Continuous Online Research Articles

Mentoring in education: an analysis of international practices and their implementation in Russia

Introduction. In the context of an ongoing discussion about the necessity to enhance the institution of mentoring in Russia, the examination and critical analysis of the experiences of countries where the system of mentoring for novice teachers is regulated at the state level is of undeniable interest. Aim. The study aimed to identify the main features of mentoring in France and determine the potential for applying international experience in the Russian educational system. Methodology and research methods. The research employed the following methods: an analysis of pedagogical literature and normative documents in the field of education in France, and a questionnaire survey of novice teachers working as trainee teachers in primary schools in Paris. As a survey technique, a continuous online survey with a group was employed. Results and scientific novelty. The process of becoming a fully qualified lower primary teacher in France is outlined: it involves passing a competitive examination (CRPE) and successfully completing a probation period. During this probation period, candidates are required to work for one year in a school as a trainee teacher under the guidance of mentor teachers. The research focuses on mentoring practices in France, the pathways to becoming a new teacher mentor, their job requirements, professional training, certification processes, as well as their roles and responsibilities. Based on the results of a survey of trainee teachers, a social profile of a novice teacher in France was created. The main challenges and shortcomings of the participants were identified, and their interactions with mentors were analysed. The collected data enabled the authors to emphasise the benefits of the French education policy regarding entry into the teaching profession and to formulate recommendations for mentor selection and training in Russia. Practical significance. The content of the article will be beneficial for specialists in the field of comparative education and for members of the scientific and pedagogical community engaged in teacher training programmes.

BREASTFEEDING ATTITUDES AND EXPERIENCES OF BREASTFEEDING WOMEN INFECTED WITH COVID-19: A MIXED METHODS STUDY

Aim: The aim of this study was to learn more about the attitudes and experiences of breastfeeding women infected with COVID-19. Method: A convergent parallel mixed methods design was used to conduct the study. The study included breastfeeding women who presented COVID-19 symptoms and tested positive for COVID-19, and who sought care at a community health center in a province located in the Black Sea region of Turkey. Quantitative and qualitative data for the study were collected online between July 2021 and February 2022. Data for the study were collected using the 'Socio-demographic Information Form', the 'Breastfeeding Attitude Scale' and the 'Semi-structured Interview Form'. Data were collected from 82 women for the quantitative part and seven women for the qualitative part. Before the research began, study approval was obtained from the TR Ministry of Health and ethical approval was granted by the Clinical Research Ethics Committee of Ondokuz Mayıs University (25 June 2021/ OMU KAEK2021/316). Results: The number of children, age of the baby, breastfeeding initiation and nutritional status of the baby were found to influence breastfeeding attitudes. The overall mean score of the breastfeeding attitude rating scale was 91.7710.63. Five themes were identified as a result of the content analysis: "Women's feelings during the disease," "How the disease affects the babies," "Woman-baby contact," "COVID -19 and breastfeeding," and "Status of support for women".Conclusions and Suggestions: It was found that women's attitudes towards breastfeeding during the disease process are moderate, that they experience considerable anxiety during this time, and that the support they receive affects both their psychological state and their attitudes towards breastfeeding. During this process, continuous online or phone support may improve women's attitudes towards breastfeeding.

Diagnosis of early creep degradation in 12Cr1MoVG steel based on a hybrid magnetic NDE approach

Accurate identification of microstructural degradation that occurs prior to crack coalescence and growth is crucial for continuous online monitoring of creep damage in main stream pipelines. The magnetic non-destructive testing technique is well-established for quantitative property measurement of creep degradation in steels. During the interrupted creep of 12Cr1MoVG steel at 580 ℃ and 110 MPa, the magnetic properties of crept specimens were evaluated using a combination of magnetic hysteresis loop and Barkhausen noise techniques, the microstructural evolution was also examined by means of scanning electron microscopy combined with EBSD method. The microscopic coercive force and Barkhausen noise characteristic values were chosen as the main parameters of diagnosis, as the most sensitive to the microstructural evolution such as dislocations, precipitates and voids, reflects the changes in material condition indicative of early degradation. Besides, these magnetic signatures even can identify the locations that potentially susceptible to future creep damage in the material, which were validated by microhardness measurement, grain misorientation and dislocation density distributions. It suggested that this work could be used as the technical basis for proactive management of material early degradation in the steam pipelines.

