Experimental Design Techniques Research Articles

Optimizing Sr-doped CaSiO3 synthesis for enhanced bioceramic properties: A Plackett–Burman approach

Doping Sr has the potential to enhance bioceramic properties. However, controlling purity, Sr concentration, and properties is crucial. Therefore, precise evaluation of synthesis variables is essential, achieved through experimental design techniques. This study synthesized Sr-doped calcium silicate wollastonite using a Plackett–Burman design with 11 variables to identify key factors affecting synthesis yield and physical, structural, and chemical attributes for tissue engineering. Results indicate that the strontium proportion significantly impacts material properties, affecting porosity, surface area, and pore volume. Moreover, observations reveal that calcination temperature contributes to heightened crystallinity and strontium doping. Adjusting calcium and strontium precursor concentrations while reducing the silicon to calcium and strontium ratio improves efficiency crystallinity, purity, and promotes the formation of the preferred β-CaSiO3 phase. Overall, consistent and reproducible findings provide valuable insights into the variables influencing the process and facilitate optimization for future ceramic synthesis.

Unveiling influential factors in the capital asset pricing model: A novel approach utilizing Taguchi method

This study uses a Design of Experiment (DOE) approach and the Taguchi method to examine how different factors affect the expected return within the framework of the CAPM. The Capital Asset Pricing Model (CAPM) is a financial framework that forecasts an investment’s expected return by taking into account its systematic risk. This model factors elements like the risk-free rate, market risk premium, and beta to ascertain the expected return for a given asset. Typically, historical market data and financial analysis provide the inputs for CAPM. This method, frequently used in manufacturing and engineering, is modified for use in the financial domain to evaluate the importance of variables like beta, market risk premium, and the risk-free rate. The research identifies the factors that influence investment performance and shows how much each factor contributes to the expected returns. It is possible to ascertain each factor's percentage contribution to the expected return through statistical analysis. This work improves knowledge of CAPM and creates opportunities for improving investment decision-making processes by bridging the gap between financial theory and experimental design. Financial practitioners can achieve more reliable and accurate investment return forecasts by incorporating DOE techniques into their existing analytical toolkit.

Using Response Surface Methodology Approach to Modeling and Optimization of the Combustion Process in Cement Kiln

AbstractIn this study, this process has been modeled and optimized using the design of experiment technique and response surface methodology (RSM). The RSM was employed to optimize the combustion process in the cement kiln. Kiln feed (X1 = 160–190 t h−1), fan speed (X2 = 840–870 rpm), kiln rotational speed (X3 = 2.8–3.4 rpm), kiln fuel rate (X4 = 3800–4150 L h−1), calciner fuel rate (X5 = 5700–6200 L h−1), pressure of kiln inlet (X6 = 98–102 Pa), and limestone saturation factor (LSF) (X7 = 94–95.8 %) as independent variables were investigated. The carbon monoxide and the kiln body temperature in RSM were considered as response variables. The absolute average error in the model predictions for the combustion process in the cement kiln, for the kiln body's temperature, is about 1.6 % and for carbon monoxide is about 15.5 %.

Development of a mathematical model for the prediction of drill quality in voice-activated ultrasonic vibration assisted drilling process.

In different industries, drilling quality play an important role in the success of operations by producing millions of quality drilled parts. The production of high-quality parts at the lowest possible cost to the manufacturer is the primary goal of contemporary process and assembly industries. To assess the quality index of drilling holes; circularity index and surface roughness are important metrics. In order to produce better drilled parts at lower prices, this study work attempts to determine the key process variables using voice acting modules to investigate the induced vibrations effects that have the greatest impact on surface roughness and circularity index in a vertical CNC drilling machine. In order to forecast the surface roughness and circularity index of drilled holes within a given operational range, a mathematical model has been proposed by adopting a coupled algorithm using design expert and statistical approach. As an experimental design technique, Response Surface Methodology with small central composite design is used to enable the evaluation of various process parameters related with machining and vibration parameters and their effects on surface roughness and circularity index. The created model is improved using statistical tools to ensure the best match to the experimental data. It has been observed with the increase of RPM and vibration frequency the surface roughness increases up to a certain limit and then decrease whereas with the increase feed the surface roughness increases. But in terms of circularity index with the increase of the process parameters the plotted graph trends under perturbation analysis shows the similar pattern CI decreases up to a certain limit and then increases.

