Experimental Design Process Research Articles

Relationship between Verbal Interaction and Experimental Design Ability of Middle School Science Gifted in the Group Experimental Design Process

Relationship between Verbal Interaction and Experimental Design Ability of Middle School Science Gifted in the Group Experimental Design Process

Implementing distinct spatial proteogenomic technologies: opportunities, challenges, and key considerations.

Our ability to understand the cellular complexity of tissues has been revolutionized in recent years with significant advances in proteogenomic technologies including those enabling spatial analyses. This has led to numerous consortium efforts, such as the human cell atlas initiative which aims to profile all cells in the human body in healthy and diseased contexts. The availability of such information will subsequently lead to the identification of novel biomarkers of disease and of course therapeutic avenues. However, before such an atlas of any given healthy or diseased tissue can be generated, several factors should be considered including which specific techniques are optimal for the biological question at hand. In this review, we aim to highlight some of the considerations we believe to be important in the experimental design and analysis process, with the goal of helping to navigate the rapidly changing landscape of technologies available.

Exploring Engineering Philosophy from an Epistemological Perspective

This study explores engineering philosophy from an epistemological perspective, aiming to improve students' critical thinking skills, foster interdisciplinary understanding, promote ethical reflection, and deepen engagement with the philosophical foundations of technology. The study uses a comparative analysis and critical review of the literature on the philosophy of engineering. The results are as follows: First, the essence of engineering knowledge lies in practical application, problem-solving orientation, and interdisciplinary integration. Second, epistemological methods in engineering emphasize the importance of balancing intuition and analysis, critical thinking, rigorous experimental design, and verification and validation processes. Third, while experience and expertise are certainly crucial in shaping engineering knowledge, it is necessary to critically examine the potential biases and limitations inherent in this process. Fourth, the epistemological challenges of interdisciplinary engineering necessitate humility, open-mindedness, and interdisciplinary integration. Finally, the ethical and social dimensions of engineering knowledge are essential considerations for responsible and sustainable engineering practices. Integrating ethical reflection into decision-making and embracing social responsibility and community involvement can enable engineers to contribute to social advancement while upholding professional integrity and ethical standards. Future research suggestions include conducting a meta-epistemological analysis to study the fundamental assumptions, methodologies, and conceptual frameworks employed in engineering philosophy research from an epistemological perspective. It can explore the intersection of epistemology, ethics, and social theory in engineering practice and education. Finally, conducting historical and comparative studies of engineering epistemology in diverse cultural, historical, and technological contexts can offer meaningful insights.

A novel deep learning strategy to optimize Al2O3–SiO2 porous ceramics with phosphate tailings as raw material

The Al2O3–SiO2 porous ceramics (ASPC) prepared from phosphorus tailings usually showed a large fluctuation in material properties due to the complex composition of the raw material, which required extensive experimental exploration to optimize their properties. To solve the above problem, a novel deep learning strategy for automatically adjust hyperparameters had been proposed to predict the property of ASPC based on the small experimental datasets in this work. Initially, phosphate tailings and bauxite were used as raw materials. Active carbon, ranging from 0 to 15 wt%, was added as a pore-forming agent. After mixing the raw materials, they were shaped under a pressure of 5 MPa and sintered in the temperature range of 1000–1200 °C to produce ASPC, the density and compressive strength of the ASPC were measured to train the artificial neural network (ANN). An exhaustive search was conducted to identify the optimal combination of hyperparameters suitable for small datasets, then the influence of each hyperparameter on the training performance of ANN was analyzed. Subsequently, the trained model was used to predict the feature space of ASPC, and the key processes for regulating ASPC performance were explored, thereby the experimental schemes that meet target performance (density and compressive strength) were identified. Three groups of experimental schemes were predicted, whose properties were compared with the experimental ones. The results showed that the mean squared errors (MSE) of 0.03 and 0.85 for density and compressive strength, respectively. Furthermore, the ASPC exhibited excellent comprehensive properties when 70 wt% phosphorus tailings and 30 wt% bauxite were added, whose apparent porosity was 59.11 %, density was 1.16 g/cm3 and cold compressive strength was 6.49 MPa. And the predicted data trend influence of the pore-forming agent content and sintering temperature on the performance of ASPC is the same as the experimental data. This research provides valuable guidance for addressing the limited controllability of traditional experimental design processes and accelerate the preparation of ASPC from solid waste.

