Set Of Experiments Research Articles

Experimental Uncertainty Evaluation in Optical Measurements of Micro-Injection Molded Products

Optical measurements are increasingly widely used as preferential techniques to evaluate dimensional and surface quantities in micro-products. However, uncertainty estimation is more critical on micro-products than macro, and it needs careful attention for evaluating the obtained quality, the requested tolerance, and the correct setting of experimental process settings. In this study, optical measurements characterized micro-injected products by linear and surface acquisition and considered all the sources contributing to uncertainties. The results show that the measure uncertainty could be underestimated if only the standard deviation on simple measurements is considered; this could cause a significant restriction of the estimated range covering the measured values. Furthermore, the findings confirm that the correct evaluation of the potential uncertainties contributes to accurately assessing the process behavior and improving product quality.

A holistic approach to the recovery of valuable substances from the treatment sludge formed from chemical precipitation of fruit processing industry wastewater

In this study, recovery of phenolic substances with Soxhlet extraction, (SE) ultrasound-assisted extraction (UAS), and supercritical CO2 (SC-CO2) extraction methods from chemical sludge obtained with chemical precipitation (FeCl3/PACS, Ca(OH)2/PACS, perlite/PACS, FeCl3/cationic polyelectrolyte) of lemon processing wastewater was investigated. The effect of used coagulants/flocculants and pH on COD and total phenolic substance content (TPC) removal was researched. Recovered phenolic substance profiles were also determined with HPLC-DAD. Additionally, response surface methodology was used to determine optimum treatment conditions. ANOVA analysis showed that pH is a more important variable than coagulant/flocculant doses for all chemical precipitation experimental sets. The highest removal efficiencies for COD and TPC was obtained in FeCl3/PACS (COD: 72.0 %, TPC: 93.7 %). Optimum dose values were determined as pH: 4, FeCl3: 3000 mg/L, PACS: 400 mg/L for FeCl3/PACS, pH: 6.5, Ca(OH)2: 1500 mg/L, PACS: 300 mg/L for Ca(OH)2/PACS, pH: 5.5, PACS: 7000 mg/L, perlite: 50 g/L for perlite/PACS, pH: 4.5, FeCl3: 500 mg/L, polyelectrolyte: 4 mg/L for FeCl3/polyelectrolyte. TPC removal efficiencies were determined as 55 %, 35 %, 57 % and 58 % in these conditions, respectively. Maximum TPC in extracts was determined as 39.03 mg GAE/g extract, 8.81 mg GAE/g extract, and 4.34 mg GAE/g extract for SE, UAS, and SC-CO2, respectively. TPC recovery efficiencies (RTPC) for all chemical sludge were SE > UAS > SC-CO2. Additionally, the TPC profile has shown a difference depending on the extraction method.According to the results of this study, it was concluded that the coagulation-flocculation process may be a suitable alternative for fruit juice processing industry wastewater in terms of both reducing environmental pollution and recovering polyphenolics from formed sludge. Consequently, this study presented a different perspective on the recovery from wastes with valuable substance recovery from chemical sludge.

Scientific Deep Machine Learning Concepts for the Prediction of Concentration Profiles and Chemical Reaction Kinetics: Consideration of Reaction Conditions.

Emerging concepts from scientific deep machine learning such as physics-informed neural networks (PINNs) enable a data-driven approach for the study of complex kinetic problems. We present an extended framework that combines the advantages of PINNs with the detailed consideration of experimental parameter variations for the simulation and prediction of chemical reaction kinetics. The approach is based on truncated Taylor series expansions for the underlying fundamental equations, whereby the external variations can be interpreted as perturbations of the kinetic parameters. Accordingly, our method allows for an efficient consideration of experimental parameter settings and their influence on the concentration profiles and reaction kinetics. A particular advantage of our approach, in addition to the consideration of univariate and multivariate parameter variations, is the robust model-based exploration of the parameter space to determine optimal reaction conditions in combination with advanced reaction insights. The benefits of this concept are demonstrated for higher-order chemical reactions including catalytic and oscillatory systems in combination with small amounts of training data. All predicted values show a high level of accuracy, demonstrating the broad applicability and flexibility of our approach.