Enhancing Mycosporine-Like Amino Acids (MAAs) Extraction Yield from Fischerella sp. F5 using Experimental Design

Background: Mycosporine-like amino acids (MAAs) are ultraviolet-absorbing metabolites found in cyanobacteria, fungi, algae, and animals, known for their antioxidant and anti-inflammatory properties. Objectives: This study aimed to optimize MAAs extraction from Fischerella sp. F5 using experimental design techniques. Methods: The central composite design was employed to determine the optimal levels of five factors: Total extraction time, temperature, sonication time, methanol ratio, and solvent volume. Each run involved extracting 100 mg of dried biomass under the specified conditions. Mycosporine-like amino acids content was quantified using high performance liquid chromatography (HPLC), and the antioxidant activity of the extract was assessed using the DPPH radical scavenging test. The best model was selected based on the highest R2 value, and the optimized extraction conditions were determined. Results: Mycosporine-like amino acids content in the final extracts ranged from 27.7% to 89%, with antioxidant activities ranging from 15% to 59.3%. Overall, all factors showed a positive correlation with MAAs content in the final extract. However, no significant model was established to correlate extraction conditions with MAAs purity ratio (MAA%) or extract antioxidant activity. Conclusions: The bioactivity of partially purified MAAs may be influenced by factors such as associated impurities extracted alongside MAAs, which were not investigated in this study. Further research is necessary to identify the exact factors affecting MAAs extraction yield and purity, aiming to obtain partially purified MAAs extracts with optimal bioactivity.

Determinants of funding intention of crowdfunding campaigns from India – a subregional cultural variation and transnational migrants perspective

Purpose This paper aims to explore how sub-national or regional cultural differences influence backers’ willingness to crowdfund projects. The paper also explores how migrant transnationalism influences the impact of backer’s sub-national culture and crowdfunding relationships. Design/methodology/approach This paper is based on the experimental design technique using analysis of covariance methods. The authors tested the study hypotheses on a sample of 790 respondents. Findings The study results suggest that individuals differ in their intent to crowdfund product campaigns depending on value congruence between their cultural values derived from the region to which they belong and the nature of the product category, such as environmentally friendly or happiness-enhancing products. Originality/value This paper explores the role of regional cultural differences in determining the intention to crowdfund different campaigns based on the nature of the product. Value congruence, as driven by regional cultural differences with crowdfunding campaigns, has not been explored before.

Reducing Interference Bias in Online Marketplace Experiments Using Cluster Randomization: Evidence from a Pricing Meta-experiment on Airbnb

Online marketplace designers frequently run randomized experiments to measure the impact of proposed product changes. However, given that marketplaces are inherently connected, total average treatment effect (TATE) estimates obtained through individual-level randomized experiments may be biased because of violations of the stable unit treatment value assumption, a phenomenon we refer to as “interference bias.” Cluster randomization (i.e., the practice of randomizing treatment assignment at the level of “clusters” of similar individuals) is an established experiment design technique for countering interference bias in social networks, but it is unclear ex ante if it will be effective in marketplace settings. In this paper, we use a meta-experiment or “experiment over experiments” conducted on Airbnb to both provide empirical evidence of interference bias in online marketplace settings and assess the viability of cluster randomization as a tool for reducing interference bias in marketplace TATE estimates. Results from our meta-experiment indicate that at least 20% of the TATE estimate produced by an individual-level randomized evaluation of the platform fee increase we study is attributable to interference bias and eliminated through the use of cluster randomization. We also find suggestive, nonstatistically significant evidence that interference bias in seller-side experiments is more severe in demand-constrained geographies and that the efficacy of cluster randomization at reducing interference bias increases with cluster quality. This paper was accepted by Chris Forman, information systems. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2020.01157 .

