Reading Efficiency Research Articles

Reading ability in children relates to rhythm perception across modalities

The onset of reading ability is rife with individual differences, with some children termed “early readers” and some falling behind from the very beginning. Reading skill in children has been linked to an ability to remember nonverbal rhythms, specifically in the auditory modality. It has been hypothesized that the link between rhythm skills and reading reflects a shared reliance on the ability to extract temporal structure from sound. Here we tested this hypothesis by investigating whether the link between rhythm memory and reading depends on the modality in which rhythms are presented. We tested 75 primary school-aged children aged 7–11 years on a within-participants battery of reading and rhythm tasks. Participants received a reading efficiency task followed by three rhythm tasks (auditory, visual, and audiovisual). Results showed that children who performed poorly on the reading task also performed poorly on the tasks that required them to remember and repeat back nonverbal rhythms. In addition, these children showed a rhythmic deficit not just in the auditory domain but also in the visual domain. However, auditory rhythm memory explained additional variance in reading ability even after controlling for visual memory. These results suggest that reading ability and rhythm memory rely both on shared modality-general cognitive processes and on the ability to perceive the temporal structure of sound.

Differences in echocardiography interpretation techniques among trainees and expert readers

BackgroundTrainees learn transthoracic echocardiogram (TTE) interpretation through independently completing and reviewing selected portions of the study with experts. The diagnostic accuracy of novice TTE interpretation is known to be low and schema for reading TTEs systematically are lacking. The purpose of our study is to identify techniques experts use while reading TTEs which could be used to more effectively teach novice readers.MethodsWe performed a prospective qualitative case study to observe how experts and trainees interpret TTEs in an academic institution using a concurrent think aloud (CTA) method. Three TTEs of intermediate complexity were given to 3 advanced imaging fellows, 3 first year fellows and 3 expert TTE readers Participants filled out a report while reading and described aloud their thought processes. Sessions were video and audiotaped for analysis.ResultsExperts and advanced fellows used specific techniques that novices did not including: previewing studies, reviewing multiple images simultaneously, having flexibility in image review order and disease coding, and saving hardest elements to code for the end. Direct observation of TTE reading informed trainee inefficiencies and was a well-received educational tool.ConclusionsIn this single centered study we identified several unique approaches experts use to interpret TTEs which may be teachable to novices. Although limited in generalizability the findings of this study suggests that a more systematic approach to TTE interpretation, using techniques found in experts, might be of significant value for trainees. Further study is needed to evaluate teaching practices at other institutions and to assess whether implementation of these techniques by novices improves can improve their diagnostic accuracy and efficiency of reading at an earlier stage in their training.

OP08 Automatic detection of enteric ulcers and erosions in capsule endoscopy using a convolutional neural network

Abstract Background Capsule endoscopy (CE) is recommended for the diagnosis and assessment of disease extension in patients with suspected or known inflammatory bowel disease, particularly Crohn’s disease. Ulcers and erosions of the enteric mucosa are prevalent findings in these patients. They frequently occur together, and their identification in CE is crucial for an accurate evaluation of disease severity. Nevertheless, reviewing CE images is a time-consuming task, and the risk of overlooking lesions is significant. Over the last decade, artificial intelligence (AI) has emerged as a mean for overcoming these pitfalls. Of all AI methods, convolutional neural networks (CNN), due to their complex multilayer architecture present the best results in image analysis. We aimed to develop a CNN for the automatic identification of ulcers and erosions in the small bowel mucosa. Methods A total of 1483 CE exams (PillCam SB3®) performed at a single centre between 2015–2020 were analyzed. From these exams, a sum of 11588 frames of the enteric mucosa were obtained, 3163 containing enteric ulcers and erosions, and the remaining containing normal mucosa or other findings. Ulcers and erosions were stratified according to the Saurin’s classification for bleeding potential: P1E – erosions with intermediate bleeding risk; P1U – ulcers with intermediate bleeding risk; P2U – ulcers with high bleeding risk. For automatic identification of these lesions, these images were inserted into an CNN model with transfer learning. Subsequently, the performance of the network assessed using an independent set of images. The output provided by the CNN was compared to the classification provided by a consensus of specialists (Figure 1). Results After optimizing the neural architecture of the algorithm, our model was able to automatically detect and distinguish ulcers and erosions (any bleeding potential) in the small intestine mucosa with an accuracy of 96.7%, precision of 95.9%, sensitivity of 91.7% and a specificity of 97.8% (Figure 1). The mean processing time for the validation dataset was 23 seconds (approximately 101 frames/second). Conclusion We developed and tested a deep learning model based on a CNN for the automatic detection of enteric ulcers and erosions in CE images. Our system revealed extraordinary performance marks. We believe that our study lays the foundation for the development and application of effective AI tools to clinical practice. These techniques should improve diagnostic accuracy and reading efficiency.