Experience of Older Patients with COPD Using Disease Management Apps: A Qualitative Study.

(1) Background: Digital medicine is developing in the management of chronic diseases in older people, but there is still a lack of information on the use of disease management apps in older patients with COPD. This study aims to explore the views and experience of older patients with COPD on disease management apps to provide a basis for the development and promotion of apps for geriatric diseases. (2) Methods: A descriptive qualitative research method was used. Older patients with COPD (N = 32) with experience using disease management apps participated in semi-structured interviews. Thematic analysis was used to analyze the data. (3) Results: Seven themes were defined: (a) feeling curious and worried when facing disease management apps for the first time; (b) actively overcoming barriers to use; (c) gradually becoming independent by continuous online learning; (d) feeling safe in the virtual environment; (e) gradually feeling new value in online interactions; (f) relying on disease management apps under long-term use; (g) expecting disease management apps to meet personalized needs. (4) Conclusions: The adoption and use of disease management apps by older people is a gradual process of acceptance, and they can obtain a wide range of benefits in health and life.

Internet Users' Willingness to Disclose Biometric Data for Continuous Online Account Protection: An Empirical Investigation

Continuous authentication has emerged as a promising approach to increase user account security for online services. Unlike traditional authentication methods, continuous authentication provides ongoing security throughout the session, protecting against session takeover attacks due to illegitimate access. The effectiveness of continuous authentication systems relies on the continuous processing of users' sensitive biometric data. To balance security and privacy trade-offs, it's crucial to understand when users are willing to disclose biometric data for enhanced account security, addressing inevitable privacy concerns and user acceptance. To address this knowledge gap, we conducted an online study with 830 participants from the U.S., aiming to investigate user perceptions towards continuous authentication across different classes of online services. Our analysis identified four groups of biometric traits that directly reflect users' willingness to disclose them. Our findings demonstrate that willingness to disclose is influenced by both the specific biometric traits and the type of online service involved. User perceptions are strongly shaped by factors such as response efficacy, perceived privacy risks associated with the biometric traits, and concerns about the service providers' handling of such data. Our results emphasize the inadequacy of one-size-fits-all solutions and provide valuable insights for the design and implementation of continuous authentication systems.

Stochastic Bayesian Optimization with Unknown Continuous Context Distribution via Kernel Density Estimation

Bayesian optimization (BO) is a sample-efficient method and has been widely used for optimizing expensive black-box functions. Recently, there has been a considerable interest in BO literature in optimizing functions that are affected by context variable in the environment, which is uncontrollable by decision makers. In this paper, we focus on the optimization of functions' expectations over continuous context variable, subject to an unknown distribution. To address this problem, we propose two algorithms that employ kernel density estimation to learn the probability density function (PDF) of continuous context variable online. The first algorithm is simpler, which directly optimizes the expectation under the estimated PDF. Considering that the estimated PDF may have high estimation error when the true distribution is complicated, we further propose the second algorithm that optimizes the distributionally robust objective. Theoretical results demonstrate that both algorithms have sub-linear Bayesian cumulative regret on the expectation objective. Furthermore, we conduct numerical experiments to empirically demonstrate the effectiveness of our algorithms.

Simplifying Complex Observation Models in Continuous POMDP Planning with Probabilistic Guarantees and Practice

Solving partially observable Markov decision processes (POMDPs) with high dimensional and continuous observations, such as camera images, is required for many real life robotics and planning problems. Recent researches suggested machine learned probabilistic models as observation models, but their use is currently too computationally expensive for online deployment. We deal with the question of what would be the implication of using simplified observation models for planning, while retaining formal guarantees on the quality of the solution. Our main contribution is a novel probabilistic bound based on a statistical total variation distance of the simplified model. We show that it bounds the theoretical POMDP value w.r.t. original model, from the empirical planned value with the simplified model, by generalizing recent results of particle-belief MDP concentration bounds. Our calculations can be separated into offline and online parts, and we arrive at formal guarantees without having to access the costly model at all during planning, which is also a novel result. Finally, we demonstrate in simulation how to integrate the bound into the routine of an existing continuous online POMDP solver.