Analysis of Fluid Pressure Drop through a Globe Valve using Computational Fluid Dynamics and Statistical Techniques

Fluid mechanics plays a crucial role in everyday life, enabling the selection of accessories, materials, and various components essential for a system through which fluid flows. Pressure drop stands out as one of the most relevant factors in the design of fluid flow systems. However, analytical and experimental physical methods can increase these analyses' costs and time. Hence, in this study, statistical tools are employed to carry out specific experiments supported by numerical fluid simulation, aiming to comprehend the pressure drop behavior in a fluid as it passes through a globe valve. This valve, in turn, possesses distinct operating and manufacturing characteristics. The methods employed encompass a complete factorial system of response surface as support to construct the experimental design path through computational fluid dynamics. Among the key findings, it is demonstrated that, for systems with relatively low flow rates, the valve opening percentage does not exhibit a significant relationship with fluid pressure drop. Conversely, significant effects are observed for systems with relatively high flow rates regarding the valve opening percentage and pressure drop, reaching values of up to 73% pressure drop in this study. It can be inferred that the integration of statistical experimental design techniques and computational fluid dynamics constitutes a valuable resource for studying the pressure drop of a fluid passing through a system.

A quality-based sustainable supply chain architecture for perishable products using image processing in the era of industry 4.0

In India, approximately one-third of agricultural produce is wasted every year due to the different issues present in the post-harvest supply chain stages. The supply chain management of perishable products becomes complex and challenging due to the inclusion of its perishability dynamics. Quality is the main factor that governs the buying or discarding of perishable products by consumers. Therefore, the main aim of this research work is to develop an accurate and efficient image processing model for the classification of the product (Tomato) based on its quality for managing the supply chain. There are three novelties in this research work. A four-stage supply chain architecture integrated with the image processing system at mandi, and warehouses is proposed (First). This image processing system is developed in two stages. In stage I, the acquired images of tomato during its life cycle are labelled with the help of machine learning algorithms (Second). This labelled data is used in stage II for the development of a classification model to segregate the product into various grades. For this, an optimized architecture of seven-layer Convolutional Neural Network (CNN) model is developed followed by optimization of its hyperparameters simultaneously using Design of Experiments (DOE) technique (Third). The optimized CNN model achieved maximum accuracy of 88.40% and reported an execution time of 7 min. Further, the results of standard hyperparameter optimization techniques like Grid search, Random search, Bayesian, and Hyperband are compared with the proposed DOE technique on the optimized CNN architecture. The work done in this paper enables the supply chain managers to take accurate and rapid decisions for pricing, procurement, storage, and transportation at various stages of the supply chain leading to Industry 4.0. This will result in reduced post-harvest losses and simultaneously achieve the benefits across social, economic, and environmental dimensions of sustainability leading to better supply chain management.

Modeling, landscape analysis, and solving the capacitated single-allocation hub maximal covering problem using the GARVND hybrid algorithm

