Research on Information Architecture Design of Short-Form Video Social Platforms Based on Cognitive Psychology

This study investigates how cognitive psychology principles can be integrated into the information architecture design of short-form video platforms,like TikTok,to enhance user experience,engagement,and sharing.Using a questionnaire,it explores TikTok users’habits and preferences,highlighting how social media fatigue(SMF)impacts their interaction with the platform.The paper offers strategies to optimize TikTok’s design.It suggests refining the organizational system using principles like chunking,schema theory,and working memory capacity.Additionally,it proposes incorporating shopping features within TikTok’s interface to personalize product suggestions and enable monetization for influencers and content creators.Furthermore,the study underlines the need to consider gender differences and user preferences in improving TikTok’s sharing features,recommending streamlined and customizable sharing options,collaborative sharing,and a system to acknowledge sharing milestones.Aiming to strengthen social connections and increase sharing likelihood,this research provides insights into enhancing information architecture for short-form video platforms,contributing to their growth and success.

Interface Design and Functional Optimization of Chinese Learning Apps Based on User Experience

This research paper investigates the interface design and functional optimization of Chinese learning apps through the lens of user experience.With the increasing popularity of Chinese language learning apps in the era of rapid mobile internet development,users'demands for enhanced interface design and interaction experience have grown significantly.The study aims to explore the influence of user feedback on the design and functionality of Chinese learning apps,proposing optimization strategies to improve user experience and learning outcomes.By conducting a comprehensive literature review,utilizing methods such as surveys and user interviews for data collection,and analyzing user feedback,this research identifies existing issues in the interface design and interaction experience of Chinese learning apps.The results present user opinions,feedback analysis,identified problems,improvement directions,and specific optimization strategies.The study discusses the potential impact of these optimization strategies on enhancing user experience and learning outcomes,compares findings with previous research,addresses limitations,and suggests future research directions.In conclusion,this research contributes to enriching the design theory of Chinese learning apps,offering practical optimization recommendations for developers,and supporting the continuous advancement of Chinese language learning apps.

Process Characterization of the Transesterification of Rapeseed Oil to Biodiesel Using Design of Experiments and Infrared Spectroscopy

For optimization of production processes and product quality,often knowledge of the factors influencing the process outcome is compulsory.Thus,process analytical technology(PAT)that allows deeper insight into the process and results in a mathematical description of the process behavior as a simple function based on the most important process factors can help to achieve higher production efficiency and quality.The present study aims at characterizing a well-known industrial process,the transesterification reaction of rapeseed oil with methanol to produce fatty acid methyl esters(FAME)for usage as biodiesel in a continuous micro reactor set-up.To this end,a design of experiment approach is applied,where the effects of two process factors,the molar ratio and the total flow rate of the reactants,are investigated.The optimized process target response is the FAME mass fraction in the purified nonpolar phase of the product as a measure of reaction yield.The quantification is performed using attenuated total reflection infrared spectroscopy in combination with partial least squares regression.The data retrieved during the conduction of the DoE experimental plan were used for statistical analysis.A non-linear model indicating a synergistic interaction between the studied factors describes the reactor behavior with a high coefficient of determination(R^(2))of 0.9608.Thus,we applied a PAT approach to generate further insight into this established industrial process.

Design of Experiments (DOE)—A Valuable Multi-Purpose Methodology

The DOE methodology is an effective tool for upgrading the level of measurement and assessment. In any design, planning or control problem the designer is faced with many alternatives. He/she is challenged to develop design approaches that can meet both quality and cost criteria. The way experiments are designed greatly affects the effective use of the experimental resources and the easiness with which the measured results can be analyzed. This paper does not present new evidence based on designed experiments. Its objective is solely to show how useful application of multifactor experiments is in a variety of circumstances and decision making scenarios. The paper reviews three published examples where this method was used in different contexts: quality control, flexible manufacturing systems (FMS) and logistics systems. The physical experiment has been carried out to improve the quality of a special type of batteries. The simulation experiment has been carried out to investigate the impact of several flexibility factors in a flexible manufacturing system. The numerical value of a complex analytical expression representing a customer oriented logistics performance measure has been calculated for different values of its parameters, i.e. the given numerical values of the investigated factors. It enabled a methodical examination of all factor effects and especially their interactions, thus shedding light on complex aspects of the logistics decision problem. In these examples, cases from different contexts were presented, enabling to view design of experiments as a powerful ingredient for improving decision making in a variety of circumstances.