Unveiling yield strength of metallic materials using physics-enhanced machine learning under diverse experimental conditions

In the materials science domain, the accurate prediction of the yield strength of metallic compositions has often resulted in extensive experimental endeavors, leading to inefficiencies in both time and resources. Here, we introduce an innovative approach to predict yield strength, which can be applied to a variety of metallic substances ranging from the simplest pure metals to the most intricate alloys under varying temperatures and strain rates. The fusion of grain boundary sliding mechanism and cutting-edge machine-learning algorithm forges an expansive framework, which can help realize the critical factors influencing yield strength. The validity and wide applicability of the proposed framework were rigorously confirmed through experimental evaluations conducted on selected Fe-based alloys, such as Fe60Ni25Cr15, Fe60Ni30Cr10, and Fe64Ni15Co8Mn8Cu5. This breakthrough study significantly streamlines experimental design processes, optimizes resource utilization, and marks a significant leap forward in creating a reliable predictive framework for realizing material properties.

Optimization of Red Ginger (Zingiber officinale var. Rubrum) Extraction Using Microwave Assisted Hydrodistillation Method

Red ginger is a spice plant that has high economic and social value. One of the uses of red ginger is to process it into a product, namely essential oil. This study aims to determine the optimization of the extraction process conditions on solvent volume, time, and extraction power that can produce optimum yield and residual content of red ginger essential oil solvent. The extraction was carried out with the help of microwaves. The research method is experimental design and optimization process with Response Surface Methodology (RSM) type Box-Behnken Design (BBD). The results showed that the optimum yield was at 700 mL solvent volume, 1 min extraction time, and 10% power (69.9 watts) with the equation Y = 0.2076 + 0.0262A – 0.0300B – 0.0013C and the concentration conditions the optimum remaining solvent with the equation Y = 4.98 – 1.0000A + 4.56B + 5.44C. The optimal yield of red ginger essential oil was 0.205%, with a residual solvent content of 3.8%, specific gravity 0.885, acid number 1.399, refractive index 1.485. The results of the optimum residual solvent content of -6.023%. Based on the results obtained, the yield value with the help of microwaves is higher than that without the help of microwaves. Keywords: Essential oil, Microwave, Residual solvent content, Response surface methodology (RSM), Yield.

A review of counseling research using single‐case research design

AbstractSingle‐case research designs (SCRDs) are a vital tool for researchers and practitioners in counseling to evaluate intervention and treatment effectiveness. This content analysis reviews the application of SCRDs in counseling to highlight knowledge accrued and existing gaps in the literature. We evaluated the methodological design and reporting adherence against recommended guidelines to provide guidance for future research. We included 50 SCRDs published between 2015 and 2021 across 15 journals (42 reviewed). Content results showed several trends, strengths, and areas of improvement in counseling SCRDs. No studies completely met the What Works Clearinghouse (2022) design standards or Single‐Case Reporting Guideline in Behavioural Interventions (SCRIBE) reporting standards, yet several indicators had high levels of adherence. We discuss how the experimental design process can be enhanced to improve the internal validity, ways to improve transparency in reporting SCRD results, and the importance of contextualizing SCRD methodology to the counseling field.

Application of artificial neural network to evaluation of dimensional accuracy of 3D‐printed polylactic acid parts

AbstractAdditive manufacturing (AM) has begun to replace traditional fabrication because of its advantages, such as easy manufacturing of parts with complex geometry, and mass production. The most important limitation of AM is that dimensional accuracy cannot be achieved in all parts. Dimensional accuracy is essential for high reliability, high performance, and useful final products. This study investigates the impact of printing parameters on the dimensional accuracy of samples fabricated through fused deposition modeling (FDM), an additive manufacturing (AM) method utilizing polylactic acid (PLA) material. The experimental design process was performed using Taguchi methodology. ANOVA was used to determine the most important parameter affecting accuracy. Based on experimental studies, the optimal printing parameters for parts are determined as follows: concentric infill pattern, 3 mm wall thickness, 70% infill density, and a layer thickness of 200 μm. Artificial neural network (ANN) was used in the evaluation and prediction of the results. The R‐square (R2) performance evaluation criterion was above 95% from the ANN results. This value shows that the results are significant. The data acquired from this study may assist in identifying optimal parameters that contribute to the fabrication of samples with high dimensional accuracy using the FDM method.