Pricing the Illiquidity Premium for Private Market Assets

The purpose of this paper is to examine the effect of illiquidity on the value of assets using an options approach. The paper uses two experiments (Hydropower & Damodaran data) to determine the illiquidity. Evidence suggests that erroneous assumptions of the illiquidity premium priced into an asset may ultimately misrepresent an investments value and the risk-return profile. Determining a liquidity standard and an appropriate independent measurement of illiquidity premiums are compared using common yet varying illiquidity pricing theories. Current insights and illiquidity impacts through measurement practices being followed by asset managers are assessed using data of highly illiquid private market assets. Experimental analysis of illiquidity pricing theories are measured against common industry practices of private market assets to reveal inaccuracies in illiquidity premiums. In this paper, the researcher has conducted two sets of experiments to determine the illiquidity premium. Results of the study conclude that the current illiquidity premium being paid to the investors are substantially below the premium that we find using an options approach.

Essential parameters needed for a U-Net-based segmentation of individual bones on planning CT images in the head and neck region using limited datasets for radiotherapy application.

Objective.The field of radiotherapy is highly marked by the lack of datasets even with the availability of public datasets. Our study uses a very limited dataset to provide insights on essential parameters needed to automatically and accurately segment individual bones on planning CT images of head and neck cancer patients.Approach.The study was conducted using 30 planning CT images of real patients acquired from 5 different cohorts. 15 cases from 4 cohorts were randomly selected as training and validation datasets while the remaining were used as test datasets. Four experimental sets were formulated to explore parameters such as background patch reduction, class-dependent augmentation and incorporation of a weight map on the loss function.Main results.Our best experimental scenario resulted in a mean Dice score of 0.93 ± 0.06 for other bones (skull, mandible, scapulae, clavicles, humeri and hyoid), 0.93 ± 0.02 for ribs and 0.88 ± 0.03 for vertebrae on 7 test cases from the same cohorts as the training datasets. We compared our proposed solution approach to a retrained nnU-Net and obtained comparable results for vertebral bones while outperforming in the correct identification of the left and right instances of ribs, scapulae, humeri and clavicles. Furthermore, we evaluated the generalization capability of our proposed model on a new cohort and the mean Dice score yielded 0.96 ± 0.10 for other bones, 0.95 ± 0.07 for ribs and 0.81 ± 0.19 for vertebrae on 8 test cases.Significance.With these insights, we are challenging the utilization of an automatic and accurate bone segmentation tool into the clinical routine of radiotherapy despite the limited training datasets.

Enhancing anomaly detection with adaptive node inspection in large-scale networks with binary sensors

Maintaining the stability and reliability of large-scale networks and graph structures is a practical challenge, particularly in sensor-intensive systems. One critical task in such networks is to identify anomalies and the origin of disturbances in a timely manner. However, successful anomaly detection requires sufficient and accurate data from sensors across the network. This work aims to develop an innovative framework to improve the accuracy of anomaly detection tasks with binary sensor data through intelligent node selection and inspection. Instead of relying only on stochastic insights obtained from network sensors, we explore how a specific small set of nodes can be inspected using a Bayesian framework so that the anomaly detection performance is improved. We demonstrate the effectiveness of the proposed model with a set of numerical experiments.