Uncertainty-Aware Online Learning of Dynamic Thermal Control in Data Center with Imperfect Pretrained Models

The growing demand for cloud computing and storage necessitates the expansion of Data Centers, thereby increasing energy consumption and environmental footprint. The data-driven dynamic thermal control presents a promising solution to mitigate this issue, as it can offer improved thermal control strategies and address the intricate and stochastic nature of the thermal environment. Nonetheless, existing solutions typically exhibit deficiencies in terms of reliability and practicability. To this end, this paper proposes a feasible uncertainty-aware online learning method with reliable dynamics models and controllers. Specifically, Deep Learning models with the capacity for uncertainty quantification are employed to account for the imperfections in pre-trained models, hence providing reliable predictions. The Cross-Entropy Method and Monte Carlo trajectory sampling solve optimization problems while considering uncertainties. It is mathematically analyzed that the uncertainty quantified by the system dynamics model can be captured with sufficient trajectory samples, rendering a reliable controller. A practical framework is proposed and tested on a widely used Data Center model to mitigate the insufficient capacity of Deep Learning models to quantify uncertainties. The results demonstrate its superior performance during the initial training stage, achieving a 66% reduction in violations and 3.76% power savings compared to the default controller. Continuous active online learning enhances its adaptability, resulting in an 80% reduction in violations and a 6% energy saving. The thermal control strategies are thus improved by the proposed new solutions and insights.

Online marketingeszközök hatékonyságának vizsgálata a horgászok körében

The use of online marketing tools has now become essential for businesses operating in the commercial sector. In order for businesses to increase their sales volume and brand value of their company, they need a continuous online presence while keeping up with current trends. High-quality, unique content captures the attention of consumers, who later become regular followers of the business not only online, but their personal presence can also be strengthened in physical stores. Using questionnaire research, I assessed the effectiveness of online marketing tools among anglers.

Continuous Online Learning-Based CSI Feedback in Massive MIMO Systems

Continuous Online Learning-Based CSI Feedback in Massive MIMO Systems

교직이론 과목에서 현장 역량 강화를 위한 혼합형 PBL 수업 사례 연구

This study attempted to present a specific case of blended Problem-Based Learning (PBL) in teacher education course to enhance the field competency of pre-service teachers. The participants were first-year adult learners majoring in Early Childhood Education who were enrolled in the educational psychology course. The blended PBL process was organized by appropriately combining face-to-face and online sessions, following the sequence of 'Course Introduction,' 'Problem Presentation,' 'Subsequent Problem Steps,' 'Presentation of Results and Discussion,' and 'Review and Evaluation of Learning Outcomes.' Five PBL problems were developed, and during the initial five weeks, students were assigned to individually contemplate one problem per week. In the 10th week, a face-to-face group discussion session was conducted. Subsequently, continuous online communication through Learning Management System (LMS) and Social Networking Services (SNS) was maintained, leading to the group presentation of the final project at the end of the semester. The analysis of reflection journals submitted by students after the class was conducted to assess the effectiveness of a blended PBL course. The case presented in this study proposes a form of instructional design that integrates real-world experiences, suggesting a hybrid approach to PBL in education. Furthermore, the application of blended PBL in educational psychology courses suggests the potential to enhance field competency in teacher education courses.

β-D-glucuronidase activity triggered monitoring of fecal contamination using microbial and chemical source tracking markers at drinking water intakes

Intense rainfall and snowmelt events may affect the safety of drinking water, as large quantities of fecal material can be discharged from storm or sewage overflows or washed from the catchment into drinking water sources. This study used β-d-glucuronidase activity (GLUC) with microbial source tracking (MST) markers: human, bovine, porcine mitochondrial DNA markers (mtDNA) and human-associated Bacteroidales HF183 and chemical source tracking (CST) markers including caffeine, carbamazepine, theophylline and acetaminophen, pathogens (Giardia, Cryptosporidium, adenovirus, rotavirus and enterovirus), water quality indicators (Escherichia coli, turbidity) and hydrometeorological data (flowrate, precipitation) to assess the vulnerability of 3 drinking water intakes (DWIs) and identify sources of fecal contamination. Water samples were collected under baseline, snow and rain events conditions in urban and agricultural catchments (Québec, Canada). Dynamics of E. coli, HF183 and WWMPs were similar during contamination events, and concentrations generally varied over 1 order of magnitude during each event. Elevated human-associated marker levels during events demonstrated that urban DWIs were impacted by recent contamination from an upstream municipal water resource recovery facility (WRRF). In the agricultural catchment, mixed fecal pollution was observed with the occurrences and increases of enteric viruses, human bovine and porcine mtDNA during peak contaminating events. Bovine mtDNA qPCR concentrations were indicative of runoff of cattle-derived fecal pollutants to the DWI from diffuse sources following rain events. This study demonstrated that the suitability of a given MST or CST indicator depend on river and catchment characteristics. The sampling strategy using continuous online GLUC activity coupled with MST and CST markers analysis was a more reliable source indicator than turbidity to identify peak events at drinking water intakes.

