In this paper, a bearing-based three-dimensional self-localization and distributed circumnavigation with connectivity preservation and collision avoidance are investigated for a group of quadrotor-type unmanned aerial...In this paper, a bearing-based three-dimensional self-localization and distributed circumnavigation with connectivity preservation and collision avoidance are investigated for a group of quadrotor-type unmanned aerial vehicles (UAVs). A leader–follower structure is adopted, wherein the leader moves with reference dynamics (a target). Different from the existing approaches that necessitate full knowledge of the time-varying reference trajectory, in this paper, it is assumed that only some vehicles (at least one) have access to the bearing relative to the target, and all other vehicles are equipped with sensors capable of measuring the bearings relative to neighboring vehicles. In this paper, a consensus estimator is proposed to estimate the global position for each vehicle using relative bearing measurements and an estimate of neighboring vehicles received from a direct communication network. Then, a continuous robust integral of the sign of the error (RISE) control approach is effectively integrated with the distributed vector field approach to ensure UAV formation orbiting around the moving target while avoiding obstacles and maintaining network links within available communication ranges. In contrast to the classical RISE control rule, a \(\tanh (\cdot )\) function is used instead of the \(\text {sgn}(\cdot )\) function to further decrease the high-gain feedback and to obtain a smoother control signal. Furthermore, by using the localized radial basis function (RBF) neural networks (NNs) in a cooperative way, deterministic learning theory is employed to accurately identify/learn model uncertainties resulting from the attitude dynamics. The convergence of the entire closed-loop system is illustrated using the Lyapunov theory and is shown to be uniformly ultimately bounded. Finally, numerical simulations show the effectiveness of the proposed approach.展开更多
Objective:To investigate the total phenolics,flavonoids and tannins content and the in vitro antioxidant activities of methanolic extracts of six wild Mentha species which are Mentha aquatica,Mentha arvensis,Mentha pi...Objective:To investigate the total phenolics,flavonoids and tannins content and the in vitro antioxidant activities of methanolic extracts of six wild Mentha species which are Mentha aquatica,Mentha arvensis,Mentha piperita,Mentha pulegium,Mentha rotundifolia and Mentha villosa.Methods:The Folin–Ciocalteu method was used to determine the total phenols content while flavonoids were estimated according to the aluminum chloride colorimetric method.To evaluate tannins content,vanillin and HCl were added to methanolic extracts.The antioxidant potential was measured by 1,1-diphenyl-2-picrylhydrazyl radical scavenging,ferrous ion chelating and the inhibition of b-carotene bleaching assays.Results:The methanol extracts of Algerian mints were rich in phenolic compounds and exhibited powerful antioxidant activity ranging from 7.5 mg/mL to 44.66 mg/mL,which varied significantly among species.Mentha aquatica stood out with efficient antioxidant ability which was correlated to the high total phenolics content,followed by Mentha arvensis and Mentha piperita with very close values,comparing to Mentha pulegium,Mentha rotundifolia and Mentha villosa with lowest values.Conclusions:These results show that methanolic extracts of Mentha species from Algeria have a great potential of polyphenols which can be used as a natural food preservative and antioxidant source.展开更多
Complex oxides are an important class of materials with enormous potential for electrochemical appli-cations.Depending on their composition and structure,such complex oxides can exhibit either a single conductivity(ox...Complex oxides are an important class of materials with enormous potential for electrochemical appli-cations.Depending on their composition and structure,such complex oxides can exhibit either a single conductivity(oxygen-ionic or protonic,or n-type,or p-type electronic)or a combination thereof gener-ating distinct dual-conducting or even triple-conducting materials.These properties enable their use as diverse functional materials for solid oxide fuel cells,solid oxide electrolysis cells,permeable membranes,and gas sensors.The literature review shows that the field of solid oxide materials and related electro-chemical cells has a significant level of research engagement,with over 8,000 publications published since 2020.The manual analysis of such a large volume of material is challenging.However,by examining the review articles,it is possible to identify key patterns,recent achievements,prospects,and remaining obstacles.To perform such an analysis,the present article provides,for the first time,a comprehensive summary of previous review publications that have been published since 2020,with a special focus on solid oxide materials and electrochemical systems.Thus,this study provides an important reference for researchers specializing in the fields of solid state ionics,high-temperature electrochemistry,and energyconversiontechnologies.展开更多
Objective To determine the medicinal potential of various plants and their parts extracted with different solvents. Methods The total phenolic content of acetonitrile/water（60%-40%）（ACN/W） and aqueous（W） extract...Objective To determine the medicinal potential of various plants and their parts extracted with different solvents. Methods The total phenolic content of acetonitrile/water（60%-40%）（ACN/W） and aqueous（W） extract fractions was determined by high-performance liquid chromatography（HPLC）, and terpenic compounds were detected by gas chromatography/mass spectrometry（GC/MS）. Antioxidant activity of the samples was evaluated using 2,2-diphenyl-1-picrylhydrazyl（DPPH） assay and β-carotene bleaching method. Cell viability was investigated by thiazolyl blue tetrazolium bromide [3-（4,5-dimethylthiazol）-2-yl 2,5-diphenyltetrazolium bromide]（MTT） assay. The mechanisms involved in cytotoxic activity were investigated in a murine macrophage cell line（RAW 264.7） and cancer lines. Results Our findings show that 11 plant species exhibited biological activity. In addition, moderate antibacterial activity was reported against one or more of the tested bacterial strains at two concentrations： 300 μg and 3 mg/disc. Furthermore, our data reveal that among all plants investigated, some extract and hydrophobic fractions were potent scavengers of the DPPH radical（6.78 μg/m L 〈 EC50 〈 8.55 μg/mL）. Taken together, our results show that Nerium oleander（NOACN/W） and Pituranthos tortuosus（PTACN/W） were highly cytotoxic against RAW 264.7 cells with IC80 values of 0.36, and 1.55 μg/mL, respectively. In contrast, murine macrophage cell lines had low growth and were significantly sensitive to water extracts of Thymus hirtus sp. algeriensis（THW）, Lavandula multifida（LMW）, and ACN/W extract of Erica multiflora（EMACN/W） at doses 〉 400, 47.20, and 116.74 μg/m L, respectively. The current work demonstrates that RAW 264.7 cell proliferation was inhibited by samples in a dose-dependent manner. Conclusion Our findings, validated through free radical scavenging activity, agar diffusion assay, and cytotoxicity of essential oils towards cancer cells, show that ethnomedicinal plants used in this work have a novel application as a tumor suppressor.展开更多
Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse languages.While numerous scholars conduct sentime...Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse languages.While numerous scholars conduct sentiment analysisin widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grapplingwith resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language,characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu,Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguisticfeatures, presents an additional hurdle due to the lack of accessible datasets, rendering sentiment analysis aformidable undertaking. The limited availability of resources has fueled increased interest among researchers,prompting a deeper exploration into Urdu sentiment analysis. This research is dedicated to Urdu languagesentiment analysis, employing sophisticated deep learning models on an extensive dataset categorized into fivelabels: Positive, Negative, Neutral, Mixed, and Ambiguous. The primary objective is to discern sentiments andemotions within the Urdu language, despite the absence of well-curated datasets. To tackle this challenge, theinitial step involves the creation of a comprehensive Urdu dataset by aggregating data from various sources such asnewspapers, articles, and socialmedia comments. Subsequent to this data collection, a thorough process of cleaningand preprocessing is implemented to ensure the quality of the data. The study leverages two well-known deeplearningmodels, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for bothtraining and evaluating sentiment analysis performance. Additionally, the study explores hyperparameter tuning tooptimize the models’ efficacy. Evaluation metrics such as precision, recall, and the F1-score are employed to assessthe effectiveness of the models. The research findings reveal that RNN surpasses CNN in Urdu sentiment analysis,gaining a significantly higher accuracy rate of 91%. This result accentuates the exceptional performance of RNN,solidifying its status as a compelling option for conducting sentiment analysis tasks in the Urdu language.展开更多
