This study investigated the feasibility of hyperspectral imaging techniques to estimate the vigor of heatdamaged Quercus variabilis seeds.Four thermal damage grades were classified according to heat treatment duration...This study investigated the feasibility of hyperspectral imaging techniques to estimate the vigor of heatdamaged Quercus variabilis seeds.Four thermal damage grades were classified according to heat treatment duration(0,2,5,and 10 h).After obtaining hyperspectral images with a 370–1042 nm hyperspectral imager that included visible and near infrared light,germination was tested to confirm estimates.The Savitzky–Golay(SG)second derivative was used to preprocess the spectrum to reduce any noise impact.The successive projections algorithm(SPA),principal component analysis,and local linear embedding algorithm were used to extract the characteristic spectral bands related to seed vigor.Finally,a model for seed vigor classifi-cation of Q.variabili s based on partial least squares support vector machine(LS-SVM)with different spectral data sets was developed.The results show that the spectrum after SG second derivative preprocessing was better for developing the model,and SPA performed the best among the three feature band selection methods.The combination SG second derivative-LS-SVM provided the best classification model for Q.variabilis seed vigor,with the prediction set reaching 98.81%.This study provides an important basis for rapid and nondestructive assessment of the vigor of heat-damaged seeds using hyperspectral imaging techniques.展开更多
Supercapacitors are gaining popularity due to their high cycling stability,power density,and fast charge and discharge rates.Researchers are ex-ploring electrode materials,electrolytes,and separat-ors for cost-effecti...Supercapacitors are gaining popularity due to their high cycling stability,power density,and fast charge and discharge rates.Researchers are ex-ploring electrode materials,electrolytes,and separat-ors for cost-effective energy storage systems.Ad-vances in materials science have led to the develop-ment of hybrid nanomaterials,such as combining fil-amentous carbon forms with inorganic nanoparticles,to create new charge and energy transfer processes.Notable materials for electrochemical energy-stor-age applications include MXenes,2D transition met-al carbides,and nitrides,carbon black,carbon aerogels,activated carbon,carbon nanotubes,conducting polymers,carbon fibers,and nanofibers,and graphene,because of their thermal,electrical,and mechanical properties.Carbon materials mixed with conducting polymers,ceramics,metal oxides,transition metal oxides,metal hydroxides,transition metal sulfides,trans-ition metal dichalcogenide,metal sulfides,carbides,nitrides,and biomass materials have received widespread attention due to their remarkable performance,eco-friendliness,cost-effectiveness,and renewability.This article explores the development of carbon-based hybrid materials for future supercapacitors,including electric double-layer capacitors,pseudocapacitors,and hy-brid supercapacitors.It investigates the difficulties that influence structural design,manufacturing(electrospinning,hydro-thermal/solvothermal,template-assisted synthesis,electrodeposition,electrospray,3D printing)techniques and the latest car-bon-based hybrid materials research offer practical solutions for producing high-performance,next-generation supercapacitors.展开更多
With rapid urbanization,fires pose significant challenges in urban governance.Traditional fire detection methods often struggle to detect smoke in complex urban scenes due to environmental interferences and variations...With rapid urbanization,fires pose significant challenges in urban governance.Traditional fire detection methods often struggle to detect smoke in complex urban scenes due to environmental interferences and variations in viewing angles.This study proposes a novel multimodal smoke detection method that fuses infrared and visible imagery using a transformer-based deep learning model.By capturing both thermal and visual cues,our approach significantly enhances the accuracy and robustness of smoke detection in business parks scenes.We first established a dual-view dataset comprising infrared and visible light videos,implemented an innovative image feature fusion strategy,and designed a deep learning model based on the transformer architecture and attention mechanism for smoke classification.Experimental results demonstrate that our method outperforms existing methods,under the condition of multi-view input,it achieves an accuracy rate of 90.88%,precision rate of 98.38%,recall rate of 92.41%and false positive and false negative rates both below 5%,underlining the effectiveness of the proposed multimodal and multi-view fusion approach.The attention mechanism plays a crucial role in improving detection performance,particularly in identifying subtle smoke features.展开更多
The fact that Morocco is an agricultural country and the large volume of biodegradable waste produced by the population make composting so important.The degradation of organic matter is facilitated by faunal and flora...The fact that Morocco is an agricultural country and the large volume of biodegradable waste produced by the population make composting so important.The degradation of organic matter is facilitated by faunal and floral macro and micro-organisms that act in different stages of maturation;studies on this fauna are quite rare both nationally and internationally.On a sample of two tons of household waste,we documented invertebrates that colonized compost heaps and then assessed the changes in the structure of the invertebrate population during the different phases.Our study revealed the presence of several zoological groups colonizing the compost heaps during the different composting phases;we noted the presence of:(1)Macroscopic invertebrates,in order of number of individuals:insect larvae,ants,earthworms,sowbugs,spiders,springtails,and millipedes,and(2)Microscopic invertebrates,the most abundant in terms of individuals:mites and nematodes.As for the order of appearance,we observed that insect larvae were the first to colonize the compost heap from the very first days of installation,followed by woodlice observed during the thermophilic phase and disappearing towards the end of the process.Earthworms were observed during the end of the thermophilic phase,while springtails were observed more during the cooling and maturation phases.Our study revealed the presence of a good quality of fauna during the composting process,which are indicators of good compost quality and play a major role in the circulation of nutrients,thus ensuring the provision of essential elements for plant nutrition.展开更多
In this paper,the isogeometric analysis(IGA)method is employed to analyze the oscillation characteristics of functionally graded triply periodic minimal surface(FG-TPMS)curved-doubly shells integrated with magneto-ele...In this paper,the isogeometric analysis(IGA)method is employed to analyze the oscillation characteristics of functionally graded triply periodic minimal surface(FG-TPMS)curved-doubly shells integrated with magneto-electric surface layers(referred to as"FG-TPMS-MEE curved-doubly shells")subjected to low-velocity impact loads.This study presents low-velocity impact load model based on a single springmass(S-M)approach.The FG-TPMS-MEE curved-doubly shells are covered with two magneto-electric surface layers,while the core layer consists of three types:I-graph and Wrapped Package-graph(IWP),Gyroid(G),and Primitive(P),with various graded functions.These types are notable for their exceptional stiffness-to-weight ratios,enabling a wide range of potential applications.The Maxwell equations and electromagnetic boundary conditions are applied to compute the change in electric potentials and magnetic potentials.The equilibrium equations of the shell are derived from a refined higher-order shear deformation theory(HSDT),and the transient responses of the FG-TPMS-MEE curveddoubly shells are subsequently determined using Newmark's direct integration method.These results have applications in structural vibration control and the analysis of structures subjected to impact or explosive loads.Furthermore,this study provides a theoretical prediction of the low-velocity impact load and magneto-electric-elastic effects on the free vibration and transient response of FG-TPMS-MEE curved-doubly shells.展开更多