In this paper, a new variation to the Hub Maximal Covering problem called the Capacitated Single-Allocation Hub Maximal Covering Problem (CSAHMCP) is addressed for the first time and a novel binary-integer mathematical model has been proposed. To analyze the features of the problem’s solution space and to determine the most effective heuristic approach to solve it, a comprehensive Fitness Landscape Analysis is performed which reveals that the problem is of ‘rugged plain’ type and can best be solved using an algorithm with good exploration and exploitation capabilities at the same time. Accordingly, we proposed a new algorithm named GARVND, which is a hybrid of the Genetic Algorithm acting as the main framework for diversification, and a variant of the Variable Neighborhood Descent algorithm called ‘Reduced VND’ acting as a means to improve individual solutions (intensification) by searching in the spaces generated by four types of neighborhood operators. We also introduced two original crossover and mutation operators that can be used for any hub location solution method. The parameters of GARVND are tuned toward less computational times and higher solution quality by using the Taguchi experiment design technique. We solved 114 CSAHMCP instances (several times each) from the Australian Post (AP) dataset using the proposed GARVND, as well as by the Genetic Algorithm, Variable Neighborhood Search, Simulated Annealing, Tabu Search algorithms, and a hybrid algorithm consisting of Grey Wolf Optimizer and Simulated Annealing, in addition to the Gurobi exact solver. Extensive computational results and statistical analyses showed that GARVND yields favorable solutions, outperforming all the compared algorithms by producing smaller gaps with global optimal objective values. In addition, to illustrate the practical value of the CSAHMCP and the GARVND method, we performed a cost-flow sensitivity analysis for the AP Central Sydney network and came up with the best expansion scenarios to be adopted by the management.

Fine root production, mortality, and turnover in response to simulated nitrogen deposition in the subtropical Abies georgei (Orr) forest

Increased nitrogen deposition has important effects on below-ground ecological processes. Fine roots are the most active part of the root system in terms of physiological activity and the main organs for nutrient and water uptake by plants. However, there is still a limited understanding of how nitrogen deposition affects the fine root dynamics in subtropical Abies georgei (Orr) forests. Consequently, a three-year field experiment was conducted to quantify the effects of three forms of nitrogen sources ((NH4)2SO4, NaNO3, and NH4NO3) at four levels (0, 5, 15, and 30 kg N·ha−1·yr−1) on the fine root dynamics in Abies georgei forests using a randomized block-group experimental design and minirhizotron technique. The first year of nitrogen addition did not affect the first-class fine roots (FR1, 0 < diameter < 0.5 mm) and second-class fine roots (FR2, 0.5 < diameter < 1.0 mm). The next two years of nitrogen addition significantly increased the production, mortality, and turnover of FR1 and FR2; the three year of nitrogen addition did not affect the dynamics of the third- class fine roots (FR3, 1.0 < diameter < 1.5 mm) and fourth- class fine roots (FR4,1.5 < diameter < 2.0 mm). Nitrogen addition positively affected the dynamics of FR1, FR2, FR3 and FR4 by positively affecting the carbon, nitrogen, and phosphorus contents of fine roots and indirectly affecting the soil pH. Increased carbon allocation to FR1 and FR2 may represent a phosphorus acquisition strategy when nitrogen is not the limiting factor. The nitrogen addition forms and levels affected the fine root dynamics in the following orde: NH4NO3 > (NH4)2SO4 > NaNO3 and high nitrogen > medium nitrogen > low nitrogen. The results suggest that the different-diameter fine root dynamics respond differently to different nitrogen addition forms and levels, and linking the different-diameter fine roots to nitrogen deposition is crucial.

Peer-aggressive humor and cyberbullying among Chinese and American university students: mediating and moderating roles of frustration and peer norms

ABSTRACT The impact of aggressive humor on workplace dynamics has been extensively examined; however, its implications for university students remain underexplored. Drawing on frustration–aggression theory, this study aims to uncover the consequences of peer-aggressive humor and its relationship with cyberbullying behavior. We employed a 2 (peer aggressive humor: present vs. absent) * 2 (perceived peer norms regarding tolerating aggressive humor: high vs. low) between-subject experimental design and random sampling techniques via Credamo and MTurk to recruit university students from China and the United States as participants. Data analysis was conducted using SPSS 21 PROCESS macros to validate the proposed research model. The results revealed that the effects of peer-aggressive humor are contingent upon students’ perceptions of peer norms. When students perceived a high tolerance for aggressive humor among their peers, they became frustrated after experiencing peer aggressive humor, consequently intensifying their inclination to engage in cyberbullying perpetration. Conversely, when students perceived peer norms of low tolerance for aggressive humor, they were less likely to feel frustrated and less inclined to perpetrate cyberbullying. These findings suggest that educators should consider the potential consequences of peer aggressive humor, emphasizing the role of negative emotions, and guiding the establishment of peer norms. As such, it contributes to the broader understanding of strategies aimed at mitigating cyberbullying incidents among students.