The advent of the COVID-19 pandemic has adversely affected the entire world and has put forth high demand for techniques that remotely manage crowd-related tasks.Video surveillance and crowd management using video ana...The advent of the COVID-19 pandemic has adversely affected the entire world and has put forth high demand for techniques that remotely manage crowd-related tasks.Video surveillance and crowd management using video analysis techniques have significantly impacted today’s research,and numerous applications have been developed in this domain.This research proposed an anomaly detection technique applied to Umrah videos in Kaaba during the COVID-19 pandemic through sparse crowd analysis.Managing theKaaba rituals is crucial since the crowd gathers from around the world and requires proper analysis during these days of the pandemic.The Umrah videos are analyzed,and a system is devised that can track and monitor the crowd flow in Kaaba.The crowd in these videos is sparse due to the pandemic,and we have developed a technique to track the maximum crowd flow and detect any object(person)moving in the direction unlikely of the major flow.We have detected abnormal movement by creating the histograms for the vertical and horizontal flows and applying thresholds to identify the non-majority flow.Our algorithm aims to analyze the crowd through video surveillance and timely detect any abnormal activity tomaintain a smooth crowd flowinKaaba during the pandemic.展开更多
This article is aimed at describing the way rotation and magnetic field affect the propagation of waves in an infinite poroelastic cylindrical bone.It offers a solution with an exact closed form.The authors got and ex...This article is aimed at describing the way rotation and magnetic field affect the propagation of waves in an infinite poroelastic cylindrical bone.It offers a solution with an exact closed form.The authors got and examined numerically the general frequency equation for poroelastic bone.Moreover,they calculated the frequencies of poroelastic bone for different values of the magnetic field and rotation.Unlike the results of previous studies,the authors noticed little frequency dispersion in the wet bone.The proposed model will be applicable to wide-range parametric projects of bone mechanical response.Examining the vibration of surface waves in rotating cylindrical,long human bones under the magnetic field can have an impact.The findings of the study are offered in graphs.Then,a comparison with the results of the literature is conducted to show the effect of rotation and magnetic field on the wave propagation phenomenon.It is worth noting that the results of the study highly match those of the literature.展开更多
The software development process mostly depends on accurately identifying both essential and optional features.Initially,user needs are typically expressed in free-form language,requiring significant time and human re...The software development process mostly depends on accurately identifying both essential and optional features.Initially,user needs are typically expressed in free-form language,requiring significant time and human resources to translate these into clear functional and non-functional requirements.To address this challenge,various machine learning(ML)methods have been explored to automate the understanding of these requirements,aiming to reduce time and human effort.However,existing techniques often struggle with complex instructions and large-scale projects.In our study,we introduce an innovative approach known as the Functional and Non-functional Requirements Classifier(FNRC).By combining the traditional random forest algorithm with the Accuracy Sliding Window(ASW)technique,we develop optimal sub-ensembles that surpass the initial classifier’s accuracy while using fewer trees.Experimental results demonstrate that our FNRC methodology performs robustly across different datasets,achieving a balanced Precision of 75%on the PROMISE dataset and an impressive Recall of 85%on the CCHIT dataset.Both datasets consistently maintain an F-measure around 64%,highlighting FNRC’s ability to effectively balance precision and recall in diverse scenarios.These findings contribute to more accurate and efficient software development processes,increasing the probability of achieving successful project outcomes.展开更多
In this study, aluminum-doped zinc oxide(AZO) thin films were deposited onto a low-temperature polyethylene terephthalate(PET) substrate using DC magnetron sputtering. Deposition parameters included power range of 100...In this study, aluminum-doped zinc oxide(AZO) thin films were deposited onto a low-temperature polyethylene terephthalate(PET) substrate using DC magnetron sputtering. Deposition parameters included power range of 100-300 W, a working pressure of 15 mTorr, and a substrate temperature of 50 ℃. Post-deposition, flash lamp annealing(FLA) was employed as a rapid thermal processing method with a pulse duration of 1.7 ms and energy density of 7 J·cm-2, aimed at enhancing the film's quality while preserving the temperature-sensitive PET substrate. FLA offers advantages over conventional annealing,including shorter processing times and improved material properties. The structural, optical, and electrical characteristics of the AZO films were assessed using X-ray diffraction, field emission scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy, ultraviolet-visible spectroscopy, and Hall effect measurements. The results demonstrated that properties of AZO films varied with deposition and annealing conditions. Films deposited at 200 W and subjected to FLA exhibited superior crystallinity, with average visible light transmittance exceeding 80% and resistivity as low as 0.38 Ω·cm representing 95%improvement in transmittance. Electrical analysis revealed that carrier concentration, mobility, and resistivity were influenced by both sputtering and annealing parameters. These findings underscore the effectiveness of FLA in optimizing AZO thin film properties, highlighting potential in optoelectronics applications.展开更多
With the expansion of cultivation scale,pitaya diseases are gradually increasing.Traditionally relying on human observation to judge the disease is limited by the skills and experience of the observer,which cannot gua...With the expansion of cultivation scale,pitaya diseases are gradually increasing.Traditionally relying on human observation to judge the disease is limited by the skills and experience of the observer,which cannot guarantee the accuracy and real-time of the judgment,and consumes much manpower and time.In this study,by collecting,segmenting,and labeling images of 4 main diseases of pitaya in the field,an image database of main diseases of pitaya in the field was constructed to provide a basis for computer image recognition of pitaya diseases.Thereby,it benefits reducing manual error and improving the accuracy and real-time of disease identification for agricultural production,but also lays a foundation for the future development of intelligent agriculture.展开更多