光学玻璃磨削亚表面损伤预测模型及DOE实验设计

为了掌握光学玻璃材料杯型砂轮研磨与表面粗糙度(SR)和亚表面损伤(SSD)机理,本文建立BK7光学玻璃杯型砂轮研磨表面粗糙度的预测模型,通过改变磨削参数来研究对表面粗糙度的影响。设计DOE试验,研究影响SR与SSD的显著性特征因子,并分析了各因子的交互作用。实验结果表明预测模型的可靠性,得到表面粗糙度的预测模型数据与实验数据的平均误差为5.47%。采用角抛光法,通过电子显微镜观测表面裂纹,并测量裂纹的深度。最后,基于Li的模型,建立基于磨削工艺参数的亚表面损伤的新预测模型。实验结果表明:实验和预测模型结果具有很好的一致性,模型数据与实验数据的平均误差为6.19%,并且新预测模型结果要优于Li的模型。

Computer modeling of high-pressure leaching process of nickel laterite by design of experiments and neural networks

Due to the complex chemical composition of nickel ores, the requests for the decrease of production costs, and the increase of nickel extraction in the existing depletion of high-grade sulfide ores around the world, computer modeling of nickel ore leaching process be- came a need and a challenge. In this paper, the design of experiments （DOE） theory was used to determine the optimal experimental design plan matrix based on the D optimality criterion. In the high-pressure sulfuric acid leaching （HPSAL） process for nickel laterite in ＂Rudjinci＂ ore in Serbia, the temperature, the sulfuric acid to ore ratio, the stirring speed, and the leaching time as the predictor variables, and the degree of nickel extraction as the response have been considered. To model the process, the multiple linear regression （MLR） and response surface method （RSM）, together with the two-level and four-factor full factorial central composite design （CCD） plan, were used. The proposed re- gression models have not been proven adequate. Therefore, the artificial neural network （ANN） approach with the same experimental plan was used in order to reduce operational costs, give a better modeling accuracy, and provide a more successful process optimization. The model is based on the multi-layer neural networks with the back-propagation （BP） learning algorithm and the bipolar sigmoid activation function.

A CASE STUDY IN THE APPLICATION OF SUPERSATURATED DESIGNS TO COMPUTER EXPERIMENTS

Supersaturated design is essentially a fractional factorial design in which the number of potential effects is greater than the number of runs. In this article, the supersaturated design is applied to a computer experiment through an example of steady current circuit model problem. A uniform mixed-level supersaturated design and the centered quadratic regression model are used. This example shows that supersaturated design and quadratic regression modeling method are very effective for screening effects and building the predictor. They are not only useful in computer experiments but also in industrial and other scientific experiments.

Optimizing Bucket Elevator Performance through a Blend of Discrete Element Method, Response Surface Methodology, and Firefly Algorithm Approaches

This research introduces a novel approach to enhancing bucket elevator design and operation through the integration of discrete element method(DEM)simulation,design of experiments(DOE),and metaheuristic optimization algorithms.Specifically,the study employs the firefly algorithm(FA),a metaheuristic optimization technique,to optimize bucket elevator parameters for maximizing transport mass and mass flow rate discharge of granular materials under specified working conditions.The experimental methodology involves several key steps:screening experiments to identify significant factors affecting bucket elevator operation,central composite design(CCD)experiments to further explore these factors,and response surface methodology(RSM)to create predictive models for transport mass and mass flow rate discharge.The FA algorithm is then applied to optimize these models,and the results are validated through simulation and empirical experiments.The study validates the optimized parameters through simulation and empirical experiments,comparing results with DEM simulation.The outcomes demonstrate the effectiveness of the FA algorithm in identifying optimal bucket parameters,showcasing less than 10%and 15%deviation for transport mass and mass flow rate discharge,respectively,between predicted and actual values.Overall,this research provides insights into the critical factors influencing bucket elevator operation and offers a systematic methodology for optimizing bucket parameters,contributing to more efficient material handling in various industrial applications.