On the reliable estimation of sequential Monod kinetic parameters

While dozens of studies have attempted to estimate the Monod kinetic parameters of microbial reductive dechlorination, published values in the literature vary by 2–6 orders of magnitude. This lack of consensus can be attributed in part to limitations of both experimental design and parameter estimation techniques. To address these issues, Hamiltonian Monte Carlo was used to produce more than one million sets of realistic simulated microcosm data under a variety of experimental conditions. These data were then employed in model fitting experiments using a number of parameter estimation algorithms for determining Monod kinetic parameters. Analysis of data from conventional triplicate microcosms yielded parameter estimates characterized by high collinearity, resulting in poor estimation accuracy and precision. Additionally, confidence intervals computed by commonly used classical regression analysis techniques contained true parameter values much less frequently than their nominal confidence levels. Use of an alternative experimental design, requiring the same number of analyses as conventional experiments but comprised of microcosms with varying initial chlorinated ethene concentrations, is shown to result in order-of-magnitude decreases in parameter uncertainty. A Metropolis algorithm which can be run on a typical personal computer is demonstrated to return more reliable parameter interval estimates.

Experimental tribological and mechanical behavior of aluminium alloy 6061 composites incorporated ceramic particulates using Taguchi analysis

Aluminium metal-matrix composites are broadly employed in industries like aerospace, marine, automobile-sector, and many others, generating a broad research interest. They are preferred for their high strength and low wear rate. This research work is mainly directed towards the fabrication of two types of metal-matrix composite materials using aluminium-alloy 6061(AA6061) as the matrix material and incorporating aluminium oxide (Al2O3), aluminium nitride (AlN), and silicon carbide (SiC) as a reinforcement material. AA6061 has been reinforced with Al2O3 and SiC particulates in AS series composites, while AA series composites, AA6061 has been reinforced with Al2O3 and AlN particulates. This hybrid metal composite was constructed using the stir-casting method. These hybrid composites have been investigated for their physical and mechanical characteristics, viz. density, hardness, impact strength, void content, flexural strength, and tensile strength following the ASTM standards. Tribological characteristics of these hybrid composites were performed by Pin-on-disc (POD) tribometer. Taguchi’s design of experimental technique was applied taking four factors viz. composition, sliding-velocity (SV), sliding-distance (SD), and load, and each having 4 levels. ANOVA was employed to find out meaningful parameters. SEM analysis of worn-surface was executed to find out the dominant wear mechanism. Experimental density was highest for AS42, and AA42 composites and void content were lowest for the same. Mechanical properties viz. density, hardness, impact strength, void content, flexural strength, and tensile strength were higher than the base alloy. From the response table of Taguchi analysis of AS and AA composite, the load factor significantly impacted the specific wear rate (SWR) to follow filler content (FC). It can be followed from the ANOVA table for AS composite that the load factor had a magnificent impact on the SWR. The SV and SD show no significant effect on SWR.

Separation of rare earth elements from waste NdFeB magnets: The influence of hematite seeds and agents on the hematite precipitation in nitric acid medium

ABSTRACT Nowadays with the evolution of technology and rare earth elements (REE) are raw materials for a multitude of products, especially in technological applications. Large amounts of electronic waste rich in REE are produced, and this can be utilized as a valuable resource. For these reasons, efforts and research are focusing on secondary production, which is recycling and recovery of REE and specifically neodymium (Nd) from end-of-life components. In this study, the recovery of REE from NdFeB was investigated based on Box-Behnken experimental design technique. The effect of the experimental parameters in the hydrolysis of Fe3+ to hematite in a nitric acid medium in the hydrothermal reactor was examined in detail. Afterwards, the influence of the temperature, time, and hematite precipitation agents including starch (C6H10O5) n, sucrose (C12H22O11), and hematite seeds (Fe2O3) on the recovery of REE, and hematite precipitation were analyzed by statistical analyses. The optimal parameters for maximum REE recovery and iron removal under pressure and temperature were determined to be a process temperature of approximately 145°C, a process time of approximately 6 hours with the starch addition. The optimum parameters are in harmony with the validation experiments.

