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.

Research on Landscape Design of Waterfront Public Space in Mountainous Cities Based on User Experience-Taking the Waterfront Landscape Design of Art Peninsula in Chongqing as an Example

Mountainous cities are dominated by mountainous,hilly,and steep terrain,which brings certain complexity and particularity to the planning and construction of waterfront spaces in these cities compared to plain cities.Waterfront spaces,often serving as the core areas of city development,possess favorable location advantages and special attributes of water-land intersection,giving them more possibilities for functional transformation[1].However,the ultimate goal of design is to provide users with a vibrant waterfront area.The design of waterfront spaces should focus more on people’s behavioral needs,allowing users to feel a good interaction between the place and their behavioral needs during space usage[2].Therefore,the design incorporates human environmental behavior,increases interactive experiences,and enriches spatial interest.

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.

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.

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 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。

Design of experiment study on hardness variations in friction stir welding of AM60 Mg alloy

Identification of process parameters,their effects and contributions to the outcomes of the system using experimental approach could be a daunting,time consuming,and costly course.Using proper statistical methods,i.e.,Taguchi method,could significantly reduce the number of required experiments and statistical significance of the parameter can be identified.Friction stir welding is one of those welding techniques with many parameters which have different effects on the quality of the welds.In friction stir welding the tool rotational speed(RPM)and transverse speed(mm/min)influence the strength(i.e.,hardness distribution)of the stirred zone.In this study,these two factors are investigated to determine the effect they will have on the hardness in the stirred zone of the friction stir welds and how the two factors are related to one another for as-cast magnesium alloy AM60 with nominal chemical composition of Mg-(5.5-6.5)Al-(0.24-0.6)Mn-0.22Zn-0.1Si.Experimental data was taken at three different tool rotational speeds and three different transverse speeds.The data obtained was then analyzed using a 32 factorial design to find the contribution of these parameters.It was determined that both tool rotational speed and transverse speed possess significant effects on the stir zone hardness.Also,the interactions between the two factors were statistically assessed.

Cavitation Optimization for a Centrifugal Pump Impeller by Using Orthogonal Design of Experiment

Cavitation is one of the most important performance of centrifugal pumps. However, the current optimization works of centrifugal pump are mostly focusing on hydraulic efficiency only, which may result in poor cavitation performance. Therefore, it is necessary to find an appropriate solution to improve cavitation performance with acceptable efficiency. In this paper, to improve the cavitation performance of a centrifugal pump with a vaned diffuser, the influence of impeller geometric parameters on the cavitation of the pump is investigated using the orthogonal design of experiment （DOE） based on computational fluid dynamics. The impeller inlet diameter D1, inlet incidence angle Aft, and blade wrap angle ~0 are selected as the main impeller geometric parameters and the orthogonal experiment of L9（3＂3） is performed. Three-dimensional steady simulations for cavitation are conducted by using constant gas mass fraction model with second-order upwind, and the predicated cavitation performance is validated by laboratory experiment. The optimization results are obtained by the range analysis method to improve cavitation performance without obvious decreasing the efficiency of the centrifugal pump. The internal flow of the pump is analyzed in order to identify the flow behavior that can affect cavitation performance. The results show that D1 has the greatest influence on the pump cavitation and the final optimized impeller provides better flow distribution at blade leading edge. The final optimized impeller accomplishes better cavitation and hydraulic performance and the NPSHR decreases by 0.63m compared with the original one. The presented work supplies a feasible route in engineering practice to optimize a centrifugal pump impeller for better cavitation performance.

Study on solder joint reliability of ceramic ball grid array component based on design of experiment method

Four process parameters, pad diameter, stencil thickness, ball diameter and stand-off were chosen as four control factors. By using an L25 （5^6 ） orthogonal array the ceramic ball grid array （ CBGA ） solder joints which have 25 different combinations of process parameters were designed. The numerical models of all the 25 CBGA solder joints were developed using the Sugrace Evolver. Utilizing the sugrace coordinate exported from the 25 CBGA solder joints numerical models, the finite element analysis models were set up and the nonlinear finite element analysis of the CBGA solder joints under thermal cycles were pegrormed by ANSYS. The thermal fatigue life of CBGA solder joint was calculated using Coffin-Manson equation. Based on the calculated thermal fatigue life results, the range analysis and the variance analysis were pegrormed. The results show that the fatigue life of CBGA solder joint is affected by the pad diameter, the stencil thickness, the ball diameter and the stand-off in a descending order, the best combination of process parameters results in the longest fatigue life is 0.07 mm stand-off, 0.125 mm stencil thickness of, 0.85 mm ball diameter and 0. 89 mm pad diameter. With 95% confidence the pad diameter has a significant effect on the reliability of CBGA solder joints whereas the stand-off, the stencil thickness and the ball diameter have little effect on the reliability of CBGA solder joints.

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.