This study describes the advantages of using iron-oxide-pillared MF （montmorillonite） and EDTA （ethylenediaminetetraacetic acid）-pillared MD （montmorillonite） for the removal of fluoride in the phosphoric acid. ...This study describes the advantages of using iron-oxide-pillared MF （montmorillonite） and EDTA （ethylenediaminetetraacetic acid）-pillared MD （montmorillonite） for the removal of fluoride in the phosphoric acid. The MF and MD were characterized by using BET （Brunauer-Emmett-Teller） and SEM （scanning electron microscope） for there to appreciate their textural and structural properties and in order to appraise their capacity in the fixation of the fluorine experimental results of fluorine adsorption show a defluoration of the phosphoric acid passing beyond 90% with both clays with a light performance for the MD. The kinetics of fluorine adsorption on MF and on MD has been measured at different temperatures. Indeed, the adsorbed quantity increases more favorably to temperatures more high. This kinetics could be described by two models： pseudo-first-order or pseudo-second-order, which depends on temperature and the nature of adsorbent. At different temperatures used and with various adsorbents （MD and MF, the pseudo second order model was the most reliable to determine the order of kinetics of adsorption of fluorine, which is also reflected a good correlation coefficient （above 0.99）. The fluoride sorption was reasonably explained with Langmuir or Freundlich isotherms. Thermodynamic parameters such as AG~, AH~, AS~ and kinetic one E,, （apparent activation energy） were calculated in order to understand the nature of sorption process. Results indicate that the mechanism of adsorption of the ion of fluoride on the pillared montmorillonite is an exothermic reaction. During equilibrium conditions, both the systems showed negative Gibbs energy, which indicates stability of system. The values of apparent activation energy for the both clays show that the adsorption of F ions is easier on the MF.展开更多
Ions Sensitive Field Effect Transistors (ISFETs) are becoming the platform sensors for important chemical and biomedical applications. However, the accuracy of ISFET output measurement is greatly affected by the prese...Ions Sensitive Field Effect Transistors (ISFETs) are becoming the platform sensors for important chemical and biomedical applications. However, the accuracy of ISFET output measurement is greatly affected by the presences of low-frequency noise, drift and slow response of the device. This requires more safety in measured results and the tools of analysis. In this paper, we present fundamental limits on the sensitivity of ISFETs micro-sensors, arising from intrinsic and extrinsic noise sources. We developed an algorithm in MATLAB in order to model the frequency analysis of the 1/f noise in ISFET sensor using Hooge theory. We have shown that the 1/f noise of the ISFETs sensors is due to both the electrochemical system (pH solution) and the MOS component (canal size, insulator thickness). The temperature effect on the ISFET noise and the signal conditioning are also performed.展开更多
Fenugreek and carob seed were germinated in the dark for 4 days and 15 days respectively. The two species are rich in vitamins A, B1, B3, B8. Germination promotes the increase in their concentrations. To ensure preser...Fenugreek and carob seed were germinated in the dark for 4 days and 15 days respectively. The two species are rich in vitamins A, B1, B3, B8. Germination promotes the increase in their concentrations. To ensure preservation and increased bioavailability of the vitamins, it is necessary to apply a DIC of 30s to 400 kPa making it possible to support the cellular expansion and the decontamination of germinated seeds. Vitamin A content in germinated carob seed increased by 82.54%, which is not the case for fenugreek. A decrease in vitamin A concentration of 4 μg/100 g of dry matter. DIC increase vitamin B1, B3 and B8 in germinated fenugreek seeds. A slight decrease of B vitamins in germinated carob seed after treatment with DIC has been noticed. This loss can be explained by the low bioavailability caused mainly by the botanical structures of the seeds.展开更多
The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real life.The development of the Internet of Things(I...The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real life.The development of the Internet of Things(IoT)relies on the support of base stations,which provide a solid foundation for achieving a more intelligent way of living.In a specific area,achieving higher signal coverage with fewer base stations has become an urgent problem.Therefore,this article focuses on the effective coverage area of base station signals and proposes a novel Evolutionary Particle Swarm Optimization(EPSO)algorithm based on collective prediction,referred to herein as ECPPSO.Introducing a new strategy called neighbor-based evolution prediction(NEP)addresses the issue of premature convergence often encountered by PSO.ECPPSO also employs a strengthening evolution(SE)strategy to enhance the algorithm’s global search capability and efficiency,ensuring enhanced robustness and a faster convergence speed when solving complex optimization problems.To better adapt to the actual communication needs of base stations,this article conducts simulation experiments by changing the number of base stations.The experimental results demonstrate thatunder the conditionof 50 ormore base stations,ECPPSOconsistently achieves the best coverage rate exceeding 95%,peaking at 99.4400%when the number of base stations reaches 80.These results validate the optimization capability of the ECPPSO algorithm,proving its feasibility and effectiveness.Further ablative experiments and comparisons with other algorithms highlight the advantages of ECPPSO.展开更多
The Suizhou meteorite is a heavily shock-met-amorphosed L6 chondrite which contains thin shock melt veins.So far,26 high-pressure phases have been identified from the meteorite.Among the high-pressure phases,ten of th...The Suizhou meteorite is a heavily shock-met-amorphosed L6 chondrite which contains thin shock melt veins.So far,26 high-pressure phases have been identified from the meteorite.Among the high-pressure phases,ten of them were approved as new minerals which include tuite,xieite,wangdaodeite,chenmingite,hemleyite,poirierite,asimowite,hiroseite,elgoresyite,and ohtaniite,by the Commission on New Minerals,Nomenclature and Classification of the International Mineralogical Association.Other high-pressure phases identified from the meteorite are ahrensite,akimotoite,bridgmanite,lingunite,magnesiowüstite,majorite,majorite-pyrope_(ss),maskelynite,riesite,ringwoodite,wadsleyite,and 5 other phases including phase A,vitrified phase B and phase C,phase D(Ca-rich majorite),and partly inverted ringwoodite.The occurrence and abundance of high-pressure phases makes this meteorite the one with the richest variety of high-pressure minerals to date.展开更多
Ensuring the integrity and confidentiality of patient medical information is a critical priority in the healthcare sector.In the context of security,this paper proposes a novel encryption algorithm that integrates Blo...Ensuring the integrity and confidentiality of patient medical information is a critical priority in the healthcare sector.In the context of security,this paper proposes a novel encryption algorithm that integrates Blockchain technology,aiming to improve the security and privacy of transmitted data.The proposed encryption algorithm is a block-cipher image encryption scheme based on different chaotic maps:The logistic Map,the Tent Map,and the Henon Map used to generate three encryption keys.The proposed block-cipher system employs the Hilbert curve to perform permutation while a generated chaos-based S-Box is used to perform substitution.Furthermore,the integration of a Blockchain-based solution for securing data transmission and communication between nodes and authenticating the encrypted medical image’s authenticity adds a layer of security to our proposed method.Our proposed cryptosystem is divided into two principal modules presented as a pseudo-random number generator(PRNG)used for key generation and an encryption and decryption system based on the properties of confusion and diffusion.The security analysis and experimental tests for the proposed algorithm show that the average value of the information entropy of the encrypted images is 7.9993,the Number of Pixels Change Rate(NPCR)values are over 99.5%and the Unified Average Changing Intensity(UACI)values are greater than 33%.These results prove the strength of our proposed approach,demonstrating that it can significantly enhance the security of encrypted images.展开更多
