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Objective The purpose of this study was to develop a natural language processing (NLP) pipeline to identify incidental thyroid nodules (ITNs) meeting criteria for sonographic follow-up and to assess both adherence rates to white paper recommendations and downstream outcomes related to these incidental findings. Methods 21583 non-contrast chest CT reports from 2017 and 2018 were retrospectively evaluated to identify reports which included either an explicit recommendation for thyroid ultrasound, a description of a nodule ≥ 1.5 cm, or description of a nodule with suspicious features. Reports from 2018 were used to train an NLP algorithm called fastText for automated identification of such reports. Algorithm performance was then evaluated on the 2017 reports. Next, any patient from 2017 with a report meeting criteria for ultrasound follow-up was further evaluated with manual chart review to determine follow-up adherence rates and nodule-related outcomes. Results NLP identified reports with ITNs meeting criteria for sonographic follow-up with an accuracy of 96.5% (95% CI 96.2-96.7) and sensitivity of 92.1% (95% CI 89.8-94.3). In 10006 chest CTs from 2017, ITN follow-up ultrasound was indicated according to white paper criteria in 81 patients (0.8%), explicitly recommended in 46.9% (38/81) of patients, and obtained in less than half of patients in which it was appropriately recommended (17/35, 48.6%). Discussion NLP accurately identified chest CT reports meeting criteria for ITN ultrasound follow-up. Radiologist adherence to white paper guidelines and subsequent referrer adherence to radiologist recommendations showed room for improvement.

Paper prototypes have been successful for gathering concrete, grounded user feedback. Most of these prototypes are used to elicit feedback during bounded design sessions. However, for systems that support new social practices these bounded sessions do not fully materialize the interactions the prototype supports. This paper presents a case study of a six-month paper prototype deployment that materialized collaborative interactions in care retirement community. Throughout this deployment the prototype was situated within the lives of participants, where they shaped the interactions and usage of the prototype over time. As these interactions materialized, the evoked community practice was unlike what participants envisioned during our first, more traditional paper prototyping and design sessions. This article shows how our interactive, paper prototype provided critical guidance in how realistic our imagined practice would be.

Exam paper generation is an indispensable part of teaching. Existing methods focus on the use of question extraction algorithms with labels for each question provided. Obviously, manual labeling is inefficient and cannot avoid label bias. Furthermore, the quality of the exam papers generated by the existing methods is not guaranteed. To address these problems, we propose a novel approach to generating exam papers based on prediction of exam performance. As such, we update the quality of the initially generated questions one by using dynamic programming, as well as in batches by using genetic algorithms. We performed the prediction task by using Deep Knowledge Tracing. Our approach considered the skill weight, difficulty, and distribution of exam scores. By comparisons, experimental results indicate that our approach performed better than the two baselines. Furthermore, it can generate exam papers with adaptive difficulties closely to the expected levels, and the related student exam scores will be guaranteed to be relatively reasonable distribution. In addition, our approach was evaluated in a real learning scenarios and shows advantages.

Thermal papers, commonly used for printed receipts or lottery tickets, are omnipresent in our everyday life. They are regarded as semi-porous substrates, and yet can be critical to analyze when looking for latent fingermarks due to their thermosensibility. The aim of this study was to investigate a development sequence that would better combine the adequate detection techniques in order to maximize the chances to develop latent fingermarks left on these substrates. Different methods of development have been compared on test substrates: black magnetic powder, Lumicyano™, thermal development, ninhydrin and 1,2-indanedione/ZnCl2. Whitening stages and thermal development have been focused on, tested and optimized. The results of these preliminary tests enabled the study of three development sequences. They have subsequently been compared to the one currently used in the Gendarmerie's laboratories and the best results have been provided during pseudo-operational comparative trials by one of these sequences, consisting in 6 stages.

One of the benefits of Internet of Vehicles (IoV) is improved traffic safety and efficiency, for example due to the capability to share vehicular messages in real-time. While most of the vehicular messages only need to be shared by nearby vehicles, some messages (e.g., announcement messages) may need to be more broadly distributed, for example to vehicles in a wider region. Finding a single trusted entity to store and distribute such messages can be challenging, and vehicles may not be inclined to participate (e.g., generation and distribution of announcement messages) unless they can benefit from such participation. In addition, achieving both security and privacy can be challenging. In this paper, we propose a blockchain based secure data sharing system to address the above challenges in an IoV setting. Specifically, in our system, announcement messages are stored using blockchain. To encourage/incentivize participation, vehicles that faithfully broadcast the announcement messages and/or contribute to the block generation will be rewarded by some cryptocurrency. Our system is also designed to be privacy-preserving and realizes both priori and posteriori countermeasures.

