Determining interfacial tension and critical micelle concentrations of surfactants from atomistic molecular simulations.

Hypothesis Atomistically-detailed models of surfactants provide quantitative information on the molecular interactions and spatial distributions at fluid interfaces. Hence, it should be possible to extract from this information, macroscopical thermophysical properties such as interfacial tension, critical micelle concentrations and the relationship between these properties and the bulk fluid surfactant concentrations. Simulations and Experiments Molecular-scale interfacial of systems containing n-dodecyl ß-glucoside (APG12) are simulated using classical molecular dynamics. The bulk phases and the corresponding interfacial regions are all explicitly detailed using an all-atom force field (PCFF+). During the simulation, the behaviour of the interface is analyzed geometrically to obtain an approximated value of the critical micelle concentration (CMC) in terms of the surfactant area number density and the interfacial tension is assessed through the analysis of the forces amongst molecules. New experimental determinations are reported for the surface tension of APG12 at the water/air and at the water/n-decane interfaces. Findings We showcase the application of a thermodynamic framework that inter-relates interfacial tensions, surface densities, CMCs and bulk surfactant concentrations, which allows the in silico quantitative prediction of interfacial tension isotherms.

Merits and Demerits of Machine Learning of Ferroelectric, Flexoelectric, and Electrolytic Properties of Ceramic Materials.

In the present review, the merits and demerits of machine learning (ML) in materials science are discussed, compared with first principles calculations (PDE (partial differential equations) model) and physical or phenomenological ODE (ordinary differential equations) model calculations. ML is basically a fitting procedure of pre-existing (experimental) data as a function of various factors called descriptors. If excellent descriptors can be selected and the training data contain negligible error, the predictive power of a ML model is relatively high. However, it is currently very difficult for a ML model to predict experimental results beyond the parameter space of the training experimental data. For example, it is pointed out that all-dislocation-ceramics, which could be a new type of solid electrolyte filled with appropriate dislocations for high ionic conductivity without dendrite formation, could not be predicted by ML. The merits and demerits of first principles calculations and physical or phenomenological ODE model calculations are also discussed with some examples of the flexoelectric effect, dielectric constant, and ionic conductivity in solid electrolytes.

Enhancing inferences and conclusions in body image focused non-experimental research via a causal modelling approach: A tutorial.

Causal inference is often the goal of psychological research. However, most researchers refrain from drawing causal conclusions based on non-experimental evidence. Despite the challenges associated with producing causal evidence from non-experimental data, it is crucial to address causal questions directly rather than avoiding them. Here we provide a clear, non-technical overview of the fundamental concepts (including the counterfactual framework and related assumptions) and tools that permit causal inference in non-experimental data, intended as a starting point for readers unfamiliar with the literature. Certain tools, such as the target trial framework and causal diagrams, have been developed to assist with the identification and reduction of potential biases in study design and analysis and the interpretation of findings. We apply these concepts and tools to a motivating example from the body image field. We assert that more precise and detailed elucidation of the barriers to causal inference within one's study is arguably a key first step in the enhancement of non-experimental research and future intervention development and evaluation.

The development and testing of a smart sensorized guide wire for catheterization in a "blood" vessel phantom to support aortic valve implementation.

PURPOSE: Heart valve disease is commonly treated by minimally invasive procedures with guide wires and catheterization. The main purpose of this study is to find out whether an extension of the guide wire with a sensor can support the surgeon within the blood vessel to reduce X-ray necessity. METHODS: A smart guide wire is developed by an extension with a flex-bending sensor to evaluate the sensor signal with and without "blood" flow at a constant compression force. Various surgically relevant investigations are performed. For assessment, the mean temporal average of the moving averaged filtered ADC signal and a subsequent FFT are carried out. RESULTS: Results show that there is a smaller sensor signal when the applied force or bending at the sensor is higher. In all investigations, there was a different sensor signal. The flex-bending sensor can detect the effect of pulsatile flow. The smallest temporal averaged signal difference between reference and clamp in the front wire's tip is 1.09%. For example, the mean temporal average of the filtered ADC signal for different clinically relevant scenarios is between 2550 and 2900. CONCLUSIONS: The results show that the sensorized guide wire developed for catheterization can support aortic valve implementation. The sensor sensitivity is sufficient to detect even very small variations within the blood vessel and therefore is promising to support catheterization heart valve surgeries in future.

Self-consolidating concrete: Dataset on mixture design and key properties.