P113 Automatic detection of colonic ulcers and erosions in colon capsule endoscopy images using a convolutional neural network

Abstract Background Conventional colonoscopy is gold standard for the diagnosis and monitoring of patients with suspected or known inflammatory bowel disease (IBD). Nevertheless, it is a potentially painful procedure with risk of complications, including bleeding and perforation. Colon capsule endoscopy (CCE) is a minimally invasive alternative for patients unwilling to undergo colonoscopy or when it is contraindicated or unfeasible. However, CCE produces thousands of frames and their revision is a time-consuming and monotonous task. The detection of ulcers and erosions is paramount for the diagnosis and assessment of the activity of IBD. However, these lesions may appear in a very small number of frames, thus increasing the risk of overlooking significant lesions. Our aim was to develop an Artificial Intelligence (AI) algorithm, based on a multilayer Convolutional Neural Network (CNN) for automatic detection of ulcers and erosions in CCE exams. Methods A total of 24 CCE exams (PillCam COLON 2®) performed at a single centre between 2010–2020 were analysed. From these exams, 3 635 frames of the colonic mucosa were extracted, 770 containing colonic ulcers or erosions and 2 865 showing normal colonic mucosa. These images were used for construction of training (80% of the frames) and validation (20% of the frames) datasets. For automatic identification of these lesions, these images were inputted in a CNN model with transfer learning. The output provided by the CNN (Figure 1) was compared to the classification provided by a consensus of specialists. Performance marks included sensitivity, specificity, positive and negative predictive values, accuracy and area under the receiving operator characteristics curve (AUROC). Results After the optimization of the neural architecture of the CNN, our model automatically detected colonic ulcers and erosions with a sensitivity of 90.3%, specificity of 98.8% and an accuracy of 97.0% (Figure 2). The AUROC was 0.99 (Figure 3). The mean processing time for the validation dataset was 11s (approximately 66 frames/s). Conclusion We developed a CNN model which demonstrated high levels of efficiency for the automatic detection of ulcers and erosions in CCE images. Our study lays the foundations for the development of effective AI tools for application to CCE exams. These systems may enhance the diagnostic accuracy and reading efficiency of CCE, thus expanding the role of minimally-invasive colonic exploration in the management of IBD patients.

Investigating the effects of Citrullus colocynthis on cognitive performance and anxiety-like behaviors in STZ-induced diabetic rats

Background: Diabetes can impair cognitive performance and lead to dementia. Patients with type 1 diabetes mellitus (T1DM) are reported with different levels of cognitive dysfunctions in various cognitive domains ranging from general intellectual testing to specific deficits with visuospatial abilities, motor speed, writing, attention, reading, and psychomotor efficiency. The present study aimed to investigate the effect of Citrullus colocynthis on cognitive functions. Methods: A total of 42 male Wistar rats (3-4 months old and weighing 200-250 g) were tested in the current study. Rats were randomly allocated into 3 groups of control, Diabetes, and Diabetes + Drug. The diabetic rats received Citrullus colocynthis extraction orally. The behavioral tests included the open field, elevated plus maze (EPM), novel object recognition (NOR), passive avoidance tests, and Morris Water Maze (MWM) tests. Data were analyzed using student and paired t-tests via SPSS software version 16. Results: Our results showed the protective effects of Citrullus colocynthis administration against cognitive impairments. This is followed by STZ-induced diabetes in the MWM, novel object recognition, and passive avoidance tasks. Also, it was found that Citrullus colocynthis improved anxiety in diabetic rats. Conclusion According to the findings of this study, the administration of 200 mg/kg C. colocynthis once per day for 40 days can lead to ameliorated cognitive impairments and antidiabetic effects such as increasing body weight and decreasing FBS.