P009 IL-17 mediates visceral hypersensitivity by sensitising colonic nociceptors

Abstract Background Comparison of cytokine expression in colitic tissue with respective cytokine receptor expression in pain sensing neurons highlights a likely contribution of IL-17 to the production of abdominal pain during flare. To explore this pro-nociceptive potential of IL-17 in colitis we examined the ability of IL-17 to stimulate sensory neurons and the colonic afferent response to noxious distension. Methods IL-17 mediated sensory neuron activation: changes in the intracellular [Ca2+]i within thoracolumbar (T12-L5 spinal segments) dorsal root ganglia sensory neurons were examined in cells cultured overnight and loaded with Fluo-4-AM (30min) prior to imaging. Responses were measured to IL-17 (10-300 ng/mL) alone and following pre-treatment with either TRPV1 or TRPA1 inhibitors. Afterwards cells were exposed to capsaicin (1 µM) followed by 50 mM KCl, to confirm the presence of nociceptors and neurons respectively by positive response. In a separate set of experiments the effect of IL-17 was also examine on a neuron pure cell culture in which non-neuronal cells were removes by magnet assisted cell sorting. IL-17 mediated colonic nociceptor activation: electrophysiological recordings were made of lumbar splanchnic nerve activity and the response to noxious luminal distension of the isolated colorectum (ex-vivo), perfused with carbogenated Krebs solution, examined following pre-treatment with IL-17 (50 ng/mL) alone or in the presence of respective inhibitors. All experiments were performed using tissue from male C57BL/6J mice euthanaised by rising concentration of CO2followed by exsanguination in accordance with Schedule 1 of the UK Animals Scientific Procedures act (1986). Results Consistent with the ability of IL-17 to stimulate sensory neurons including those classified as nociceptors by their co-sensitivity to capsaicin, pre-treatment with IL-17 also produced a significant sensitisation of the colonic afferent response to luminal distension at noxious distending pressures. The increase in [Ca2+]i produced by IL-17 in sensory neurons was blocked by pre-treament with a TRPV1 antagonist but not a TRPA1 antagonist. Furthermore, the proportion of neurons stimulated by IL-17 was comparable between standard DRG cell cultures and neuron pure cell cultures demonstrating a direct effect of IL-17 on sensory neurons. Finally, IL-17 mediated mechanosensitisation of colonic nociceptors was unaffected by TRPV1 inhibition. Conclusion Our findings demonstrate that IL-17 stimulates capsaicin-sensitive nociceptors and mediates visceral hypersensitivity in colonic nociceptors consistent with a causative role for IL-17 is the generation of abdominal pain in people with colitis.

Research on the Critical Value of Sand Permeability Particle Size and Its Permeability Law after Mixing

The permeability of sand is an important factor in determining the movement and occurrence of liquids and gases in sand. The current work studied the influence of soil particle size and gradation on permeability by testing the permeability of different sand samples that consist of defined sand particles in certain ratios. The results of a total of 640 sets of experiments were analyzed and compared with the corresponding theoretical calculations. We found that the sand can be divided into four groups based on particle size: high-permeability particles, medium-permeability particles, low-permeability particles and non-permeable particles; and the critical particle size of sand for permeability is 0.050 mm. Permeation will be lost when non-permeable particles account for >75% of the total in the mixture of high-permeability particles and non-permeable particles. Permeation will be very easily lost when medium- or low-permeability particles are mixed with non-permeable particles. The current findings are of importance for assessing the permeability of sand based on particle size.

Designing optimal behavioral experiments using machine learning.

Computational models are powerful tools for understanding human cognition and behavior. They let us express our theories clearly and precisely and offer predictions that can be subtle and often counter-intuitive. However, this same richness and ability to surprise means our scientific intuitions and traditional tools are ill-suited to designing experiments to test and compare these models. To avoid these pitfalls and realize the full potential of computational modeling, we require tools to design experiments that provide clear answers about what models explain human behavior and the auxiliary assumptions those models must make. Bayesian optimal experimental design (BOED) formalizes the search for optimal experimental designs by identifying experiments that are expected to yield informative data. In this work, we provide a tutorial on leveraging recent advances in BOED and machine learning to find optimal experiments for any kind of model that we can simulate data from, and show how by-products of this procedure allow for quick and straightforward evaluation of models and their parameters against real experimental data. As a case study, we consider theories of how people balance exploration and exploitation in multi-armed bandit decision-making tasks. We validate the presented approach using simulations and a real-world experiment. As compared to experimental designs commonly used in the literature, we show that our optimal designs more efficiently determine which of a set of models best account for individual human behavior, and more efficiently characterize behavior given a preferred model. At the same time, formalizing a scientific question such that it can be adequately addressed with BOED can be challenging and we discuss several potential caveats and pitfalls that practitioners should be aware of. We provide code to replicate all analyses as well as tutorial notebooks and pointers to adapt the methodology to different experimental settings.