Factorial Experimental Design to Study the Effects of Layers and Fiber Content on Concrete Flexural Behavior

Experimentation has come a long in helping researchers achieve breakthroughs in their different scientific areas and engineering happens to be one of those areas with the most impact from experimental advancement. The need for valid experimental results free from biases and confounding conclusions has prompted the development of new experimental techniques that takes consideration of all applicable factor and combinations in providing answers on a research topic, and the Factorial Experimental design credited to Sir Ronald Fisher is one technique yielding highly valid results. This paper uses the factorial design of experiments to research the flexural impact of polyvinyl acetate fiber and layered concrete in construction. The experiment considered two levels of fiber contents and two levels of layers, and prepared samples with all combinations of the variable factors. The samples were tested after 7 days from casting for flexural strength and an advance statistical analysis was performed on the flexural responses of the samples using R-program. The results from the analyses revealed the significance of the variables to the flexural strength of the samples, as well as their interactions. The experiment concluded that based on the number of layers and fiber content used for the experiment, casting concrete in layers does have a significant negative effect on the flexural strength of concrete, and the failure pattern of concrete members under flexural load in evidently influenced by the material composition of the concrete, and that it can be evidently influenced by casting the concrete in layers.

Application of Monte-Carlo statistical experiments in design of ocean engineering - Estimating the parameters, models and probabilities

Recently, some results have been acquired with the Monte- Carlo statistical experiments in the design of ocean en gineering. The results show that Monte-Carlo statistical experiments can be widely used in estimating the parameters of wave statistical distributions, checking the probability model of the long- term wave extreme value distribution under a typhoon condition and calculating the failure probability of the ocean platforms.

Numerical Simulation on Radon Retardation Behavior of Covering Floats in Radon-Containing Water

Objective This study aimed to efficiently reduce the release of radon from water bodies to protect the environment.Methods Based on the sizes of the experimental setup and modular float,computational fluid dynamics(CFD)was used to assess the impact of the area coverage rate,immersion depth,diffusion coefficient,and radon transfer velocity at the gas–liquid interface on radon migration and exhalation of radon-containing water.Based on the numerical simulation results,an estimation model for the radon retardation rate was constructed.The effectiveness of the CFD simulation was evaluated by comparing the experimental and simulated variation values of the radon retardation rate with the coverage area rates.Results The effect of radon transfer velocity on radon retardation in water bodies was minor and insignificant according to the appropriate value;therefore,an estimation model of the radon retardation rate of the coverage of a radon-containing water body was constructed using the synergistic impacts of three factors:area coverage rate,immersion depth,and diffusion coefficient.The deviation between the experimental and simulated results was<4.3%.Conclusion Based on the numerical simulation conditions,an estimation model of the radon retardation rate of covering floats in water bodies under the synergistic effect of multiple factors was obtained,which provides a reference for designing covering floats for radon retardation in radoncontaining water.

Polylactic Acid (PLA) Foaming: Design of Experiments for Cell Size Control

In this study, a design of experiments (DoE) approach was used to develop a PLA open-cell foam morphology using the compression molding technique. The effect of three molding parameters (foaming time, mold opening temperature, and weight concentration of the ADA blowing agent) on the cellular structure was investigated. A regression equation relating the average cell size to the above three processing parameters was developed from the DoE and the analysis of variance (ANOVA) was used to find the best dimensional fitting parameters based on the experimental data. With the help of the DoE technique, we were able to develop various foam morphologies having different average cell size distribution levels, which is important in the development of open-cell PLA scaffolds for bone regeneration for which the control of cell morphology is crucial for osteoblasts proliferation. For example, at a constant ADA weight concentration of 5.95 wt%, we were able to develop a narrow average cell size distribution ranging between 275 and 300 μm by varying the mold opening temperature between 106°C and 112°C, while maintaining the foaming time constant at 8 min, or by varying the mold foaming time between 6 and 11 min and maintaining the mold opening temperature at 109°C.