Pill image recognition is an important field in computer vision.It has become a vital technology in healthcare and pharmaceuticals due to the necessity for precise medication identification to prevent errors and ensur...Pill image recognition is an important field in computer vision.It has become a vital technology in healthcare and pharmaceuticals due to the necessity for precise medication identification to prevent errors and ensure patient safety.This survey examines the current state of pill image recognition,focusing on advancements,methodologies,and the challenges that remain unresolved.It provides a comprehensive overview of traditional image processing-based,machine learning-based,deep learning-based,and hybrid-based methods,and aims to explore the ongoing difficulties in the field.We summarize and classify the methods used in each article,compare the strengths and weaknesses of traditional image processing-based,machine learning-based,deep learning-based,and hybrid-based methods,and review benchmark datasets for pill image recognition.Additionally,we compare the performance of proposed methods on popular benchmark datasets.This survey applies recent advancements,such as Transformer models and cutting-edge technologies like Augmented Reality(AR),to discuss potential research directions and conclude the review.By offering a holistic perspective,this paper aims to serve as a valuable resource for researchers and practitioners striving to advance the field of pill image recognition.展开更多
Attacks are growing more complex and dangerous as network capabilities improve at a rapid pace.Network intrusion detection is usually regarded as an efficient means of dealing with security attacks.Many ways have been...Attacks are growing more complex and dangerous as network capabilities improve at a rapid pace.Network intrusion detection is usually regarded as an efficient means of dealing with security attacks.Many ways have been presented,utilizing various strategies and focusing on different types of visitors.Anomaly-based network intrusion monitoring is an essential area of intrusion detection investigation and development.Despite extensive research on anomaly-based network detection,there is still a lack of comprehensive literature reviews covering current methodologies and datasets.Despite the substantial research into anomaly-based network intrusion detection algorithms,there is a dearth of a research evaluation of new methodologies and datasets.We explore and evaluate 50 highest publications on anomaly-based intrusion detection using an in-depth review of related literature techniques.Our work thoroughly explores the technological environment of the subject in order to help future research in this sector.Our examination is carried out from the relevant angles:application areas,data preprocessing and threat detection approaches,assessment measures,and datasets.We select unresolved research difficulties and underexplored research areas from every viewpoint recommendation of the study.Finally,we outline five potentially increased research areas for the future.展开更多
This study investigates the corrosion inhibition potential of Datura stramonium seed extracts on mild steel in 1.0 mol·L^(-1)HCl and 0.5 mol·L^(-1)H_(2)SO_(4),utilizing both ethanolic and aqueous extracts as...This study investigates the corrosion inhibition potential of Datura stramonium seed extracts on mild steel in 1.0 mol·L^(-1)HCl and 0.5 mol·L^(-1)H_(2)SO_(4),utilizing both ethanolic and aqueous extracts as ecofriendly inhibitors.Electrochemical techniques,thermodynamic studies,and quantum chemical calculations were employed to evaluate the adsorption mechanism and inhibitory action at the metal/electrolyte interface.Maximum inhibition efficie ncies of 93.1%in HCl and 97.7%in H_(2)SO_(4) were achieved with the ethanolic extract at a concentration of 0.2 g·L^(-1),while the aqueous extract demonstrated 93.8%inhibition in HCl and 96.6%in H_(2)SO_(4).Polarization curves indicated mixed-type inhibition with a slight anodic bias.The thermodynamic analysis of two extracts in both environments indicated that the K_(ads)increased and that theΔG_(ads)were close to-40 kJ·mol^(-1),suggesting that the adsorption followed the Langmuir isotherm,indicating a combination of physical and chemical adsorption.SEM/EDX analysis confirmed the formation of a protective layer,while quantum chemical studies further validated strong adsorption,evidenced by a lowΔE of 2.396 eV and an adsorption energy of-878 kcal·mol^(-1)(1kcal·mol^(-1)=4.18 kJ·mol^(-1)).These results demonstrate that Datura stramonium extracts are promising inhibitors,particularly in sulfuric acid,for industrial applications.Reason:Improved clarity,vocabulary,and technical accuracy while maintaining the original meaning.展开更多
A conventional solid-state process was used to synthesize the double perovskite materials HoRCoMnO_(6)(R=Ho,Gd,Eu,Nd).The structural properties of the compounds were investigated using X-ray powder diffraction(XRD).Th...A conventional solid-state process was used to synthesize the double perovskite materials HoRCoMnO_(6)(R=Ho,Gd,Eu,Nd).The structural properties of the compounds were investigated using X-ray powder diffraction(XRD).The results revealed that Ho_(2)CoMnO_(6) crystallizes in a monoclinic structure with the P2_(1)/n space group.In contrast,the other compounds HoRCoMnO_(6)(R=Gd,Eu,or Nd) exhibit an orthorhombic structure with the Pnma space group.As a result,the average crystallite size also changes as a function of rare-earth element doping.This investigation reveals that the magnetic properties of the compounds studied are significantly dependent on the doping elements.The Curie temperature T_C,for example,increases from 80 to 118℃ with the ionic radii of rare earths increasing.Furthermore,the study of the magnetocaloric effect(MCE) shows that the maximum of the entropy variation(-ΔS_(M)^(max)) increases from 4.97 to 6.06 J/(kg·K) under a magnetic field of 5 T with substitution by rare-earth ions.To examine the efficiency of MCE materials,the relative cooling power(RCP) was evaluated and is found to increase with increment of rare-earth radius till 406.69 J/kg for Nd.The mean entropy variation with tempe rature(TEC) was also studied.Due to their significant magnetocaloric performance,HoRCoMnO_(6)(noted as HRCMO) compounds(with R=Ho,Gd,Eu or Nd) could be good candidates for low-temperature magnetic cooling applications.展开更多
The Moroccan automotive industry is experiencing steady growth,positioning itself as the largest manufacturer of passenger cars in Africa.This expansion is leading to a significant increase in waste generation,particu...The Moroccan automotive industry is experiencing steady growth,positioning itself as the largest manufacturer of passenger cars in Africa.This expansion is leading to a significant increase in waste generation,particularly from end-of-life vehicles(ELVs),which require proper dismantling and disposal to minimize environmental harm.Millions of tonnes of automotive waste are generated annually,necessitating efficient waste management strategies to mitigate environmental and health risks.ELVs contain hazardous substances such as heavy metals,oils,and plastics,which,if not properly managed,can contaminate soil and water resources.To address this challenge,reverse logistics networks play a crucial role in optimizing the recovery of used components,enhancing recycling efficiency,and ensuring the safe disposal of hazardous and non-recyclable waste.This paper introduces a mathematical programming model designed to minimize the total costs associated with ELVs collection,treatment,and transportation while also accounting for revenues from the resale of repaired,directly reusable,or recycled components.The proposed model determines the optimal locations for processing facilities and establishes efficient material flows within the reverse logistics network.By integrating economic and environmental considerations,this model supports the development of a sustainable and cost-effective automotive waste management system,ultimately contributing to a circular economy approach in the industry.展开更多