Regularizing cross-attention learning for end-to-end speech translation with ASR and MT attention matrices

The cross-attention mechanism enables Transformer to capture correspondences between the input and output. However, in the domain of end-to-end (E2E) speech-to-text translation (ST), the learned cross-attention weights often struggle to accurately correspond with actual alignments, given the need to align speech and text across different modalities and languages. In this paper, we present a simple yet effective method called regularized cross-attention learning, for end-to-end speech translation in a multitask learning (MTL) framework. RCAL leverages the knowledge from auxiliary automatic speech recognition (ASR) and machine translation (MT) tasks to generate a teacher cross-attention matrix, serving as prior alignment knowledge to enhance cross-attention learning within the ST task. An additional loss function is introduced as part of the MTL framework to facilitate this process. We conducted experiments on the MuST-C benchmark dataset to evaluate the effectiveness of RCAL. The results demonstrate that the proposed approach yields significant improvements over the baseline, with an average enhancement of +0.8 BLEU across four translation directions in two experimental settings, outperforming state-of-the-art E2E and cascaded speech translation models. Further analysis and visualization reveal that the model with RCAL effectively learns high-quality alignment information from auxiliary ASR and ST tasks, thereby improving the ST alignment quality. Moreover, the experiments with different sizes of MT and ST data provide strong evidence supporting our model’s robustness in various scenarios.

The Live Attenuated Vaccine Strain "ARRIAH" Completely Protects Goats from a Virulent Lineage IV Field Strain of Peste Des Petits Ruminants Virus.

Peste des petits ruminants (PPR) is a transboundary viral disease that affects small ruminants, such as goats and sheep, in Africa, the Middle East, and Asia, causing substantial damage to livelihoods and disrupting livestock trade. Although Russia is PPR virus (PPRV)-free, controlling PPRV in neighboring countries is the top national priority. Recent PPR outbreaks in Mongolia and other countries in the Middle East caused by a lineage IV virus represent a risk of transboundary emergence in neighboring countries, including China, Kazakhstan, and Russia. In the present study, we assessed the potency and safety of the ARRIAH live attenuated PPRV vaccine (lineage II) in Zaannen and Nubian goat breeds by challenging them with a virulent lineage IV Mongolia/2021 isolate. For comparison, two commercial vaccines of Nigeria75/1 strain were used. The ARRIAH-vaccinated animals showed an increase in body temperature of 1-1.5 °C above the physiological norm, similar to the animals vaccinated with Nigeria75/1 vaccines. In all vaccinated groups, the average rectal temperature never exceeded 39.4-39.7 °C throughout the infection period, and no clinical signs of the disease were observed, demonstrating vaccine efficacy and safety in the current experimental setting. However, the control group (mock vaccinated) challenged with Mongolia/2021 PPRV exhibited moderate-to-severe clinical signs. Overall, the findings of the present study demonstrate that the ARRIAH vaccine strain has a promising protective phenotype compared with Nigeria75/1 vaccines, suggesting its potential as an effective alternative for curbing and controlling PPR in affected countries. Although the ARRIAH vaccine against PPR is not currently endorsed by the World Organization for Animal Health due to its incomplete safety and potency profile, this study is the first step to provide experimentally validated data on the ARRIAH vaccine.

Benefitting productivity and the environment: Current and future maize cropping systems improve yield while reducing nitrate load.

