Information For Teachers Research Articles

Dynamic pulse-to-pulse thermal load effects in pulse-train-mode self-seeded X-ray free-electron laser.

Thermal load has been a haunting factor that undermines the brightness and coherence of the self-seeded X-ray free-electron laser. Different from uniformly pulsed mode, in pulse train mode a thermal quasi-steady state of the crystal monochromator may not be reached. This leads to a dynamic thermal distortion of the spectral transmission curves and seed quality degradation. In this paper, the pulse-to-pulse thermal load effects on the spectral transmission curves and seed quality are shown, and some instructive information for the tuning process is provided.

RETRACTED: Professional development and ICT literacy of college teachers based on FPGA and image target recognition education

This work is the Integration of teacher's Information and Communication Technology (ICT) in all aspects of the school environment. Studies have shown that the potential for Teachers Professional Development (TPD) is based on the benefits of relevant local conditions and global factors. A non-describing term used for the headways which are used to assemble, store, recuperate, cycle, study, and send information. Information and correspondence advancement moves have gradually diminished organization information and engage individuals and relationships to perform information related tasks even more reasonable and have cost various improvements in things, practices, and progressive structures. The purpose of this article is to address the issue of TPD, about the use of information and communication technology, as a lever for educational reform in a systematic way, in terms of local applications and international research. This article should combine some of the key issues and challenges of TPD. This article proposes four cases, which combine practical and theoretical elements.

Integrating Eye-Tracking to Augmented Reality System for Surgical Training

Augmented Reality has been utilized for surgical training. During the implementation, displaying instructional information at the right moment is critical for skill acquisition. We built a new surgical training platform combining augmented reality system (HoloLens, Microsoft) with an eye-tracker (Pupil labs, Germany). Our goal is to detect the moments of performance difficulty using the integrated eye-tracker so that the system could display instructions at the precise moment when the user is seeking instructional information during a surgical skill practice in simulation. In the paper, we describe the system design, system calibration and data transferring between these devices.

ФОКУС РЕЗУЛЬТАТИВНОСТІ БАЗОВОГО ПРОФЕСІЙНОГО ЕТАПУ ОБРАЗОТВОРЧОЇ ПІДГОТОВКИ МАЙБУТНІХ ФАХІВЦІВ МИСТЕЦЬКИХ СПЕЦІАЛЬНОСТЕЙ

The article identifies fine art training in the system of art specialties (fine art teachers, artists of fine and decorative art, artists-restorers, art critics and architectures) of institutions of high education. In the focus of educational and professional programs the appropriateness of its effective regulating with professional and graphic specific features of a definite profession has been highlighted.Based on the sense of the conceptions “profession”, “professional”, “specialist” the following summary has been revealed: in high education in the context of identified profession the applying specialty, art specialty in particular, is presented by two necessary and appropriate professional and graphic features of vocational training. They are literacy (knowledge, abilities and skills) and professional skills (job skillfulness).Taking into consideration the mix of canonic, academic, ethnic and cultural, cultural and synthetic, operative, creative and uncontrolled models of basic professional stage of fine art training, the presence of specific features, diversity and difference of its results have been indicated. It has been proved that for every art specialty they should be presented by the single professional and graphic specific feature like “descriptive literacy” and “art skillfulness”.Using the materials on art criticism, art and pedagogical information, the preceding fact has been stated – during fine art training for would-be professionals of art specialties at high school based on the results of educational and creative activities the following contradictions have been confirmed: between the image realty and artistic value of the object pictured; between descriptive literacy as a main factor of educational and creative works producing and their artistic features; between descriptive literacy in learning its academic basis and the quality of fine art producing in the context of presenting of objects illusion and real phenomena. The reason for present disbalance in the forming of descriptive literacy and the students’ art skillfulness that implies in emphasizing at realty or imagery has been revealed. They may perform as the art criteria in the process to reach qualitative art work producing in educational and creative contexts.

Linking Genes to Shape in Plants Using Morphometrics.