Reconstruction of a traumatic distal femur defect remains a therapeutic challenge.Bone defect implants have been proposed to substitute the bone defect,and their biomechanical performances can be analyzed via a numeri...Reconstruction of a traumatic distal femur defect remains a therapeutic challenge.Bone defect implants have been proposed to substitute the bone defect,and their biomechanical performances can be analyzed via a numerical approach.However,the material assumptions for past computational human femur simulations were mainly homogeneous.Thus,this study aimed to design and analyze scaffolds for reconstructing the distal femur defect using a patient-specific finite element modeling technique.A three-dimensional finite element model of the human femur with accurate geometry and material distribution was developed using the finite element method and material mapping technique.An intact femur and a distal femur defect model treated with nine microstructure scaffolds and two solid scaffolds were investigated and compared under a single-leg stance loading.The results showed that the metal solid scaffold design could provide the most stable fixation for reconstructing the distal femur defect.However,the fixation stability was affected by various microstructure designs and pillar diameters.A microstructure scaffold can be designed to satisfy all the biomechanical indexes,opening up future possibilities for more stable reconstructions.A three-dimensional finite element model of the femur with real bone geometry and bone material distribution can be developed,and this patient-specific femur model can be used for studying other femoral fractures or injuries,paving the way for more comprehensive research in the field.Besides,this patient-specific finite element modeling technique can also be applied to developing other human or animal bone models,expanding the scope of biomechanical research.展开更多
The electrochemical conversion of carbon dioxide into valuable products is pivotal for maintaining the global carbon cycle and mitigating global warming.This review explores the advancements in electrochemical CO_(2) ...The electrochemical conversion of carbon dioxide into valuable products is pivotal for maintaining the global carbon cycle and mitigating global warming.This review explores the advancements in electrochemical CO_(2) conversion,particularly focusing on producing methanol,ethanol,and n-propanol using various catalysts such as metals,metal oxides,metal alloys,and metal organic frameworks.Additionally,it covers the photoelectrochemical(PEC)conversion of CO_(2) into alcohols.The primary objective is to identify efficient electrocatalysts for ethanol,methanol,and n-propanol production,prioritizing selectivity,stability,Faradaic efficiency(FE),and current density.Notable catalysts include PtxZn nanoalloys,which exhibit an FE of~81.4% for methanol production,and trimetallic Pt/Pb/Zn nanoalloys,aimed at reducing Pt costs while enhancing catalyst stability and durability.Metal oxide catalysts like thin film Cu_(2)O/CuO on nickel foam and Cu_(2)O/ZnO achieve FE values of~38% and~16.6% for methanol production,respectively.Copper-based metal-organic frameworks,such as Cu@Cu_(2)O,demonstrate an FE of~45% for methanol production.Similarly,Ag_(0.14)/Cu_(0.86) and Cu-Zn alloys exhibit FEs of~63% and~46.6%,respectively,for ethanol production.Notably,n-propanol production via Pd–Cu alloy and graphene/ZnO/Cu_(2)O yields FEs of~13.7% and~23%,respectively.Furthermore,the review discusses recent advancements in PEC reactor design,photoelectrodes,reaction mechanisms,and catalyst durability.By evaluating the efficiency of these devices in liquid fuel production,the review addresses challenges and prospects in CO_(2) conversion for obtaining various valuable products.展开更多
Several optimization methods,such as Particle Swarm Optimization(PSO)and Genetic Algorithm(GA),are used to select the most suitable Static Synchronous Compensator(STATCOM)technology for the optimal operation of the po...Several optimization methods,such as Particle Swarm Optimization(PSO)and Genetic Algorithm(GA),are used to select the most suitable Static Synchronous Compensator(STATCOM)technology for the optimal operation of the power system,as well as to determine its optimal location and size to minimize power losses.An IEEE 14 bus system,integrating three wind turbines based on Squirrel Cage Induction Generators(SCIGs),is used to test the applicability of the proposed algorithms.The results demonstrate that these algorithms are capable of selecting the most appropriate technology while optimally sizing and locating the STATCOM to reduce power losses in the network.Specifically,the optimized STATCOM allocation using the Particle Swarm Optimization(PSO)achieves a 7.44%reduction in total active power loss compared to the optimized allocation using the Genetic Algorithm(GA).Furthermore,the voltage magnitudes at buses 4,9,and 10,which initially had exceeded the upper voltage limit,were reduced and brought within acceptable ranges,thereby improving the system’s overall voltage profile.Consequently,the optimal allocation of the STATCOM significantly enhances the efficiency and performance of the power network.展开更多
Agriculture plays an important role in the economy of all countries.However,plant diseases may badly affect the quality of food,production,and ultimately the economy.For plant disease detection and management,agricult...Agriculture plays an important role in the economy of all countries.However,plant diseases may badly affect the quality of food,production,and ultimately the economy.For plant disease detection and management,agriculturalists spend a huge amount of money.However,the manual detection method of plant diseases is complicated and time-consuming.Consequently,automated systems for plant disease detection using machine learning(ML)approaches are proposed.However,most of the existing ML techniques of plants diseases recognition are based on handcrafted features and they rarely deal with huge amount of input data.To address the issue,this article proposes a fully automated method for plant disease detection and recognition using deep neural networks.In the proposed method,AlexNet and VGG19 CNNs are considered as pre-trained architectures.It is capable to obtain the feature extraction of the given data with fine-tuning details.After convolutional neural network feature extraction,it selects the best subset of features through the correlation coefficient and feeds them to the number of classifiers including K-Nearest Neighbor,Support Vector Machine,Probabilistic Neural Network,Fuzzy logic,and Artificial Neural Network.The validation of the proposed method is carried out on a self-collected dataset generated through the augmentation step.The achieved average accuracy of our method is more than 96%and outperforms the recent techniques.展开更多
文摘In this paper, a bearing-based three-dimensional self-localization and distributed circumnavigation with connectivity preservation and collision avoidance are investigated for a group of quadrotor-type unmanned aerial vehicles (UAVs). A leader–follower structure is adopted, wherein the leader moves with reference dynamics (a target). Different from the existing approaches that necessitate full knowledge of the time-varying reference trajectory, in this paper, it is assumed that only some vehicles (at least one) have access to the bearing relative to the target, and all other vehicles are equipped with sensors capable of measuring the bearings relative to neighboring vehicles. In this paper, a consensus estimator is proposed to estimate the global position for each vehicle using relative bearing measurements and an estimate of neighboring vehicles received from a direct communication network. Then, a continuous robust integral of the sign of the error (RISE) control approach is effectively integrated with the distributed vector field approach to ensure UAV formation orbiting around the moving target while avoiding obstacles and maintaining network links within available communication ranges. In contrast to the classical RISE control rule, a \(\tanh (\cdot )\) function is used instead of the \(\text {sgn}(\cdot )\) function to further decrease the high-gain feedback and to obtain a smoother control signal. Furthermore, by using the localized radial basis function (RBF) neural networks (NNs) in a cooperative way, deterministic learning theory is employed to accurately identify/learn model uncertainties resulting from the attitude dynamics. The convergence of the entire closed-loop system is illustrated using the Lyapunov theory and is shown to be uniformly ultimately bounded. Finally, numerical simulations show the effectiveness of the proposed approach.
