Carbon-based flexible self-supporting cathode for lithium-sulfur batteries:Progress and perspective

The flexible self-supporting electrode can maintain good mechanical and electrical properties while retaining high specific capacity,which meets the requirements of flexible batteries.Lithium-sulfur batteries(LSBs),as a new generation of energy storage system,hold much higher theoretical energy density than traditional batteries,and they have attracted extensive attention from both the academic and industrial communities.Selection of a proper substrate material is important for the flexible self-supporting electrode.Carbon materials,with the advantages of light weight,high conductivity,strong structural plasticity,and low cost,provide the electrode with a large loading space for the active material and a conductive network.This makes the carbon materials meet the mechanical and electrochemical requirements of flexible electrodes.In this paper,the commonly used fabrication methods and recent research progresses of the flexible self-supporting cathode with a carbon material as the substrate are introduced.Various sulfur loading methods are summarized,which provides useful information for the structural design of the cathode.As the first review article of the carbon-based flexible self-supporting LSB cathodes,it provides valuable guidance for the researchers working in the field of LSB.

Self-supporting and hierarchically porous Ni_(x)Fe-S/NiFe_(2)O_(4) heterostructure as a bifunctional electrocatalyst for fluctuating overall water splitting

Stable non-noble metal bifunctional electrocatalysts are one of the challenges to the fluctuating overall water splitting driven by re-newable energy.Herein,a novel self-supporting hierarchically porous Ni_(x)Fe-S/NiFe_(2)O_(4) heterostructure as bifunctional electrocatalyst was constructed based on porous Ni-Fe electrodeposition on three-dimensional(3D)carbon fiber cloth,in situ oxidation,and chemical sulfuration.Results showed that the Ni_(x)Fe-S/NiFe_(2)O_(4) heterostructure with a large specific surface area exhibits good bifunctional activity and stability for both hydrogen evolution reaction(HER)and oxygen evolution reaction(OER)because of the abundance of active sites,synergistic effect of the heterostructure,superhydrophilic surface,and stable,self-supporting structure.The results further confirmed that the Ni_(x)Fe-S phase in the heterostructure is transformed into metal oxides/hydroxides and Ni_(3)S_(2) during OER.Compared with the commercial 20wt%Pt/C||IrO_(2)-Ta_(2)O_(5) electrolyzer,the self-supporting Ni1/5Fe-S/NiFe_(2)O_(4)||Ni1/2Fe-S/NiFe_(2)O_(4) electrolyzer exhibits better stability and lower cell voltage in the fluctu-ating current density range of 10-500 mA/cm^(2).Particularly,the cell voltage of Ni1/5Fe-S/NiFe_(2)O_(4)||Ni1/2Fe-S/NiFe_(2)O_(4) is only approximately 3.91 V at an industrial current density of 500 mA/cm^(2),which is lower than that of the 20wt%Pt/C||IrO_(2)-Ta_(2)O_(5) electrolyzer(i.e.,approximately 4.79 V).This work provides a promising strategy to develop excellent bifunctional electrocatalysts for fluctuating overall water splitting.

Self-supporting NiFe LDH-MoS_(x) integrated electrode for highly efficient water splitting at the industrial electrolysis conditions