A transition from qualitative to quantitative descriptors of morphology has been facilitated through the growing field of morphometrics, representing the conversion of shapes and patterns into numbers. The analysis of plant form at the macromorphological scale using morphometric approaches quantifies what is commonly referred to as a phenotype. Quantitative phenotypic analysis of individuals with contrasting genotypes in turn provides a means to establish links between genes and shapes. The path from a gene to a morphological phenotype is, however, not direct, with instructive information progressing both across multiple scales of biological complexity and through nonintuitive feedback, such as mechanical signals. In this review, we explore morphometric approaches used to perform whole-plant phenotyping and quantitative approaches in capture processes in the mesoscales, which bridge the gaps between genes and shapes in plants. Quantitative frameworks involving both the computational simulation and the discretization of data into networks provide a putative path to predicting emergent shape from underlying genetic programs.

Ergonomic Design is the Best Weapon to Reduce Risk

“Do it by Design” written by Dick Sawyer and the CDRH work group is a premiere reference and introduction to human factors in medical devices published by HHS, FDA CDRH. In this publication, the relationship of device design to safe and effective use is described and then built upon in subsequent key industry standards such as ANSI/AAMI HE 75. Yet the importance of completing good design and considering all the possible usability aspects continues to be overlooked or under-represented in the design process. Information for safety is required to be provided to medical device users (MDR 2017, FDA 21 CFR 801). However, risk mitigations in the form of training, labeling and instructions for use (IFU) are required to be used as a last resort, avoided or used sparingly (MDR 2017 and ISO 14971). Instead devices should be inherently safe by design and construction as well as implement adequate protection measures, such as alarms, for those risks that cannot be eliminated. As often observed in usability studies, users disregard labeling and instructional information or do not even realize it is provided to them. In cases where information for safety is the risk control method, can it be considered effective in reducing risk and should they be acceptable to medical device manufacturers? This article discusses rules of thumb in good product design that limit or eliminate the reliance on instructions and when that’s not possible, the rules of design for instructional materials that provide solid support for users.

Engineering Heterostructured Nanocatalysts for CO2 Transformation Reactions: Advances and Perspectives.

As a conceivable route to achieving anthropological carbon looping, carbon capture and utilization (CCU) technologies employ waste CO2 as an accessible C1 building block to generate upgraded chemicals or fuels, thereby simultaneously remedying environmental issues and energy crises. However, efficient CO2 conversion is disfavored by both its thermodynamics and its kinetics. Heterostructured materials with well-controlled interfaces have great potential for enhanced catalytic performance in various CO2 transformation reactions, owing to the synergistic effects among components, numerous interfacial and/or surface active sites, increased CO2 adsorption capacity, promoted charge transfer efficiency, and unique physicochemical properties. This Review highlights the state of the art in typical heterostructures, such as core-shell, yolk-shell, Janus, hierarchical (branched and hollow), and 2D/2D layered structures, applied for CO2 conversion with various energy inputs (radiation, electricity, heat). Fabrication methods of different heterostructures and structure-composition-performance relationships are also discussed concisely. Finally, a brief summary and prospective research directions are provided. The motivation of this Review is to offer instructive information on the applicability of inorganic heterostructures for CO2 transformation reactions, and it is hoped that further enlightening studies in this field could emerge in the future.

Disaster-resilient communication ecosystem in an inclusive society – A case of foreigners in Japan

The number of foreign residents and tourists in Japan has been dramatically increasing in recent years. Despite the fact that Japan is prone to natural disasters, with each climate-related event turning into an emergency such as with record rainfalls, floods and mudslides almost every year, non-Japanese communication infrastructure and everyday disaster drills for foreigners have received little attention. This study aims to understand how a resilient communication ecosystem forms in various disaster contexts involving foreigners. Within a framework of information ecology we try to get an overview of the communication ecosystem in literature and outline its structure and trends in social media use. Our empirical case study uses Twitter API and R programming software to extract and analyze tweets in English during Typhoon 19 (Hagibis) in October 2019. It reveals that many information sources transmit warnings and evacuation orders through social media but do not convey a sense of locality and precise instructions on how to act. For future disaster preparedness, we argue that the municipal government, as a responsible agent, should (1) make available instructional information in foreign languages on social media, (2) transfer such information through collaboration with transmitters, and (3) examine the use of local hashtags in social media to strengthen non-Japanese speaker's capacity to adapt.