What is text reading fluency and is it a predictor or an outcome of reading comprehension? A longitudinal investigation.

Text reading fluency refers to the ability to read connected texts with accuracy, speed, and expression (prosody), and has garnered substantial attention as an important skill for reading comprehension. However, two fundamental questions remain-the dimensionality of text reading fluency including text reading efficiency (accuracy and speed) and reading prosody, and the directionality of the relation between text reading fluency and reading comprehension. These questions were addressed using longitudinal data from Grade 1 (Mage = 6.36 years) to Grade 3 (Mage = 8.34 years). Majority of children were White (approximately 60%) and African American (26%) with 39% to 52% from low-SES backgrounds, depending on the grade. Text reading fluency, word reading, listening comprehension, and reading comprehension were measured. Results from confirmatory factor analysis revealed that text reading fluency is a multidimensional construct with a trifactor structure, which has a general factor that captures common ability across text reading efficiency and reading prosody as well as local and specific factors that are unique beyond the general factor. However, the general factor was the most reliable factor, whereas local and specific factors were not reliable. The directionality of the relation between text reading fluency and reading comprehension was addressed by examining two competing structural equation models-text-reading-fluency-as-a-predictor/mediator model and text-reading-fluency-as-an-outcome model-and data supported the former. These results indicate that text reading fluency is a multidimensional construct, and it acts as a predictor, mediating the relations of word reading and listening comprehension to reading comprehension. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Effective intervention for adolescents with reading disabilities: Combining reading and motivational remediation to improve outcomes.

Adolescents with reading disability (RD) participated in a randomized controlled trial evaluating the efficacy of a multiple-component reading intervention with motivational components (PHAST). A total of 514 youth in 6th, 7th, and 8th grade formed instructional groups (4–8) that were randomly assigned to one of three conditions—one of two PHAST interventions (additional comprehension or fluency training) or a remedial reading control condition. Intervention occurred in participants’ schools, 40–60 min daily, 3–5×/week, for 100–125 hr total. Over four outcome assessments, multilevel growth models evaluated intervention/control differences in growth over time, and post-intervention effect sizes. The two PHAST interventions were associated with equivalent positive outcomes, and their data combined. PHAST participants out-performed Control participants on 8 of 16 outcomes, demonstrating greater growth on standardized and experimental reading and spelling outcomes. PHAST-instructed students demonstrated higher sense of reading competence, and increased attributions of reading success to their own abilities. Intervention effect sizes (Hedge’s g) comparing PHAST versus Control growth were larger for foundational reading skills (.78 for nonword decoding, .56 for word identification) than for reading comprehension (.36 for passage comprehension), for which effects were more equivocal. An effect size of .61 was obtained for sense of reading competence. A year later, the PHAST participants demonstrated continued improvement on later-developing reading skills like word reading efficiency, reading fluency, and reading comprehension. Intensive reading intervention in middle school can produce gains on multiple dimensions of reading skill and motivation and foster continuing growth of higher-order reading skills. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

Visual recognition efficiency of handheld infrared thermometer interface information under low ambient illuminance