Increases in cereal crop yield per area have increased global food security. "Era" studies compare historical and modern crop varieties in controlled experimental settings and are routinely used to understand how advances in crop genetics and management affect crop yield. However, to date, no era study has explored how advances in maize (Zea mays L.) genetics and management (i.e., cropping systems) have affected environmental outcomes. Here, we developed a cropping systems era study in Iowa, USA, to examine how yield and nitrate losses have changed from "Old" systems common in the 1990s to "Current" systems common in the 2010s, and to "Future" systems projected to be common in the 2030s. We tested the following hypothesis: If maize yield and nitrogen use efficiency have improved over previous decades, Current and Future maize systems will have benefits to water quality compared to Old systems. We show that not only have maize yield and nitrogen use efficiency (kg grain kg-1 N), on average, improved over time but also yield-scaled nitrate load + soil nitrate was reduced by 74% and 91% from Old to Current and Future systems, respectively. Continuing these trajectories of improvement will be critical to meet the needs of a growing and more affluent population while reducing deleterious effects of agricultural systems on ecosystem services.

Detecting phishing websites through improving convolutional neural networks with Self-Attention mechanism

Emerging technologies have made internet connection a vital activity facilitating access to many services. However, internet connection raises many security concerns, such as illegally acquiring private information, passwords, and identifiers. Phishing websites are the first choice for attackers that try to have users' private space. Social engineering attacks are performed by designing fake websites similar to real ones and inviting the victim to access those websites to collect their sensitive information and then redirect them to the actual site. Due to the importance of detecting phishing websites, building a robust detector that filters them and blocks their activity on the Internet is necessary. In this paper, we proposed a phishing website detector based on improving the convolutional neural network (CNN) with a self-attention mechanism. The proposed detector collects phishing Uniform Resource Locators (URLs) by treating them as strings. CNN models have proved their efficiency when dealing with text strings compared to Long Short-Term Memory (LSTM) which focuses on temporal features. Using CNN allows learning comprehensive features of the URLs and facilitates the detection of phishing ones. The self-attention mechanism was added to enhance the model's focus and detection accuracy. Besides, the training dataset was balanced by generating phishing URLs using a Generative Adversarial Network (GAN). A set of experiments has proved the robustness of the proposed detector by achieving high detection accuracy on the test set. Besides, the proposed detector was tested using unknown URLs and achieved excellent results. The improved CNN's detection precision of 99.7 is higher than the regular CNN model by 2.74%. The reported results show that using the self-attention mechanism has improved the detection accuracy and made the CNN model more efficient for detecting phishing websites.

Effects of individual and collective decision rule on cooperation in public goods game

Research on public goods game has traditionally focused on studying the effects of punishment and reward in experimental settings, commonly known as standard games. However, these standard games often examine isolated interactions among individuals, leading to varying results based on factors such as the population studied, number of participants, and geographical context. Consequently, the understanding of cooperation evolution remains imprecise.To address this limitation, we are trying to generalize these experimental studies, we employ an agent-based model in this study to simulate interactions in public goods game and explore the influence of punishment and reward on the evolution of cooperation. Unlike standard games, evolutionary games offer a more comprehensive framework to capture and analyze a broader range of strategic interactions and behaviors.In our approach, we introduce two methods for applying punishment or reward: Individual Punishment or Reward and Collective Punishment or Reward. Under the first method, actors can individually reward or punish each other based on their contributions or free-riding behavior towards the collective good. In the second method, decisions for punishment or reward are made collectively after achieving a consensus or agreement among the members of the group. We provide a model for comparing Individual Decision Rule and Collective Decision Rule under punishment and reward, to study their effects on the evolution of contributions.Consistent with recent experimental findings, our results indicate that these mechanisms lead to a significant increase in contributions compared to the outcomes observed in standard public goods game. Additionally, we demonstrate the significant role of rewards in promoting contribution within public goods game.