How novice and expert anaesthetists understand expertise in anaesthesia: a qualitative study

BackgroundThe development of expertise in anaesthesia requires personal contact between a mentor and a learner. Because mentors often are experienced clinicians, they may find it difficult to understand the challenges novices face during their first months of clinical practice. As a result, novices’ perspectives may be an important source of pedagogical information for the expert. The aim of this study was to explore novice and expert anaesthetists understanding of expertise in anaesthesia using qualitative methods.MethodsSemi-structured interviews were conducted with 9 novice and 9 expert anaesthetists from a German University Hospital. Novices were included if they had between 3 and 6 months of clinical experience and experts were determined by peer assessment. Interviews were intended to answer the following research questions: What do novices think expertise entails and what do they think they will need to become an expert? What do experts think made them the expert person and how did that happen? How do both groups value evidence-based standards and how do they negotiate following written guidance with following one’s experience?ResultsThe clinical experience in both groups differed significantly (novices: 4.3 mean months vs. experts: 26.7 mean years; p < 0.001). Novices struggled with translating theoretical knowledge into action and found it difficult to talk about expertise. Experts no longer seem to remember being challenged as novice by the complexity of routine tasks. Both groups shared the understanding that the development of expertise was a socially embedded process. Novices assumed that written procedures were specific enough to address every clinical contingency whereas experts stated that rules and standards were essentially underspecified. For novices the challenge was less to familiarise oneself with written standards than to learn the unwritten, quasi-normative rules of their supervising consultant(s). Novices conceptualized decision making as a rational, linear process whereas experts added to this understanding of tacit knowledge and intuitive decision making.ConclusionsMajor qualitative differences between a novice and an expert anaesthetist’s understanding of expertise can create challenges during the first months of clinical training. Experts should be aware of the problems novices may have with negotiating evidence-based standards and quasi-normative rules.

Genetic Music System with Synthetic Biology.

This article introduces GeMS, a system for music composition informed by synthetic biology. GeMS generates music with simulations of genetic processes, such as transcription, translation, and protein folding, with which biological systems render chains of amino acids from DNA strands. The system comprises the following components: the Miranda machine, the rhythmator, and the pitch processor. The Miranda machine is an abstract Turing-machine-like processor, which manipulates a sequence of DNA symbols according to a set of programming instructions. This process generates a pool of new DNA strands, which are subsequently translated into rhythms. GeMS represents the musical equivalent of amino acids in terms of rhythms, referred to as rhythmic codons. This enables the rhythmator to convert DNA sequences into rhythmic sequences. The pitch processor generates pitches for such rhythmic sequences. It is inspired by the phenomenon of protein folding. The pitch processor considers orientation information of DNA instructions yielded by the Miranda machine in order to activate algorithms for generating pitches. A musical composition, entitled Artibiotics, for percussion ensemble and electronic instruments, is presented to demonstrate the system.

Molecular insights into the hydrodenitrogenation mechanism of pyridine over Pt/γ-Al2O3 catalysts