基金Supported by Ministry of Higher Education Scientific Research and Technology,Algeria(Grant No.F03120130013/2014)
文摘Objective:To investigate the total phenolics,flavonoids and tannins content and the in vitro antioxidant activities of methanolic extracts of six wild Mentha species which are Mentha aquatica,Mentha arvensis,Mentha piperita,Mentha pulegium,Mentha rotundifolia and Mentha villosa.Methods:The Folin–Ciocalteu method was used to determine the total phenols content while flavonoids were estimated according to the aluminum chloride colorimetric method.To evaluate tannins content,vanillin and HCl were added to methanolic extracts.The antioxidant potential was measured by 1,1-diphenyl-2-picrylhydrazyl radical scavenging,ferrous ion chelating and the inhibition of b-carotene bleaching assays.Results:The methanol extracts of Algerian mints were rich in phenolic compounds and exhibited powerful antioxidant activity ranging from 7.5 mg/mL to 44.66 mg/mL,which varied significantly among species.Mentha aquatica stood out with efficient antioxidant ability which was correlated to the high total phenolics content,followed by Mentha arvensis and Mentha piperita with very close values,comparing to Mentha pulegium,Mentha rotundifolia and Mentha villosa with lowest values.Conclusions:These results show that methanolic extracts of Mentha species from Algeria have a great potential of polyphenols which can be used as a natural food preservative and antioxidant source.
文摘Complex oxides are an important class of materials with enormous potential for electrochemical appli-cations.Depending on their composition and structure,such complex oxides can exhibit either a single conductivity(oxygen-ionic or protonic,or n-type,or p-type electronic)or a combination thereof gener-ating distinct dual-conducting or even triple-conducting materials.These properties enable their use as diverse functional materials for solid oxide fuel cells,solid oxide electrolysis cells,permeable membranes,and gas sensors.The literature review shows that the field of solid oxide materials and related electro-chemical cells has a significant level of research engagement,with over 8,000 publications published since 2020.The manual analysis of such a large volume of material is challenging.However,by examining the review articles,it is possible to identify key patterns,recent achievements,prospects,and remaining obstacles.To perform such an analysis,the present article provides,for the first time,a comprehensive summary of previous review publications that have been published since 2020,with a special focus on solid oxide materials and electrochemical systems.Thus,this study provides an important reference for researchers specializing in the fields of solid state ionics,high-temperature electrochemistry,and energyconversiontechnologies.
文摘Objective To determine the medicinal potential of various plants and their parts extracted with different solvents. Methods The total phenolic content of acetonitrile/water（60%-40%）（ACN/W） and aqueous（W） extract fractions was determined by high-performance liquid chromatography（HPLC）, and terpenic compounds were detected by gas chromatography/mass spectrometry（GC/MS）. Antioxidant activity of the samples was evaluated using 2,2-diphenyl-1-picrylhydrazyl（DPPH） assay and β-carotene bleaching method. Cell viability was investigated by thiazolyl blue tetrazolium bromide [3-（4,5-dimethylthiazol）-2-yl 2,5-diphenyltetrazolium bromide]（MTT） assay. The mechanisms involved in cytotoxic activity were investigated in a murine macrophage cell line（RAW 264.7） and cancer lines. Results Our findings show that 11 plant species exhibited biological activity. In addition, moderate antibacterial activity was reported against one or more of the tested bacterial strains at two concentrations： 300 μg and 3 mg/disc. Furthermore, our data reveal that among all plants investigated, some extract and hydrophobic fractions were potent scavengers of the DPPH radical（6.78 μg/m L 〈 EC50 〈 8.55 μg/mL）. Taken together, our results show that Nerium oleander（NOACN/W） and Pituranthos tortuosus（PTACN/W） were highly cytotoxic against RAW 264.7 cells with IC80 values of 0.36, and 1.55 μg/mL, respectively. In contrast, murine macrophage cell lines had low growth and were significantly sensitive to water extracts of Thymus hirtus sp. algeriensis（THW）, Lavandula multifida（LMW）, and ACN/W extract of Erica multiflora（EMACN/W） at doses 〉 400, 47.20, and 116.74 μg/m L, respectively. The current work demonstrates that RAW 264.7 cell proliferation was inhibited by samples in a dose-dependent manner. Conclusion Our findings, validated through free radical scavenging activity, agar diffusion assay, and cytotoxicity of essential oils towards cancer cells, show that ethnomedicinal plants used in this work have a novel application as a tumor suppressor.