This study explored the relationship between syntactic complexity and rhetorical functions and the relationship between syntactic complexity and level of move/step realization in learner-generated research paper introductions. We created a corpus of 79 introductions written by undergraduate L2 students at intermediate to high-intermediate levels. We annotated the introductions for moves and steps, measured their syntactic complexity at the global, clausal, and phrasal levels, and rated their level of move/step realization across four levels (no, low, intermediate, and high). We conducted one-way MANOVA, one-way ANOVA, Pearson correlation, and stepwise multiple linear regression analyses. We found sentences realizing Reviewing items of previous research function to be significantly longer than other steps in Move1 and to contain significantly more clausal and phrasal complexity features. Sentences performing Claiming centrality function also demonstrated significantly more phrasal complexity features as measured by complex nominals per clause. We also found a higher level of phrasal complexity in introductions with a higher level of move/step realization, which can be attributed to introductions including Step1 and Step3, rather than containing all steps. Our findings imply that there is value in developing explicit instructions on how to use complex structures in performing rhetorical steps in research paper introductions. © 2021 Elsevier Ltd

Spatial visualization—the ability to mentally imagine and manipulate objects—has frequently been measured using the Paper Folding Test (PFT). In this task, participants view diagrams of a paper being folded and a hole being punched. They then identify the resulting punch pattern. Although task instructions promote mentally unfolding the paper, the extent to which people follow this spatial visualization strategy is unknown. The present work assesses hypothesized PFT solution strategies and their relation to problem attributes. Accuracy was impacted by the interaction between fold types, linear mixed models revealed greater use of simple heuristics compared to the suggested unfolding. Furthermore, most participants used a single strategy but simple heuristics were more often used than unfolding. Given this, we recommend redesigning the PFT to utilize the prevalence of strategy use to assess individual differences.

Images can be helpful for eliciting data in the form of responses from participants. Sometimes photographs can help participants speak about issues, events, thoughts and emotions which they find difficult to talk about. This paper discusses how photos can be used and how the data collected through their use in a discussion can be analysed using Correspondence Analysis (CA). Although CA is considered a quantitative tool for analysing data, this paper describes how it can be used with qualitative data.

Background In Software Engineering (SE), conference publications have high importance both in effective communication and in academic careers. Researchers actively discuss how a paper should be organized to be accepted in mainstream conferences. Aiming This work tackles the problem of generalizing and characterizing the type of papers accepted at SE conferences. Method The paper offers a new perspective in the analysis of SE literature: a categorization scheme for SE papers is obtained by merging, extending and revising related proposals from a few existing studies. The categorization scheme is used to classify the papers accepted at three top-tier SE conferences during five years (2012–2016). Results While a broader experience is certainly needed for validation and fine-tuning, preliminary outcomes can be observed relative to what problems and topics are addressed, what types of contributions are presented and how they are validated. Conclusions The results provide insights to paper writers, paper reviewers and conference organizers in focusing their future efforts, without any intent to provide judgments or authoritative guidelines.

The aim of the present study was to explore the relationship between testing mode (taking a test on computer versus paper) and two other factors: (1) cognitive load and (2) scratch paper use in an undergraduate general chemistry class setting. Cognitive load was measured with two self-report questions (perceived mental effort and level of difficulty) and scratch paper use was analyzed manually. All 221 students completed three assessments administered either on computer or paper. The assessments included a variety of chemistry topics with an equal number of three question types (algorithmic, conceptual, and definition). There was no significant difference in the cognitive load imposed by computer or paper-based tests at the overall test level or by question type. Students utilized scratch paper more on paper-based than online tests, especially when the questions were conceptual, and they used scratch paper the most for algorithmic questions. Altogether, these results provide further support that online testing can be implemented in educational settings without imposing additional cognitive load on students.