The hydrodenitrogenation of nitrogen-containing heterocycles over noble metals has fundamental importance for energy and environmental science. To develop more efficient catalyst, mechanistic investigation has been conducted with a method combining in situ Fourier transformation infrared experiments and density functional theory calculations in this work. The in situ experiments indicate flatly adsorbed pyridine molecules convert to pyridinium and α-pyridyl species at higher temperature on metallic Pt of Pt/γ-Al2O3 catalysts. Pyridine hydrogenation distinctly takes place at 150 °C with the appearance of methylene stretching vibrations, and a stepwise mechanism is identified as the temperature further increases. The adsorption, hydrogenation and hydrogenolysis of pyridine on Pt are studied in detail by theoretical calculations. In line with these findings, the geometry optimization confirms pyridine preferentially adsorbs on Pt(111) and Pt(211) both in a parallel configuration. Based on the Langmuir–Hinshelwood mechanism, the results show successive hydrogenation markedly lowers the energy barrier for subsequent hydrogenolysis. The CN bond cleavage occurs via nucleophilic attack of pentahydropyridine, rather than piperidine, which determines the reaction products, including piperidine, n-pentylamine and n-pentane. The comparative study reveals both hydrogenation and hydrogenolysis are kinetically and thermodynamically more competitive on Pt(211) than Pt(111). Especially for hydrogenolysis, the coordinatively unsaturated Pt step atoms play an essential role in CN bond cleavage. Thus, hydrogenolysis is more geometric‒dependent than hydrogenation. This provides instructive information for the design of catalysts with adjustable product selectivity.

Particle Swarm Optimization Based Swarm Intelligence for Active Learning Improvement: Application on Medical Data Classification

Semi-supervised learning targets the common situation where labeled data are scarce but unlabeled data are abundant. It uses unlabeled data to help supervised learning tasks. In practice, it may make sense to utilize active learning in conjunction with semi-supervised learning. That is, we might allow the learning algorithm to pick a set of unlabeled instances to be labeled by a domain expert, which will then be used as the labeled data set. However, existing approaches are computationally expensive and require searching through an entire unlabeled dataset, which may contain redundant instances that provide no instructive information to the classifier and can decrease the performance. To address this optimization problem, a hybrid system that combines active learning (AL) and particle swarm optimization (PSO) algorithms is proposed to reduce the cost of labeling while building a more efficient classifier. The novelty of this work resides in the integration of a bio-inspired optimization algorithm in the machine learning strategy. Furthermore, a novel uncertainty measure was integrated into the particle swarm optimization algorithm as an objective function to select from massive amounts of medical instances those that are deemed most informative. To evaluate the effectiveness of the proposed approach, eighteen (18) benchmark datasets were used and compared against three best-known classifiers with different learning paradigms: AL–NB an active learning algorithm using Naive Base classifier and Margin Sampling strategy, SVM (Support Vector Machine), ELM (Extreme Learning Machine) with supervised learning, and TSVM (Transductive Support Vector Machine) with the semi-supervised learning. Experiments showed that the proposed approach is effective in reducing the efforts required by experts for medical data annotation to produce an accurate classifier. The active learning approach has been utilized to optimize the expensive task of labeling. Based on a novel uncertainty measure, the nature-inspired algorithm PSO attempts to select from massive amounts of unlabeled medical instances those considered informative, at the same time improving the classifier performance. The experiments carried out confirm that the proposed strategy significantly enhances the performance of the AL algorithm compared with the commonly used uncertainty strategies. It achieves a performance similar to that of fully supervised and semi-supervised algorithms while requiring much less labeling. As a future extension of this work, it would be interesting to integrate other evolutionary optimization algorithms and compare them with our approach. In addition, it is beneficial to test the impact of using other variants of PSO algorithm in our approach. Also, it is aimed to test more classification algorithms in the experimentation process.

Assessment of teachers' awareness and proficiency in utilization of information and communication technology (ICT) packages in classroom instruction

Assessment of teachers' awareness and proficiency in utilization of information and communication technology (ICT) packages in classroom instruction

An Adaptive Clock Scheme Exploiting Instruction-Based Dynamic Timing Slack for a GPGPU Architecture