文摘Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse languages.While numerous scholars conduct sentiment analysisin widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grapplingwith resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language,characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu,Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguisticfeatures, presents an additional hurdle due to the lack of accessible datasets, rendering sentiment analysis aformidable undertaking. The limited availability of resources has fueled increased interest among researchers,prompting a deeper exploration into Urdu sentiment analysis. This research is dedicated to Urdu languagesentiment analysis, employing sophisticated deep learning models on an extensive dataset categorized into fivelabels: Positive, Negative, Neutral, Mixed, and Ambiguous. The primary objective is to discern sentiments andemotions within the Urdu language, despite the absence of well-curated datasets. To tackle this challenge, theinitial step involves the creation of a comprehensive Urdu dataset by aggregating data from various sources such asnewspapers, articles, and socialmedia comments. Subsequent to this data collection, a thorough process of cleaningand preprocessing is implemented to ensure the quality of the data. The study leverages two well-known deeplearningmodels, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for bothtraining and evaluating sentiment analysis performance. Additionally, the study explores hyperparameter tuning tooptimize the models’ efficacy. Evaluation metrics such as precision, recall, and the F1-score are employed to assessthe effectiveness of the models. The research findings reveal that RNN surpasses CNN in Urdu sentiment analysis,gaining a significantly higher accuracy rate of 91%. This result accentuates the exceptional performance of RNN,solidifying its status as a compelling option for conducting sentiment analysis tasks in the Urdu language.
基金The authors extend their appreciation to the Deputyship for Research and Innovation,Ministry of Education in Saudi Arabia for funding this research work through the Project Number QURDO001Project title:Intelligent Real-Time Crowd Monitoring System Using Unmanned Aerial Vehicle(UAV)Video and Global Positioning Systems(GPS)Data。
文摘The advent of the COVID-19 pandemic has adversely affected the entire world and has put forth high demand for techniques that remotely manage crowd-related tasks.Video surveillance and crowd management using video analysis techniques have significantly impacted today’s research,and numerous applications have been developed in this domain.This research proposed an anomaly detection technique applied to Umrah videos in Kaaba during the COVID-19 pandemic through sparse crowd analysis.Managing theKaaba rituals is crucial since the crowd gathers from around the world and requires proper analysis during these days of the pandemic.The Umrah videos are analyzed,and a system is devised that can track and monitor the crowd flow in Kaaba.The crowd in these videos is sparse due to the pandemic,and we have developed a technique to track the maximum crowd flow and detect any object(person)moving in the direction unlikely of the major flow.We have detected abnormal movement by creating the histograms for the vertical and horizontal flows and applying thresholds to identify the non-majority flow.Our algorithm aims to analyze the crowd through video surveillance and timely detect any abnormal activity tomaintain a smooth crowd flowinKaaba during the pandemic.
基金funded by the Deanship of Scientific Research(DSR),King Abdulaziz University,Jeddah,under grant No.D-668-305-1441.
文摘This article is aimed at describing the way rotation and magnetic field affect the propagation of waves in an infinite poroelastic cylindrical bone.It offers a solution with an exact closed form.The authors got and examined numerically the general frequency equation for poroelastic bone.Moreover,they calculated the frequencies of poroelastic bone for different values of the magnetic field and rotation.Unlike the results of previous studies,the authors noticed little frequency dispersion in the wet bone.The proposed model will be applicable to wide-range parametric projects of bone mechanical response.Examining the vibration of surface waves in rotating cylindrical,long human bones under the magnetic field can have an impact.The findings of the study are offered in graphs.Then,a comparison with the results of the literature is conducted to show the effect of rotation and magnetic field on the wave propagation phenomenon.It is worth noting that the results of the study highly match those of the literature.
基金This work is supported by EIAS(Emerging Intelligent Autonomous Systems)Data Science Lab,Prince Sultan University,Kingdom of Saudi Arabia,by paying the APC.
文摘The software development process mostly depends on accurately identifying both essential and optional features.Initially,user needs are typically expressed in free-form language,requiring significant time and human resources to translate these into clear functional and non-functional requirements.To address this challenge,various machine learning(ML)methods have been explored to automate the understanding of these requirements,aiming to reduce time and human effort.However,existing techniques often struggle with complex instructions and large-scale projects.In our study,we introduce an innovative approach known as the Functional and Non-functional Requirements Classifier(FNRC).By combining the traditional random forest algorithm with the Accuracy Sliding Window(ASW)technique,we develop optimal sub-ensembles that surpass the initial classifier’s accuracy while using fewer trees.Experimental results demonstrate that our FNRC methodology performs robustly across different datasets,achieving a balanced Precision of 75%on the PROMISE dataset and an impressive Recall of 85%on the CCHIT dataset.Both datasets consistently maintain an F-measure around 64%,highlighting FNRC’s ability to effectively balance precision and recall in diverse scenarios.These findings contribute to more accurate and efficient software development processes,increasing the probability of achieving successful project outcomes.
基金supported by the MOTIE (Ministry of Trade,Industry,and Energy)in Korea,under the Fostering Global Talents for Innovative Growth Program (P0017308)supervised by the Korea Institute for Advancement of Technology (KIAT)+1 种基金supported by the MSIT (Ministry of Science and ICT),Korea,under the ITRC (Information Technology Research Center)support program (IITP-2024-2020-0-01655)supervised by the IITP (Institute of Information and Communications Technology Planning and Evaluation).
文摘In this study, aluminum-doped zinc oxide(AZO) thin films were deposited onto a low-temperature polyethylene terephthalate(PET) substrate using DC magnetron sputtering. Deposition parameters included power range of 100-300 W, a working pressure of 15 mTorr, and a substrate temperature of 50 ℃. Post-deposition, flash lamp annealing(FLA) was employed as a rapid thermal processing method with a pulse duration of 1.7 ms and energy density of 7 J·cm-2, aimed at enhancing the film's quality while preserving the temperature-sensitive PET substrate. FLA offers advantages over conventional annealing,including shorter processing times and improved material properties. The structural, optical, and electrical characteristics of the AZO films were assessed using X-ray diffraction, field emission scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy, ultraviolet-visible spectroscopy, and Hall effect measurements. The results demonstrated that properties of AZO films varied with deposition and annealing conditions. Films deposited at 200 W and subjected to FLA exhibited superior crystallinity, with average visible light transmittance exceeding 80% and resistivity as low as 0.38 Ω·cm representing 95%improvement in transmittance. Electrical analysis revealed that carrier concentration, mobility, and resistivity were influenced by both sputtering and annealing parameters. These findings underscore the effectiveness of FLA in optimizing AZO thin film properties, highlighting potential in optoelectronics applications.