Sustainably managing vehicles at their end-of-life stage(ELVs)presents significant potential forresource recovery,effectively addressing resource scarcity through the closure of the material loop.While ELVs in countri...Sustainably managing vehicles at their end-of-life stage(ELVs)presents significant potential forresource recovery,effectively addressing resource scarcity through the closure of the material loop.While ELVs in countries like Morocco have traditionally been treated as waste rather than secondaryresource material(SRM),they have the potential to reduce reliance on primary materials when usedjudiciously.Despite policymakers aiming for increased resource efficiency in the automobile sector,there is limited research exploring the role of the informal sector in recovering materials and parts fromELVs.This study investigates the ELV processing scenario at Salmia scrap market,recognized as one of Africa’s largest informal markets for ELVs.Using a mass-balance approach,the disposal of sedan cars isexamined,and a conceptual framework illustrating the process flow and interactions among multiplestakeholders is developed.From sampled sedan cars,approximately 7% of aluminum and 76%of iron,by weight,are recovered.These findings contribute to estimating the potential for recycling andrecovering materials from ELVs processed by the informal sector in Morocco.In a standard operationalcontext,estimations suggest that the sector holds substantial potential to recover aluminum and iron by2030.This underscores the importance of formalizing operations and integrating informal players intothe value chain to effectively address resource scarcity within a circular economy.展开更多
The Mekkam inlier is located 50 km southeast of the town of Taourirt,in northeastern Morocco.It offers a great opportunity for the study of Variscan magmatism in Morocco.This inlier is punctuated by small magmatic bod...The Mekkam inlier is located 50 km southeast of the town of Taourirt,in northeastern Morocco.It offers a great opportunity for the study of Variscan magmatism in Morocco.This inlier is punctuated by small magmatic bodies which we will characterize through a petrographic and geochemical study to situate this inlier in its geotectonic context.The petrographic study revealed the existence of three trends:acidic,intermediate,and basic,which are represented by facies ranging from granites to basanites,including andesites,rhyolites,trachytes,dacites,quartz microdiorites,Aplite and microgranites.All these facies have a mineralogical assemblage dominated by quartz,plagioclase,oligoclase,potassium feldspar,pyroxene,and biotite;the most abundant accessory minerals are zircon and apatite.Green hornblende is found in microdiorites and dacites.The geochemical analysis,conducted through the examination of major elements,trace elements,and rare earth elements,has uncovered the presence of two distinct magmatic series:a calc-alkaline series of the island arc type or active continental margin,and another alkaline series of syn-collision.Based on this combined data,we propose that the Mekkam sector represents a magmatic arc developed within a compressional tectonic regime located above a subduction zone,which was later followed by an intracontinental collision phase.展开更多
In this paper,Isogeometric analysis(IGA)is effectively integrated with machine learning(ML)to investigate the bearing capacity of strip footings in layered soil profiles,with a focus on a sand-over-clay configuration....In this paper,Isogeometric analysis(IGA)is effectively integrated with machine learning(ML)to investigate the bearing capacity of strip footings in layered soil profiles,with a focus on a sand-over-clay configuration.The study begins with the generation of a comprehensive dataset of 10,000 samples from IGA upper bound(UB)limit analyses,facilitating an in-depth examination of various material and geometric conditions.A hybrid deep neural network,specifically the Whale Optimization Algorithm-Deep Neural Network(WOA-DNN),is then employed to utilize these 10,000 outputs for precise bearing capacity predictions.Notably,the WOA-DNN model outperforms conventional ML techniques,offering a robust and accurate prediction tool.This innovative approach explores a broad range of design parameters,including sand layer depth,load-to-soil unit weight ratio,internal friction angle,cohesion,and footing roughness.A detailed analysis of the dataset reveals the significant influence of these parameters on bearing capacity,providing valuable insights for practical foundation design.This research demonstrates the usefulness of data-driven techniques in optimizing the design of shallow foundations within layered soil profiles,marking a significant stride in geotechnical engineering advancements.展开更多
Accurate daily suspended sediment load(SSL)prediction is essential for sustainable water resource management,sediment control,and environmental planning.However,SSL prediction is highly complex due to its nonlinear an...Accurate daily suspended sediment load(SSL)prediction is essential for sustainable water resource management,sediment control,and environmental planning.However,SSL prediction is highly complex due to its nonlinear and dynamic nature,making traditional empirical models inadequate.This study proposes a novel hybrid approach,integrating the Adaptive Neuro-Fuzzy Inference System(ANFIS)with the Gradient-Based Optimizer(GBO),to enhance SSL forecasting accuracy.The research compares the performance of ANFIS-GBO with three alternative models:standard ANFIS,ANFIS with Particle Swarm Optimization(ANFIS-PSO),and ANFIS with Grey Wolf Optimization(ANFIS-GWO).Historical SSL and streamflow data from the Bailong River Basin,China,are used to train and validate the models.The input selection process is optimized using the Multivariate Adaptive Regression Splines(MARS)method.Model performance is evaluated using statistical metrics such as Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE),Nash Sutcliffe Efficiency(NSE),and Determination Coefficient(R^(2)).Additionally,visual assessments,including scatter plots,Taylor diagrams,and violin plots,provide further insights into model reliability.The results indicate that including historical SSL data improves predictive accuracy,with ANFIS-GBO outperforming the other models.ANFIS-GBO achieves the lowest RMSE and MAE and the highest NSE and R^(2),demonstrating its superior learning ability and adaptability.The findings highlight the effectiveness of nature-inspired optimization algorithms in enhancing sediment load forecasting and contribute to the advancement of AI-based hydrological modeling.Future research should explore the integration of additional environmental and climatic variables to enhance predictive capabilities further.展开更多
Floods and storm surges pose significant threats to coastal regions worldwide,demanding timely and accurate early warning systems(EWS)for disaster preparedness.Traditional numerical and statistical methods often fall ...Floods and storm surges pose significant threats to coastal regions worldwide,demanding timely and accurate early warning systems(EWS)for disaster preparedness.Traditional numerical and statistical methods often fall short in capturing complex,nonlinear,and real-time environmental dynamics.In recent years,machine learning(ML)and deep learning(DL)techniques have emerged as promising alternatives for enhancing the accuracy,speed,and scalability of EWS.This review critically evaluates the evolution of ML models—such as Artificial Neural Networks(ANN),Convolutional Neural Networks(CNN),and Long Short-Term Memory(LSTM)—in coastal flood prediction,highlighting their architectures,data requirements,performance metrics,and implementation challenges.A unique contribution of this work is the synthesis of real-time deployment challenges including latency,edge-cloud tradeoffs,and policy-level integration,areas often overlooked in prior literature.Furthermore,the review presents a comparative framework of model performance across different geographic and hydrologic settings,offering actionable insights for researchers and practitioners.Limitations of current AI-driven models,such as interpretability,data scarcity,and generalization across regions,are discussed in detail.Finally,the paper outlines future research directions including hybrid modelling,transfer learning,explainable AI,and policy-aware alert systems.By bridging technical performance and operational feasibility,this review aims to guide the development of next-generation intelligent EWS for resilient and adaptive coastal management.展开更多