Structural reliability analysis based on probability density evolution method and stepwise truncated variance reduction

To address the substantial computational burden associated with probability density evolution method (PDEM) in structural reliability analysis, this study proposes a novel look-ahead learning function named stepwise truncated variance reduction (STVR), integrating polynomial chaos Kriging (PCK) and PDEM. Three key features of STVR are highlighted. First, it enables quantifying the maximum reduction in predictive errors of PCK within the regions of interest (ROI) when adding a new point. Second, closed-form expression for STVR is derived through Kriging update formulas, eliminating the need for computationally intensive Gauss–Hermite quadrature or extensive conditional simulations of PCK. Third, a dynamic adjustment procedure is proposed for the probability level-related parameter in STVR, with the aim of achieving a good balance between the exploitation and exploration of ROI during the sequential experimental design process. The performance of STVR is demonstrated through two benchmark analytical functions and three numerical examples of varying complexity. Results indicate that the dynamic adjustment procedure for the probability level-related parameter in STVR outperforms the empirical setting of a minor value. Then, STVR proves more advantageous than existing pointwise and look-ahead learning functions, particularly in addressing complex dynamic reliability problems.

ClimMob: Software to support experimental citizen science in agriculture

Experimental citizen science offers new ways to organize on-farm testing of crop varieties and other agronomic options. Its implementation at scale requires software that streamlines the process of experimental design, data collection and analysis, so that different organizations can support trials. This article considers ClimMob software developed to facilitate implementing experimental citizen science in agriculture. We describe the software design process, including our initial design choices, the architecture and functionality of ClimMob, and the methodology used for incorporating user feedback. Initial design choices were guided by the need to shape a workflow that is feasible for farmers and relevant for farmers, breeders and other decision-makers. Workflow and software concepts were developed concurrently. The resulting approach supported by ClimMob is triadic comparisons of technology options (tricot), which allows farmers to make simple comparisons between crop varieties or other agricultural technologies tested on farms. The software was built using Component-Based Software Engineering (CBSE), to allow for a flexible, modular design of software that is easy to maintain. Source is open-source and built on existing components that generally have a broad user community, to ensure their continuity in the future. Key components include Open Data Kit, ODK Tools, PyUtilib Component Architecture. The design of experiments and data analysis is done through R packages, which are all available on CRAN. Constant user feedback and short communication lines between the development teams and users was crucial in the development process. Development will continue to further improve user experience, expand data collection methods and media channels, ensure integration with other systems, and to further improve the support for data-driven decision-making.

Co-Design of a Public Space and the Implementation: Atakent (Car) Park

Citizen Design Science is a new co-design strategy for urban and architectural systems that improves the planning, design, management, and renewal of cities, urban habitats, and architectural structures, using active design tools through citizens' observation, experience, and local knowledge. The aim of this study is to describe how Atakent Parking Area is transformed into a public space design and implementation through both digital and analog active design tools in the co-design process that includes citizens' spatial experiences, needs, and desires through the method of citizen design science. The objective indicators and subjective perception applied in the study were combined in the co-design process to implement an urban design project. The experimental collaborative urban design process is realized on a democratic platform based on the tendencies and expectations of the participants. Two conceptual urban design projects were prepared with design science data including citizens' wishes, needs, and suggestions about the area, and participating citizens were asked to vote for the project democratically in the urban space. The selected conceptual design project was transformed into an implementation project in the urban area.

A kind of numerical model combined with genetic algorithm and back propagation neural network for creep-fatigue life prediction and optimization of double-layered annulus metal hydride reactor and verification of ASME-NH code

The safety and stability of metal hydride reactor (MHR) structure are significantly important for normal operations of hydrogen isotope storage and supply system. Moreover, considering that the experiments of studying the fatigue and creep properties of MHR structure are always time, money, difficulty and energy consuming. To further improve the service life of MHR as well as lower the failure risk, this paper proposes a system integration method based on ASME code validation, which combines genetic algorithm (GA) and back propagation neural network (BPNN) for rapid assessment of creep-fatigue life (CFL) and optimal design of structural parameters. First, numerical simulation is applied to perform thermal-mechanical coupled finite element analysis and verified by ASME code. Then, parametric sensitivity analysis and experimental design of parameter-based training set based on Taguchi method were performed, and response values are obtained from physical models combined with numerical simulations. Finally, a parametric optimization procedure combining BPNN and GA is developed based on the training data. In the workflow, parametric data flow is passed through the experimental design, numerical simulation, prediction and optimization processes. The results show that the proposed BPNN proxy model based on ASME code validation has good performance in predicting the life of MHR. Based on this, the life of the MHR is improved by 8% compared to the reference sample.