One of the symptoms of plagues, epidemics or pandemics is often a fever, so during such unprecedented times, handheld infrared thermometers are vital devices for monitoring symptomatic individuals. It is therefore very important to enhance reading efficiency when these thermometers are used for lengthy periods of time in a low illuminance environment. The need for the efficient reading of infrared thermometers or a fast reaction time when reading the information is even more relevant now during the COVID-19 pandemic. In this study, a target search experiment of digital characters is carried out through a simulated interface and use scenarios of a handheld thermometer based on three variables: the inclination angle or slant of the seven segment display characters, screen brightness, and ambient illuminance. The experimental results show that the inclination angle or slant of the characters and ambient illuminance have a significant effect on the reaction speed. In general, the slowest reaction time is found when reading characters with a slant of 10° to the left and the reaction time is the fastest with a right slant of 20°. A continued reduction in ambient illuminance does not affect the visual recognition performance but instead further enhances reading efficiency. Increasing the screen brightness increases the reaction time more in relatively low ambient illuminance conditions as opposed to relatively high ambient illuminance, which implies that in higher ambient illuminance conditions, a brighter screen needs to be used to obtain the same reaction speed as that in lower ambient illuminance conditions.

Rich-club structure contributes to individual variance of reading skills via feeder connections in children with reading disabilities

The present work considers how connectome-wide differences in brain organization might distinguish good and poor readers. The connectome comprises a ‘rich-club’ organization in which a small number of hub regions play a focal role in assisting global communication across the whole brain. Prior work indicates that this rich-club structure is associated with typical and impaired cognitive function although no work so far has examined how this relates to skilled reading or its disorders. Here we investigated the rich-club structure of brain’s white matter connectome and its relationship to reading subskills in 64 children with and without reading disabilities. Among three types of white matter connections, the strength of feeder connections that connect hub and non-hub nodes was significantly correlated with word reading efficiency and phonemic decoding. Phonemic decoding was also positively correlated with connectivity between connectome-wide hubs and nodes within the left-hemisphere reading network, as well as the local efficiency of the reading network. Exploratory analyses also identified sex differences indicating these effects were stronger in girls. This work highlights the independent roles of connectome-wide structure and the more narrowly-defined reading network in understanding the neural bases of skilled and impaired reading in children.

Diagnostic performance of artificial intelligence model for pneumonia from chest radiography.

ObjectiveThe chest X-ray (CXR) is the most readily available and common imaging modality for the assessment of pneumonia. However, detecting pneumonia from chest radiography is a challenging task, even for experienced radiologists. An artificial intelligence (AI) model might help to diagnose pneumonia from CXR more quickly and accurately. We aim to develop an AI model for pneumonia from CXR images and to evaluate diagnostic performance with external dataset.MethodsTo train the pneumonia model, a total of 157,016 CXR images from the National Institutes of Health (NIH) and the Korean National Tuberculosis Association (KNTA) were used (normal vs. pneumonia = 120,722 vs.36,294). An ensemble model of two neural networks with DenseNet classifies each CXR image into pneumonia or not. To test the accuracy of the models, a separate external dataset of pneumonia CXR images (n = 212) from a tertiary university hospital (Gachon University Gil Medical Center GUGMC, Incheon, South Korea) was used; the diagnosis of pneumonia was based on both the chest CT findings and clinical information, and the performance evaluated using the area under the receiver operating characteristic curve (AUC). Moreover, we tested the change of the AI probability score for pneumonia using the follow-up CXR images (7 days after the diagnosis of pneumonia, n = 100).ResultsWhen the probability scores of the models that have a threshold of 0.5 for pneumonia, two models (models 1 and 4) having different pre-processing parameters on the histogram equalization distribution showed best AUC performances of 0.973 and 0.960, respectively. As expected, the ensemble model of these two models performed better than each of the classification models with 0.983 AUC. Furthermore, the AI probability score change for pneumonia showed a significant difference between improved cases and aggravated cases (Δ = -0.06 ± 0.14 vs. 0.06 ± 0.09, for 85 improved cases and 15 aggravated cases, respectively, P = 0.001) for CXR taken as a 7-day follow-up.ConclusionsThe ensemble model combined two different classification models for pneumonia that performed at 0.983 AUC for an external test dataset from a completely different data source. Furthermore, AI probability scores showed significant changes between cases of different clinical prognosis, which suggest the possibility of increased efficiency and performance of the CXR reading at the diagnosis and follow-up evaluation for pneumonia.

