of leaf essential oil of Forsythia koreana(F. koreana) and evaluate its ef ects on bacterial strains. Methods: The essential oil of leaf of F. koreana was extracted by using hydrodistillation process and the volatile components investigated with the help of gas chromatography coupled with mass spectrometry. The antibacterial study was carried out with the help of agar disc dif usion method, MIC, MBC and viable count. The mode of action was determined with help of potassium ion l ux, cellular material release and scanning electron microscopy. The antioxidant activity was determined with the help of 2, 3-diphenyl-2-picrylhydrazyl method, nitric oxide scavenging activity and superoxide anion radical scavenging assay. Results: Total ten compounds were identii ed as trans-phytol(42.73%), cis-3-hexenol(12.95%), 毬-linalool(10.68%), trans-2-hexenal(8.86%), trans-2-hexenol(8.86%), myrcenol(3.86%), 4-vinylphenyl acetate(3.86%),(4Z)-4,6-heptadien-1-ol(3.18%), lemonol(2.73%) and benzeneacetaldehyde(2.27%) by gas chromatography coupled with mass spectrometry. The antibacterial study was demonstrated that leaf essential oil of F. koreana act against foodborne and other pathogenic bacteria. The mode of action revealed that this essential oil acted on the cytoplasmic membrane, resulting in loss of integrity and increased permeability. In addition, leaf essential oil of F. koreana was shown to be rich in linalool, which contributes to improved antioxidant activity. Conclusions: These results show that leaf essential oil of F. koreana has great potential as a natural food preservative, antibacterial and antioxidant agent.展开更多
This study investigated the effect of credit constraints and credit unconstraints on the technical efficiency of hybrid maize growers in the Khyber Pakhtunkhwa Province(KPK) of Pakistan. The primary data were collecte...This study investigated the effect of credit constraints and credit unconstraints on the technical efficiency of hybrid maize growers in the Khyber Pakhtunkhwa Province(KPK) of Pakistan. The primary data were collected by a direct elicitation method from 510 maize growers of KPK Province. Stochastic frontier model techniques were used for technical efficiency analyses. The results revealed that the mean technical efficiency difference between the two groups was 10.2%. The results of technical inefficiency effect modeling demonstrated that education of the household head, family size, number of married family members, off-farm income, farming experience, tractor drill, water irrigation through a lined course, certified seed, extension services, household saving variables, and a credit size variable had positive effects on technical efficiency for both credit constrained farmers(CCFs) and credit unconstrained farmers(UCCFs). In addition, age of household head and fragmented land values had negative effects on technical efficiency for both groups. However, the interest rate had positive and negative impacts on the technical efficiency of CCFs and UCCFs, respectively. Our results have significant implications for policies related to land use, interest rate, and banking sector expansion in the rural areas of Pakistan.展开更多
文摘从一块腐烂的鲸鱼骨头分离鉴定了一株真菌Penicillium sp. S2014503。利用大米固体发酵培养技术从该菌产物中获得10个化合物,通过核磁共振波谱数据比对分析结合质谱数据分析,手性化合物测定旋光值或者进行X-射线单晶衍射分析后将这10个化合物分别鉴定为emodin(1)、citreorosein(2)、tetrahydroaltersolanolB(3)、conioxanthoneA(4)、chrysogine(5)、pyramidamycin B (6)、germicidin O (7)、2-(6-hydroxy-5,7-dimethylbenzefuranone-4-yl) acetaldehyde (8)、astrophenone (9)和chenopodolans A (10)。经生物活性测试发现,化合物1和2对藤黄微球菌具有弱的抑制活性,化合物1、2和4对卤虫幼虫具有显著的致死毒性。此外,化合物1是中药大黄和虎杖的主要致泻成分。文章首次报道了来源于海洋动物样品鲸鱼骨的真菌Penicillium sp.能够生产大黄素类等活性天然产物。
基金financially supported by the Deanship of Scientific Research at King Khalid University under Research Grant Number(R.G.P.2/549/44).
文摘Algorithms for steganography are methods of hiding data transfers in media files.Several machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image analysis.Images with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for classification.To address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of images.Support Vector Machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or cover.The Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the proposedmethod.Using WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods.