This article presents an adaptive clock scheme to exploit instruction-based dynamic timing slack (DTS) for a general-purpose graphics processor unit (GPGPU) architecture. Based on the developed transitional static timing analysis, the deterministic DTS can be identified for each instruction at different pipeline stages. A critical path (CP) messenger scheme was designed to monitor the runtime utilization of CPs. Both real-time issued instruction information and CP messengers are utilized to determine the runtime DTS margin and guide the cycle-by-cycle clock period adjustment. To apply the proposed adaptive clock on GPGPU, a hierarchical clocking scheme is built including a global phase-locked loop (PLL) and local delay-locked loop (DLL)-based clock generator inside each compute unit (CU). Each CU core contains its own clock domain with adjustable local clocking. In addition, to exploit error-resilient characteristics of the neural network, an elastic pipeline clocking scheme is developed to redistribute the timing margin across pipeline stages for machine learning computations. Measurement results from the implemented open-source GPGPU architecture on a 65 nm CMOS process demonstrate up to 18% performance improvement or equivalent 30% energy saving can be obtained by exploiting the deterministic instruction-based DTS. The proposed elastic pipeline clocking can gain an additional 8% energy saving with small accuracy degradation for neural network inference operations.

Informações pedagógicas de cursos de psicologia em sites de instituições de ensino superior

Nos últimos 30 anos, o ensino superior brasileiro cresceu vertiginosamente, e cursos de Psicologia apresentaram um dos maiores crescimentos de matrículas. Este artigo objetivou analisar a disponibilidade on-line de informações pedagógicas sobre cursos de Psicologia. Trata-se de estudo exploratório, documental, que analisou estatisticamente as taxas de disponibilidade das informações pesquisadas nos sites das respectivas Instituições de Ensino Superior. Os resultados apontam que cursos públicos e privados incorreram em omissão na publicização de informações relevantes, em desacordo com as noções de transparência e accountability e contrariando disposições legais.

ERF: An Empirical Recommender Framework for Ascertaining Appropriate Learning Materials from Stack Overflow Discussions

Computer programmers require various instructive information during coding and development. Such information is dispersed in different sources like language documentation, wikis, and forums. As an information exchange platform, programmers broadly utilize Stack Overflow, a Web-based Question Answering site. In this paper, we propose a recommender system which uses a supervised machine learning approach to investigate Stack Overflow posts to present instructive information for the programmers. This might be helpful for the programmers to solve programming problems that they confront with in their daily life. We analyzed posts related to two most popular programming languages—Python and PHP. We performed a few trials and found that the supervised approach could effectively manifold valuable information from our corpus. We validated the performance of our system from human perception which showed an accuracy of 71%. We also presented an interactive interface for the users that satisfied the users’ query with the matching sentences with most instructive information.

Preference of individuals in the treatment strategies of acute myocardial infarction in China: a discrete choice experiment

BackgroundAcute myocardial infarction (AMI) is a significant cause of mortality and morbidity worldwide. Today, with increasing life quality and social economy, people pay much attention to the cost-effectiveness of a treatment strategy. This study investigated the preferences of individuals who would be potential caregivers or patients for AMI treatment in order to provide liable and instructive information for cardiologists and other related physicians.MethodsA discrete choice experiment was conducted among people to assess preferences for hypothetical AMI treatment scenarios characterized by the attributes of treatment method, mortality within 5 years, complication rate within 1 year, treatment duration and expense. A conditional logit regression model and latent class analysis were used to interpret the collected data systematically. The relative importance of each attribute and willingness to pay of people on the trade-offs between different treatment strategies were estimated.ResultsParticipants valued mortality within 5 years most highly (average importance: 40.9, 95%CI 0.447–0.530). Three classes of participants were identified: Class 1 placed the most importance on treatment duration, class 2 corresponded with the overall result while expense was regarded as the most important attribute in class 3. Individuals favored an intermediate treatment duration of about 10 days instead of the shortest (95% CI 1.044–1.248, P < 0.001). People’s characteristics (sex, age, marriage, education and income) affected their preferences (P < 0.01).ConclusionPeople considered a mortality rate within 5 years as the most crucial attribute in the MI treatment and preferred an intermediate treatment duration of about 10 days. Furthermore, the findings estimated the trade-offs acceptable to patients and heterogeneity in preferences for AMI treatment.