Developing effective and practical electrocatalyst under industrial electrolysis conditions is critical for renewable hydrogen production.Herein,we report the self-supporting NiFe LDH-MoS_(x) integrated electrode for water oxidation under normal alkaline test condition(1 M KOH at 25℃)and simulated industrial electrolysis conditions(5 M KOH at 65℃).Such optimized electrode exhibits excellent oxygen evolution reaction(OER)performance with overpotential of 195 and 290 mV at current density of 100 and 400 mA·cm^(-2) under normal alkaline test condition.Notably,only over-potential of 156 and 201 mV were required to achieve the current density of 100 and 400mA·cm^(-2) under simulated industrial electrolysis conditions.No significant degradations were observed after long-term durability tests for both conditions.When using in two-electrode system,the operational voltages of 1.44 and 1.72 V were required to achieve a current density of 10 and 100 mA·cm^(-2) for the overall water splitting test(NiFe LDH-MoS_(x)/INF||20%Pt/C).Additionally,the operational voltage of employing NiFe LDH-MoS_(x)/INF as both cathode and anode merely require 1.52 V at 50mA·cm^(-2) at simulated industrial electrolysis conditions.Notably,a membrane electrode assembly(MEA)for anion exchange membrane water electrolysis(AEMWEs)using NiFe LDH-MoS_(x)/INF as an anode catalyst exhibited an energy conversion efficiency of 71.8%at current density of 400 mA·cm^(-2)in 1 M KOH at 60℃.Further experimental results reveal that sulfurized substrate not only improved the conductivity of NiFe LDH,but also regulated its electronic configurations and atomic composition,leading to the excellent activity.The easy-obtained and cost-effective integrated electrodes are expected to meet the large-scale application of industrial water electrolysis.

Topology Optimization of Self-Supporting Structures for Additive Manufacturing with Adaptive Explicit Continuous Constraint

The integration of topology optimization(TO)and additive manufacturing(AM)technologies can create significant synergy benefits,while the lack of AM-friendly TO algorithms is a serious bottleneck for the application of TO in AM.In this paper,a TO method is proposed to design self-supporting structures with an explicit continuous self-supporting constraint,which can be adaptively activated and tightened during the optimization procedure.The TO procedure is suitable for various critical overhang angles(COA),which is integrated with build direction assignment to reduce performance loss.Besides,a triangular directional self-supporting constraint sensitivity filter is devised to promote the downward evolution of structures and maintain stability.Two numerical examples are presented;all the test cases have successfully converged and the optimized solutions demonstrate good manufacturability.In the meanwhile,a fully self-supporting design can be obtained with a slight cost in performance through combination with build direction assignment.

Underwater four-quadrant dual-beam circumferential scanning laser fuze using nonlinear adaptive backscatter filter based on pauseable SAF-LMS algorithm

The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant dual-beam circumferential scanning laser fuze to distinguish various interference signals and provide more real-time data for the backscatter filtering algorithm.This enhances the algorithm loading capability of the fuze.In order to address the problem of insufficient filtering capacity in existing linear backscatter filtering algorithms,we develop a nonlinear backscattering adaptive filter based on the spline adaptive filter least mean square(SAF-LMS)algorithm.We also designed an algorithm pause module to retain the original trend of the target echo peak,improving the time discrimination accuracy and anti-interference capability of the fuze.Finally,experiments are conducted with varying signal-to-noise ratios of the original underwater target echo signals.The experimental results show that the average signal-to-noise ratio before and after filtering can be improved by more than31 d B,with an increase of up to 76%in extreme detection distance.

Research on self-supporting T-shaped gate structure of GaN-based HEMT devices

A self-supporting T-shaped gate(SST-gate) GaN device and process method using electron beam lithography are proposed.An AlGaN/GaN high-electron-mobility transistor(HEMT) device with a gate length of 100 nm is fabricated by this method.The current gain cutoff frequency(f_(T)) is 60 GHz,and the maximum oscillation frequency(f_(max)) is 104 GHz.The current collapse has improved by 13% at static bias of(V_(GSQ),V_(DSQ))=(-8 V,10 V),and gate manufacturing yield has improved by 17% compared with the traditional floating T-shaped gate(FT-gate) device.