Properties of cellulosic materials, such as average molar mass (MMvis), crystallinity index and hemicelluloses content may influence the efficiency of the enzymatic conversion of cellulose to glucose. Studies on the simultaneous influence of these parameters have been scarcely found in the literature. In the present study, the conversion of cellulose to glucose was investigated using cellulosic materials with different MMvis, crystallinity, and hemicelluloses content: microcrystalline cellulose (MCC, MMvis = 22104 g mol−1, CI = 79%, no hemicelluloses detected), sisal pulp (SP, MMvis = 94618 g mol−1, CI = 66%, hemicelluloses content = 2.6%) and filter paper (FP, MMvis = 98530 g mol−1, CI = 63%, hemicelluloses content = 19.2%). During the reactions, aliquots were withdrawn and, in addition to the liquors, the unreacted cellulosic materials were evaluated by MMvis, CI, SEM, length and thickness, which can help further understand the reaction as a whole. The liquors were characterized by high-performance liquid chromatography (HPLC) and Miller's method (or DNS). The highest yield for the conversion of cellulose to glucose was observed for SP (88%), followed by MCC (64%) and FP (52%). The results indicated that the presence of high hemicelluloses content (FP) had a more significant interference effect than high crystallinity (MCC).

The development of microfluidic devices for point-of-care (POC) testing has been widely applied for food quality control, environmental monitoring, and clinical diagnoses. Regarding the latter, microfluidic paper-based analytical devices (µPADs) have a great potential in developing portable and disposable platforms for fast disease-relevant biomarker detection in remote areas. In this work, k-nearest neighbors (k-NN) classifiers were applied to demonstrate the reliability of using chemical data obtained from µPADs for diagnosis of diseases like diabetes mellitus and hyperuricemia. Predictors based on glucose, lactate, and uric acid biomarkers were evaluated to reach this goal. Healthy adults were differentiated from the diagnosed patients with a perfect success rate. Metabolic disorders were suitably predicted with 95.0% overall accuracy. This model also showed sensitivities and specificities in the ranges 75–100% and 93–100%, respectively, indicating that the classification protocol is robust. The k-NN modeling proved the potential of the µPADs to be applied in telemedicine for reliable off-site diagnoses.

The Internet of Things (IoT) based remote monitoring of the environment gained tremendous attention from researchers. Wireless monitoring is offered through the IoT-enabled technologies under applications like Intelligent Transport System (ITS), healthcare, home automation, precision agriculture, etc. The working of IoT applications has been the basis on different kinds regards sensor nodes that were deployed for field data sensing & transmitting through a base station (BS). The Wireless Sensor Network (WSN)- assisted IoT networks to suffer from two challenges like energy-efficiency & various security threats such as sinkhole, black hole, Daniel of Service attacks, etc. This survey paper, studying the recent security measures to protect communications from such attackers. A systematic review conducted on lightweight security solutions like trust-based & probe route-based mechanisms under this research. Comparative analysis of such methods presented in terms of security technique, attacks, performance metrics, etc. The outputs of this paper have been critics of research observed through the comparative analysis of reviewed methods.

This is a short, seemingly superficial, highly recursive, unstructured and confusingly non-scientific essay on epistemology, methodology and related topics that violates many firmly entrenched rules and numerous others that are not so deeply engrained. It therefore has to be rejected if the standard and well-established reviewing processes are applied. It will be shown in an inconclusive and inconsistent way why this inconclusive and inconsistent paper suffers from many shortcomings that will prevent its publication unless editors and reviewers accept the fact that many shortcomings are shortcomings only in the eye of the beholder. At least this paper contains relatively few convoluted statements such as the previous sentence, but instead comprises a carefully drafted list of pending problems and proposed solutions that can be used as starting points for further important discussion, as well as quite a few provocative innuendos. Down the rabbit hole …

The integration of telecommunications and information systems in health care first began 4 decades ago with 500 patient consultations performed via interactive television. The use of telemedicine services and technology to deliver health care at a distance is increasing exponentially. Concomitant with this rapid expansion is the exciting ability to provide enhancements in quality and safety of care. Telemedicine enables increased access to care, improvement in health outcomes, reduction in medical costs, better resource use, expanded educational opportunities, and enhanced collaboration between patients and physicians. These potential benefits should be weighed against the risks and challenges of using telemedicine. The American College of Allergy, Asthma, and Immunology advocates for incorporation of meaningful and sustained use of telemedicine in allergy and immunology practice. This article serves to offer policy and position statements of the use of telemedicine pertinent to the allergy and immunology subspecialty.