基金the Key Research and Development Project of Hainan Province of China(ZDYF2017066)Natural Science Foundation of Hainan Province of China(619MS028)the Research on Education and Teaching Reform of Hainan University(hdjy1954)。
文摘With the expansion of cultivation scale,pitaya diseases are gradually increasing.Traditionally relying on human observation to judge the disease is limited by the skills and experience of the observer,which cannot guarantee the accuracy and real-time of the judgment,and consumes much manpower and time.In this study,by collecting,segmenting,and labeling images of 4 main diseases of pitaya in the field,an image database of main diseases of pitaya in the field was constructed to provide a basis for computer image recognition of pitaya diseases.Thereby,it benefits reducing manual error and improving the accuracy and real-time of disease identification for agricultural production,but also lays a foundation for the future development of intelligent agriculture.
文摘This study describes the advantages of using iron-oxide-pillared MF （montmorillonite） and EDTA （ethylenediaminetetraacetic acid）-pillared MD （montmorillonite） for the removal of fluoride in the phosphoric acid. The MF and MD were characterized by using BET （Brunauer-Emmett-Teller） and SEM （scanning electron microscope） for there to appreciate their textural and structural properties and in order to appraise their capacity in the fixation of the fluorine experimental results of fluorine adsorption show a defluoration of the phosphoric acid passing beyond 90% with both clays with a light performance for the MD. The kinetics of fluorine adsorption on MF and on MD has been measured at different temperatures. Indeed, the adsorbed quantity increases more favorably to temperatures more high. This kinetics could be described by two models： pseudo-first-order or pseudo-second-order, which depends on temperature and the nature of adsorbent. At different temperatures used and with various adsorbents （MD and MF, the pseudo second order model was the most reliable to determine the order of kinetics of adsorption of fluorine, which is also reflected a good correlation coefficient （above 0.99）. The fluoride sorption was reasonably explained with Langmuir or Freundlich isotherms. Thermodynamic parameters such as AG~, AH~, AS~ and kinetic one E,, （apparent activation energy） were calculated in order to understand the nature of sorption process. Results indicate that the mechanism of adsorption of the ion of fluoride on the pillared montmorillonite is an exothermic reaction. During equilibrium conditions, both the systems showed negative Gibbs energy, which indicates stability of system. The values of apparent activation energy for the both clays show that the adsorption of F ions is easier on the MF.
文摘Ions Sensitive Field Effect Transistors (ISFETs) are becoming the platform sensors for important chemical and biomedical applications. However, the accuracy of ISFET output measurement is greatly affected by the presences of low-frequency noise, drift and slow response of the device. This requires more safety in measured results and the tools of analysis. In this paper, we present fundamental limits on the sensitivity of ISFETs micro-sensors, arising from intrinsic and extrinsic noise sources. We developed an algorithm in MATLAB in order to model the frequency analysis of the 1/f noise in ISFET sensor using Hooge theory. We have shown that the 1/f noise of the ISFETs sensors is due to both the electrochemical system (pH solution) and the MOS component (canal size, insulator thickness). The temperature effect on the ISFET noise and the signal conditioning are also performed.
文摘Fenugreek and carob seed were germinated in the dark for 4 days and 15 days respectively. The two species are rich in vitamins A, B1, B3, B8. Germination promotes the increase in their concentrations. To ensure preservation and increased bioavailability of the vitamins, it is necessary to apply a DIC of 30s to 400 kPa making it possible to support the cellular expansion and the decontamination of germinated seeds. Vitamin A content in germinated carob seed increased by 82.54%, which is not the case for fenugreek. A decrease in vitamin A concentration of 4 μg/100 g of dry matter. DIC increase vitamin B1, B3 and B8 in germinated fenugreek seeds. A slight decrease of B vitamins in germinated carob seed after treatment with DIC has been noticed. This loss can be explained by the low bioavailability caused mainly by the botanical structures of the seeds.
基金supported by the National Natural Science Foundation of China(Nos.62272418,62102058)Basic Public Welfare Research Program of Zhejiang Province(No.LGG18E050011)the Major Open Project of Key Laboratory for Advanced Design and Intelligent Computing of the Ministry of Education under Grant ADIC2023ZD001,National Undergraduate Training Program on Innovation and Entrepreneurship(No.202410345054).
文摘The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real life.The development of the Internet of Things(IoT)relies on the support of base stations,which provide a solid foundation for achieving a more intelligent way of living.In a specific area,achieving higher signal coverage with fewer base stations has become an urgent problem.Therefore,this article focuses on the effective coverage area of base station signals and proposes a novel Evolutionary Particle Swarm Optimization(EPSO)algorithm based on collective prediction,referred to herein as ECPPSO.Introducing a new strategy called neighbor-based evolution prediction(NEP)addresses the issue of premature convergence often encountered by PSO.ECPPSO also employs a strengthening evolution(SE)strategy to enhance the algorithm’s global search capability and efficiency,ensuring enhanced robustness and a faster convergence speed when solving complex optimization problems.To better adapt to the actual communication needs of base stations,this article conducts simulation experiments by changing the number of base stations.The experimental results demonstrate thatunder the conditionof 50 ormore base stations,ECPPSOconsistently achieves the best coverage rate exceeding 95%,peaking at 99.4400%when the number of base stations reaches 80.These results validate the optimization capability of the ECPPSO algorithm,proving its feasibility and effectiveness.Further ablative experiments and comparisons with other algorithms highlight the advantages of ECPPSO.
基金Science and Technology Planning Project of Guangdong Province(2023B1212060048).
文摘The Suizhou meteorite is a heavily shock-met-amorphosed L6 chondrite which contains thin shock melt veins.So far,26 high-pressure phases have been identified from the meteorite.Among the high-pressure phases,ten of them were approved as new minerals which include tuite,xieite,wangdaodeite,chenmingite,hemleyite,poirierite,asimowite,hiroseite,elgoresyite,and ohtaniite,by the Commission on New Minerals,Nomenclature and Classification of the International Mineralogical Association.Other high-pressure phases identified from the meteorite are ahrensite,akimotoite,bridgmanite,lingunite,magnesiowüstite,majorite,majorite-pyrope_(ss),maskelynite,riesite,ringwoodite,wadsleyite,and 5 other phases including phase A,vitrified phase B and phase C,phase D(Ca-rich majorite),and partly inverted ringwoodite.The occurrence and abundance of high-pressure phases makes this meteorite the one with the richest variety of high-pressure minerals to date.
基金supported by the Large Group Project under grant number(RGP2/473/46).