Improving reading skills in children with dyslexia: efficacy studies on a newly proposed remedial intervention-repeated reading with vocal music masking (RVM).

In this work, two different studies are examined to evaluate the effectiveness of a novel intervention program for the improvement of reading ability in children with dyslexia, known as repeated reading with vocal music masking (RVM). The proposed remedial approach is inspired by Breznitz's original work. The studies assess a 5-week program of intensive RVM training in a pre-post-test clinical paradigm, as well as a longitudinal paradigm where it is compared to 8 months of the standard remediation program (SRP). The results of both studies support the efficacy of the newly proposed RVM method. Notably in the longitudinal study, the reading speed of children, as well as related phonological, visuo-attentional, and cognitive skills, and attitudes toward reading, were measured regularly. Significant improvements in reading efficiency and related skills were observed, as well as greater motivation to read after RVM training. A modeling of the data specifically linked executive and processing speed skills to be involved in RVM training, suggesting that RVM may help rebalance the phonological and orthographic coding procedures necessary for efficient reading. The short, intensive, and focused nature of RVM training makes it a viable and attractive intervention for clinical practice. As preliminary results are promising, RVM training may prove to be a valuable tool that clinicians can call upon to effectively treat reading fluency disorders, especially when standard programs do not provide results.

Knowledge aggregation of the WeChat Official Accounts Platform based on tag clustering

As an important part of social networks, tags are widely used. Tags are similar to keywords, which can express and convey the main ideas or key knowledge of the article. With the increase in the number of visits to the WeChat Official Accounts Platform (referred to as WeChat platform), the number of scientific research published by the platform has also increased rapidly. However, with the increase in the number of posts, readers spend considerable time and energy. Since the articles published on the WeChat platform do not have corresponding keywords or tags, readers cannot quickly acquire knowledge, nor can they realize navigation and query of knowledge resources. To save users' effective reading time and improve users' reading efficiency, the articles published on the platform can be preprocessed to enable quick matching between readers and articles. How to extract the knowledge that users need from articles in different fields to help users find valuable knowledge quickly and effectively, in addition to how to realize domain knowledge resource retrieval and navigation, are important issues facing the current WeChat platform to carry out knowledge services. To solve the problems, this research extracts keywords from the articles published on the WeChat platform as article tags and proposes a framework for knowledge aggregation on the WeChat platform based on tag clustering.

Hadoop-Based Medical Image Storage and Access Method for Examination Series

Under the background of electronic medical data, doctors use electronic images to replace the traditional film for diagnosis, and patients can view examination images at any time through various electronic means. The storage and frequent reading of massive data bring new challenges. Given the characteristics of the size and quantity of image files generated by different examination types, different merging strategies are proposed to improve the storage performance of the files; according to the characteristics of medical data with examination as the basic unit, a two-level model combined with medical imaging information is proposed. The indexing mechanism solves the problem that SEQ files cannot be read randomly without an index; given the time characteristics of data access, an improved 2Q algorithm is proposed to cache the prefetched files and the read files in different cache queues, which improves the efficiency of file reading. In the experimental comparison, the proposed algorithm surpasses the baseline method in storage and access performance.

An Efficient Dual-Channel Data Storage and Access Method for Spaceborne Synthetic Aperture Radar Real-Time Processing

With the development of remote sensing technology and very large-scale integrated circuit (VLSI) technology, the real-time processing of spaceborne Synthetic Aperture Radar (SAR) has greatly improved the ability of Earth observation. However, the characteristics of external memory have led to matrix transposition becoming a technical bottleneck that limits the real-time performance of the SAR imaging system. In order to solve this problem, this paper combines the optimized data mapping method and reasonable hardware architecture to implement a data controller based on the Field-Programmable Gate Array (FPGA). First of all, this paper proposes an optimized dual-channel data storage and access method, so that the two-dimensional data access efficiency can be improved. Then, a hardware architecture is designed with register manager, simplified address generator and dual-channel Double-Data-Rate Three Synchronous Dynamic Random-Access Memory (DDR3 SDRAM) access mode. Finally, the proposed data controller is implemented on the Xilinx XC7VX690T FPGA chip. The experimental results show that the reading efficiency of the data controller proposed is 80% both in the range direction and azimuth direction, and the writing efficiency is 66% both in the range direction and azimuth direction. The results of a comparison with the recent implementations show that the proposed data controller has a higher data bandwidth, is more flexible in its design, and is suitable for use in spaceborne scenarios.