THE DESIGN OF A LIMITED AREA MESOSCALE MODEL AND PREDICTION EXPERIMENTS

Based on the dynamic frame of a 6-layer primitive equations model, a mesoscale primitive equations model is designed that includes larger range of physical processes. It is run in (x, y, σ) system with the model atmosphere topping at 10 hPa and availability of multiple conditions of horizontal boundaries, both horizontal and vertical resolution are adjustable; the precipitation scheme includes large scale and deep cumulus convective precipitation; the ground temperature is computed using surface heat budget equations; exchanges between land atmosphere and between ocean and atmosphere are considered, the Liouis format is used in the computation of vertical exchange budget; a scheme that combines the second and fourth order is employed in horizontal diffusion in which the coefficient is the function of the location of grid points and wind fields; the integration scheme is in the form of economic central difference.With the resolution that horizontal grids are spaced at intervals of 80 km and vertical length is unequally spaced into 16 layers, the model is experimented with 26 cases of forecast. The result has shown stable model computation, good prediction of major synoptic patterns and close reproduction of real precipitation. Statistics for a number of assessment indexes are given in this paper and comparisons are made to the original 6-layer model in respect of the forecasting ability and model properties.

Design of Experiment (DoE): Implementation in Determining Optimum Design Parameters of Portable Workstation

In the modern era of manufacturing, it is important to optimize every design parameter in product development stage to reduce cost, material usage and to achieve the desired efficacy level. There are various models which serve those purposes, for instance, Design of Experiment (DoE) is used to check the parameters after adopting optimization tactics which results in reduced cost or saving operating time. In this regard, this research aims to construct a DoE model on a portable workstation to optimize its design parameters. The methodology of DOE would be a 2 level 3 factors full factorial DOE which is conducted to determine the optimal value for three design parameters (factors) which are material density, the length of the table and the length of the table stand in terms of the response which is the required time of fold ability function of the portable workstation. Based upon the evaluated interactions between the parameters, the optimized parameters are chosen for responses. Here, the resultant design parameters are at their lowest level, so the goal of time efficiency in fold ability function is achieved. This similar sort of DoE can be implemented in the furniture and other manufacturing industries who wish to optimize their material usage as well as increase efficiency and reduce cycle time.

Design and Implementation of Virtual Experiments for Computer Architecture Based on Simulators

In china,many students are unable to do experiments in computer architecture courses,which is very important in helping them to understand many key points.The reason is that the cost of the hardware required is too much.Besides,it is very difficult to do research study in hardware experiments.In our course,we adopted an alternative way to deal with the problem: to use software simulators,and designed a set of virtual experiments based on these simulators,which are described in detail in this paper.

Design of experiments unravels insights into selective ethylene or methane production on evaporated Cu catalysts

As a highly tempting technology to close the carbon cycle,electrochemical CO_(2)reduction calls for the development of highly efficient and durable electrocatalysts.In the current study,Design of Experiments utilizing the response surface method is exploited to predict the optimal process variables for preparing high-performance Cu catalysts,unraveling that the selectivity towards methane or ethylene can be simply modulated by varying the evaporation parameters,among which the Cu film thickness is the most pivotal factor to determine the product selectivity.The predicted optimal catalyst with a low Cu thickness affords a high methane Faradaic efficiency of 70.6%at the partial current density of 211.8 m A cm^(-2),whereas that of a high Cu thickness achieves a high ethylene selectivity of 66.8%at267.2 m A cm^(-2)in the flow cell.Further structure-performance correlation and in-situ electrospectroscopic measurements attribute the high methane selectivity to isolated Cu clusters with low packing density and monotonous lattice structure,and the high ethylene efficiency to coalesced Cu nanoparticles with rich grain boundaries and lattice defects.The high Cu packing density and crystallographic diversity is of essence to promoting C–C coupling by stabilizing*CO and suppressing*H coverage on the catalyst surface.This work highlights the implementation of scientific and mathematic methods to uncover optimal catalysts and mechanistic understandings toward selective electrochemical CO_(2)reduction.

Parametric Study of Calibration Blackbody Uncertainty Using Design of Experiments