This manuscript delineates the assembly and structure of an extensive dataset encompassing more than 2500 self-consolidating concrete (SCC) mixtures, meticulously compiled from 176 scholarly sources. The dataset has been subjected to a thorough curation process to eliminate feature redundancy, rectify transcriptional inaccuracies, and excise duplicative entries. This refinement process has culminated in a dataset primed for advanced data-driven inquiries within the SCC research domain, marking a novel contribution to the field. The dataset serves as a robust foundational resource, poised for subsequent augmentations and stringent applications in data-centric studies. It facilitates a detailed characterization of SCC properties, potentially through the implementation of machine learning algorithms, or serves as a comparative benchmark to assess the performance across diverse SCC formulations. In conclusion, the dataset serves as a crucial resource for scholars engaged in studying SCC and similar substances. It offers deep insights into the ecological benefits of substituting conventional Portland concrete with SCC alternatives. This compilation not only advances the understanding of SCC properties but also contributes to the broader conversation about sustainable construction practices.

Dataset of a numerical model of a cold-formed, C-shaped section in compression according to EN 1993-1-14.

Numerical simulations have gained increasing importance in civil engineering in the recent past. The potential for research on the one hand and for practical application on the other hand is great. EN 1993-1-14 was developed to support users in the generation of numerical models, to provide security in the procedure and to achieve comparable results across the board. The accompanying publication of application examples is of extraordinary assistance. The data set presented in this article was also used to generate the benchmark examples No. 5 of Technical Report EN/TR 1993-1-141:2023 (E)_v2023-06 on EN 1993-1-14. Based on the data, the procedure for validating a numerical model on experimental component tests can be reproduced using a series of tests on cold-formed, C-shaped columns in axial compression. In this article, the measured imperfections of the test specimens and the recorded measured values during the test are first discussed in detail. The corresponding data are provided. Subsequently, the numerical models are provided and described as input files in the program language ANSYS Parametric Design Language (APDL). The entire data set can be used as an aid to generate numerical models according to the specifications of EN 1993-1-14.

Analysis of changes in nutrient salts and other water quality indexes in the pond water for largemouth bass (micropterus salmoides) farming.

To explore the changes in nitrite nitrogen, ammoniacal nitrogen, nitrate nitrogen, phosphates, pH, dissolved oxygen, salinity, and water temperature over time and the correlations and mutual influences between these indexes in the traditional farming of largemouth bass, this study selected three ponds in Lizigu Farm in Baodi District of Tianjin, China as research objects. From May to October 2021, nutrient salts and other water quality indexes in the ponds were measured, and water samples were collected at different depths for repetition, Water is collected from the ponds using Plexiglas samplers and sent back to the lab for determination of water quality indexes using our national laboratory standards. According to the analysis of the measurement results, in traditional farming, nitrite nitrogen, ammoniacal nitrogen, nitrate nitrogen, phosphates, pH, dissolved oxygen, salinity, and water temperature in the ponds for largemouth bass all change significantly over time, with different changing trends and certain correlations with each other. In particular, nutrient salts indexes in ponds are influenced by other water quality indexes, human activities, and phytoplankton. During the breeding process, strengthening the dynamic monitoring of nutrient salts and other water quality indexes in the ponds and adjusting the nitrogen, phosphorus, and ammonia levels in the ponds artificially play an important role in preventing eutrophication in the water and promoting the green and sustainable production of pond ecosystems, in particular, allowing better quality growth of the largemouth bass, as well as ensuring the production and economic efficiency. This study provides a theoretical basis and data support for further optimization of traditional pond aquaculture in similar regions, in order to provide aquatic products with better quality and achieve higher economic benefits.

Accurate prediction of pressure losses using machine learning for the pipeline transportation of emulsions.

One of the significant challenges to designing an emulsion transportation system is predicting frictional pressure losses with confidence. The state-of-the-art method for enhancing reliability in prediction is to employ artificial intelligence (AI) based on various machine learning (ML) tools. Six traditional and tree-based ML algorithms were analyzed for the prediction in the current study. A rigorous feature importance study using RFECV method and relevant statistical analysis was conducted to identify the parameters that significantly contributed to the prediction. Among 16 input variables, the fluid velocity, mass flow rate, and pipe diameter were evaluated as the top predictors to estimate the frictional pressure losses. The significance of the contributing parameters was further validated by estimation error trend analyses. A comprehensive assessment of the regression models demonstrated an ensemble of the top three regressors to excel over all other ML and theoretical models. The ensemble regressor showcased exceptional performance, as evidenced by its high R2 value of 99.7 % and an AUC-ROC score of 98 %. These results were statistically significant, as there was a noticeable difference (within a 95 % confidence interval) compared to the estimations of the three base models. In terms of estimation error, the ensemble model outperformed the top base regressor by demonstrating improvements of 6.6 %, 11.1 %, and 12.75 % for the RMSE, MAE, and CV_MSE evaluation metrics, respectively. The precise and robust estimations achieved by the best regression model in this study further highlight the effectiveness of AI in the field of pipeline engineering.