基金financially supported by the Deanship of Scientific Research,Qassim University,Saudi Arabia for funding the publication of this project.
文摘The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments,soil conditions and higher human consumption.It is cultivated in vast areas of Asian and Non-Asian countries,including Pakistan.The guava plant is vulnerable to diseases,specifically the leaves and fruit,which result in massive crop and profitability losses.The existing plant leaf disease detection techniques can detect only one disease from a leaf.However,a single leaf may contain symptoms of multiple diseases.This study has proposed a hybrid deep learning-based framework for the real-time detection of multiple diseases from a single guava leaf in several steps.Firstly,Guava Infected Patches Modified MobileNetV2 and U-Net(GIP-MU-NET)has been proposed to segment the infected guava patches.The proposed model consists of modified MobileNetv2 as an encoder,and the U-Net model’s up-sampling layers are used as a decoder part.Secondly,the Guava Leaf SegmentationModel(GLSM)is proposed to segment the healthy and infected leaves.In the final step,the Guava Multiple Leaf Diseases Detection(GMLDD)model based on the YOLOv5 model detects various diseases from a guava leaf.Two self-collected datasets(the Guava Patches Dataset and the Guava Leaf Diseases Dataset)are used for training and validation.The proposed method detected the various defects,including five distinct classes,i.e.,anthracnose,insect attack,nutrition deficiency,wilt,and healthy.On average,the GIP-MU-Net model achieved 92.41%accuracy,the GLSM gained 83.40%accuracy,whereas the proposed GMLDD technique achieved 73.3%precision,73.1%recall,71.0%mAP@0.5 and 50.3 mAP@0.5:0.95 scores for all the aforesaid classes.
基金funding this work through the Research Group Program under the Grant Number:(R.G.P.2/382/44).
文摘Many plant species have a startling degree of morphological similarity,making it difficult to split and categorize them reliably.Unknown plant species can be challenging to classify and segment using deep learning.While using deep learning architectures has helped improve classification accuracy,the resulting models often need to be more flexible and require a large dataset to train.For the sake of taxonomy,this research proposes a hybrid method for categorizing guava,potato,and java plumleaves.Two new approaches are used to formthe hybridmodel suggested here.The guava,potato,and java plum plant species have been successfully segmented using the first model built on the MobileNetV2-UNET architecture.As a second model,we use a Plant Species Detection Stacking Ensemble Deep Learning Model(PSD-SE-DLM)to identify potatoes,java plums,and guava.The proposed models were trained using data collected in Punjab,Pakistan,consisting of images of healthy and sick leaves from guava,java plum,and potatoes.These datasets are known as PLSD and PLSSD.Accuracy levels of 99.84%and 96.38%were achieved for the suggested PSD-SE-DLM and MobileNetV2-UNET models,respectively.
文摘Software Product Line(SPL)is a group of software-intensive systems that share common and variable resources for developing a particular system.The feature model is a tree-type structure used to manage SPL’s common and variable features with their different relations and problem of Crosstree Constraints(CTC).CTC problems exist in groups of common and variable features among the sub-tree of feature models more diverse in Internet of Things(IoT)devices because different Internet devices and protocols are communicated.Therefore,managing the CTC problem to achieve valid product configuration in IoT-based SPL is more complex,time-consuming,and hard.However,the CTC problem needs to be considered in previously proposed approaches such as Commonality VariabilityModeling of Features(COVAMOF)andGenarch+tool;therefore,invalid products are generated.This research has proposed a novel approach Binary Oriented Feature Selection Crosstree Constraints(BOFS-CTC),to find all possible valid products by selecting the features according to cardinality constraints and cross-tree constraint problems in the featuremodel of SPL.BOFS-CTC removes the invalid products at the early stage of feature selection for the product configuration.Furthermore,this research developed the BOFS-CTC algorithm and applied it to,IoT-based feature models.The findings of this research are that no relationship constraints and CTC violations occur and drive the valid feature product configurations for the application development by removing the invalid product configurations.The accuracy of BOFS-CTC is measured by the integration sampling technique,where different valid product configurations are compared with the product configurations derived by BOFS-CTC and found 100%correct.Using BOFS-CTC eliminates the testing cost and development effort of invalid SPL products.