A stochastic data-driven Bayesian optimization approach for intensified ethanol–water separation systems

The optimization of advanced intensified distillation operation systems is crucial for achieving sustainability in the chemical industry. In this paper, we present a stochastic data-driven Bayesian optimization approach for the design and operation of ethanol distillation separation systems. The proposed optimal design methodology does not require a first-principles mathematical model to draw optimal operating conditions. The approach involves an initial sampling of input/output system measurements, followed by the construction of an approximate input/output Gaussian process model. The next best sampling point is then drawn using an acquisition function, and the algorithm converges when the optimization function value does not change or when the maximum number of iterations is reached. We address the optimal parametric design of advanced ethanol–water intensified distillation separation systems, assuming a mixture of the ethanol–water system is available, and almost anhydrous ethanol is required for mobility applications. We also consider the impact of noisy measurements on the optimality operating region. The proposed approach can be extended to the optimization of merged discrete and continuous systems. Bayesian optimization techniques represent black-box optimization strategies that can be employed for the continuous online optimization of intricate separation systems. These techniques aid in mitigating sustainability issues by reducing mass and energy requirements.

Escaping the rough life of the street: Roofless people and mobile social media

Roofless people are among the most vulnerable, marginalized, and silenced societal groups. However, access to social media through mobile devices may provide opportunities for battling their stigmatization, social exclusion, low self-esteem, and self-acceptance. This study aims to explore how people who are experiencing rooflessness use and represent themselves on social media. This qualitative study, using thematic analysis of 16 in-depth interviews, reveals that roofless people may use social media to increase their social capital, advocate for other homeless people, and, most importantly, escape or reconstruct the reality of life on the street through continuous online social interaction and revalidation of oneself.

Toward a Comprehensive Evaluation on the Online Methods for Monitoring Transformer Turn-to-Turn Faults

Transformer winding turn-to-turn fault is the prominent cause of transformer total failure, so detecting the winding fault in real time to stop the failure development in advance is imperative. However, existing techniques entailing periodic offline inspections fail to continuously monitor transformer winding states while causing extra costs due to the outage during inspections. This has driven researchers to consider effective continuous online monitoring methods from several technical perspectives, including typically port voltage current analysis, online frequency response analysis, and vibration analysis. Since these methods are conventionally evaluated with qualitative comparisons focusing only on feasibility, quantitative assessments indispensable for the targeted improvement of the methods and the most suitable method decision in specific scenarios are still missing. To this end, we conduct a comprehensive evaluation on the three methods by leveraging both experiment and theoretical analysis. Specifically, a customized experiment platform has been designed to support data acquisition under different operating conditions. As conventional feature mining algorithms cannot process the monitoring data produced by different methods in a uniform manner, a feature extraction algorithm leveraging image mining is proposed to extract data features after mapping the test data into a high-dimensional image. This novel algorithm allows us to fully assess several fundamental aspects (i.e., sensitivity, repeatability, and anti-interference capability) of these monitoring methods.

A Parallel Sequential SBAS Processing Framework Based on Hadoop Distributed Computing