Advancements in Soil Stabilization: The Efficacy of Fly Ash and GGBS

Engineered soil qualities can be modified mechanically, chemically, or biologically to achieve soil stabilisation. Soil stabilisation is a method used in civil engineering to enhance and enhance a soil’s structural qualities. Mechanical strength, permeation, compressibility, resilience, and plasticity are some of these attributes. The systematic investigation of soil stabilization with fly ash and ground granulated blast furnace slag (GGBS) as binders is presented in this work. The goal of using geopolymer technology is to enhance the mechanical qualities of clay soils for the building of road pavement by substituting conventional cementitious materials. The ideal ratio of Fly Ash to GGBS is found using a Taguchi experimental design technique, more precisely a L9 Orthogonal Array, with the goal of improving soil liquid limit, plastic limit, and plasticity index. The effectiveness of various Fly Ash and GGBS ratios is assessed, as well as their influence on soil behavior. In order to develop models for predicting soil parameters, regression analysis is used. Diagnostic plots show a reasonable fit but also highlight small amounts of unexplained variability. These discoveries have important ramifications for sustainable construction methods and are essential for improving soil stabilizing procedures, especially when it comes to road pavement engineering.

The Effect of Video Media-Based Jigsaw Type Cooperative Learning Model to Increase Motivation and Science Learning Outcomes

The purpose of this research is to ascertain whether or not the Jigsaw method, which makes use of video media, improves the scientific motivation and cognitive learning results for fifth graders at SDN 2 Tekorejo. One component of the quantitative approach used in this research is the genuine experiment design technique. The experimental group and control group were selected using simple random selection from all fifth graders in five public primary schools in the Melati Cluster, Buay Madang Timur District, East OKU. Instruments for data collecting include surveys, exams, and observations; tests for normality and homogeneity, as well as independent t-tests, t-test samples, and gain tests, are used for data analysis. Students' levels of intrinsic desire to learn were found to be 0.49 in the control group and 0.78 in the experimental group, according to the N-Gain test. The experimental group's N-Gain test for cognitive learning outcomes was 0.65, but the control group's was just 0.43. Students' motivation and cognitive learning results were improved in the video-assisted jigsaw method class compared to the lecture method control class. It follows that students' motivation and cognitive learning results are impacted by the Jigsaw method with video media assistance.

A person-centred approach to help services and commissioners to improve the experience of people who are homeless and secure better sustainability.