NASA is developing the Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission to provide accurate measurements to substantially improve understanding of climate change. CLARREO will include a Reflected Solar (RS) Suite, an Infrared (IR) Suite, and a Global Navigation Satellite System-Radio Occultation (GNSS-RO). The IR Suite consists of a Fourier Transform Spectrometer (FTS) covering 5 to 50 micrometers (2000-200 cm-1 wavenumbers) and on-orbit calibration and verification systems. The IR instrument will use a cavity blackbody view and a deep space view for on-orbit calibration. The calibration blackbody and the verification system blackbody will both have Phase Change Cells (PCCs) to accurately provide a SI reference to absolute temperature. One of the most critical parts of obtaining accurate CLARREO IR scene measurements relies on knowing the spectral radiance output from the blackbody calibration source. The blackbody spectral radiance must be known with a low uncertainty, and the magnitude of the uncertainty itself must be reliably quantified. This study focuses on determining which parameters in the spectral radiance equation of the calibration blackbody are critical to the blackbody accuracy. Fourteen parameters are identified and explored. Design of Experiments (DOE) is applied to systematically set up an experiment (i.e., parameter settings and number of runs) to explore the effects of these 14 parameters. The experiment is done by computer simulation to estimate uncertainty of the calibration blackbody spectral radiance. Within the explored ranges, only 4 out of 14 parameters were discovered to be critical to the total uncertainty in blackbody radiance, and should be designed, manufactured, and/or controlled carefully. The uncertainties obtained by computer simulation are also compared to those obtained using the “Law of Propagation of Uncertainty”. The two methods produce statistically different uncertainties. Nevertheless, the differences are small and are not considered to be important. A follow-up study has been planned to examine the total combined uncertainty of the CLARREO IR Suite, with a total of 47 contributing parameters. The DOE method will help in identifying critical parameters that need to be effectively and efficiently designed to meet the stringent IR measurement accuracy requirements within the limited resources.

基于遗传算法优化的风洞试验MDOE方法

为提高风洞试验效率,现代试验设计(MDOE)方法正越来越频繁地用于各类飞行器风洞试验。然而,基于MDOE方法的风洞试验尽管理论上减少了试验点数量,有效缩短了风洞吹风时间,但也带来了试验数据精度下降的问题。为此发展了一种基于D-最优设计准则和二元三次多项式响应面模型的MDOE方法,同时引入遗传算法进一步提高响应面模型的拟合精度。结果表明,所开发的MDOE风洞试验方法仅需单变量(OFAT)方法30%左右的试验点数,吹风时间较OFAT方法大约缩短了60%;与最初拟合的多项式响应面模型相比,遗传算法优化后的响应面拟合精度提高了5%左右,内插得到的数据误差更小。

Design and Analysis of Computer Experiments Based on a Simulation Model of Air Traffic Flow Optimization in Panama

There is a growing in number of operations in aviation all over the world.This growing is increasing the necessity of innovation and new technology to respond the increment of the demand.As a respond of this demand,FAA(Federal Aviation Administration)is working with NextGen in the United States and the EUROCONTROL is implementing the Point Merge as solution in the air traffic flow management in Europe.However,the FAA alternative and EUROCONTROL alternative are not mutually exclusive since Panama,a small country in Latin America,is trying to use a combination between the vectoring approach and the Point Merge in the air traffic flow management.In addition,the AAC(Autoridad de Aereonautica Civil)and the Tocumen(Tocumen International Airport)are working in a continuous collaboration between FAA and Panama with the mutual challenge to improve the actual system.As a result,the main airline of Panama,the Compania Panamena de Aviacion(COPA Airlines),and the Autoridad de Aeronautica Civil(AAC)constructed a simulation model to select an air traffic flow alternative that can be able to change the actual situation.In other words,COPA Airlines and AAC are pursuing the minimization of the numbers of conflicts,the number of sequence actions,the flight time,the track flight distance and the fuel burn.Furthermore,this study aims to use the final draft of this previous analysis based on a simulation methodology to conduct a Design and Analysis of Computer Experiments with the final objective to increment the statistical significance of the actual model.

Multidisciplinary Design and Optimization of Satellite Launch Vehicle Using Latin Hypercube Design of Experiments

The design of new Satellite Launch Vehicle （SLV） is of interest, especially when a combination of Solid and Liquid Propulsion is included. Proposed is a conceptual design and optimization technique for multistage Low Earth Orbit （LEO） bound SLV comprising of solid and liquid stages with the use of Genetic Algorithm （GA） as global optimizer. Convergence of GA is improved by introducing initial population based on the Design of Experiments （DOE） Technique. Latin Hypercube Sampling （LHS）-DOE is used for its good space filling properties. LHS is a stratified random procedure that provides an efficient way of sampling variables from their multivariate distributions. In SLV design minimum Gross Lift offWeight （GLOW） concept is traditionally being sought. Since the development costs tend to vary as a function of GLOW, this minimum GLOW is considered as a minimum development cost concept. The design approach is meaningful to initial design sizing purpose for its computational efficiency gives a quick insight into the vehicle performance prior to detailed design.