Optimization of cadmium adsorption on metal organic frameworks MIL-53(Fe) using response surface methodology

This research aims to optimize the cadmium adsorption on metal organic frameworks MIL-53(Fe) using response surface methodology. Specifically, the effects of the adsorption parameters, such as contact time, initial cadmium ion concentration and adsorbent dosage were examined. The experimental design and optimization process consisted of a central composite design and a response surface analysis. The adsorption experimental data were analyzed to validate the the quadratic model produced by the response surface methodology. The results indicated that the interaction between contact time and initial cadmium ion concentration affects the cadmium uptake. The maximum cadmium uptake was obtained at the optimum conditions of 85 min contact time, 46 mg adsorbent dosage and 10 mg/L initial concentration of cadmium. A maximum of 74.94% adsorption efficiency was achieved under optimum conditions, with an F-value of 65.37. The model fitting was satisfactory, as the correlation coefficient of experimental (0.9882) and predicted (0.9731) was in good agreement. This study demonstrates the effectiveness of a combined application of central composite design and response surface methodology in evaluating the effects of the factors influencing cadmium adsorption adsorption on MIL-53(Fe).

Optimizing photocatalytic oxidative desulfurization using novel BiOI/B4C heterojunction in the presence of air and visible-light irradiation through response surface methodology (RSM)

In this study, different BiOI/B4C binary composites were evaluated as novel heterojunctions for the first time in the photocatalytic oxidative desulfurization process, and good results were obtained under visible light for the removal of dibenzothiophene sulfur compound with air oxidizer. The composites with different mass ratios were synthesized by hydrothermal method. In addition, different analysis including XRD, FT-IR, FE-SEM, EDX-MAP, TEM, BET, UV–vis DRS, PL, EIS and Mott-Schottky were used to identify the structure, morphology and performance of the composites. Hydroxyl and superoxide active species performed the three steps of electron-hole processing in the best way while improving the oxidation process with Z-scheme heterojunction. Experiment design process was carried out with Design Expert software and optimizing the parameters was performed in order to find the best operating conditions. The results showed that the optimal composite achieved the highest degradation performance of dibenzothiophene with a value of 95.1% in 30 min. In order to determine the degradation mechanism, trapping test was performed, and it was found that •O2– and •OH– radicals played a significant role in the photocatalytic process. In addition, due to the proper recovery of the BiOI/B4C heterojunction, this binary composite was proposed for industrial use.

Bioprocess intensification with model-assisted DoE-strategies for the production of biopharmaceuticals

Abstract The demand for highly effective biopharmaceuticals and the need to reduce manufacturing costs are increasing the pressure to develop productive and efficient bioprocesses. For this purpose, model-based process design concepts have been developed. Although first approaches were proposed, model-based process designs are still not state-of-the-art for cell culture processes during development or manufacturing. This highlights a need for improved methods and tools for optimal experimental design, optimal and robust process design and process optimization for the purposes of monitoring and control during manufacturing. In this review, an overview of the state of the art of model-based methods, their applications, further challenges, possible solutions and specific case studies for intensification of process development for production of biopharmaceuticals is presented. As a special focus, problems related to data generation (culture systems, process mode, specifically designed experiments) will be addressed.

Designing UAV Swarm Experiments: A Simulator Selection and Experiment Design Process.

The rapid advancement and increasing number of applications of Unmanned Aerial Vehicle (UAV) swarm systems have garnered significant attention in recent years. These systems offer a multitude of uses and demonstrate great potential in diverse fields, ranging from surveillance and reconnaissance to search and rescue operations. However, the deployment of UAV swarms in dynamic environments necessitates the development of robust experimental designs to ensure their reliability and effectiveness. This study describes the crucial requirement for comprehensive experimental design of UAV swarm systems before their deployment in real-world scenarios. To achieve this, we begin with a concise review of existing simulation platforms, assessing their suitability for various specific needs. Through this evaluation, we identify the most appropriate tools to facilitate one's research objectives. Subsequently, we present an experimental design process tailored for validating the resilience and performance of UAV swarm systems for accomplishing the desired objectives. Furthermore, we explore strategies to simulate various scenarios and challenges that the swarm may encounter in dynamic environments, ensuring comprehensive testing and analysis. Complex multimodal experiments may require system designs that may not be completely satisfied by a single simulation platform; thus, interoperability between simulation platforms is also examined. Overall, this paper serves as a comprehensive guide for designing swarm experiments, enabling the advancement and optimization of UAV swarm systems through validation in simulated controlled environments.