Initial Coin Offerings (ICOs) are a new form of venture capital financing that raised more than 30 billion US dollars between 2016 and 2019. ICOs opened up the venture capital market to a more diverse investor base previously limited to “sophisticated” investors. This study analyses 1,258 white papers and finds that the length is positively correlated with the amount raised during the ICO and the likelihood of successfully completing the ICO. In contrast, the readability grade of the white paper is only positively correlated with the amount raised. The study highlights the role of voluntary disclosure in the ICO market.

Exposing learners to cognitive computing concepts involves new learning strategies. Because cognitive computing is probabilistic, using massive sets of data is fundamental in understanding these concepts. One data set is the release of the Offshore Panama Papers Leaks Database (LeaksDB) in May 2016, in which researchers were able to access this graph database as part of the International Consortium of Investigative Journalists (ICIJ) Offshore Leaks investigation and to draw conclusions about companies, trusts, foundations, and funds incorporated in 21 tax havens. For the purpose of this research, GraphDB Server was installed and configured by faculty members from a mid-western university. In addition, DBPedia and GeoNames repositories were linked to LeaksDB, leading to the discovery of interesting patterns about these Geo facade relationships between Officers (persons or companies) and Countries.

In order to satisfy the demands for both safety and global warming reduction, a high-strength seismic reinforced steel bar is required in the structural steel market. Recent developments in computational thermodynamics and related application software have made it possible to design a suitable material as well as support engineers of steel manufacturing companies in the production of the designed material with minimum benchmarks in practical operations. This paper reports our recent success in developing grade 600 MPa reinforced steel bars for seismic safety in South Korea. First, conventional alloy design based on CALPHAD-type computational thermodynamics was carried out. For this purpose, a typical alloy system of Fe-0.30C-0.23Si-1.37Mn-0.14V-0.22Cu (in wt%) was selected, and thermodynamic and kinetic calculations were carried out using MatCalc and JMatPro software. Second, in order to reduce V content in the steel for economic reasons, a cooling process designed using finite element (FE) simulation based on the thermodynamic database was performed. For this application, Fe-0.34C-0.22Si-1.34Mn-0.04 V (in wt%) alloy was chosen, and the FE software ABAQUS was applied for modeling the TempCore process. The mechanical properties of the steel products with a diameter of 32 mm produced based on the simulated results satisfy the required properties for grade 600 MPa seismic reinforced steel bars.

We examine voluntary disclosures made by issuers of initial coin offerings (ICOs) in their “white papers” and use them to rate the blockchain technology being developed by the ICO issuers. First, we find that most ICOs receive a low rating. In fact, over 80% of the ICOs do not need to use a blockchain. Second, ICO projects with a higher rating raised more funds than their counterparts, after controlling for other project-, issuer-, and token-related features. Third, ICOs with a high rating are more likely to have their digital coins/tokens listed on a cryptocurrency exchange within 180 days after their ICO end dates. Finally, results indicate that providing technical details in the white paper can be an effective way to signal the quality of an ICO project. Overall, these results are consistent with ICO token buyers/investors taking into consideration the underlying blockchain technology of the ICO projects when making their purchasing/investing decisions. Since the white paper is the main source of ICO information, our findings imply that the credibility of this information is important to protect the interests of ICO buyers/investors and to ensure the long-term viability of the ICO as a capital-raising tool for blockchain-based ventures.

Sensitivity analysis (SA) has been used to evaluate the behavior and quality of environmental models by estimating the contributions of potential uncertainty sources to quantities of interest (QoI) in the model output. Although there is an increasing literature on applying SA in environmental modeling, a pragmatic and specific framework for spatially distributed environmental models (SD-EMs) is lacking and remains a challenge. This article reviews the SA literature for the purposes of providing a step-by-step pragmatic framework to guide SA, with an emphasis on addressing potential uncertainty sources related to spatial datasets and the consequent impact on model predictive uncertainty in SD-EMs. The framework includes: identifying potential uncertainty sources; selecting appropriate SA methods and QoI in prediction according to SA purposes and SD-EM properties; propagating perturbations of the selected potential uncertainty sources by considering the spatial structure; and verifying the SA measures based on post-processing. The proposed framework was applied to a SWAT (Soil and Water Assessment Tool) application to demonstrate the sensitivities of the selected QoI to spatial inputs, including both raster and vector datasets - for example, DEM and meteorological information - and SWAT (sub)model parameters. The framework should benefit SA users not only in environmental modeling areas but in other modeling domains such as those embraced by geographical information system communities.