With the rapid development of microwave remote sensing and SAR satellite systems, the use of InSAR techniques has been greatly encouraged due to the abundance of SAR data with unprecedented temporal and spatial coverage. Small Baseline Subset (SBAS) is a promising time-series InSAR method for applications involving deformation monitoring of the Earth’s crust, and the sequential SBAS method is an extension of SBAS that allows long-term and large-scale surface displacements to be obtained with continuously auto-updating measurement results. As the Chinese LuTan-1 SAR system has begun acquiring massive SAR image data, the need for an efficient and lightweight InSAR processing platform has become urgent in various research fields. However, traditional sequential algorithms are incapable of meeting the huge challenges of low efficiency and frequent human interaction in large-scale InSAR data processing. Therefore, this study proposes a distributed parallel sequential SBAS (P2SBAS) processing chain based on Hadoop by effectively parallelizing and improving the current sequential SBAS method. P2SBAS mainly consists of two components: (1) a distributed SAR data storage platform based on HDFS, which supports efficient inter-node data transfer and continuous online data acquisition, and (2) several parallel InSAR processing algorithms based on the MapReduce model, including image registration, filtering, phase unwrapping, sequential SBAS processing, and so on. By leveraging the capabilities associated with the distributed nature of the Hadoop platform, these algorithms are able to efficiently utilize the segmentation strategy and perform careful boundary processing. These parallelized InSAR algorithm modules can achieve their goals on different nodes in the Hadoop distributed environment, thereby maximizing computing resources and improving the overall performance while comprehensively considering performance and precision. In addition, P2SBAS provides better computing and storage capabilities for small- and medium-sized teams compared to popular InSAR processing approaches based on cloud computing or supercomputing platforms, and it can be easily deployed on clusters thanks to the integration of various existing computing components. Finally, to demonstrate and evaluate the efficiency and accuracy of P2SBAS, we conducted comparative experiments on a set of 32 TerraSAR images of Beijing, China. The results demonstrate that P2SBAS can fully utilize various computing nodes to improve InSAR processing and can be applied well in large-scale LuTan-1 InSAR applications in the future.

On line monitoring of transmission line

Continuous online monitoring of transmission line is required for proper functioning of power system. This paper presents two simulink model one for single transmission line, singly fed and the other for parallel transmission line doubly fed and presents the simulation result which shows the continuous variation in transmission line impedance before, during and after the fault. This paper shows the effect of different factors like prefault system condition, ground fault resistance, series compensation effect individually and then taking together on impedance plot under line to ground fault and three phase to ground fault. In case of parallel transmission line the paper have taken into account of mutual coupling effect also.

Factors Affecting Consumers' Early Stage and Continuous Online Shopping Behaviour

Factors Affecting Consumers' Early Stage and Continuous Online Shopping Behaviour

A novel artificial neural network approach for residual life estimation of paper insulation in oil‐immersed power transformers

AbstractAvoiding financial losses requires preventing catastrophic oil‐filled power transformer breakdowns. Continuous online transformer monitoring is needed. The authors use paper insulation to evaluate transformer health for continuous online transformer monitoring. The study suggests a new artificial intelligence method for estimating paper insulation residual life in oil‐immersed power transformers. The four artificial intelligence models use backpropagation‐based neural networks to predict paper insulation lifespan. Four primary transformer insulating paper failure indices—degree of polymerisation, 2‐furfuraldehyde, carbon monoxide, and carbon dioxide—form the basis of these models. Each model, including the backpropagation‐based neural networks, estimates paper insulation life using one failure index, along with moisture and temperature data. Optimisation techniques enhance hidden layer neurons and epoch count for improved performance. Results are validated against literature‐based life models, establishing a precise input–output correlation. This method accurately predicts the remaining useable life of power transformer paper insulation, enabling utilities to take proactive measures for safe and efficient transformer operation.

Mask R-CNN based droplet detection in liquid–liquid systems, Part 3: Model generalization for accurate processing performance independent of image quality

Operating processes involving liquid–liquid systems requires a sound understanding of fluid dynamics, mass transfer, and reaction kinetics, which strongly depend on droplet size distribution (DSD). Optical measurement is the most commonly used technique for DSD determination, followed by an appropriate image-processing method. However, the image-processing method is parameterized for a specific image quality, which varies depending on operating conditions and the setup of optical hardware. Generalizing the image-processing method Mask Region-Based Convolutional Neural Network (MRCNN) has the potential to robustly determine DSDs independently from varying image qualities. This work applies three generalization methods, namely augmentation, pre-processing, and training domain diversification, to generalize MRCNN. The droplet detection performance of the resulting generalized MRCNN model is compared with the manual processing for eight image qualities. The generalization of MRCNN by training domain diversification results in a robust model with high DSD detection accuracy. Averaged over the eight image qualities, the relative error in characteristic DSD properties is: Sauter mean diameter – 3%, minimum diameter – 4%, and maximum diameter – 10%. Since MRCNN shows an accurate and robust processing performance independent of the photo-optical measurement equipment and operating conditions, it can be implemented in continuous online monitoring of the DSD without training on specific image quality.