Juniperus oxycedrus(J.oxycedrus)is a traditional culinary spice and medicinal herb with a longstanding history of ethnopharmacological applications across diverse cultures.While prior research has explored the biologi...Juniperus oxycedrus(J.oxycedrus)is a traditional culinary spice and medicinal herb with a longstanding history of ethnopharmacological applications across diverse cultures.While prior research has explored the biological activities and phytochemical constituents of extracts derived from its leaves and seed cones,the present study systematically investigates their mineral and phenolic profiles alongside their multifunctional bioactive potential.Inductively coupled plasma-atomic emission spectroscopy(ICP-AES)analysis revealed a substantial abundance of essential macro-and microelements.Reversed-phase high-performance liquid chromatography(RP-HPLC)further identified high concentrations of phenolic acids(e.g.,p-coumaric acid)and flavonoids(e.g.,rutin and quercetin).The extracts exhibited potent radical scavenging activity against 2,2-diphenyl-1-picrylhydrazyl(DPPH),robust antioxidant capacity against hydrogen peroxide,and significant inhibition of xanthine oxidase(XO)activity.Notably,both extracts demonstrated marked antibacterial efficacy.In silico molecular docking studies suggested that the antimicrobial activity may stem from the phenolic constituents,which exhibited favorable binding affinities to the active site of bacterial target proteins.These findings underscore J.oxycedrus as a promising reservoir of bioactive natural compounds,warranting further exploration for therapeutic and nutraceutical applications.展开更多
This study developed a novel heterogeneous Vis-Photo+Fenton-like system by integrating visible-light-responsive Co_(3)O_(4)/TiO_(2) photocatalysis with peroxymonosulfate(PMS)activation for efficient atrazine(ATZ)degra...This study developed a novel heterogeneous Vis-Photo+Fenton-like system by integrating visible-light-responsive Co_(3)O_(4)/TiO_(2) photocatalysis with peroxymonosulfate(PMS)activation for efficient atrazine(ATZ)degradation.The synergistic process achieved complete ATZ removal within 60 min under near-neutral pH(6.9),outperform-ing individual Fenton-like(39%)and photocatalytic(24%)processes.Key factors influencing the degradation efficiency included light sources(UV>visible),pH(optimal at 6.9),catalyst dosage(0.01 g Co_(3)O_(4)/TiO_(2)),and PMS:ATZ molar ratio(1:2).The system exhibited a synergistic coefficient of 5.03(degradation)and 1.97(miner-alization),attributed to enhanced radical generation and accelerated Co^(3+)/Co^(2+)redox cycling through photoin-duced electron transfer.Intermediate analysis revealed dealkylation,dechlorination,and oxidation pathways,with reduced toxicity of by-products(e.g.,CEAT,CIAT)confirmed by ecotoxicity assessments.The mineralization efficiency(Vis-Photo+Fenton-like)reached 83.1%,significantly higher than that of standalone processes(Fenton-like:43.2%;photocatalysis:30.5%).The catalyst demonstrated excellent stability(nearly 90%recov-ery,<1μg/L Co leaching)and practical applicability.This study provides an efficient,sludge-free,and solar-compatible strategy for eliminating persistent herbicides in water treatment.展开更多
Text-mining technologies have substantially affected financial industries.As the data in every sector of finance have grown immensely,text mining has emerged as an important field of research in the domain of finance....Text-mining technologies have substantially affected financial industries.As the data in every sector of finance have grown immensely,text mining has emerged as an important field of research in the domain of finance.Therefore,reviewing the recent literature on text-mining applications in finance can be useful for identifying areas for further research.This paper focuses on the text-mining literature related to financial forecasting,banking,and corporate finance.It also analyses the existing literature on text mining in financial applications and provides a summary of some recent studies.Finally,the paper briefly discusses various text-mining methods being applied in the financial domain,the challenges faced in these applications,and the future scope of text mining in finance.展开更多
The disadvantageous effects of colloidal SiO2 layer and micro-content of metal oxide adsorbed on SiC powder surface on SiC slurry stable dispersion were studied, and the novel method to avoid this disadvantage was pro...The disadvantageous effects of colloidal SiO2 layer and micro-content of metal oxide adsorbed on SiC powder surface on SiC slurry stable dispersion were studied, and the novel method to avoid this disadvantage was proposed. By acidwashing, on the one hand, because the maximum Zeta potential of SiC powder increases to 72.49 mV with the decreasing content of metal oxide adsorbed on the SiC powder surface, the repulsion force between SiC powders that dispersed in slurry is enhanced, thus the SiC powder can be fully dispersed in slurry. On the other hand, after HF acidwashing, with the OH^- group adsorbed on SiC powder surface destroyed and replaced by the Fion, the hydrogen bond adsorbed on the OHgroup is also destroyed. Therefore, the surface property of the SiC powder is changed from hydrophilic to hydrophobic; H2O that adsorbed on SiC powder surface is released and can flow freely, and it actually increases the content of the effective flow phase in the slurry. These changes of SiC powder surface property can be proved by XPS and FTIR analysis. Finally, the viscosity of SiC slurry is decreased greatly, and when the viscosity of the slurry is lower than 1 Pa·s, the solid volume fraction of SiC powder in the slurry is maximized to 61.5 vol.%.展开更多
Japanese larch is one of the main plantation tree species in China.A lack of engineered wood products made by Japanese larch,however,limits its application in wood stnuctures.In this study,based on optimum process par...Japanese larch is one of the main plantation tree species in China.A lack of engineered wood products made by Japanese larch,however,limits its application in wood stnuctures.In this study,based on optimum process parameters,such as pressure(12 MPa),adhesive spread rate(200 g/m^(2))and adhesive(one-component polyurethane),the mechanical properties of Japanese larch-made cross-laminated timber(CLT)with different lay-ups were evaluated by means of the static method.Results of this study showed that variations in lay-ups significantly affected the mechanical properties of CLT.The strength and modulus of bending and parallel compression for CLT increased with the thickness of lumber,while that of bending,parallel compression and rolling shear all decreased with the number of layers.Thickness,layup orientation and the number of layers all had an impact on the strength of CLT.Failure modes obtained from numerical simulation were basically the same as those of experimental tests.There was also strong alignment between theoretical value and test value for effective bending stifness and shear stifness.Thus,the shear analogy method can be used to predict the mechanical properties of CLT effectively.This study proved great potential in using Japanese larch wood for manufacturing CLT due to its good mechanical properties.展开更多
基金funded by the National Natural Science Foundation of China(Grant No.31770769)the National Key Research and Development Program of China(No.2017YFC0504403)the Fundamental Research Funds for the Central Universities(No.2015ZCQ-GX-03).