基金extend their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through Project Number RI-44-0345.
文摘With the capacities of self-learning,acquainted capacities,high-speed looking for ideal arrangements,solid nonlin-ear fitting,and mapping self-assertively complex nonlinear relations,neural systems have made incredible advances and accomplished broad application over the final half-century.As one of the foremost conspicuous methods for fake insights,neural systems are growing toward high computational speed and moo control utilization.Due to the inborn impediments of electronic gadgets,it may be troublesome for electronic-implemented neural systems to make the strides these two exhibitions encourage.Optical neural systems can combine optoelectronic procedures and neural organization models to provide ways to break the bottleneck.This paper outlines optical neural networks of feedforward repetitive and spiking models to give a clearer picture of history,wildernesses,and future optical neural systems.The framework demonstrates neural systems in optic communication with the serial and parallel setup.The graphene-based laser structure for fiber optic communication is discussed.The comparison of different balance plans for photonic neural systems is made within the setting of hereditary calculation and molecule swarm optimization.In expansion,the execution comparison of routine photonic neural,time-domain with and without extending commotion is additionally expounded.The challenges and future patterns of optical neural systems on the growing scale and applications of in situ preparing nonlinear computing will hence be uncovered.
基金Deanship of Scientific Research at Taif University for the grant received for this research.This research was supported by Taif University with research grant(TURSP-2020/77).
文摘The growing number of decarbonization standards in the transportation sector has resulted in an increase in demand for electric cars.Renewable energy sources have the ability to bring the fossil fuel age to an end.Electrochemical storage devices,particularly lithium-ion batteries,are critical for this transition’s success.This is owing to a combination of favorable characteristics such as high energy density and minimal self-discharge.Given the environmental degradation caused by hazardous wastes and the scarcity of some resources,recycling used lithium-ion batteries has significant economic and practical importance.Many efforts have been undertaken in recent years to recover cathode materials(such as high-value metals like cobalt,nickel,and lithium).Regrettably,the regeneration of lower-value-added anode materials(mostly graphite)has received little attention.However,given the widespread use of carbon-based materials and the higher concentration of lithium in the anode than in the environment,anode recycling has gotten a lot of attention.As a result,this article provides the most recent research progress in the recovery of graphite anode materials from spent lithium ion batteries,analyzing the strengths and weaknesses of various recovery routes such as direct physical recovery,heat treatment recovery,hydrometallurgy recovery,heat treatment-hydrometallurgy recovery,extraction,and electrochemical methods from the perspectives of energy,environment,and economy;additionally,the reuse of recycled anode mats is discussed.Finally,the problems and future possibilities of anode recycling are discussed.To enable the green recycling of wasted lithium ion batteries,a low energy-consuming and ecologically friendly solution should be investigated.
基金the support from Taif University Researchers Supporting Project Number(TURSP-2020/331)Taif University,Taif,Saudi Arabia.This research was supported by the MSIT(Ministry of Science and ICT),Korea,under the National Research Foundation(NRF),Korea(2022R1A2C4001270).
文摘The non-orthogonal multiple access(NOMA)method is a novel multiple access technique that aims to increase spectral efficiency(SE)and accommodate enormous user accesses.Multi-user signals are superimposed and transmitted in the power domain at the transmitting end by actively implementing controllable interference information,and multi-user detection algorithms,such as successive interference cancellation(SIC),are performed at the receiving end to demodulate the necessary user signals.Although its basic signal waveform,like LTE baseline,could be based on orthogonal frequency division multiple access(OFDMA)or discrete Fourier transform(DFT)-spread OFDM,NOMA superimposes numerous users in the power domain.In contrast to the orthogonal transmission method,the nonorthogonal method can achieve higher spectrum utilization.However,it will increase the complexity of its receiver.Different power allocation techniques will have a direct impact on the system’s throughput.As a result,in order to boost the system capacity,an efficient power allocation mechanism must be investigated.This research developed an efficient technique based on conjugate gradient to solve the problem of downlink power distribution.The major goal is to maximize the users’maximum weighted sum rate.The suggested algorithm’s most notable feature is that it converges to the global optimal solution.When compared to existing methods,simulation results reveal that the suggested technique has a better power allocation capability.