FRET Dyes Significantly Affect SAXS Intensities of Proteins

AbstractStructural analyses in biophysics aim at revealing a relationship between a molecule's dynamic structure and its physiological function. Förster resonance energy transfer (FRET) and small‐angle X‐ray scattering (SAXS) are complementary experimental approaches to this. Their concomitant application in combined studies has recently opened a lively debate on how to interpret FRET measurements in the light of SAXS data with the popular example of the radius of gyration, commonly derived from both FRET and SAXS. There still is a lack of understanding in how to mutually relate and interpret quantities equally obtained from FRET or SAXS, and to what extent FRET dyes affect SAXS intensities in combined applications. In the present work, we examine the interplay of FRET and SAXS from a computational simulation perspective. Molecular simulations are a valuable complement to experimental approaches and supply instructive information on dynamics. As FRET depends not only on the mutual separation but also on the relative orientations, the dynamics, and therefore also the shapes of the dyes, we utilize a novel method for simulating FRET‐dye‐labeled proteins to investigate these aspects in atomic detail. We perform structure‐based simulations of four different proteins with and without dyes in both folded and unfolded conformations. In‐silico derived radii of gyration are different with and without dyes and depend on the chosen dye pair. The dyes apparently influence the dynamics of unfolded systems. We find that FRET dyes attached to a protein have a significant impact on theoretical SAXS intensities calculated from simulated structures, especially for small proteins. Radii of gyration from FRET and SAXS deviate systematically, which points to further underlying mechanisms beyond prevalent explanation approaches.

Using Social Media to Recruit Youth Who Use Electronic Cigarettes

Objective: Research is needed to inform effective regulatory policy that can help curb electronic cigarette (e-cigarette) use among youth. However, several challenges interfere with studying e-cigarette use among youth. Social media may provide an outlet to overcome these challenges. Our objective was to provide instructional information on tested methods for using social media to study e-cigarette use among youth. Methods: We developed 13 advertisements that were used to recruit e-cigarette users 14-24 years old who lived in the United States. We measured the effectiveness of the advertisements and quality of the resulting data. Results: The advertisements reached 422,228 adolescents and young adults, 408 completed the survey, and 212 (52%) passed the quality check measures. The average cost per click was $0.36. Conclusions: Based on the current study, key recommendations for behavioral scientists interested in conducting e-cigarette research among youth include: (1) developing and testing multiple advertisements with the target audience; (2) advertising on social media platforms most commonly used by the target group; (3) tailoring (ie, age and interest) message options; and (4) using multiple types of data quality assurance items.

Lightweight multi-scale residual networks with attention for image super-resolution

In recent years, constructing various deep convolutional neural networks (CNNs) for single-image super-resolution (SISR) tasks has made significant progress. Despite their high performance, numerous CNNs are limited in practical applications, owing to the requirement of heavy computation. This paper proposes a lightweight network for SISR, known as attention-based multi-scale residual network (AMSRN). In detail, a residual atrous spatial pyramid pooling (ASPP) block as well as a spatial and channel-wise attention residual (SCAR) block is stacked alternately to support the main framework of the entire network. The residual ASPP block utilizes parallel dilated convolutions of different dilation rates to achieve the purpose of capturing multi-scale features. The SCAR block adds the channel attention (CA) and spatial attention (SA) mechanisms based on a double-layer convolution residual block. In addition, group convolution is introduced in the SCAR block to further reduce the parameters while preventing over-fitting. Moreover, a multi-scale feature attention module is designed to provide instructive multi-scale attention information for shallow features. Particularly, we propose a novel upscale module, which adopts dual paths to upscale the features by jointly using sub-pixel convolution and nearest interpolation layers, instead of using deconvolution layer or sub-pixel convolution layer alone. The experimental results demonstrate that our method achieves comparable performance to the state-of-the-art methods, both quantitatively and qualitatively.