Self-supporting and dual-active 3D Co-S nanosheets constructed by ligand replacement reaction from MOF for rechargeable Al battery

Metal sulfides with high theoretical capacities are expected as promising cathode materials of Al batteries(AIBs). However, powdery active materials are mainly synthesized and loaded on current collector by insulating binder without capacity. Meanwhile, S as inert element in metal sulfides can not usually provide capacity. So, powdery metal sulfides only exhibit limiting practical capacity and poor cycling stability due to weak conductivity and low mass utilization. Herein, the novel self-supporting and dual-active Co-S nanosheets on carbon cloth (i.e. Co-S/CC) with hierarchically porous structure are constructed as cathode of AIBs. Co-S nanosheets are derived from ZIF-67 nanosheets on CC by a facile ligand replacement reaction. As a result, the binder-free Co-S/CC cathode with good conductivity delivers excellent initial discharge capacity of 383.4 m Ah g^(-1)(0.211 m Ah cm^(-2)) at current density of 200 m A g^(-1)and maintain reversible capacity of 156.9 m Ah g^(-1)(0.086 m Ah cm^(-2)) with Coulombic efficiency of 95.8% after 500 cycles,which are much higher than those of the traditional slurry-coating cathodes. Both Co and S as active elements in Co-S/CC contribute to capacity, which leads to a high mass utilization. This work provides a significant strategy for the construction of self-supporting metallic cathode for advanced high-energy density Al battery.

Recent progress of self-supported air electrodes for flexible Zn-air batteries

Smart wearable devices are regarded to be the next prevailing technology product after smartphones and smart homes,and thus there has recently been rapid development in flexible electronic energy storage devices.Among them,flexible solid-state zinc-air batteries have received widespread attention because of their high energy density,good safety,and stability.Efficient bifunctional oxygen electrocatalysts are the primary consideration in the development of flexible solid-state zinc-air batteries,and self-supported air cathodes are strong candidates because of their advantages including simplified fabrication process,reduced interfacial resistance,accelerated electron transfer,and good flexibility.This review outlines the research progress in the design and construction of nanoarray bifunctional oxygen electrocatalysts.Starting from the configuration and basic principles of zinc-air batteries and the strategies for the design of bifunctional oxygen electrocatalysts,a detailed discussion of self-supported air cathodes on carbon and metal substrates and their uses in flexible zinc-air batteries will follow.Finally,the challenges and opportunities in the development of flexible zinc-air batteries will be discussed.

MCWOA Scheduler:Modified Chimp-Whale Optimization Algorithm for Task Scheduling in Cloud Computing

Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud platforms.However,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing times.The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution space.Moreover,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating process.This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model.Simulated outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection scenarios.MCWOA excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage detection.The efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and delay.The simulated data indicates that the new MCWOA outpaces other methods across all metrics.The study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).

Synthesis of Zeolite LTN-(SOD) Self-Supporting Membranes from SiO2-Rich Industrial Waste

Synthesis of zeolite LTN (“Linde Type N”) was investigated under insertion of a SiO2-rich filtration residue (FR) from waste water cleaning of the silane production. A new synthesis procedure was therefore developed applying a flotation mechanism with the aim to grow LTN in form of thin membrane like sheets. Preparation starts with preactivation of FR by slurrying first in alkaline solution, followed by an addition of aluminate solution and citric acid. The latter was added as suitable chelating agent for the initiation of the flotation process. In the course of these experiments, we succeeded in synthesizing zeo-lite LTN with more or less zeolite SOD as byproduct in the form of a stable compact membrane-like layer at low temperature of 60℃. The crystallization was performed under isotherm static conditions in an open reaction system without addition of organic templates as structure directing agents (OSDA’s). FR was utilized as a total substitute of sodium silicate in all experiments and an expansive pre-treatment procedure like calcinations was not needed. Furthermore, membrane formation with LTN of usual synthesis needs chemically functionalized supports. In contrast self-supporting membranous LTN layers were grown for the first time in the present study.

Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

Rao Algorithms-Based Structure Optimization for Heterogeneous Wireless Sensor Networks

The structural optimization of wireless sensor networks is a critical issue because it impacts energy consumption and hence the network’s lifetime.Many studies have been conducted for homogeneous networks,but few have been performed for heterogeneouswireless sensor networks.This paper utilizes Rao algorithms to optimize the structure of heterogeneous wireless sensor networks according to node locations and their initial energies.The proposed algorithms lack algorithm-specific parameters and metaphorical connotations.The proposed algorithms examine the search space based on the relations of the population with the best,worst,and randomly assigned solutions.The proposed algorithms can be evaluated using any routing protocol,however,we have chosen the well-known routing protocols in the literature:Low Energy Adaptive Clustering Hierarchy(LEACH),Power-Efficient Gathering in Sensor Information Systems(PEAGSIS),Partitioned-based Energy-efficient LEACH(PE-LEACH),and the Power-Efficient Gathering in Sensor Information Systems Neural Network(PEAGSIS-NN)recent routing protocol.We compare our optimized method with the Jaya,the Particle Swarm Optimization-based Energy Efficient Clustering(PSO-EEC)protocol,and the hybrid Harmony Search Algorithm and PSO(HSA-PSO)algorithms.The efficiencies of our proposed algorithms are evaluated by conducting experiments in terms of the network lifetime(first dead node,half dead nodes,and last dead node),energy consumption,packets to cluster head,and packets to the base station.The experimental results were compared with those obtained using the Jaya optimization algorithm.The proposed algorithms exhibited the best performance.The proposed approach successfully prolongs the network lifetime by 71% for the PEAGSIS protocol,51% for the LEACH protocol,10% for the PE-LEACH protocol,and 73% for the PEGSIS-NN protocol;Moreover,it enhances other criteria such as energy conservation,fitness convergence,packets to cluster head,and packets to the base station.

Multi-Strategy Assisted Multi-Objective Whale Optimization Algorithm for Feature Selection

In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate.Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter,but the results obtained depend on the value of the parameter.To eliminate this parameter’s influence,the problem can be reformulated as a multi-objective optimization problem.The Whale Optimization Algorithm(WOA)is widely used in optimization problems because of its simplicity and easy implementation.In this paper,we propose a multi-strategy assisted multi-objective WOA(MSMOWOA)to address feature selection.To enhance the algorithm’s search ability,we integrate multiple strategies such as Levy flight,Grey Wolf Optimizer,and adaptive mutation into it.Additionally,we utilize an external repository to store non-dominant solution sets and grid technology is used to maintain diversity.Results on fourteen University of California Irvine(UCI)datasets demonstrate that our proposed method effectively removes redundant features and improves classification performance.The source code can be accessed from the website:https://github.com/zc0315/MSMOWOA.

Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)transmission.Vehicular networks give a safe and more effective driving experience by presenting time-sensitive and location-aware data.The communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with time.Therefore,the scheme of an effectual routing protocol for reliable and stable communications is significant.Current research demonstrates that clustering is an intelligent method for effectual routing in a mobile environment.Therefore,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in VANETS.The FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the VANET.To accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust level.For the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR method.The experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

Application of DSAPSO Algorithm in Distribution Network Reconfiguration with Distributed Generation

With the current integration of distributed energy resources into the grid,the structure of distribution networks is becoming more complex.This complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent algorithms.Consequently,traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local optima.To tackle this issue,a more advanced particle swarm optimization algorithm is proposed.To address the varying emphases at different stages of the optimization process,a dynamic strategy is implemented to regulate the social and self-learning factors.The Metropolis criterion is introduced into the simulated annealing algorithm to occasionally accept suboptimal solutions,thereby mitigating premature convergence in the population optimization process.The inertia weight is adjusted using the logistic mapping technique to maintain a balance between the algorithm’s global and local search abilities.The incorporation of the Pareto principle involves the consideration of network losses and voltage deviations as objective functions.A fuzzy membership function is employed for selecting the results.Simulation analysis is carried out on the restructuring of the distribution network,using the IEEE-33 node system and the IEEE-69 node system as examples,in conjunction with the integration of distributed energy resources.The findings demonstrate that,in comparison to other intelligent optimization algorithms,the proposed enhanced algorithm demonstrates a shorter convergence time and effectively reduces active power losses within the network.Furthermore,it enhances the amplitude of node voltages,thereby improving the stability of distribution network operations and power supply quality.Additionally,the algorithm exhibits a high level of generality and applicability.