Machinability of Titanium Grade 5 Alloy for Wire Electrical Discharge Machining Using a Hybrid Learning Algorithm

Titanium alloys have found widespread use in aviation, automotive, and marine applications, which makes their implementation in mass production more challenging. Conventional methods of removing these alloy materials are unsuitable because of the high wear rate of cutting and slower rate of processing. The complexities of these materials have prompted the creation of cutting-edge machining methods. Wire Electrical Discharge Machining (WEDM) is a technique that has the potential to be useful for the removal of materials that are harder and electrically conductive. In order to create intricate designs, this method is frequently employed. The input factors, including pulse duration (on/off) and peak current, were taken into account during the experimental design process. The rate of material removal, surface roughness, dimensional deviation, and GD&T errors were opted for as performance indicators. The approach proposed by Taguchi was selected for the investigation of the process factors, and an Analysis of Variance was selected to find out the relative momentousness of each factor. From the analysis it is perceived that the applied current is the predominant factor that influences the chosen output characteristics. The aspiration of this article is to evolve a decision-making model based on a hybrid learning method which can be adopted to predict the selected output measures that affect the WEDM process. According to the findings, the value of the ANFIS-GRG, which was predicted to be 0.7777, was in fact closer to that value than any other value. The proposed model has the ability to help make a variety of different production processes more efficient. The analysis showed that the model’s functionality was enhanced, which helps producers make well-informed decisions.

A Tale of Three Proteomes: Visualizing Protein and Transcript Abundance Relationships in the Breast Cancer Proteome Portal.

Molecular characterization is transforming research on novel therapeutics in breast cancer. High-throughput methodologies are unbiased to hypotheses; thus, data produced are relevant to address unlimited questions and provide resources for the experimental design process. However, the opportunity is often overlooked because data are not readily accessed or analyzed. Herein, the Breast Cancer Proteome Portal, the only online tool for analyzing protein and transcript abundances across the three breast cancer proteomics studies, is presented. The tool is applied to demonstrate that cofunctioning protein abundances are highly correlated and, conversely, high abundance correlation may be an indicator of cofunction. Furthermore, the cofunction-correlation relationship is less resolved at the transcript level. By applying analysis and visualization tools within the Breast Cancer Proteome Portal, insights are garnered about serine synthesis and the compartmentalization of one-carbon metabolism in breast cancer, and a transcription factor tumorigenic regulatory network of glutamine deamination and oxidation is proposed, illustrating that the Breast Cancer Proteome Portal provides an interface for garnering insights from the information-rich studies of the breast cancer proteome.

Two-stage non-submodular maximization

The sheer size of modern datasets has led to an urgent need for summarization techniques that can identify representative elements of the data set. Fortunately, the vast majority of data summarization tasks satisfy an intuitive diminishing returns condition known as submodularity, which allows us to find nearly-optimal solutions in linear time. However, for many applications in practice, including experimental design and sparse Gaussian processes, the objective is in general not submodular. To solve these optimization problems, an important research method is to describe the characteristics of the non-submodular functions. The non-submodular function is a hot research topic in the study of nonlinear combinatorial optimizations. In this paper, we combine and generalize the curvature and the generic submodularity ratio to design an approximation algorithm for two-stage non-submodular maximization under a matroid constraint.

Numerical Optimization of Mini Centrifuge Modelling Test Design of Excavation Unloading Influence on Existing Tunnel Controlled by Partition Piles

Partition piles are widely used in tunnel displacement control under excavation. However, the control effect of partition piles has not been systematically studied, and the centrifuge model test is lacking. In view of this situation, the centrifuge model test design of the deformation of the adjacent existing tunnel caused by the excavation in terms of the partition piles under the dry sand foundation was carried out, and a series of optimization designs were carried out in the experimental design process according to the numerical simulation results. In addition, the numerical simulation of the centrifuge model test was carried out, and the calculated results were discussed and analyzed. The conclusions were obtained as follows: The magnesium-aluminum alloy materials were used to model the structural members, and its elastic modulus was close to that of common reinforced concrete; the high precision digital image correlation technology (DIC) was used to measure the horizontal and vertical displacement of the tunnel. The results of numerical simulation show that the embedded partition piles can play a better role in controlling the tunnel displacement than the non-partition piles and the non-embedded partition piles.