A discussion and evaluation of statistical procedures used by JIMB authors when comparing means.

Out of the 166 articles published in Journal of Industrial Microbiology and Biotechnology (JIMB) in 2019-2020 (not including special issues or review articles), 51 of them used a statistical test to compare two or more means. The most popular test was the (Standard) t-test, which often was used to compare several pairs of means. Other statistical procedures used included Fisher's least significant difference (LSD), Tukey's honest significant difference (HSD), and Welch's t-test; and to a lesser extent Bonferroni, Duncan's Multiple Range, Student-Newman-Keuls, and Kruskal-Wallis tests. This manuscript examines the performance of some of these tests with simulated experimental data, typical of those reported by JIMB authors. The results show that many of the most common procedures used by JIMB authors result in statistical conclusions that are prone to have large false positive (Type I) errors. These error-prone procedures included the multiple t-test, multiple Welch's t-test, and Fisher's LSD. These multiple comparisons procedures were compared with alternatives (Fisher-Hayter, Tukey's HSD, Bonferroni, and Dunnett's t-test) that were able to better control Type I errors. NON-TECHNICAL SUMMARY: The aim of this work was to review and recommend statistical procedures for Journal of Industrial Microbiology and Biotechnology authors who often compare the effect of several treatments on microorganisms and their functions.

Prediction for Future Yaw Rate Values of Vehicles Using Long Short-Term Memory Network.

Currently, electric mobility and autonomous vehicles are of top priority from safety, environmental and economic points of view. In the automotive industry, monitoring and processing accurate and plausible sensor signals is a crucial safety-critical task. The vehicle's yaw rate is one of the most important state descriptors of vehicle dynamics, and its prediction can significantly contribute to choosing the correct intervention strategy. In this article, a Long Short-Term Memory network-based neural network model is proposed for predicting the future values of the yaw rate. The training, validating and testing of the neural network was conducted based on experimental data gathered from three different driving scenarios. The proposed model can predict the yaw rate value in 0.2 s in the future with high accuracy, using sensor signals of the vehicle from the last 0.3 s in the past. The R2 values of the proposed network range between 0.8938 and 0.9719 in the different scenarios, and in a mixed driving scenario, it is 0.9624.

NAPS Fusion: A framework to overcome experimental data limitations to predict human performance and cognitive task outcomes.

In the area of human performance and cognitive research, machine learning (ML) problems become increasingly complex due to limitations in the experimental design, resulting in the development of poor predictive models. More specifically, experimental study designs produce very few data instances, have large class imbalances and conflicting ground truth labels, and generate wide data sets due to the diverse amount of sensors. From an ML perspective these problems are further exacerbated in anomaly detection cases where class imbalances occur and there are almost always more features than samples. Typically, dimensionality reduction methods (e.g., PCA, autoencoders) are utilized to handle these issues from wide data sets. However, these dimensionality reduction methods do not always map to a lower dimensional space appropriately, and they capture noise or irrelevant information. In addition, when new sensor modalities are incorporated, the entire ML paradigm has to be remodeled because of new dependencies introduced by the new information. Remodeling these ML paradigms is time-consuming and costly due to lack of modularity in the paradigm design, which is not ideal. Furthermore, human performance research experiments, at times, creates ambiguous class labels because the ground truth data cannot be agreed upon by subject-matter experts annotations, making ML paradigm nearly impossible to model. This work pulls insights from Dempster-Shafer theory (DST), stacking of ML models, and bagging to address uncertainty and ignorance for multi-classification ML problems caused by ambiguous ground truth, low samples, subject-to-subject variability, class imbalances, and wide data sets. Based on these insights, we propose a probabilistic model fusion approach, Naive Adaptive Probabilistic Sensor (NAPS), which combines ML paradigms built around bagging algorithms to overcome these experimental data concerns while maintaining a modular design for future sensor (new feature integration) and conflicting ground truth data. We demonstrate significant overall performance improvements using NAPS (an accuracy of 95.29%) in detecting human task errors (a four class problem) caused by impaired cognitive states and a negligible drop in performance with the case of ambiguous ground truth labels (an accuracy of 93.93%), when compared to other methodologies (an accuracy of 64.91%). This work potentially sets the foundation for other human-centric modeling systems that rely on human state prediction modeling.