Finding the man amongst many: A developmental perspective on mechanisms of morphological decomposition

Skilled reading is characterized by rapid recognition of morphologically complex words. Evidence suggests that adult readers segment complex words into their constituent morphemes during visual word recognition, and that this extends to items that have only a surface morphological structure (e.g., corner), a process termed ‘morpho-orthographic segmentation’. It is not yet known how and when this mechanism is established over the course of reading development, although data from English-speaking children suggest that it may be a relatively late-acquired milestone. The purpose of this study was to examine for the first time the mechanisms driving morphological processing across late childhood and adolescence. A cross-sectional sample of 204 children and adolescents from South-East England, ranging in age from 9 to 18 years (M age = 13.74 years, SD = 2.68; 110 female), completed a visual masked prime lexical decision task using three sets of prime-target pairs: morphological (e.g., teacher – TEACH), pseudomorphological (sharing an apparent morphological relationship in the absence of a semantic relationship, e.g., corner – CORN), and form (sharing an orthographic relationship only, e.g., window – WIND). Linear mixed effects models revealed both morphological and pseudomorphological priming in the absence of form priming, with priming magnitude increasing in line with age, and stronger evidence of morpho-orthographic segmentation emerging in line with word reading efficiency. Our findings reveal advances in the reading system during adolescence which may reflect accumulated exposure to regularities in the writing system, facilitating rapid access to meaning from print.

Integration Path of College Classic Reading and Ideological and Political Education from the Perspective of Ideology-oriented Course

Classic reading has high educational value, but the integration of classic reading and Ideological and Political Education is low due to the cultivation of different majors in Colleges and universities. Ideology-oriented Course is a kind of collaborative concept of curriculum. The integration of classical reading and Ideological and Political Education in the goal setting, curriculum implementation and curriculum evaluation of classical reading education can improve the educational value and efficiency of classical reading.

Tight Tradeoffs in Searchable Symmetric Encryption

A searchable symmetric encryption (SSE) scheme enables a client to store data on an untrusted server while supporting keyword searches in a secure manner. Recent experiments have indicated that the practical relevance of such schemes heavily relies on the tradeoff between their space overhead, locality (the number of non-contiguous memory locations that the server accesses with each query), and read efficiency (the ratio between the number of bits the server reads with each query and the actual size of the answer). These experiments motivated Cash and Tessaro (EUROCRYPT ’14) and Asharov et al. (STOC ’16) to construct SSE schemes offering various such tradeoffs and to prove lower bounds for natural SSE frameworks. Unfortunately, the best-possible tradeoff has not been identified, and there are substantial gaps between the existing schemes and lower bounds, indicating that a better understanding of SSE is needed. We establish tight bounds on the tradeoff between the space overhead, locality and read efficiency of SSE schemes within two general frameworks that capture the memory access pattern underlying all existing schemes. First, we introduce the “pad-and-split” framework, refining that of Cash and Tessaro while still capturing the same existing schemes. Within our framework we significantly strengthen their lower bound, proving that any scheme with locality L must use space $$\Omega ( N \log N / \log L )$$ for databases of size N. This is a tight lower bound, matching the tradeoff provided by the scheme of Demertzis and Papamanthou (SIGMOD ’17) which is captured by our pad-and-split framework. Then, within the “statistical-independence” framework of Asharov et al. we show that their lower bound is essentially tight: We construct a scheme whose tradeoff matches their lower bound within an additive $$O(\log \log \log N)$$ factor in its read efficiency, once again improving upon the existing schemes. Our scheme offers optimal space and locality, and nearly optimal read efficiency that depends on the frequency of the queried keywords: For a keyword that is associated with $$n = N^{1 - \epsilon (n)}$$ document identifiers, the read efficiency is $$\omega (1) \cdot {\epsilon }(n)^{-1}+ O(\log \log \log N)$$ when retrieving its identifiers (where the $$\omega (1)$$ term may be arbitrarily small, and $$\omega (1) \cdot {\epsilon }(n)^{-1}$$ is the lower bound proved by Asharov et al.). In particular, for any keyword that is associated with at most $$N^{1 - 1/o(\log \log \log N)}$$ document identifiers (i.e., for any keyword that is not exceptionally common), we provide read efficiency $$O(\log \log \log N)$$ when retrieving its identifiers.