文摘This study investigated the feasibility of hyperspectral imaging techniques to estimate the vigor of heatdamaged Quercus variabilis seeds.Four thermal damage grades were classified according to heat treatment duration(0,2,5,and 10 h).After obtaining hyperspectral images with a 370–1042 nm hyperspectral imager that included visible and near infrared light,germination was tested to confirm estimates.The Savitzky–Golay(SG)second derivative was used to preprocess the spectrum to reduce any noise impact.The successive projections algorithm(SPA),principal component analysis,and local linear embedding algorithm were used to extract the characteristic spectral bands related to seed vigor.Finally,a model for seed vigor classifi-cation of Q.variabili s based on partial least squares support vector machine(LS-SVM)with different spectral data sets was developed.The results show that the spectrum after SG second derivative preprocessing was better for developing the model,and SPA performed the best among the three feature band selection methods.The combination SG second derivative-LS-SVM provided the best classification model for Q.variabilis seed vigor,with the prediction set reaching 98.81%.This study provides an important basis for rapid and nondestructive assessment of the vigor of heat-damaged seeds using hyperspectral imaging techniques.
文摘Supercapacitors are gaining popularity due to their high cycling stability,power density,and fast charge and discharge rates.Researchers are ex-ploring electrode materials,electrolytes,and separat-ors for cost-effective energy storage systems.Ad-vances in materials science have led to the develop-ment of hybrid nanomaterials,such as combining fil-amentous carbon forms with inorganic nanoparticles,to create new charge and energy transfer processes.Notable materials for electrochemical energy-stor-age applications include MXenes,2D transition met-al carbides,and nitrides,carbon black,carbon aerogels,activated carbon,carbon nanotubes,conducting polymers,carbon fibers,and nanofibers,and graphene,because of their thermal,electrical,and mechanical properties.Carbon materials mixed with conducting polymers,ceramics,metal oxides,transition metal oxides,metal hydroxides,transition metal sulfides,trans-ition metal dichalcogenide,metal sulfides,carbides,nitrides,and biomass materials have received widespread attention due to their remarkable performance,eco-friendliness,cost-effectiveness,and renewability.This article explores the development of carbon-based hybrid materials for future supercapacitors,including electric double-layer capacitors,pseudocapacitors,and hy-brid supercapacitors.It investigates the difficulties that influence structural design,manufacturing(electrospinning,hydro-thermal/solvothermal,template-assisted synthesis,electrodeposition,electrospray,3D printing)techniques and the latest car-bon-based hybrid materials research offer practical solutions for producing high-performance,next-generation supercapacitors.
基金supported by the National Natural Science Foundation of China(32171797)Chunhui Project Foundation of the Education Department of China(HZKY20220026).
文摘With rapid urbanization,fires pose significant challenges in urban governance.Traditional fire detection methods often struggle to detect smoke in complex urban scenes due to environmental interferences and variations in viewing angles.This study proposes a novel multimodal smoke detection method that fuses infrared and visible imagery using a transformer-based deep learning model.By capturing both thermal and visual cues,our approach significantly enhances the accuracy and robustness of smoke detection in business parks scenes.We first established a dual-view dataset comprising infrared and visible light videos,implemented an innovative image feature fusion strategy,and designed a deep learning model based on the transformer architecture and attention mechanism for smoke classification.Experimental results demonstrate that our method outperforms existing methods,under the condition of multi-view input,it achieves an accuracy rate of 90.88%,precision rate of 98.38%,recall rate of 92.41%and false positive and false negative rates both below 5%,underlining the effectiveness of the proposed multimodal and multi-view fusion approach.The attention mechanism plays a crucial role in improving detection performance,particularly in identifying subtle smoke features.
文摘The fact that Morocco is an agricultural country and the large volume of biodegradable waste produced by the population make composting so important.The degradation of organic matter is facilitated by faunal and floral macro and micro-organisms that act in different stages of maturation;studies on this fauna are quite rare both nationally and internationally.On a sample of two tons of household waste,we documented invertebrates that colonized compost heaps and then assessed the changes in the structure of the invertebrate population during the different phases.Our study revealed the presence of several zoological groups colonizing the compost heaps during the different composting phases;we noted the presence of:(1)Macroscopic invertebrates,in order of number of individuals:insect larvae,ants,earthworms,sowbugs,spiders,springtails,and millipedes,and(2)Microscopic invertebrates,the most abundant in terms of individuals:mites and nematodes.As for the order of appearance,we observed that insect larvae were the first to colonize the compost heap from the very first days of installation,followed by woodlice observed during the thermophilic phase and disappearing towards the end of the process.Earthworms were observed during the end of the thermophilic phase,while springtails were observed more during the cooling and maturation phases.Our study revealed the presence of a good quality of fauna during the composting process,which are indicators of good compost quality and play a major role in the circulation of nutrients,thus ensuring the provision of essential elements for plant nutrition.
