Structural,Electrical and Photoresponse Properties of Si-based Diode with Organic Interfacial Layer Containing Novel Cyclotriphosphazene Compound

The electrical and photoresponse properties of Al/p-Si/organic layer/Al diode were investigated. Organic layer containing novel 2,2-bis[spiro(7,8-dioxy- 4-methylcoumarin)]-4,4,6,6-bis[spiro(2’,2”-dioxy-1’,1”- biphenylyl)]cyclotriphosphazene compound was coated by drop casting method on p-Si having ohmic contact. The structural characterization of novel cyclotriphosphazene compound was confirmed by using ¹H, ¹³C and ³¹P-NMR, elemental analysis and FT-IR spectroscopic techniques. The diode exhibits a photoconducting and photodiode behavior under solar light illumination. The electrical parameters such as ideality factor, barrier height and series resistance of the diode were determined from I-V characteristics. It is seen that the photocurrent of the diode under illumination is higher than dark current. Also, the frequency dependence of capacitance (C) and conductance (G) was explained on the basis of interface states. It is evaluated that the hybrid photodiode can be used as a photosensor in organic photodetector applications.     

Barriers to engagement in sleep restriction and stimulus control in chronic insomnia.

Sleep restriction (SRT) and stimulus control (SC) have been found to be effective interventions for chronic insomnia (Morgenthaler et al., 2006), and yet adherence to SRT and SC varies widely. The objective of this study was to investigate correlates to adherence to SC/SRT among 40 outpatients with primary or comorbid insomnia using a correlational design. Participants completed a self-report measure of sleepiness prior to completion of a 6-week cognitive behavioral treatment group for insomnia. At the posttreatment period, they rated their ability to engage in SC/SRT using a survey. Results from standard multiple regression analyses showed that perceiving fewer barriers (i.e., less boredom, annoyance) to engaging in SC/SRT and experiencing less pretreatment sleepiness were each associated with better adherence to SC/SRT. Adherence to SC/SRT was associated with outcome. Implications of these findings are that more work is needed to make SC/SRT less uncomfortable, possibly by augmenting energy levels prior to introducing these approaches. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Cannabis and motor function: fMRI changes following 28 days of discontinuation.

The authors hypothesized that supplementary motor cortex (SMA) and anterior cingulate cortex (ACC) activation in chronic cannabis users, studied 4 to 36 hours after their last episode of use, would disappear by Day 28 of abstinence during finger-tapping tests. Eleven cannabis users and 16 comparison subjects were scanned during right (RFT) and left (LFT) finger-tapping tasks on a GE 1.5 Tesla scanner retrofitted with a whole body echo planar coil. Image analyses were conducted in SPM99 using an ROI approach to define each Brodmann area (BA). Differences in cerebral activation were examined in the left and right primary motor cortex (BA4), SMA (BA6), and ACC (BA24 and BA32 separately). The authors found diminished activation for contralateral BA6 from Day 0 to Day 28. For LFT, the authors also found: ipsilaterally diminished BA6 activation on Day 7, but not Day 0 or Day 28; ipsilaterally diminished BA32 activation on Day 0, but not Day 7 or Day 28; contralaterally diminished BA 4 activation on Day 28, but not Day 0 or Day 7; and contralaterally diminished BA32 activation on Day 0 and Day 28, but not Day 7. For RFT, the authors found ipsilaterally diminished BA32 activation on Days 0 and 7 but not on Day 28; contralaterally diminished BA32 activation on Days 0, 7, and 28; and ipsilaterally diminished BA6 activation on Days 0, 7, and 28. These results suggest that residual diminished brain activation is still observed after discontinuing cannabis use in motor cortical circuits. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A Review of Smart Contract Blockchain Based on Multi-Criteria Analysis: Challenges and Motivations

A smart contract is a digital program of transaction protocol (rules of contract) based on the consensus architecture of blockchain. Smart contracts with Blockchain are modern technologies that have gained enormous attention in scientific and practical applications. A smart contract is the central aspect of a blockchain that facilitates blockchain as a platform outside the cryptocurrency spectrum. The development of blockchain technology, with a focus on smart contracts, has advanced significantly in recent years. However, research on the smart contract idea has weaknesses in the implementation sectors based on a decentralized network that shares an identical state. This paper extensively reviews smart contracts based on multi-criteria analysis, challenges and motivations. Therefore, implementing blockchain in multi-criteria research is required to increase the efficiency of interaction between users via supporting information exchange with high trust. Implementing blockchain in the multi-criteria analysis is necessary to increase the efficiency of interaction between users via supporting information exchange and with high confidence, detecting malfunctioning, helping users with performance issues, reaching a consensus, deploying distributed solutions and allocating plans, tasks and joint missions. The smart contract with decision-making performance, planning and execution improves the implementation based on efficiency, sustainability and management. Furthermore, the uncertainty and supply chain performance lead to improved users’ confidence in offering new solutions in exchange for problems in smart contacts. Evaluation includes code analysis and performance, while development performance can be under development.     