基金supported by the Ministry of Education,Malaysia(Grant Code:FRGS/1/2018/ICT02/UKM/02/6).
文摘Physical sensors,intelligent sensors,and output recommenda-tions are all examples of smart health technology that can be used to monitor patients’health and change their behavior.Smart health is an Internet-of-Things(IoT)-aware network and sensing infrastructure that provides real-time,intelligent,and ubiquitous healthcare services.Because of the rapid development of cloud computing,as well as related technologies such as fog computing,smart health research is progressively moving in the right direction.Cloud,fog computing,IoT sensors,blockchain,privacy and security,and other related technologies have been the focus of smart health research in recent years.At the moment,the focus in cloud and smart health research is on how to use the cloud to solve the problem of enormous health data and enhance service performance,including cloud storage,retrieval,and calculation of health big data.This article reviews state-of-the-art edge computing methods that has shifted to the collection,transmission,and calculation of health data,which includes various sensors and wearable devices used to collect health data,various wireless sensor technologies,and how to process health data and improve edge performance,among other things.Finally,the typical smart health application cases,blockchain’s application in smart health,and related privacy and security issues were reviewed,as well as future difficulties and potential for smart health services.The comparative analysis provides a reference for the the mobile edge computing in healthcare systems.
文摘Energy sustainability is a hot topic in both scientific and political cir-cles.To date,two alternative approaches to this issue are being taken.Some peo-ple believe that increasing power consumption is necessary for countries’economic and social progress,while others are more concerned with maintaining carbon consumption under set limitations.To establish a secure,sustainable,and economical energy system while mitigating the consequences of climate change,most governments are currently pushing renewable growth policies.Energy mar-kets are meant to provide consumers with dependable electricity at the lowest pos-sible cost.A profit-maximization optimal decision model is created in the electric power market with the combined wind,solar units,loads,and energy storage sys-tems,based on the bidding mechanism in the electricity market and operational principles.This model utterly considers the technological limits of new energy units and storages,as well as the involvement of new energy and electric vehicles in market bidding through power generation strategy and the output arrangement of the virtual power plant’s coordinated operation.The accuracy and validity of the optimal decision-making model of combined wind,solar units,loads,and energy storage systems are validated using numerical examples.Under multi-operating scenarios,the effects of renewable energy output changes on joint sys-tem bidding techniques are compared.
基金funded by King Abdulaziz University,Jedda Saudi Arabia and King Abdulah City for Atomic and Renewable Energy,Riyadh,Saudi Arabia Grant No.(KCR-KFL-13-20)thereforethe authors gratefully acknowledge their technical and financial support.
文摘The economics,infrastructure,transportation,and level of living of a country are all influenced by energy.The gap between energy usage and availabil-ity is a global issue.Currently,all countries rely on fossil fuels for energy genera-tion,and these fossil fuels are not sustainable.The hydrogen proton exchange membrane fuel cell(PEMFC)power system is both clean and efficient.The fuel delivery system and the PEMFC make up the majority of the PEMFC power sys-tem.The lack of an efficient,safe,and cost-effective hydrogen storage system is still a major barrier to its widespread use.Solid hydrogen storage has the large capacity,safety and good reversibility.As a hydrogen source system,the hydro-gen supply characteristics affect the characteristics of the PEMFC at the output.In this paper,a mathematical model of a hydrogen source reactor and PEMFC based on chemical absorption/desorption of solid hydrogen storage is established,and a simulation model of a PEMFC power system coupled with solid hydrogen storage is established using MATLAB/SIMULINK software,and the hydrogen supply of the reactor is analyzed in detail.The influence of prominent factors is evaluated.The research results show that the proposed method improved the system perfor-mance.At the same time,increasing the PEMFC temperature,increasing the area of the proton exchange membrane and the oxygen supply pressure can increase the output power of the power system.
基金The authors extend their appreciation to the Deputy ship for the Research&innovation,Ministry of Education in Saudi Arabia for funding this research work through the Project Number(IF-PSAU-2021/01/18432).