文摘In this paper,the isogeometric analysis(IGA)method is employed to analyze the oscillation characteristics of functionally graded triply periodic minimal surface(FG-TPMS)curved-doubly shells integrated with magneto-electric surface layers(referred to as"FG-TPMS-MEE curved-doubly shells")subjected to low-velocity impact loads.This study presents low-velocity impact load model based on a single springmass(S-M)approach.The FG-TPMS-MEE curved-doubly shells are covered with two magneto-electric surface layers,while the core layer consists of three types:I-graph and Wrapped Package-graph(IWP),Gyroid(G),and Primitive(P),with various graded functions.These types are notable for their exceptional stiffness-to-weight ratios,enabling a wide range of potential applications.The Maxwell equations and electromagnetic boundary conditions are applied to compute the change in electric potentials and magnetic potentials.The equilibrium equations of the shell are derived from a refined higher-order shear deformation theory(HSDT),and the transient responses of the FG-TPMS-MEE curveddoubly shells are subsequently determined using Newmark's direct integration method.These results have applications in structural vibration control and the analysis of structures subjected to impact or explosive loads.Furthermore,this study provides a theoretical prediction of the low-velocity impact load and magneto-electric-elastic effects on the free vibration and transient response of FG-TPMS-MEE curved-doubly shells.
文摘Pill image recognition is an important field in computer vision.It has become a vital technology in healthcare and pharmaceuticals due to the necessity for precise medication identification to prevent errors and ensure patient safety.This survey examines the current state of pill image recognition,focusing on advancements,methodologies,and the challenges that remain unresolved.It provides a comprehensive overview of traditional image processing-based,machine learning-based,deep learning-based,and hybrid-based methods,and aims to explore the ongoing difficulties in the field.We summarize and classify the methods used in each article,compare the strengths and weaknesses of traditional image processing-based,machine learning-based,deep learning-based,and hybrid-based methods,and review benchmark datasets for pill image recognition.Additionally,we compare the performance of proposed methods on popular benchmark datasets.This survey applies recent advancements,such as Transformer models and cutting-edge technologies like Augmented Reality(AR),to discuss potential research directions and conclude the review.By offering a holistic perspective,this paper aims to serve as a valuable resource for researchers and practitioners striving to advance the field of pill image recognition.
文摘Attacks are growing more complex and dangerous as network capabilities improve at a rapid pace.Network intrusion detection is usually regarded as an efficient means of dealing with security attacks.Many ways have been presented,utilizing various strategies and focusing on different types of visitors.Anomaly-based network intrusion monitoring is an essential area of intrusion detection investigation and development.Despite extensive research on anomaly-based network detection,there is still a lack of comprehensive literature reviews covering current methodologies and datasets.Despite the substantial research into anomaly-based network intrusion detection algorithms,there is a dearth of a research evaluation of new methodologies and datasets.We explore and evaluate 50 highest publications on anomaly-based intrusion detection using an in-depth review of related literature techniques.Our work thoroughly explores the technological environment of the subject in order to help future research in this sector.Our examination is carried out from the relevant angles:application areas,data preprocessing and threat detection approaches,assessment measures,and datasets.We select unresolved research difficulties and underexplored research areas from every viewpoint recommendation of the study.Finally,we outline five potentially increased research areas for the future.
基金supported by the NRF(National Research Foundation)of Koreafunded by the Basic Science Research Program through the Ministry of Education(2020R1I1A3052258)carried out with the support of the“2024 System Semiconductor Technology Development Support Project”of Chungbuk Technopark。
文摘This study investigates the corrosion inhibition potential of Datura stramonium seed extracts on mild steel in 1.0 mol·L^(-1)HCl and 0.5 mol·L^(-1)H_(2)SO_(4),utilizing both ethanolic and aqueous extracts as ecofriendly inhibitors.Electrochemical techniques,thermodynamic studies,and quantum chemical calculations were employed to evaluate the adsorption mechanism and inhibitory action at the metal/electrolyte interface.Maximum inhibition efficie ncies of 93.1%in HCl and 97.7%in H_(2)SO_(4) were achieved with the ethanolic extract at a concentration of 0.2 g·L^(-1),while the aqueous extract demonstrated 93.8%inhibition in HCl and 96.6%in H_(2)SO_(4).Polarization curves indicated mixed-type inhibition with a slight anodic bias.The thermodynamic analysis of two extracts in both environments indicated that the K_(ads)increased and that theΔG_(ads)were close to-40 kJ·mol^(-1),suggesting that the adsorption followed the Langmuir isotherm,indicating a combination of physical and chemical adsorption.SEM/EDX analysis confirmed the formation of a protective layer,while quantum chemical studies further validated strong adsorption,evidenced by a lowΔE of 2.396 eV and an adsorption energy of-878 kcal·mol^(-1)(1kcal·mol^(-1)=4.18 kJ·mol^(-1)).These results demonstrate that Datura stramonium extracts are promising inhibitors,particularly in sulfuric acid,for industrial applications.Reason:Improved clarity,vocabulary,and technical accuracy while maintaining the original meaning.
文摘A conventional solid-state process was used to synthesize the double perovskite materials HoRCoMnO_(6)(R=Ho,Gd,Eu,Nd).The structural properties of the compounds were investigated using X-ray powder diffraction(XRD).The results revealed that Ho_(2)CoMnO_(6) crystallizes in a monoclinic structure with the P2_(1)/n space group.In contrast,the other compounds HoRCoMnO_(6)(R=Gd,Eu,or Nd) exhibit an orthorhombic structure with the Pnma space group.As a result,the average crystallite size also changes as a function of rare-earth element doping.This investigation reveals that the magnetic properties of the compounds studied are significantly dependent on the doping elements.The Curie temperature T_C,for example,increases from 80 to 118℃ with the ionic radii of rare earths increasing.Furthermore,the study of the magnetocaloric effect(MCE) shows that the maximum of the entropy variation(-ΔS_(M)^(max)) increases from 4.97 to 6.06 J/(kg·K) under a magnetic field of 5 T with substitution by rare-earth ions.To examine the efficiency of MCE materials,the relative cooling power(RCP) was evaluated and is found to increase with increment of rare-earth radius till 406.69 J/kg for Nd.The mean entropy variation with tempe rature(TEC) was also studied.Due to their significant magnetocaloric performance,HoRCoMnO_(6)(noted as HRCMO) compounds(with R=Ho,Gd,Eu or Nd) could be good candidates for low-temperature magnetic cooling applications.