Experimental Data on the Role of Melatonin in the Pathogenesis of Nonalcoholic Fatty Liver Disease.

Despite the increasing prevalence of nonalcoholic fatty liver disease (NAFLD) worldwide, its complex pathogenesis remains incompletely understood. The currently stated hypotheses cannot fully clarify the interrelationships between individual pathogenetic mechanisms of the disease. No appropriate health strategies have been developed for treating NAFLD. NAFLD is characterized by an accumulation of triglycerides in hepatic cells (steatosis), with the advanced form known as nonalcoholic steatohepatitis. In the latter, superimposed inflammation can lead to fibrosis. There are scientific data on NAFLD's association with components of metabolic syndrome. Hormonal factors are thought to play a role in the development of metabolic syndrome. Endogenous melatonin, an indoleamine hormone synthesized by the pineal gland mainly at night, is a powerful chronobiotic that probably regulates metabolic processes and has antioxidant, anti-inflammatory, and genomic effects. Extrapineal melatonin has been found in various tissues and organs, including the liver, pancreas, and gastrointestinal tract, where it likely maintains cellular homeostasis. Melatonin exerts its effects on NAFLD at the cellular, subcellular, and molecular levels, affecting numerous signaling pathways. In this review article, we discuss the experimental scientific data accumulated on the involvement of melatonin in the intimate processes of the pathogenesis of NAFLD.

Experimental Data Connector (XDC): Integrating the Capture of Experimental Data and Metadata Using Standard Formats and Digital Repositories.

Accelerating the development of synthetic biology applications requires reproducible experimental findings. Different standards and repositories exist to exchange experimental data and metadata. However, the associated software tools often do not support a uniform data capture, encoding, and exchange of information. A connection between digital repositories is required to prevent siloing and loss of information. To this end, we developed the Experimental Data Connector (XDC). It captures experimental data and related metadata by encoding it in standard formats and storing the converted data in digital repositories. Experimental data is then uploaded to Flapjack and the metadata to SynBioHub in a consistent manner linking these repositories. This produces complete connected experimental data sets that are exchangeable. The information is captured using a single template Excel Workbook, which can be integrated into existing experimental workflow automation processes and semiautomated capture of results.

Simplified Modelling of the Edge Crush Resistance of Multi-Layered Corrugated Board: Experimental and Computational Study.

The edge crush test is the most popular laboratory test in the corrugated packaging industry. It measures the edge crush resistance of a sample in the cross-fiber direction (CD), also known as the ECT index. This parameter is widely used for the specification of the board by its producers. It is also utilized in most analytical formulas describing the load capacity of the packaging. On the other hand, the ECT value can be estimated from both analytical and numerical models based on the basic parameters of each constituent paper. Knowing the compressive strength in CD (commonly known as SCT) and the elastic properties of the individual layers, the sample geometry (i.e., the period and height of the corrugated layer), as well as the boundary conditions, the ECT value can be calculated. This is very useful as new boards can be virtually analyzed before being manufactured. In this work, both detailed numerical models based on finite elements (FE) methods and very simple analytical (engineering) models were used for the ECT calculations. All presented models were validated with experimental data. The surprising consistency and high precision of the results obtained with the simplest approach was additionally analyzed in the study.

EBRAINS Live Papers - Interactive Resource Sheets for Computational Studies in Neuroscience.

We present here an online platform for sharing resources underlying publications in neuroscience. It enables authors to easily upload and distribute digital resources, such as data, code, and notebooks, in a structured and systematic way. Interactivity is a prominent feature of the Live Papers, with features to download, visualise or simulate data, models and results presented in the corresponding publications. The resources are hosted on reliable data storage servers to ensure long term availability and easy accessibility. All data are managed via the EBRAINS Knowledge Graph, thereby helping maintain data provenance, and enabling tight integration with tools and services offered under the EBRAINS ecosystem.

In search of experimental evidence on Scratch programming and students' achievements in the first-year college computing class? Consider these datasets.

This article presents datasets representing the demographics and achievements of computer science students in their first programming courses (CS1). They were collected from a research project comparing the effects of a constructionist Scratch programming and the conventional instructions on the achievements of CS1 students from selected Nigerian public colleges. The project consisted of two consecutive quasi-experiments. In both cases, we adopted a non-equivalent pretest-posttest control group design and multistage sampling. Institutions were selected following purposive sampling, and those selected were randomly assigned to the Scratch programming class (experimental) and the conventional (comparison) class. A questionnaire and pre- and post-introductory programming achievement tests were used to collect data. To strengthen the research design, we used the Coarsened Exact Matching (CEM) algorithm to create matched samples from the unmatched data obtained from both experiments. Future studies can use these data to identify the factors influencing CS1 students' performance, investigate how programming pedagogies or tools affect CS1 students' achievements in higher education, identify important trends using machine learning techniques, and address additional research ideas.