文摘The large-scale application of renewable energy power generation technology brings new challenges to the operation of traditional power grids andenergy management on the load side. Microgrid can effectively solve this problemby using its regulation and flexibility, and is considered to be an ideal platform.The traditional method of computing total transfer capability is difficult due tothe central integration of wind farms. As a result, the differential evolutionextreme learning machine is offered as a data mining approach for extractingoperating rules for the total transfer capability of tie-lines in wind-integratedpower systems. K-medoids clustering under the two-dimensional “wind power-load consumption” feature space is used to define representative operational scenarios initially. Then, using stochastic sampling and repetitive power flow, aknowledge base for total transfer capability operating rule mining is created.Then, a novel method is used to filter redundant characteristics and find featuresthat are closely associated to the total transfer capability in order to decrease theultra-high dimensionality of operational features. Finally, by feeding the trainingdata into the proposed algorithm, the total transfer capability operation rules arederived from the knowledge base. It can be seen that, the proposed algorithmcan optimize the system performance with good accuracy and generality, according to numerical data.
文摘An effective and simple screening technique for identification of salt tolerant and salt sensitive radish genotypes was observed. Sand is used as potting media. Six genotypes of radish were used for screening against four salinity levels (0, 1, 3, 5 and 7 dS/m<sup>-1</sup>). Twenty days old seedlings of radish were salinized with salt solution (NaCl). Morphological, physiological and ionic parameters were studied. Radish genotypes Laal-Pari and 40 Days executed the best performance in all the measured attributes and categorized as salt tolerant genotype while Green Neck was the poorest in retaining normal functioning at higher salinity levels thus grouped under salt sensitive cultivar.
文摘Objective: To identify the chemical constituents of leaf essential oil of Forsythia koreana(F. koreana) and evaluate its ef ects on bacterial strains. Methods: The essential oil of leaf of F. koreana was extracted by using hydrodistillation process and the volatile components investigated with the help of gas chromatography coupled with mass spectrometry. The antibacterial study was carried out with the help of agar disc dif usion method, MIC, MBC and viable count. The mode of action was determined with help of potassium ion l ux, cellular material release and scanning electron microscopy. The antioxidant activity was determined with the help of 2, 3-diphenyl-2-picrylhydrazyl method, nitric oxide scavenging activity and superoxide anion radical scavenging assay. Results: Total ten compounds were identii ed as trans-phytol(42.73%), cis-3-hexenol(12.95%), 毬-linalool(10.68%), trans-2-hexenal(8.86%), trans-2-hexenol(8.86%), myrcenol(3.86%), 4-vinylphenyl acetate(3.86%),(4Z)-4,6-heptadien-1-ol(3.18%), lemonol(2.73%) and benzeneacetaldehyde(2.27%) by gas chromatography coupled with mass spectrometry. The antibacterial study was demonstrated that leaf essential oil of F. koreana act against foodborne and other pathogenic bacteria. The mode of action revealed that this essential oil acted on the cytoplasmic membrane, resulting in loss of integrity and increased permeability. In addition, leaf essential oil of F. koreana was shown to be rich in linalool, which contributes to improved antioxidant activity. Conclusions: These results show that leaf essential oil of F. koreana has great potential as a natural food preservative, antibacterial and antioxidant agent.
基金sponsored by the National Natural Social Science Foundation of China (71573211)
文摘This study investigated the effect of credit constraints and credit unconstraints on the technical efficiency of hybrid maize growers in the Khyber Pakhtunkhwa Province(KPK) of Pakistan. The primary data were collected by a direct elicitation method from 510 maize growers of KPK Province. Stochastic frontier model techniques were used for technical efficiency analyses. The results revealed that the mean technical efficiency difference between the two groups was 10.2%. The results of technical inefficiency effect modeling demonstrated that education of the household head, family size, number of married family members, off-farm income, farming experience, tractor drill, water irrigation through a lined course, certified seed, extension services, household saving variables, and a credit size variable had positive effects on technical efficiency for both credit constrained farmers(CCFs) and credit unconstrained farmers(UCCFs). In addition, age of household head and fragmented land values had negative effects on technical efficiency for both groups. However, the interest rate had positive and negative impacts on the technical efficiency of CCFs and UCCFs, respectively. Our results have significant implications for policies related to land use, interest rate, and banking sector expansion in the rural areas of Pakistan.