We used the random forest classifier to predict Gross Motor Function Classification System (GMFCS) levels I-IV from patient reported abilities recorded on the Gillette Functional Assessment Questionnaire (FAQ). The classifier exhibited outstanding accuracy across GMFCS levels I-IV, with 83%-91% true positive rate (TPR), area under the receiver operation characteristic (ROC) curve greater than 0.96 for all levels, and misclassification by more than one level only occurring 1.2% of the time. This new approach to GMFCS level assignment overcomes several difficulties with the current method: (i) it is based on a broad spectrum of functional abilities, (ii) it resolves functional ability profiles that conflict with existing GMFCS level definitions, (iii) it is based entirely on self-reported abilities, and (iv) it removes complex age dependence. Further work is needed to examine inter-center differences in classifier performance-which would most likely reflect interpretive differences in GMFCS level definitions between centers.

Bacteria identification has predominantly been conducted using specific bioreceptors such as antibodies or nucleic acid sequences. This approach may be inappropriate for environmental monitoring when the user does not know the target bacterial species and for screening complex water samples with many unknown bacterial species. In this work, we investigate the supervised machine learning of the bacteria-particle aggregation pattern induced by the peptide sets identified from the biofilm-bacteria interface. Each peptide is covalently conjugated to polystyrene particles and loaded together with bacterial suspensions onto paper microfluidic chips. Each peptide interacts with bacterial species to a different extent, leading to varying sizes of particle aggregation. This aggregation changes the surface tension and viscosity of the liquid flowing through the paper pores, altering the flow velocity at different extents. A smartphone camera captures this flow velocity without being affected by ambient and environmental conditions, towards a low-cost, rapid, and field-ready assay. A collection of such flow velocity data generates a unique fingerprinting profile for each bacterial species. Support vector machine is utilized to classify the species. At optimized conditions, the training model can predict the species at 93.3% accuracy out of five bacteria: Escherichia coli, Staphylococcus aureus, Salmonella Typhimurium, Enterococcus faecium, and Pseudomonas aeruginosa. Flow rates are monitored for less than 6 s and the sample-to-answer assay time is less than 10 min. The demonstrated method can open a new way of analyzing complex biological and environmental samples in a biomimetic manner with machine learning classification.

This study focuses on a recently introduced type of indicator measuring disruptiveness in science. Disruptive research diverges from current lines of research by opening up new lines. In the current study, we included the initially proposed indicator of this new type (Funk & Owen-Smith, 2017; Wu, Wang, & Evans, 2019) and several variants with DI1: DI5, DI1n, DI5n, and DEP. Since indicators should measure what they propose to measure, we investigated the convergent validity of the indicators. We used a list of milestone papers, selected and published by editors of Physical Review Letters, and investigated whether this human (experts)-based list is related to values of the several disruption indicators variants and – if so – which variants show the highest correlation with expert judgements. We used bivariate statistics, multiple regression models, and (coarsened) exact matching (CEM) to investigate the convergent validity of the indicators. The results show that the indicators correlate differently with the milestone paper assignments by the editors. It is not the initially proposed disruption index that performed best (DI1), but the variant DI5 which has been introduced by Bornmann, Devarakonda, Tekles, and Chacko (2020a). In the CEM analysis of this study, the DEP variant – introduced by Bu, Waltman, and Huang (in press) – also showed favorable results.

Machine learning methods are being successfully applied in various domains of human activity, including optics. Can they become a universal instrument capable of improving user aspect of photonic devices? Is their application a necessary element of such improvement? Answers to fundamental questions arising from application of machine learning methods in ultrashort-pulsed lasers are discussed. Future prospects and current fundamental limitations of such methods are analysed.