文摘The Moroccan automotive industry is experiencing steady growth,positioning itself as the largest manufacturer of passenger cars in Africa.This expansion is leading to a significant increase in waste generation,particularly from end-of-life vehicles(ELVs),which require proper dismantling and disposal to minimize environmental harm.Millions of tonnes of automotive waste are generated annually,necessitating efficient waste management strategies to mitigate environmental and health risks.ELVs contain hazardous substances such as heavy metals,oils,and plastics,which,if not properly managed,can contaminate soil and water resources.To address this challenge,reverse logistics networks play a crucial role in optimizing the recovery of used components,enhancing recycling efficiency,and ensuring the safe disposal of hazardous and non-recyclable waste.This paper introduces a mathematical programming model designed to minimize the total costs associated with ELVs collection,treatment,and transportation while also accounting for revenues from the resale of repaired,directly reusable,or recycled components.The proposed model determines the optimal locations for processing facilities and establishes efficient material flows within the reverse logistics network.By integrating economic and environmental considerations,this model supports the development of a sustainable and cost-effective automotive waste management system,ultimately contributing to a circular economy approach in the industry.
文摘Sustainably managing vehicles at their end-of-life stage(ELVs)presents significant potential forresource recovery,effectively addressing resource scarcity through the closure of the material loop.While ELVs in countries like Morocco have traditionally been treated as waste rather than secondaryresource material(SRM),they have the potential to reduce reliance on primary materials when usedjudiciously.Despite policymakers aiming for increased resource efficiency in the automobile sector,there is limited research exploring the role of the informal sector in recovering materials and parts fromELVs.This study investigates the ELV processing scenario at Salmia scrap market,recognized as one of Africa’s largest informal markets for ELVs.Using a mass-balance approach,the disposal of sedan cars isexamined,and a conceptual framework illustrating the process flow and interactions among multiplestakeholders is developed.From sampled sedan cars,approximately 7% of aluminum and 76%of iron,by weight,are recovered.These findings contribute to estimating the potential for recycling andrecovering materials from ELVs processed by the informal sector in Morocco.In a standard operationalcontext,estimations suggest that the sector holds substantial potential to recover aluminum and iron by2030.This underscores the importance of formalizing operations and integrating informal players intothe value chain to effectively address resource scarcity within a circular economy.
文摘The Mekkam inlier is located 50 km southeast of the town of Taourirt,in northeastern Morocco.It offers a great opportunity for the study of Variscan magmatism in Morocco.This inlier is punctuated by small magmatic bodies which we will characterize through a petrographic and geochemical study to situate this inlier in its geotectonic context.The petrographic study revealed the existence of three trends:acidic,intermediate,and basic,which are represented by facies ranging from granites to basanites,including andesites,rhyolites,trachytes,dacites,quartz microdiorites,Aplite and microgranites.All these facies have a mineralogical assemblage dominated by quartz,plagioclase,oligoclase,potassium feldspar,pyroxene,and biotite;the most abundant accessory minerals are zircon and apatite.Green hornblende is found in microdiorites and dacites.The geochemical analysis,conducted through the examination of major elements,trace elements,and rare earth elements,has uncovered the presence of two distinct magmatic series:a calc-alkaline series of the island arc type or active continental margin,and another alkaline series of syn-collision.Based on this combined data,we propose that the Mekkam sector represents a magmatic arc developed within a compressional tectonic regime located above a subduction zone,which was later followed by an intracontinental collision phase.
文摘In this paper,Isogeometric analysis(IGA)is effectively integrated with machine learning(ML)to investigate the bearing capacity of strip footings in layered soil profiles,with a focus on a sand-over-clay configuration.The study begins with the generation of a comprehensive dataset of 10,000 samples from IGA upper bound(UB)limit analyses,facilitating an in-depth examination of various material and geometric conditions.A hybrid deep neural network,specifically the Whale Optimization Algorithm-Deep Neural Network(WOA-DNN),is then employed to utilize these 10,000 outputs for precise bearing capacity predictions.Notably,the WOA-DNN model outperforms conventional ML techniques,offering a robust and accurate prediction tool.This innovative approach explores a broad range of design parameters,including sand layer depth,load-to-soil unit weight ratio,internal friction angle,cohesion,and footing roughness.A detailed analysis of the dataset reveals the significant influence of these parameters on bearing capacity,providing valuable insights for practical foundation design.This research demonstrates the usefulness of data-driven techniques in optimizing the design of shallow foundations within layered soil profiles,marking a significant stride in geotechnical engineering advancements.
基金supported by the National Natural Science Foundation of China(52350410465)the General Projects of Guangdong Natural Science Research Projects(2023A1515011520).
文摘Accurate daily suspended sediment load(SSL)prediction is essential for sustainable water resource management,sediment control,and environmental planning.However,SSL prediction is highly complex due to its nonlinear and dynamic nature,making traditional empirical models inadequate.This study proposes a novel hybrid approach,integrating the Adaptive Neuro-Fuzzy Inference System(ANFIS)with the Gradient-Based Optimizer(GBO),to enhance SSL forecasting accuracy.The research compares the performance of ANFIS-GBO with three alternative models:standard ANFIS,ANFIS with Particle Swarm Optimization(ANFIS-PSO),and ANFIS with Grey Wolf Optimization(ANFIS-GWO).Historical SSL and streamflow data from the Bailong River Basin,China,are used to train and validate the models.The input selection process is optimized using the Multivariate Adaptive Regression Splines(MARS)method.Model performance is evaluated using statistical metrics such as Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE),Nash Sutcliffe Efficiency(NSE),and Determination Coefficient(R^(2)).Additionally,visual assessments,including scatter plots,Taylor diagrams,and violin plots,provide further insights into model reliability.The results indicate that including historical SSL data improves predictive accuracy,with ANFIS-GBO outperforming the other models.ANFIS-GBO achieves the lowest RMSE and MAE and the highest NSE and R^(2),demonstrating its superior learning ability and adaptability.The findings highlight the effectiveness of nature-inspired optimization algorithms in enhancing sediment load forecasting and contribute to the advancement of AI-based hydrological modeling.Future research should explore the integration of additional environmental and climatic variables to enhance predictive capabilities further.
文摘Floods and storm surges pose significant threats to coastal regions worldwide,demanding timely and accurate early warning systems(EWS)for disaster preparedness.Traditional numerical and statistical methods often fall short in capturing complex,nonlinear,and real-time environmental dynamics.In recent years,machine learning(ML)and deep learning(DL)techniques have emerged as promising alternatives for enhancing the accuracy,speed,and scalability of EWS.This review critically evaluates the evolution of ML models—such as Artificial Neural Networks(ANN),Convolutional Neural Networks(CNN),and Long Short-Term Memory(LSTM)—in coastal flood prediction,highlighting their architectures,data requirements,performance metrics,and implementation challenges.A unique contribution of this work is the synthesis of real-time deployment challenges including latency,edge-cloud tradeoffs,and policy-level integration,areas often overlooked in prior literature.Furthermore,the review presents a comparative framework of model performance across different geographic and hydrologic settings,offering actionable insights for researchers and practitioners.Limitations of current AI-driven models,such as interpretability,data scarcity,and generalization across regions,are discussed in detail.Finally,the paper outlines future research directions including hybrid modelling,transfer learning,explainable AI,and policy-aware alert systems.By bridging technical performance and operational feasibility,this review aims to guide the development of next-generation intelligent EWS for resilient and adaptive coastal management.