Successful implementation of evidence-based services in health and social care depends largely on the fit of the services with the values and priorities of users who are shaping and participating in their delivery and use. When looking at the implementation of innovative specialist hospital discharge schemes for people who are homeless, there is a lack of knowledge on homeless people’s preferences and perspectives on receiving out-of-hospital care.&#x0D; In England, the Department of Health and Social Care, Ministry for Housing and Local Government and Ministry of Justice allocated £16 million to further develop and test out hospital care for people experiencing homelessness. The out-of-hospital care model programme is seeking to scale successful hospital discharge models that were shown to be effective and cost-effective in an earlier pilot programme adapting these for new contexts and circumstances post Covid-19. Evaluation of the implementation of the out-of-hospital care model programme is currently underway in 17 local authority test sites across England. A specific component of the larger evaluation is aimed at capturing service users’ preferences for different types of specialist hospital discharge schemes.&#x0D; This is done using a flexible health economics tool for measuring choices in health and social care-related settings named discrete choice experiments (DCE). It measures preferences from individual decision-makers over alternative scenarios (or service provisions). Each alternative is described by several attributes (or characteristics) and the choices made between two or more competing scenarios subsequently determine how preferences are influenced by each attribute (eg, which attributes are valued as well as their relative importance). It can also provide a measure of the overall value attached to different alternatives (and identify optimal service provision that meets stakeholder requirements and have the best chance of sustainability in the long term). Hence a DCE survey has been developed to measure people’s preferences for what out-of-hospital care provides in terms of types of location of care, the professional who delivers most of their care, how often they receive care, how long they receive care after hospital discharge and rules about behaviour where they live.&#x0D; The attributes (characteristics) and their various levels were informed by our previous research. Webinar discussions regarding what stakeholders value about specialist services were used to validate and refine the attributes and levels. PPI stakeholders were also consulted on the development of the presentation, wording and format of the survey. Efficient experimental design techniques were applied to create the DCE questions. Due to the cognitive load involved in completing the survey, respondents are assisted in the completion of the questionnaire by members of the research team to secure responses from more than 250 people who are homeless. Data will be modelled using logit techniques and results will be ready for presentation in May 2023.&#x0D; Information on how service users value specialist hospital discharge schemes for people who are homeless is needed to better inform the implementation and development of the innovative out-of-hospital care model programme currently proposed in England at a national level. Identified learning will be shared at the conference.

New analytical method for the determination of endocrine disruptors in human faeces using gas chromatography coupled to tandem mass spectrometry.

Endocrine-disrupting chemicals are environmental pollutants that can enter our bodies and cause diverse pathologies. Some bisphenols and parabens have been shown to be capable of modifying proper functioning of the endocrine system. Among other dysfunctions, endocrine-disrupting chemicals can cause changes in intestinal microbiota. Faeces are a convenient matrix that can be useful for identifying the quantity of endocrine disruptors that reach the intestine and the extent to which the organism is exposed to these pollutants. The present work developed a new analytical method to determine 17 compounds belonging to the paraben and bisphenol families found in human faeces. The extraction method was optimized using an ultrasound-assisted extraction technique followed by a clean-up step based on the QuEChERS (Quick, Easy, Cheap, Effective, Rugged and Safe) technique. Optimization was performed using the design of experiments technique. In validation analysis, the method was proven to be linear over a wide range. R-squared outcomes were between 95 and 99%. Selectiveness and sensitivity outcomes were acceptable, with detection limits being between 1 and 10ngg-1 in all cases, whilst quantification limits were between 3 and 25ngg-1 in all instances, with the exception of bisphenol AF. The method was deemed accurate, with recovery values being close to 100% and relative standard deviations being lower than 15% in all cases. Applicability was examined by analysing 13 samples collected from volunteers (male and female). All samples were contaminated with at least one of the analytes studied. The most commonly found compounds were methylparaben and bisphenol A, which were detected in almost all samples and quantitatively determined in 11 and 12 samples, respectively. Of the 17 compounds analysed, 11 were found in at least one sample. Outcomes demonstrate that faeces can be a good matrix for the determination of exposure to contaminants of interest here.

Yoked learning in molecular data science

Active machine learning is an established and increasingly popular experimental design technique where the machine learning model can request additional data to improve the model's predictive performance. It is generally assumed that this data is optimal for the machine learning model since it relies on the model's predictions or model architecture and therefore cannot be transferred to other models. Inspired by research in pedagogy, we here introduce the concept of yoked machine learning where a second machine learning model learns from the data selected by another model. We found that in 48% of the benchmarked combinations, yoked learning performed similar or better than active learning. We analyze distinct cases in which yoked learning can improve active learning performance. In particular, we prototype yoked deep learning (YoDeL) where a classic machine learning model provides data to a deep neural network, thereby mitigating challenges of active deep learning such as slow refitting time per learning iteration and poor performance on small datasets. In summary, we expect the new concept of yoked (deep) learning to provide a competitive option to boost the performance of active learning and benefit from distinct capabilities of multiple machine learning models during data acquisition, training, and deployment.