Quantitatively characterizing sandy soil structure altered by MICP using multi-level thresholding segmentation algorithm

The influences of biological,chemical,and flow processes on soil structure through microbially induced carbonate precipitation(MICP)are not yet fully understood.In this study,we use a multi-level thresholding segmentation algorithm,genetic algorithm(GA)enhanced Kapur entropy(KE)(GAE-KE),to accomplish quantitative characterization of sandy soil structure altered by MICP cementation.A sandy soil sample was treated using MICP method and scanned by the synchrotron radiation(SR)micro-CT with a resolution of 6.5 mm.After validation,tri-level thresholding segmentation using GAE-KE successfully separated the precipitated calcium carbonate crystals from sand particles and pores.The spatial distributions of porosity,pore structure parameters,and flow characteristics were calculated for quantitative characterization.The results offer pore-scale insights into the MICP treatment effect,and the quantitative understanding confirms the feasibility of the GAE-KE multi-level thresholding segmentation algorithm.

A real-time intelligent lithology identification method based on a dynamic felling strategy weighted random forest algorithm

Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face many challenges. This paper studies the problems of difficult feature information extraction,low precision of thin-layer identification and limited applicability of the model in intelligent lithologic identification. The author tries to improve the comprehensive performance of the lithology identification model from three aspects: data feature extraction, class balance, and model design. A new real-time intelligent lithology identification model of dynamic felling strategy weighted random forest algorithm(DFW-RF) is proposed. According to the feature selection results, gamma ray and 2 MHz phase resistivity are the logging while drilling(LWD) parameters that significantly influence lithology identification. The comprehensive performance of the DFW-RF lithology identification model has been verified in the application of 3 wells in different areas. By comparing the prediction results of five typical lithology identification algorithms, the DFW-RF model has a higher lithology identification accuracy rate and F1 score. This model improves the identification accuracy of thin-layer lithology and is effective and feasible in different geological environments. The DFW-RF model plays a truly efficient role in the realtime intelligent identification of lithologic information in closed-loop drilling and has greater applicability, which is worthy of being widely used in logging interpretation.

Improvement of High-Speed Detection Algorithm for Nonwoven Material Defects Based on Machine Vision

Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.

Genetic algorithm assisted meta-atom design for high-performance metasurface optics

Metasurfaces,composed of planar arrays of intricately designed meta-atom structures,possess remarkable capabilities in controlling electromagnetic waves in various ways.A critical aspect of metasurface design involves selecting suitable meta-atoms to achieve target functionalities such as phase retardation,amplitude modulation,and polarization conversion.Conventional design processes often involve extensive parameter sweeping,a laborious and computationally intensive task heavily reliant on designer expertise and judgement.Here,we present an efficient genetic algorithm assisted meta-atom optimization method for high-performance metasurface optics,which is compatible to both single-and multiobjective device design tasks.We first employ the method for a single-objective design task and implement a high-efficiency Pancharatnam-Berry phase based metalens with an average focusing efficiency exceeding 80%in the visible spectrum.We then employ the method for a dual-objective metasurface design task and construct an efficient spin-multiplexed structural beam generator.The device is capable of generating zeroth-order and first-order Bessel beams respectively under right-handed and left-handed circular polarized illumination,with associated generation efficiencies surpassing 88%.Finally,we implement a wavelength and spin co-multiplexed four-channel metahologram capable of projecting two spin-multiplexed holographic images under each operational wavelength,with efficiencies over 50%.Our work offers a streamlined and easy-to-implement approach to meta-atom design and optimization,empowering designers to create diverse high-performance and multifunctional metasurface optics.

Product quality prediction based on RBF optimized by firefly algorithm

With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.