On-line Technique Research Articles

Application of on-line sample preconcentration by large-volume dual preconcentration by isotachophoresis and stacking (LDIS) on straight-channel microchips.

In this study, large-volume dual preconcentration by isotachophoresis and stacking (LDIS) which is an on-line sample preconcentration technique coupling large-volume sample stacking with an electroosmotic flow pump (LVSEP) with transient isotachophoresis (tITP) was applied to microchip electrophoresis (MCE) for improving both detection sensitivities and peak shapes. To realize LDIS in MCE, we investigated experimental procedures for injecting a short plug of a leading electrolyte (LE) solution into a straight microchannel without any sophisticated injector apparatus. We found that a short LE plug could be injected into a sample-filled straight-channel only by making the liquid level of the LE solution in an outlet reservoir higher than that in an inlet one. By applying a reversed-polarity voltage to the microchip, anionic analytes injected throughout the microchannel were first enriched by LVSEP, followed by tITP. Through the second preconcentration effect by tITP in LDIS, sensitivity enhancement factor (SEF) and asymmetry factor for a standard dye were improved from 878 and 0.62 to 1330 and 1.14, respectively, relative to those in conventional LVSEP. It should be noted that more viscous running buffer containing sieving polymers could be employed to the LDIS analysis, which was effective for improving the SEF and the separation efficiencies, especially for bio-polymeric compounds. Finally, LDIS was applied to the oligosaccharide and protein analyses in MCE, resulting in the SEFs of 1410 and ca. 50 for maltotriose and bovine milk casein, respectively.

Development of mobile CAT for patient feedback on pediatric consultations based on Rasch analysis of online techniques.

The Rasch Rating Scale Model (RSM) is widely used in questionnaire analysis, providing insights into how individuals respond to item-level stimuli. Existing software for Rasch RSM parameter estimation, while powerful, often presents a steep learning curve. An accessible online tool can greatly benefit novice users, particularly students and clinicians, by simplifying the analytical process. This study introduces an online tool, an intuitive online RSM analysis tool designed to facilitate questionnaire data analysis for applied researchers, students, and clinicians. The online tool employs the joint maximum likelihood method for estimation, yielding estimates, standard errors (SE), and fit statistics iteratively. A unique feature of the tool is its ability to visualize estimates on Google Maps with an opacity setting of 0, enhancing data interpretation through a user-friendly interface. This study outlines the estimation process and key features, employing data from 200 proxy participants who answered 20 5-point questions regarding doctor-patient and doctor-family interactions in pediatric consultations. Mobile computerized adaptive testing (CAT) was employed. The online tool offers 5 essential visual displays often utilized in Rasch analyses, including the Wright Map, KIDMAP, category probability curve, performance plot, and differential item functioning (DIF) graph. DIF analysis revealed that 2 items, concerning the doctor attentiveness and empathy toward the child illness, exhibited differences in female proxy participants' responses, indicating lower satisfaction with pediatricians. The online tool emerges as a user-friendly and efficient RSM analysis tool with notable advantages for newcomers, improving data visualization and comprehension. Its capacity to pinpoint key areas of concern, such as gender-related satisfaction disparities among proxy participants, enhances its utility in questionnaire analysis. The online tool holds promise as a valuable resource for researchers, students, and clinicians seeking accessible Rasch analysis solutions.

First ion source at ISOL@MYRRHA with an improved thermal profile - Theoretical considerations

ISOL@MYRRHA will be a new Radioactive Ion Beam (RIB) facility in Belgium based on the Isotope Separation On-Line (ISOL) technique, and established within the framework of MYRRHA, the world’s first large-scale accelerator driven system project at power levels scalable to industrial systems. The surface ion source, or hot cavity, is chosen as initial source for its reliability and simple design. To account for the higher flux of atoms through this cavity, a theoretical study of the processes within the ion source is discussed here, based on theoretical equations and thermal-electric simulations. In the past, the temperature was clearly identified as a key element to this source, but with the assumption that it remains constant throughout the cavity. Nonetheless, more recent thermal-electric simulations have revealed that the source Ohmic heating leads to temperature gradients along the cavity tube. The temperature profile impact on ionisation in the hot cavity will be reviewed here.