Data efficient indoor thermal comfort prediction using instance based transfer learning method

The precise prediction of indoor thermal sensation is critical to building energy efficiency and satisfaction of occupants. In this field, data-driven models have shown superior performance than Predicted Mean Vote (PMV) model. However, a reliable and accurate model of this kind relies on sufficient and high-quality data from physiological and environmental sensing, which in practice is difficult or expensive to collect. Rather than developing a separated predictive model for certain task, transfer learning method could intelligently use knowledge from similar domains to solve the target problem, and thus the requirement of data resources is reduced. In the last few years, transfer learning has been introduced into the field of thermal comfort prediction. Most of them used the source domain data as a whole for model pre-training, and the samples' suitability judgment was not fully considered. But the general situation in this field is: there are few public and available data resources and their data distributions are hardly consistent with that of specific target task. Aiming at the issue, this study proposes an instance based transfer learning model, which is based on data weighting and reusing for data efficient thermal comfort prediction. We assume that samples from source and target domains obey different distributions but have some common features that can be transferred across domains. The Nearest Neighbor Search (NNS) is used for similarity judgement and selection of source domain data; A Boosting type transfer learning algorithm, i.e., TrAdaBoost, is improved for reliable thermal comfort prediction. In the algorithm, an automatic weighting mechanism could optimally adjust the source training data's weights for performance improvement. By using field experimental data set and a public data set as target and source domains data respectively, the performance of the proposed model is investigated. Results show when target domain data is insufficient, the proposed NNS-iTrAdaBoost method has superior transfer learning ability and could achieve much better performance than traditional data-driven models. In practice, the proposed method is easy for engineering implementation and has great potential for thermal comfort prediction based on small-scale experiments.

Long-Term Study of the Synchronization Effect between Geomagnetic Field Variations and Minute-Scale Heart-Rate Oscillations in Healthy People

This study aimed to investigate the effect of human heart-rate synchronization with variations in the geomagnetic field (GMF) (“biogeophysical synchronization effect”). We analyzed 403 electrocardiogram (ECG) recordings of 100 or 120 min that were obtained in 2012–2023 from two middle-aged female volunteers in good health. The minute-value series of the GMF vector from the INTERMAGNET network was used. Each ECG recording was individually examined using cross-correlation and wavelet analysis. The findings from two separate experimental sets (306 recordings from Volunteer A and 97 from Volunteer B) displayed notable similarity in all aspects analyzed: (1) For both participants, the biogeophysical synchronization effect is observed in 40–53% of the recordings as a statistically significant (p < 0.0045) correlation between minute heart-rate (HR) time-series values and at least one of the horizontal components of the GMF, with a time shift between values of [−5, +5] min. (2) Wavelet analysis indicates that the spectra of the HR series and at least one GMF component exhibit similarity in 58–61% of cases. (3) The synchronization is most evident within the period range between 8–13 min. The probability of the synchronization effect manifestation was independent of the geomagnetic activity (GMA) level, which was recorded during the observations.

Effect of machining parameters in turning under different cooling conditions

ABSTRACT Flood lubrication is often replaced with minimal quantity lubrication (MQL) because of its eco-friendly properties. Surface smoothness, chip removal from cutting regions, improving the tool life, friction reduction at tool/work/chip tip, and temperature reduction during machining are all objectives of the lubricant. Nevertheless, choosing the correct cutting fluid for MQL is seen to be the most pressing issue, even if there are many factors to consider, such as industrial health concerns and environmental contamination. Tool wear, cutting temperature, and surface quality were the focus of this study on machining performance. The experimental settings consist of three machining conditions: oil MQL (OM), ionic liquid (IL) MQL, and LN2. Additionally, there are two various feed rates and three different cutting speeds. Results showed that compared to IL MQL and OM, using LN2 as a cooling medium dramatically reduced cutting temperature, surface roughness, and flank wear. All things considered, the waspaloy is best machined in an LN2 environment.