A GSM MODULE-BASED SMART ELECTRIC METER READER

The traditional and estimated billing system of electric energy consumed in most part of Sub-Saharan Africa has become a lingering issue to the electricity consumers. This has therefore necessitated the advent of smart electric meters. In this work, we propose a smart electric meter reader that provides an efficient and economically viable technique for measuring the consumption of electricity. This proposed method tends to solve many issues of the traditional reading system, such as reading efficiency, accuracy, and the elimination of human interface. Our proposed method, consisting of a GSM module, is used to wirelessly communicate the smart meter readings to the electricity provider and the consumer in form of a text message. The results obtained from the evaluation of this work show that our proposed method has improved the accuracy of the meter reading process for proper accountability.

A novel design of surface acoustic wave‐based chipless radio frequency identification tag based on multiphysics modeling

AbstractRadio frequency identification (RFID) technique is a kind of wireless, automatic identification technology that is being widely used. Compared to other identification methods such as bar code and two‐dimensional code (quick response code or QR code), the RFID technique has several remarkable merits, such as long reading distance, strong penetrating ability, pollution resistance, high reading efficiency, small size, and large information storage. However, the technique involves high cost and cannot be accepted by cost‐sensitive commodities or applications. To reduce its cost, chipless RFID tags were proposed in recent years using surface acoustic wave (SAW) to read and store the information is one such RFID tags. The tags feature small size, high flexibility, strong radiation resistance, high consistency and repeatability, as well as high temperature stability. In this paper, we propose a novel design for the SAW‐based RFID tags by using the commercial software COMSOL and analyze the performance of SAW tags with different structures.

Використання мультимедійних технологій у професійній діяльності вчителя-філолога

The article raises topical issue of the multimedia technologies usage in the philology teacher’s professional activity. It has been determined that under conditions of the Ukrainian society democratization essentially increases the importance of pupils’ spiritual and moral education, therefore the requirements to the quality of philological subjects teaching, which in this way have considerable pedagogical potential, considerably increase. One of the effective ways to solve this problem is to introduce modern multimedia technologies to educational process. It is specified that modern multimedia technology in the educational field is a software and hardware system that by combining various means of audio, TV, visual and virtual communications creates a compound informational and educational environment. It has been found that scientific literature offers different approaches to the multimedia technologies classification. Thus, according to the method of perception the context, visual, audio, audiovisual technologies are distinguished, according to the methodical purpose – demonstration, mentoring, training, controlling, reference, modeling, game, leisure. Each of the technologies mentioned has wide opportunities to increase the efficiency of philological subjects teaching. It has been established that while choosing a specific multimedia technology for a lesson by a philology teacher one should consider features of learning philological subjects by pupils, such as: object and subject of philological sciences is a personality; important result of the philological subjects context acquiring is accumulating the experience of expressing subjective emotional and valuable attitude to different aspects of human being; the process of philological subjects learning cannot be explained as a clear algorithm, because the holistic perception of an object is the reason to its understanding; the mentioned subjects learning by the pupils seriously influences their individual environment and motivates to self-improvement. It has been demonstrated that the scientifically proved multimedia technologies usage preserves the increase of the pupils’ development processes efficiency in reading, writing, listening and building language lexical and grammar skills.