Cider apple pomace as a source of nutrients: Evaluation of the polyphenolic profile, antioxidant and fiber properties after drying process at different temperatures.

Apple pomace, the by-product of the cider industry, contains a high content of antioxidant compounds and dietary fiber. Drying would allow its preservation for a later use. The aim of this study was to evaluate the effect of the drying temperature on the drying kinetics, antioxidant properties and the fiber characteristics. For this, drying experiments were performed at different temperatures (40-120 °C). The increase in temperature enhanced the drying rate, as was shown by the effective diffusivity and mass transfer coefficient identified by modelling. The influence of temperature was quantified through the activation energy (38.21 kJ/mol). Regarding the retention of antioxidant properties, the best results were found at 80-100 °C while 40-60 °C was the best temperature range for the fiber characteristics. Therefore, 80 °C could be an adequate temperature for drying of cider apple pomace, as it represents a good balance between kinetics, and antioxidant and fiber properties.

Dataset and ANFIS model prediction of the performance of graphene nano-LPG in domestic refrigerator system.

LPG's steady-state performance in a base lubricant and a graphene nanolubricant was investigated in this study. Step-by-step processes and procedures for preparing graphene nanolubricant concentrations and replacing them for the base lubricant in a domestic refrigerator system were presented as the measuring devices necessary and their uncertainties. The experimental dataset and the training and testing datasets for Adaptive Neuro-fuzzy Inference System. (ANFIS) are available. The use of an ANFIS approach model to forecast graphene nanolubricant performance in a domestic refrigerator is described. The Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE) are also available as statistical performance indicators for the ANFIS model prediction.

Neural representations of cryo-EM maps and a graph-based interpretation.

BACKGROUND: Advances in imagery at atomic and near-atomic resolution, such as cryogenic electron microscopy (cryo-EM), have led to an influx of high resolution images of proteins and other macromolecular structures to data banks worldwide. Producing a protein structure from the discrete voxel grid data of cryo-EM maps involves interpolation into the continuous spatial domain. We present a novel data format called the neural cryo-EM map, which is formed from a set of neural networks that accurately parameterize cryo-EM maps and provide native, spatially continuous data for density and gradient. As a case study of this data format, we create graph-based interpretations of high resolution experimental cryo-EM maps. RESULTS: Normalized cryo-EM map values interpolated using the non-linear neural cryo-EM format are more accurate, consistently scoring less than 0.01 mean absolute error, than a conventional tri-linear interpolation, which scores up to 0.12 mean absolute error. Our graph-based interpretations of 115 experimental cryo-EM maps from 1.15 to 4.0 Å resolution provide high coverage of the underlying amino acid residue locations, while accuracy of nodes is correlated with resolution. The nodes of graphs created from atomic resolution maps (higher than 1.6 Å) provide greater than 99% residue coverage as well as 85% full atomic coverage with a mean of 0.19 Å root mean squared deviation. Other graphs have a mean 84% residue coverage with less specificity of the nodes due to experimental noise and differences of density context at lower resolutions. CONCLUSIONS: The fully continuous and differentiable nature of the neural cryo-EM map enables the adaptation of the voxel data to alternative data formats, such as a graph that characterizes the atomic locations of the underlying protein or macromolecular structure. Graphs created from atomic resolution maps are superior in finding atom locations and may serve as input to predictive residue classification and structure segmentation methods. This work may be generalized to transform any 3D grid-based data format into non-linear, continuous, and differentiable format for downstream geometric deep learning applications.

Generalized linear mixed-effects models for studies using different sets of stimuli across conditions.

A non-repeated item (NRI) design refers to an experimental design in which items used in one level of experimental conditions are not repeatedly used at other levels. Recent literature has suggested the use of generalized linear mixed-effects models (GLMMs) for experimental data analysis, but the existing specification of GLMMs does not account for all possible dependencies among the outcomes in NRI designs. Therefore, the current study proposed a GLMM with a level-specific item random effect for NRI designs. The hypothesis testing performance of the newly proposed model was evaluated via a simulation study to detect the experimental condition effect. The model with a level-specific item random effect performed better than the existing model in terms of power when the variance of the item effect was heterogeneous. Based on these results, we suggest that experimental researchers using NRI designs consider setting a level-specific item random effect in the model.