The top 1000 biomedical papers by number of citations are classified by method, type of method and non-methods by examination of citation contexts. Supervised machine learning is applied to the context data for a training sample of papers which is then used to classify the full list, revealing that words indicating utility are most important for the classification of methods. Further word analysis is carried out using corpus linguistics to uncover context words that characterize non-methods. Hedging words are found to play an important role for non-methods, and several are selected for further analysis with logistic regression. Other variables in the regression are a consensus variable based on the similarity of contexts for a paper and another variable based on whether citations come from “methods” sections of citing papers. Accuracy of predictions from logistic regression is comparable to machine learning. The results are interpreted in terms of the perceived certainty or uncertainty of the underlying knowledge, that is, methods and their outputs have higher certainty, and non-methods higher uncertainty. Evidence is found that hedging is inversely related to citation frequency. Implications of this work for the study of the development of science and the role of methods and tools in biomedical research are discussed.

This paper reviews several dimensions in terms of which electronic/Internet and paper voting can be compared (vote secrecy, verifiability, ballot box integrity, transparency and trust base). We conclude that, for many vulnerabilities of Internet voting systems, there exist related weakness in paper systems as well. The main reason why paper-based elections are perceived as more secure is historical experience. We argue that recent criticisms of Internet voting have unfairly concentrated on the associated risks and neglected the benefits. Remote electronic voting lowers the cost of election participation and provides the most secure means for absentee voting. The latter is something that is urgently required in the contemporary, increasingly mobile world. Hence, we need to give Internet voting a chance, even if it means risking unknown threats and learning by trial and error.

The Panama Papers are eleven million leaked electronic documents that detail financial and attorney–client information for more than two hundred thousand offshore entities. The documents were leaked in April 2016 by an anonymous whistle-blower from the database of Panamanian law firm and corporate service provider Mossack Fonseca. In this case study, we discuss how a team of international network of journalists collaborated using data mining tools to unearth vital financial fraud information from this large chunk of unstructured data. The case study begins with a prologue of dialogue between the whistle-blower and protagonist. The case is divided into nine mini chapters where we start with how a data team were formed followed by the tools used to clean and annotate the unstructured data into a graph-based database and finally using expert network of journalists to generate financial insights from the various nodes and links of the graph. We conclude the case by providing word clouds that highlights Indian connections.

Paper cooperation network embodies expert topic similarity in an extent, thus, a novel method is proposed for expert topic analysis and extraction by combining paper cooperation network and topic model. In the method, we extract each paper’ author information and construct an expert cooperation network. At the same time, by means of LDA model, a probabilistic topic model is also built to analyze papers’ latent topics. Then, by making full use of the feature that adjacent nodes in the expert cooperation network share similar themes distribution, we makes a constraint on expert topic distribution in Gibbs sampling process of solving the probabilistic topic model. Experimental results on NIPS dataset show that the proposed method can effectively extract expert topics, and the expert paper cooperation network plays a very good supporting role on the extracting task.

The paper presents the modelling of a mixed traffic flow in which look-ahead controlled vehicles with a speed control are driven together with conventional vehicles. Since the speed profile of the lookahead control may differ from that of the conventional vehicle, the structure of the traffic flow changes. The paper analyses the impact of vehicles applying look-ahead control strategy on the traffic flow. In a simulation-based analysis it is shown that the fundamental diagram of the traffic network changes. A new mixed-traffic model incorporates the nonlinearity of the traffic dynamics using a polynomial formula, in which the look-ahead cruise control is considered with a parameter-dependent form. Using these nonlinear forms the impact of the look-ahead vehicles on the stability of the traffic is examined through controlled invariant sets. In this analysis the sum-of-squares programming method is applied. The results are demonstrated through simulation examples using the VISSIM simulation environment.

The present study focuses on the implementation of a self - assessment procedure in a Physics class, extended during a seven weeks period in a European secondary level school. The researchers used three modes of assessment based on paper and pencil, computer-web and mobile devices respectively. The aim of the study is to investigate the effect of each mode of assessment on students' motivation and achievement. Analysis of pre- and post-motivation tests revealed a more positive motivational orientation of students towards computers and mobile devices as assessment delivery mediums. Also, student evaluation implemented after the phase of the experimental procedure showed a significant increase in learning achievement for low-achieving students who participated in the mobile-based and computer-based assessment. The positive effects of computers and mobile devices on students' learning motivation suggest that they can be used as a promising alternative to paper and pencil assessment procedures. We investigate the impact of self-assessment delivery modes on student motivation and achievement.Computer- and mobile-based self-assessment increases science learning motivation.Computer- and mobile-based self-assessment contributes to better learning achievement.Low- and medium-achieving students benefit more from computer- and mobile-based self-assessments.