Steel surface roughness parameter prediction from laser reflection data using machine learning models

Control of surface texture in strip steel is essential to meet customer requirements during galvanizing and temper rolling processes. Traditional methods rely on post-production stylus measurements, while on-line techniques offer non-contact and real-time measurements of the entire strip. However, ensuring accurate measurement is imperative for their effective utilization in the manufacturing pipeline. Moreover, accurate on-line measurements enable real-time adjustments of manufacturing processing parameters during production, ensuring consistent quality and the possibility of closed-loop control of the temper mill. In this study, we formulate the manufacturing issue into a Time Series Extrinsic Regression problem and a Machine Vission problem and leverage state-of-the-art machine learning models to enhance the transformation of on-line measurements into a significantly more accurate Ra surface roughness metric. By comparing a selection of data-driven approaches, including both deep learning such as convolutional, recurrent, and transformer networks and non-deep learning methods such as Rocket and XGBoost, to the close-form transformation, we evaluate their potential using Root Mean Squared Error (RMSE) and correlation for improving surface texture control in temper strip steel manufacturing.

‘We’re not that Gullible!’ Revealing Dark Pattern Mental Models of 11-12 Year-Old Scottish Children

Deceptive techniques known as dark patterns specifically target online users. Children are particularly vulnerable as they might lack the skills to recognise and resist these deceptive attempts. To be effective, interventions to forewarn and forearm should build on a comprehensive understanding of children’s existing mental models. To this end, we carried out a study with 11-12 year old Scottish children to reveal their mental models of dark patterns. They were acutely aware of online deception, referring to deployers as being ‘up to no good’. Yet, they were overly vigilant and construed worst-case outcomes, with even a benign warning triggering suspicion. We recommend that rather than focusing on specific instances of dark patterns in awareness raising, interventions should prioritise improving children’s understanding of the characteristics of, and the motivations behind, deceptive online techniques. By so doing, we can help them to develop a more robust defence against these deceptive practices.

Voices from the algorithm: Large language models in social research

Research on energy and society often relies on online data collection. In particular, there has been an increase in the use of online techniques such as video software for qualitative research since the pandemic. We suggest that the rapid growth in generative AI and Large Language Models such as Chat-GPT mean that they may be utilised by research participants; particularly in research where participants may be less knowledgeable about the topic under discussion, such as emerging energy technologies. Drawing on examples from recent research, we argue that social scientists need to be cautious in assuming that the voices of our participants are genuinely theirs.

Structured diary introspection training: A kind of critical thinking training method can enhance the Pro-C creativity of interior designers

Pro-C creativity refers to professional creativity, which plays a key role in designing novel and practical works for designers in practical work. In order to verify the significance of critical thinking and Pro-C creativity and explore how to improve Pro-C creativity through critical thinking training, we carried out two studies. In Study 1, we recruited 58 designers using a longitudinal research approach and employed the California Critical Thinking Skills Test (CCTST) and the California Critical Thinking Disposition Inventory (CCTDI) to measure designers' critical thinking. Designers were tasked with completing assignments related to Pro-C level of creativity A, and their creativity was assessed by experts. Through a one-year follow-up analysis, we examined how their critical thinking influenced Pro-C creativity in the design domain. The results of the cross-lagged analysis indicate that scores on critical thinking positively predicted the creativity levels of designers one year later. Study 2 is a controlled intervention study. We developed an intervention program for training critical thinking, named the "Structured diary introspection Training Method", which is based on the online technique. Through a 21-day critical thinking training intervention, the results of repeated measures analysis of variance showed that, compared to the control group, designers in the intervention group scored significantly higher in a creative task related to designing a living room. Our findings support critical thinking as an important predictor of Pro-C creativity in the real world and provide a feasible training program for those who need to improve their Pro-C creativity.