Optimal design of vegetation in residential district with numerical simulation and field experiment

Vegetation plays a key role in improving wind environment of residential districts,and is helpful for creating a comfortable and beautiful living environment.The optimal design of vegetation for wind environment improvement in winter was investigated by carrying out field experiments in Heqingyuan residential area in Beijing,and after that,numerical simulation with SPOTE(simulation platform for outdoor thermal environment) experiments for outdoor thermal environment of vegetation was adopted for comparison.The conclusions were summarized as follows:1) By comparing the experimental data with simulation results,it could be concluded that the wind field simulated was consistent with the actual wind field,and the flow distribution impacted by vegetation could be accurately reflected;2) The wind velocity with vegetation was lower than that without vegetation,and the wind velocity was reduced by 46%;3) By adjusting arrangement and types of vegetation in the regions with excessively large wind velocity,the pedestrian-level wind velocity could be obviously improved through the simulation and comparison.

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.

Investigation of the optimum differential gear ratio for real driving cycles by experiment design and genetic algorithm

Experiment statistical method and genetic algorithms based optimization method are used to obtain the optimum differential gear ratio for heavy truck that provides best fuel consumption when changing the working condition that affects its torque and speed range. The aim of the study is to obtain the optimum differential gear ratio with fast and accurate optimization calculation without affecting drivability characteristics of the vehicle according to certain driving cycles that represent the new working conditions of the truck. The study is carried on a mining dump truck YT3621 with 9 for- ward shift manual transmission. Two loading conditions, no load and 40 t, and four on road real driving cycles have been discussed. The truck powertrain is modeled using GT-drive, and DOE -post processing tool of the GT-suite is used for DOE analysis and genetic algorithm optimization.

Optimization of mechanical properties using D-optimal factorial design of experiment: Electromagnetic stir casting process of A357-SiC nanocomposite

Electromagnetic stir casting process of A357-Si C nanocomposite was discussed using the D-optimal design of experiment(DODOE) method. As the main objective, nine random experiments obtained by DX-7 software were performed. By this method, A357-Si C nanocomposites with 0.5, 1.0 and 1.5 wt.% Si C were fabricated at three different frequencies(10, 35 and 60 Hz) in the experimental stage. The microstructural evolution was characterized by scanning electron and optical microscopes, and the mechanical properties were investigated using hardness and roomtemperature uniaxial tensile tests. The results showed that the homogeneous distribution of Si C nanoparticles leads to the microstructure evolution from dendritic to non-dendritic form and a reduction of size by 73.9%. Additionally, based on DODOE, F-values of 44.80 and 179.64 were achieved for yield stress(YS) and ultimate tensile strength(UTS), respectively, implying that the model is significant and the variables(Si C fraction and stirring frequency) were appropriately selected. The optimum values of the Si C fraction and stirring frequency were found to be 1.5 wt.% and 60 Hz, respectively. In this case, YS and UTS for A357-Si C nanocomposites were obtained to be 120 and 188 MPa(57.7% and 57.9 % increase compared with those of the as-cast sample), respectively.

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.

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.

Analyzing Laser-Welded NiTi-NiTi-Joints for Actuator Applications Using Design of Experiments

One promising joining method for NiTi-SMA （shape memory alloy）-components is laser welding. This joining technology bears huge potential regarding process automation and mechanical properties as well as durability, especially within the field of small- and medium-sized actuators. However, there is still need for research due to unsolved issues influencing the microstructure and thus effecting mechanical properties as well as SMA-characteristics of these joints. Therefore, the purpose of this paper is the evaluation of quality parameters of NiTi-NiTi-wire-joints. For this purpose, design of experiments with a fractional factorial design is used for the investigation, because of its high potential to decrease experimental effort. This paper provides a basis for future research in the field of SMA-actuators and joining.

Design of Calibration Experiments for Identification of Manipulator Elastostatic Parameters

The paper is devoted to the elastostatic calibration of industrial robots, which is used for precise machining of large-dimensional parts made of composite materials. In this technological process, the interaction between the robot and the workpiece causes essential elastic deflections of the manipulator components that should be compensated by the robot controller using relevant elastostatic model of this mechanism. To estimate parameters of this model, an advanced calibration technique is applied that is based on the non-linear experiment design theory, which is adopted for this particular application. In contrast to previous works, it is proposed a concept of the user-defined test-pose, which is used to evaluate the calibration experiments quality. In the frame of this concept, the related optimization problem is defined and numerical routines are developed, which allow generating optimal set of manipulator configurations and corresponding forces/torques for a given number of the calibration experiments. Some specific kinematic constraints are also taken into account, which insure feasibility of calibration experiments for the obtained configurations and allow avoiding collision between the robotic manipulator and the measurement equipment. The efficiency of the developed technique is illustrated by an application example that deals with elastostatic calibration of the serial manipulator used for robot-based machining.