文摘Juniperus oxycedrus(J.oxycedrus)is a traditional culinary spice and medicinal herb with a longstanding history of ethnopharmacological applications across diverse cultures.While prior research has explored the biological activities and phytochemical constituents of extracts derived from its leaves and seed cones,the present study systematically investigates their mineral and phenolic profiles alongside their multifunctional bioactive potential.Inductively coupled plasma-atomic emission spectroscopy(ICP-AES)analysis revealed a substantial abundance of essential macro-and microelements.Reversed-phase high-performance liquid chromatography(RP-HPLC)further identified high concentrations of phenolic acids(e.g.,p-coumaric acid)and flavonoids(e.g.,rutin and quercetin).The extracts exhibited potent radical scavenging activity against 2,2-diphenyl-1-picrylhydrazyl(DPPH),robust antioxidant capacity against hydrogen peroxide,and significant inhibition of xanthine oxidase(XO)activity.Notably,both extracts demonstrated marked antibacterial efficacy.In silico molecular docking studies suggested that the antimicrobial activity may stem from the phenolic constituents,which exhibited favorable binding affinities to the active site of bacterial target proteins.These findings underscore J.oxycedrus as a promising reservoir of bioactive natural compounds,warranting further exploration for therapeutic and nutraceutical applications.
基金supported by the Financial Supports of the National Natural Science Foundation of China(Nos.51508056,52370030 and 42007352)the Chongqing Postgraduate Joint Training Base Project(No.JDLHPYJD2022005)the special fund of Henan Key Labora-tory of Water Pollution Control and Rehabilitation Technology(No.CJSZ2024001).
文摘This study developed a novel heterogeneous Vis-Photo+Fenton-like system by integrating visible-light-responsive Co_(3)O_(4)/TiO_(2) photocatalysis with peroxymonosulfate(PMS)activation for efficient atrazine(ATZ)degradation.The synergistic process achieved complete ATZ removal within 60 min under near-neutral pH(6.9),outperform-ing individual Fenton-like(39%)and photocatalytic(24%)processes.Key factors influencing the degradation efficiency included light sources(UV>visible),pH(optimal at 6.9),catalyst dosage(0.01 g Co_(3)O_(4)/TiO_(2)),and PMS:ATZ molar ratio(1:2).The system exhibited a synergistic coefficient of 5.03(degradation)and 1.97(miner-alization),attributed to enhanced radical generation and accelerated Co^(3+)/Co^(2+)redox cycling through photoin-duced electron transfer.Intermediate analysis revealed dealkylation,dechlorination,and oxidation pathways,with reduced toxicity of by-products(e.g.,CEAT,CIAT)confirmed by ecotoxicity assessments.The mineralization efficiency(Vis-Photo+Fenton-like)reached 83.1%,significantly higher than that of standalone processes(Fenton-like:43.2%;photocatalysis:30.5%).The catalyst demonstrated excellent stability(nearly 90%recov-ery,<1μg/L Co leaching)and practical applicability.This study provides an efficient,sludge-free,and solar-compatible strategy for eliminating persistent herbicides in water treatment.
文摘Text-mining technologies have substantially affected financial industries.As the data in every sector of finance have grown immensely,text mining has emerged as an important field of research in the domain of finance.Therefore,reviewing the recent literature on text-mining applications in finance can be useful for identifying areas for further research.This paper focuses on the text-mining literature related to financial forecasting,banking,and corporate finance.It also analyses the existing literature on text mining in financial applications and provides a summary of some recent studies.Finally,the paper briefly discusses various text-mining methods being applied in the financial domain,the challenges faced in these applications,and the future scope of text mining in finance.
基金This work was financially supported by the Doctoral Foundation of Xi'an Jiaotong University (No. DFXJTU2004-04).
文摘The disadvantageous effects of colloidal SiO2 layer and micro-content of metal oxide adsorbed on SiC powder surface on SiC slurry stable dispersion were studied, and the novel method to avoid this disadvantage was proposed. By acidwashing, on the one hand, because the maximum Zeta potential of SiC powder increases to 72.49 mV with the decreasing content of metal oxide adsorbed on the SiC powder surface, the repulsion force between SiC powders that dispersed in slurry is enhanced, thus the SiC powder can be fully dispersed in slurry. On the other hand, after HF acidwashing, with the OH^- group adsorbed on SiC powder surface destroyed and replaced by the Fion, the hydrogen bond adsorbed on the OHgroup is also destroyed. Therefore, the surface property of the SiC powder is changed from hydrophilic to hydrophobic; H2O that adsorbed on SiC powder surface is released and can flow freely, and it actually increases the content of the effective flow phase in the slurry. These changes of SiC powder surface property can be proved by XPS and FTIR analysis. Finally, the viscosity of SiC slurry is decreased greatly, and when the viscosity of the slurry is lower than 1 Pa·s, the solid volume fraction of SiC powder in the slurry is maximized to 61.5 vol.%.
基金by basic operating budget of scientific research institutes for public welfare at the central level(CAFBB2018SY032)China Postdoctoral Science Foundation (No.2018M641225).
文摘Japanese larch is one of the main plantation tree species in China.A lack of engineered wood products made by Japanese larch,however,limits its application in wood stnuctures.In this study,based on optimum process parameters,such as pressure(12 MPa),adhesive spread rate(200 g/m^(2))and adhesive(one-component polyurethane),the mechanical properties of Japanese larch-made cross-laminated timber(CLT)with different lay-ups were evaluated by means of the static method.Results of this study showed that variations in lay-ups significantly affected the mechanical properties of CLT.The strength and modulus of bending and parallel compression for CLT increased with the thickness of lumber,while that of bending,parallel compression and rolling shear all decreased with the number of layers.Thickness,layup orientation and the number of layers all had an impact on the strength of CLT.Failure modes obtained from numerical simulation were basically the same as those of experimental tests.There was also strong alignment between theoretical value and test value for effective bending stifness and shear stifness.Thus,the shear analogy method can be used to predict the mechanical properties of CLT effectively.This study proved great potential in using Japanese larch wood for manufacturing CLT due to its good mechanical properties.