Investigation of the homogeneity of particle suspension

The homogeneity of particle suspension has attracted more attention in the fields of rehydrating milk beverages. This work focus on size characterization of the floating, sticking (on the wall), suspended and settling particles involving both online and offline sizing techniques. Focused Beam Reflectance Measurement (FBRM) is a powerful online technique that is capable for measuring and analysing chord length distribution (CLD) of the suspended particles in liquid. It has been widely used in terms of monitoring size evolution of crystallization and sedimentation processes. In this work, a new approach that involves FBRM probe to measure the particle size distribution has been developed. The probe is placed at three different height levels in a standard container after one hour of free sedimentation. The results suggest that the finest fraction is dominant at all three heights but the percentage of it decreases with an increase of larger particles when looking at the bottom. Besides, all the floating, sticking and settling particles are collected and analysed, and the particle size distributions (PSDs) are obtained and compared using the offline sizing techniques, such as optical microscope and Camsizer. In the results, the suspended particle is the smallest one followed by the sticking and the floating particles, while the settling particle is the largest. However, for the sticking particles sticking on the wall, a great number of agglomerates is found during the test due to adhesion force, indicating that the size distribution might be overestimated. Based on the results, the target particle size range that can be stably suspended in the model suspension was selected by eliminating the unfavourable fractions.

Assessment of Persian export pomegranate quality: A reliable non-destructive method based on spectroscopy and chemometrics

The present study investigated the feasibility of visible near-infrared (Vis-NIR) spectroscopy method as a low-cost, quick and reliable method for the prediction of qualitative characteristics (fruit juice percentage, soluble solids, acidity, taste index and vitamin C) of an export variety of Persian pomegranate. For this purpose, pomegranate samples were first analyzed by Vis-NIR spectroscopy, followed by destructive reference methods for quality determination. Subsequently, the data from Vis-NIR were evaluated with different pre-processing methods, among which the combination of the Savitzky–Golay (SG) smoothing model and standard normal variation (SNV) normalization model, generates the best outcomes in predicting six desired qualitative characteristics by partial least square regression (PLSR) model. Fruit juice percentage and pH presented the best prediction models with a correlation coefficient (rP) of 0.98 for both, and root mean square errors of prediction (RMSEP) equal to 0.036 and 0.041, respectively. The values of rp for all desired parameters in this study were higher than 0.95, which pointed to the excellent accuracy of the PLSR models in the prediction of pomegranate qualitative features. It can be concluded from the present study that, Vis-NIR spectroscopy using the PLSR model as a non-destructive functional approach is capable of verifying the quality of Persian export variety pomegranate as an online technique with the aim of grading products before the export phase in order to augment the product marketability.

Monitoring Aggregation Processes in Multiphase Systems: A Review

Particle aggregation is essential in many industrial processes, spanning the pharmaceutical and food industries, polymer production, and the environment, among others. However, aggregation can also occur, in some processes, as a non-desired side effect. Thus, to be able to monitor aggregation in industrial processes is of high importance to guarantee that the final, required product characteristics are obtained. In this paper, we present an extensive review of the different techniques available for monitoring particle characteristics in industrial processes involving particulate materials, with special emphasis on aggregation processes. These methods include both off-line and on-line techniques, based either on image acquisition techniques or different radiation scattering techniques (light-scattering and ultrasound spectroscopy). The principles behind each technique are addressed, together with their relevant applications, advantages, and disadvantages.

An extended community of inquiry framework supporting students in online and digital education

ABSTRACT In response to the COVID-19 pandemic, universities across the world were forced to move from a classroom-based delivery model to an online learning model which heavily disrupted the learning process for students. Despite proactive efforts for academic staff to embrace online teaching tools and techniques, the pressing urgency with which solutions were implemented raised concerns about the competence of academic staff to deliver online education effectively, the capabilities of digital tools and technologies required for different topics, and the quality of students’ learning experiences. Within a university postgraduate setting, this research adopts the Community of Inquiry (CoI) framework to explore the social presence, teaching presence, and cognitive presence as essential elements facilitating the educational experiences of 100 students within an online learning environment. The findings expand current literature by proposing a revised CoI framework, the Digital Community of Inquiry Framework, to highlight an additional need, that of ‘self-presence’.