Integrating an LSTM framework for predicting ankle joint biomechanics during gait using inertial sensors

The ankle joint plays a crucial role in gait, facilitating the articulation of the lower limb, maintaining foot-ground contact, balancing the body, and transmitting the center of gravity. This study aimed to implement long short-term memory (LSTM) networks for predicting ankle joint angles, torques, and contact forces using inertial measurement unit (IMU) sensors. Twenty-five healthy participants were recruited. Two IMU sensors were attached to the foot dorsum and the vertical axis of the distal anteromedial tibia in the right lower limb to record acceleration and angular velocity during running. We proposed a LSTM-MLP (multilayer perceptron) model for training time-series data from IMU sensors and predicting ankle joint biomechanics. The model underwent validation and testing using a custom nested k-fold cross-validation process. The average values of the coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE) for ankle dorsiflexion joint and moment, subtalar inversion joint and moment, and ankle joint contact forces were 0.89 ± 0.04, 0.75 ± 1.04, and 2.96 ± 4.96 for walking, and 0.87 ± 0.07, 0.88 ± 1.26, and 4.1 ± 7.17 for running, respectively. This study demonstrates that IMU sensors, combined with LSTM neural networks, are invaluable tools for evaluating ankle joint biomechanics in lower limb pathological diagnosis and rehabilitation, offering a cost-effective and versatile alternative to traditional experimental settings.

Microstructure and Properties Variation of High-Performance Grey Cast Iron via Small Boron Additions

A study was undertaken to investigate the effects of small boron additions on the solidification and microstructure of hypo-eutectic alloyed grey cast iron. The characteristic temperatures upon crystallisation of the treated metal melt were recorded, specifically those concerning small boron addition by using thermal analysis with the ATAS system. Additionally, a standardised wedge test was set to observe any changes in chill performance. The microstructures of thermal analysis samples were analysed using a light optical microscope (LOM) and field emission scanning electron microscopy (FE-SEM) equipped with energy dispersive spectroscopy (EDS), which reveal variations in graphite count number with the addition of boron within observed random and undercooled flake graphite. The effect of boron was estimated by the classical analytical and statistical approach. The solidification behaviour under equilibrium conditions was predicted by a thermodynamic approach using Thermo-Calc. Based on all gathered data, a response model was set with boron for given melt quality and melt treatment using the experimentally determined data. The study reveals that boron as a ferrite and carbide-promoting element under the experimental set shows weak nucleation potential in synergy with other heterogenic nuclei at increased solidification rates, but no considerable changes were observed by the TA samples solidified at slower cooling rates, indicating the loss of the overall inoculation effect. The potential presence of boron nitride as an inoculator for graphite precipitation for a given melt composition and melt treatment was not confirmed in this study. It seems that boron at increased solidification rates can contribute to overall inoculation, but at slower cooling rates these effects are gradually lost. In the last solidification range, an increased boron content could have a carbide forming nature, as is usually expected. The study suggests that boron in traces could affect the microstructure and properties of hypo-eutectic alloyed grey cast iron.

Bovine Serum Albumin Rejection by an Open Ultrafiltration Membrane: Characterization and Modeling.

The classic application of ultrafiltration (UF) is for the complete retention of proteins, and in that situation, the transport behavior is well established. More open membranes with fractional retention are used when separating different proteins. However, protein transport has not been well documented yet in the literature. The bovine serum albumin (∼69 kDa) observed rejection ranges from 0.65 to 1 using a 300 kDa molecular weight cut-off membrane at different pH, ionic strength, and pressure. We demonstrated that, especially with open UF, the transport of proteins through the membrane is dominated by advection, with insignificant diffusion effects (p value > 0.05). We showed that with open UF, retention is not only caused by size exclusion but also to a large extent by electrostatic interactions and oligomerization of the proteins. Mass transfer in the polarization layer was relatively independent of the pH and ionic strength. It was underestimated by common Sherwood relations due to a relatively large contribution of the reduction in the flow turbulence near the membrane by the removal of fluid through the membrane. We propose a model that allows relatively quick characterization of the rejection of proteins without prior knowledge of the pore sizes and charges based on just a limited set of experiments. Therefore, protein rejection with the open UF system can be targeted by tuning the processing conditions, which might be useful for designing protein fractionation processes.