Arabic Named Entity Recognition: A BERT-BGRU Approach

Named Entity Recognition (NER) is one of the fundamental tasks in Natural Language Processing (NLP), which aims to locate, extract, and classify named entities into a predefined category such as person, organization and location. Most of the earlier research for identifying named entities relied on using handcrafted features and very large knowledge resources, which is time consuming and not adequate for resource-scarce languages such as Arabic. Recently, deep learning achieved state-of-the-art performance on many NLP tasks including NER without requiring hand-crafted features. In addition, transfer learning has also proven its efficiency in several NLP tasks by exploiting pretrained language models that are used to transfer knowledge learned from large-scale datasets to domain-specific tasks. Bidirectional Encoder Representation from Transformer (BERT) is a contextual language model that generates the semantic vectors dynamically according to the context of the words. BERT architecture relay on multi-head attention that allows it to capture global dependencies between words. In this paper, we propose a deep learning-based model by fine-tuning BERT model to recognize and classify Arabic named entities. The pre-trained BERT context embeddings were used as input features to a Bidirectional Gated Recurrent Unit (BGRU) and were fine-tuned using two annotated Arabic Named Entity Recognition (ANER) datasets. Experimental results demonstrate that the proposed model outperformed state-of-the-art ANER models achieving 92.28% and 90.68% F-measure values on the ANERCorp dataset and the merged ANERCorp and AQMAR dataset, respectively.     

Get out of MySpace!

To understand the student experience on social software, the research aims to explore the disruptive nature and opportunity of social networking for higher education. Taking four universities, the research: (1) identifies the distinction between the students’ current usage of social software; (2) reports on the students’ experience on opportunities and challenges of learning with social software; and (3) introduces principles as a guideline in using social software for learning. Quantitative research methods (web-based questionnaires) were incorporated to investigate the pattern of learners’ usage. Qualitative methods (student interviews) were adopted to clarify and further inform this relationship and their attitudes towards social software for learning. The results demonstrate a massive use of educational technology with distinct divide between the learning space and personal space. Student voices reveal that the central problem of such divide is due to the contrast perception and experience of ‘learning/studying and social life’. We argue that online learning and social personas may overlap but that learning needs to be designed so that it addresses the individual preferences to combine or separate the two domains. The paper concludes with a few principles of learning with social software grounded in students’ experience and Vygotsky’s paradigm.     

Synthesis and evaluation of comb‐type copolymers prepared via atom transfer radical polymerization as possible cold flow improvers in GTL diesel fuels

Statistical comb‐type copolymers of styrene (Sty) and stearyl methacrylate (C18 MA) with varying [styrene]:[C18MA] ratios were synthesized by a controlled/living radical polymerization technique called atom transfer radical polymerization. The polymeric materials were evaluated in selected SASOL Fischer Tropsch gas‐to‐liquid diesels as possible cold flow improvers. Crystallization studies revealed that as the styrene content of the copolymer increased, a crystal growth inhibition mechanism was exhibited. With an increase in styrene content of the copolymer, differential scanning calorimetry and the cloud filter plugging point (CFPP) revealed a delay in onset of crystallization and lowered CFPP, respectively, whereas low‐temperature microscopy indicated modifications and size reduction of wax crystals. However, there appeared to be a styrene content, beyond which the additive's efficiency decreased. Homopolymer and copolymers with the highest styrene content led to long unfavorable needle‐shaped crystals. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

Online engagement during COVID-19: Role of agency on collaborative learning orientation and learning expectations

During the COVID-19 outbreak, students had to cope with succeeding in video-conferencing classes susceptible to technical problems like choppy audio, frozen screens and poor Internet connection, leading to interrupted delivery of facial expressions and eye-contact. For these reasons, agentic engagement during video-conferencing became critical for successful learning outcomes. This study explores the mediating effect agentic engagement has on collaborative language learning orientations (CLLO) within an EFL video-conferencing course to understand better how interactions influence academic learning expectations. A total of 329 (Male = 132, Female = 197) students were recruited from four South Korean universities to participate in this questionnaire study. Data analysis was carried out using the statistical software packages SPSS, and a series of data screening procedures were carried out. Findings revealed that collaborative language learning orientations were a statistically significant predictor of academic learning expectations, but this relationship was fully mediated when agentic engagement was added to the model. Students with a propensity for social language learning strategies believe they will succeed; however, this relationship is explained by their propensity to interact with the instructor when video-conferencing. An assortment of learning activities should be provided to support both collaborative and individual learning orientations for academic success. Students with collaborative learning tendencies and a propensity to actively engage the instructor during video conference classes are active participants in the eLearning context, possibly leading to positive course expectations.