Self-assessment of soft skills in nursing: construction and content validation of an instrument

Objective: To build and validate an instrument for self-assessment of soft skills in authentic nursing leadership. Methods: This methodological research was developed in three stages: literature review, development of the instrument entitled “LEADERSKILLS”, and validation of the instrument’s content by experts using the Delphi Online Technique. The first stage consisted of two narrative reviews. The second stage included a pilot test for the development of the instrument containing a narrative description of the most relevant soft skills (related to the exercise of authentic leadership by nurses) that were presented by the narrative literature review. The third and final stage included changes to the instrument after considerations made by the experts. Results: In the first round, the panel of experts was composed of 13 participants. Of the 17 multiple-choice questions evaluated in the first round, only two (belonging to the content and language components) did not obtain the Content Validity Index (CVI) ≥0.85. The first question was about the inadequate understanding of what the titles and subtitles of “LEADERSKILLS” were, and the second was about the instrument’s clarity and objectivity. The judges’ main suggestions were concentrated on the content, language, and layout components. The second round consisted of responses from five experts, and reaching a consensus on the questions was possible by obtaining CVI≥0.80. Conclusion: The instrument constructed to develop soft skills in nursing was considered valid in terms of content, layout, language, motivation, and culture, achieving coherent assessment and applicability to the target audience.

Prediction of operating state of hydrocyclones using vibrometry and 1D convolutional neural networks

Due to high processing capacity requirements and constraints on industrial water utilization, operation of hydrocyclones at higher feed solid concentration is often unavoidable. Hydrocyclone operation at such higher feed solid concentrations is accompanied by the risk of onset of rope discharge, adversely affecting both the size separation and thickening performance of the hydrocyclone. Numerous condition monitoring efforts have been reported in the literature regarding the development of online techniques for detection of roping in hydrocyclones. However, detection of roping, post its commencement, only ever allows for corrective maintenance in the form of measures taken for shifting the discharge back to the desired spray profile. This study focuses on the characterization and online detection of a transition discharge state which precedes the onset of roping, thereby making the prevention of onset of roping, possible. The transition discharge state was delineated from the conventional spray and rope discharge types based upon the observed differences in the decay rates of underflow discharge spray angles with increasing feed solid concentrations. A binary classification model (spray – rope) and a ternary classification model (spray – transition – rope) were developed with a 1D Convolutional neural network architecture capable of taking 5 secs of tri-axial (3-channel) vibration signal data as input, and outputting the predicted discharge type labels with an accuracy of 97.31 % and 94.94 %, respectively.

The SPES target ion source automated storage system

At the SPES (Selective Production of Exotic Species) facility, intense Radioactive Ion Beams (RIBs) are produced by the interaction of a 40 MeV proton beam with a multi-foil uranium carbide target employing the Isotope Separation On-Line (ISOL) technique. The Target Ion Source (TIS) unit constitutes the core of the isotope production process. TIS units are replaced on a periodic basis during operation to maintain high performance. An automated storage system has been designed to accept highly radioactive TIS units and house them during a cooling period prior to decommissioning. The system is conceived to meet strict functional and safety requirements. Its peculiar design allows for improved reliability and availability during critical operations, as well as minimization of staff exposure to ionizing radiation during maintenance tasks. This contribution describes the design and control architecture of the Temporary Storage System (TSS). The equipment is part of a structured framework of remote manipulation, consisting of various machines interlocked with the Access Control System (ACS) and the Machine Protection System (MPS).

Online Detection and Adaptation of Concept Drift in Streaming Data Classification

The dynamism of our digital universe systems presents a key challenge for predictive analytics. Ensuring the model’s ability to generalize beyond the training data is crucial for reliable predictions. Recent research has primarily focused on understanding and addressing the phenomenon of concept drift: the changes in the system that afect the model’s accuracy. This paper aims to resolve this issue by introducing an online technique implemented using Light Gradient Boosting Machine (LGBM) classifier for concept drift detection and adaptation. Online LGBM is adjusted incrementally based on the most recent information, allowing it to adapt to changing patterns in the data stream. The proposed technique attempts to leverage incremental learning, ensemble learning, and the sliding window method to deal with concept drift. The experiments on Electricity, Spam and MixedAbrupt Drift datasets result in higher accuracy 86.77%, 97.67%, and 65.25% as compared to the ofine LGBM with lower accuracy 71.52%, 90.86%, and 50.01% respectively. The hyperparameters of LGBM are optimized using the Bayesian method.