文摘Ensuring the integrity and confidentiality of patient medical information is a critical priority in the healthcare sector.In the context of security,this paper proposes a novel encryption algorithm that integrates Blockchain technology,aiming to improve the security and privacy of transmitted data.The proposed encryption algorithm is a block-cipher image encryption scheme based on different chaotic maps:The logistic Map,the Tent Map,and the Henon Map used to generate three encryption keys.The proposed block-cipher system employs the Hilbert curve to perform permutation while a generated chaos-based S-Box is used to perform substitution.Furthermore,the integration of a Blockchain-based solution for securing data transmission and communication between nodes and authenticating the encrypted medical image’s authenticity adds a layer of security to our proposed method.Our proposed cryptosystem is divided into two principal modules presented as a pseudo-random number generator(PRNG)used for key generation and an encryption and decryption system based on the properties of confusion and diffusion.The security analysis and experimental tests for the proposed algorithm show that the average value of the information entropy of the encrypted images is 7.9993,the Number of Pixels Change Rate(NPCR)values are over 99.5%and the Unified Average Changing Intensity(UACI)values are greater than 33%.These results prove the strength of our proposed approach,demonstrating that it can significantly enhance the security of encrypted images.
基金funded by the TaipeiMedical University-National Taiwan University of Science and Technology joint research program under Grant No.TMU-NTUST-109-09.
文摘Reconstruction of a traumatic distal femur defect remains a therapeutic challenge.Bone defect implants have been proposed to substitute the bone defect,and their biomechanical performances can be analyzed via a numerical approach.However,the material assumptions for past computational human femur simulations were mainly homogeneous.Thus,this study aimed to design and analyze scaffolds for reconstructing the distal femur defect using a patient-specific finite element modeling technique.A three-dimensional finite element model of the human femur with accurate geometry and material distribution was developed using the finite element method and material mapping technique.An intact femur and a distal femur defect model treated with nine microstructure scaffolds and two solid scaffolds were investigated and compared under a single-leg stance loading.The results showed that the metal solid scaffold design could provide the most stable fixation for reconstructing the distal femur defect.However,the fixation stability was affected by various microstructure designs and pillar diameters.A microstructure scaffold can be designed to satisfy all the biomechanical indexes,opening up future possibilities for more stable reconstructions.A three-dimensional finite element model of the femur with real bone geometry and bone material distribution can be developed,and this patient-specific femur model can be used for studying other femoral fractures or injuries,paving the way for more comprehensive research in the field.Besides,this patient-specific finite element modeling technique can also be applied to developing other human or animal bone models,expanding the scope of biomechanical research.
基金the financial support from National Science Centre Poland(NCN)based on the decision number UMO-2021/43/D/ST5/00824financial support of research project supported by the program“Excellence Initiative-Research University”for the AGH University of Krakow.
文摘The electrochemical conversion of carbon dioxide into valuable products is pivotal for maintaining the global carbon cycle and mitigating global warming.This review explores the advancements in electrochemical CO_(2) conversion,particularly focusing on producing methanol,ethanol,and n-propanol using various catalysts such as metals,metal oxides,metal alloys,and metal organic frameworks.Additionally,it covers the photoelectrochemical(PEC)conversion of CO_(2) into alcohols.The primary objective is to identify efficient electrocatalysts for ethanol,methanol,and n-propanol production,prioritizing selectivity,stability,Faradaic efficiency(FE),and current density.Notable catalysts include PtxZn nanoalloys,which exhibit an FE of~81.4% for methanol production,and trimetallic Pt/Pb/Zn nanoalloys,aimed at reducing Pt costs while enhancing catalyst stability and durability.Metal oxide catalysts like thin film Cu_(2)O/CuO on nickel foam and Cu_(2)O/ZnO achieve FE values of~38% and~16.6% for methanol production,respectively.Copper-based metal-organic frameworks,such as Cu@Cu_(2)O,demonstrate an FE of~45% for methanol production.Similarly,Ag_(0.14)/Cu_(0.86) and Cu-Zn alloys exhibit FEs of~63% and~46.6%,respectively,for ethanol production.Notably,n-propanol production via Pd–Cu alloy and graphene/ZnO/Cu_(2)O yields FEs of~13.7% and~23%,respectively.Furthermore,the review discusses recent advancements in PEC reactor design,photoelectrodes,reaction mechanisms,and catalyst durability.By evaluating the efficiency of these devices in liquid fuel production,the review addresses challenges and prospects in CO_(2) conversion for obtaining various valuable products.
文摘Several optimization methods,such as Particle Swarm Optimization(PSO)and Genetic Algorithm(GA),are used to select the most suitable Static Synchronous Compensator(STATCOM)technology for the optimal operation of the power system,as well as to determine its optimal location and size to minimize power losses.An IEEE 14 bus system,integrating three wind turbines based on Squirrel Cage Induction Generators(SCIGs),is used to test the applicability of the proposed algorithms.The results demonstrate that these algorithms are capable of selecting the most appropriate technology while optimally sizing and locating the STATCOM to reduce power losses in the network.Specifically,the optimized STATCOM allocation using the Particle Swarm Optimization(PSO)achieves a 7.44%reduction in total active power loss compared to the optimized allocation using the Genetic Algorithm(GA).Furthermore,the voltage magnitudes at buses 4,9,and 10,which initially had exceeded the upper voltage limit,were reduced and brought within acceptable ranges,thereby improving the system’s overall voltage profile.Consequently,the optimal allocation of the STATCOM significantly enhances the efficiency and performance of the power network.
基金the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2020-2016-0-00312)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation)in part by the MSIP(Ministry of Science,ICT&Future Planning),Korea,under the National Program for Excellence in SW(2015-0-00938)supervised by the IITP(Institute for Information&communications Technology Planning&Evaluation).
文摘Agriculture plays an important role in the economy of all countries.However,plant diseases may badly affect the quality of food,production,and ultimately the economy.For plant disease detection and management,agriculturalists spend a huge amount of money.However,the manual detection method of plant diseases is complicated and time-consuming.Consequently,automated systems for plant disease detection using machine learning(ML)approaches are proposed.However,most of the existing ML techniques of plants diseases recognition are based on handcrafted features and they rarely deal with huge amount of input data.To address the issue,this article proposes a fully automated method for plant disease detection and recognition using deep neural networks.In the proposed method,AlexNet and VGG19 CNNs are considered as pre-trained architectures.It is capable to obtain the feature extraction of the given data with fine-tuning details.After convolutional neural network feature extraction,it selects the best subset of features through the correlation coefficient and feeds them to the number of classifiers including K-Nearest Neighbor,Support Vector Machine,Probabilistic Neural Network,Fuzzy logic,and Artificial Neural Network.The validation of the proposed method is carried out on a self-collected dataset generated through the augmentation step.The achieved average accuracy of our method is more than 96%and outperforms the recent techniques.