Linguapragmatic characteristics of educational media discourse: American, British, and Russian visual presentations

The article is devoted to the study of current problems of educational media communication in the linguapragmatic aspect. The paper discusses the main linguapragmatic characteristics of educational media. The role of the implementation of the presentation strategy of the language personality in the educational media resource is established. The main presentations implementing the presentation strategy in the educational media resource are identified. The purpose of this work is to identify and describe American, British, and Russian visual presentations within the framework of the presentation strategy of the educational media presentation. As a research material, media texts on electronic learning of a foreign language in the educational media resource of the first quarter of the 21st century in English and Russian are used. To achieve the goal, the following methods are used: lexical, grammatical, stylistic analysis, linguasemiotic and linguapragmatic analysis, elements of content analysis and discursive analysis, interpretive and quantitative analysis. American, British, and Russian visual presentations of educational media are described and illustrated by examples. It is established that the visual presentations of online learning of a foreign language in the American, British, and Russian educational media resource are aimed at the media recipient’s perception of the shape, color, position, and movement of the object. The undoubted novelty is represented by the identified subtypes of visual presentations in the American, British, and Russian educational media history: graphemes, coloremes, proxemes, kinestems. It was established that the presentation strategy of the educational media resource on the problems of online learning of a foreign language, implemented in educational media texts on sites on the Internet in the studied linguistic cultures, has similar features, since it is aimed at informing and influencing the addressee. Information contains tools and techniques that convey the content of online courses, the online learning process, the methods, and techniques that are used in this process. The impact is implemented to attract the attention of potential students to the course and educational institution, as well as incentives to study in this educational institution. It seems promising to distinguish between conflict and cooperative strategies in the educational media discourse in different linguocultures.

A Novice Approach for the Beginners of the Professional Medical Researchers: An Introduction

Globalising the research by the transformation of the research practices using the latest research tools (Roots of the Educational Research –Redefined) is the motto of the present day and in this regard a review showing six sigma as an add-on for the combined studies of the service – quality with the living –Standards with the descriptive statistics in mind had been considered through this research practice and the approach. The main objective of this research is to provide know-how for budding researchers from the field of the medical to expand their skills towards online tools and techniques.

Innovative analysis of diazepam, zolpidem and their main metabolites in human urine by micelle-to-solvent stacking in capillary electrophoresis

Diazepam and zolpidem are the most widely used medications for managing insomnia. However, significant concerns regarding the potential risks of misuse and abuse problems arose in many literatures. While urine analysis is a valuable diagnostic tool, a challenge arises from the fact that some parent drugs may remain undetectable in urine. This necessitates concurrent monitoring of their metabolites. Here, we described an innovative on-line sample preconcentration technique known as micelle to solvent stacking (MSS) for the analysis of diazepam, zolpidem, and their main metabolites in urine. Several key parameters warrant further discussion to optimize the MSS model, enhancing its performance in terms of sensitivity and resolution. After optimizing the conditions, we conducted a validation test, achieving high correlation coefficients (greater than 0.9977) for intra-day and inter-day regression lines. Additionally, both the relative standard deviation (RSD) and relative error (RE) remained below 6.10% and 12.55%, respectively. The limits of detection (LODs, S/N = 3) for all five analytes ranged from 2.0 to 56 ng/mL. Compared to the conventional capillary zone electrophoresis method, this new approach exhibited remarkable sensitivity enhancements, ranging from 123 to 235-fold. Upon applying this method to actual urine samples from patients, we successfully detected nordiazepam, zolpidem, and its metabolites. This simple and sensitive approach has promising applications in supporting patient medication safety and bolstering forensic investigations.

Application progress of on-line sample preparation techniques coupled with liquid chromatography-mass spectrometry system in the detection of food hazards

Application progress of on-line sample preparation techniques coupled with liquid chromatography-mass spectrometry system in the detection of food hazards

A Computation-efficient Online Secondary Path Modeling Technique for Modified FXLMS Algorithm

This paper proposes an online secondary path modelling (SPM) technique to improve the performance of the modified filtered reference Least Mean Square (FXLMS) algorithm. It can effectively respond to a time-varying secondary path, which refers to the path from a secondary source to an error sensor. Unlike traditional methods, the proposed approach switches modes between adaptive ANC and online SPM, eliminating the use of destabilizing components such as auxiliary white noise or additional filters, which can negatively impact the complexity, stability, and noise reduction performance of the ANC system. The system operates in adaptive ANC mode until divergence is detected due to secondary path changes. At this moment, it switches to SPM mode until the path is remodeled and then returns to ANC mode. Furthermore, numerical simulations in the paper demonstrate that the proposed online technique effectively copes with the secondary path variations.