This paper addresses the problem of reliable control of a vehicle active suspension system under actuator faults.The methodology based on event-triggering scheme is employed to design the controller and the proposed results are expressed in the form of linear matrix inequalities (LMIs). The motivation for introducing event-triggering control is to reduce the amount of data transmission in the system. The problem is contrived as a co-design problem to find both controller gain and event-triggering parameters. Finally, the efficacy of the proposed results is demonstrated for the system under fault through a simulation example. Also, a comparison is drawn between the sampled-data control and the proposed event-triggered control strategy.

In this paper, we describe the development of a simulation model. The model deals with the role of an assembly worker in the virtual environment. He performs sequences of common tasks in manufacturing processes like reaching, grasping, walking, lifting etc. We compare time reports by the analytical MTM method and reports automatically generated by the Siemens JACK 8.4 tool. The methodology gives an insight into influential parameters, dimensions and mass of object, which have an impact on the time reports. Results of our research show that the parameters differ in importance in the MTM and JACK analyses. For one combination of influential parameters, we perform a practical experiment and compare the obtained time analysis with previous results.

As these days population is increasing hugely, we tend to face heaps problems thanks to that, pollution is one in every of the, and additionally govt have launched several schemes to create our society, country clean and healthy , we tend to here centred on a connected issue that cause pollution and additionally cause several diseases, government put in dustbins in nearly each area of the many cities to create a town clean and healthy however that doesn’t appears to successful drawback isn't resolved , as once the bin is stuffed there's nobody to create it empty not even municipal corporation aware bout that and bin is overflowed and cause diseases, thus we tend to area unit planned to remodel a traditional bin into good bins, with the assistance microcontroller Arduino UNO, inaudible device, servo motor IR device and a GSM module. Inaudible device is placed at the highest of the bin which can live the stature of the bin. the brink stature is ready as 10cm. Arduino are going to be programmed in such the simplest way that once the bin is being stuffed with facilitate of Arduino and GSM module a message is shipped to the closest swiper thereto bin. Once the bin is press, individuals will recycle the bin. At regular intervals bin are going to be press. Once these good bins area unit enforced on an oversized scale, by commutation our ancient bins gift nowadays, waste may be managed with efficiency because it avoids surplus lumping of wastes on wayside. Foul smell from these rotten wastes that stay untreated for a protracted time, thanks to negligence of authorities and carelessness of public could result in future issues.

This article centers on the specific algorithm, devised to help students select the theme of the qualifying paper. The article also reviews the contents of the customs affairs specialists qualifying paper. The method of selecting the theme of the qualifying paper using the mind-map is offered. The Mind-map offers a convenient scheme for a student to select his or her own research advisor, work experience placement, object and subject of the qualifying paper. This article represents an analysis of example of student's qualifying paper on currency control theme choice, lays out the contents of theoretical and practical parts of the qualifying paper named “Institutional analysis of currency control on the territory of the Chelyabinsk region”. Methods of currency control scientific analysis with the aid of statistical methods and graphic techniques are offered.

The bodily aspects of reading/writing practices are investigated in Finland.The paper is considered more adaptive to varied reading purposes than the screen.Handwriting is described as a more flexible and relaxed practice than typing.Skills determine less than materials and images the practices of reading/writing.Digital interfaces determine the ways we read and write less than suggested. This article investigates the different shapes in which reading and writing practices occur when paper/pen are compared with keyboard/screen. The focus is on the bodily aspects of these practices. Reading and writing are viewed as techniques of the body, which over the years have become increasingly mediated by technologies. The analysis is grounded in the theory of social practice. Research material consists of written essays collected from 25 students at the University of Jyvaskyla, Finland, in 2013. Results show that paper as material is considered suitable and adaptive to numerous reading purposes and bodily positions, while regarding the screen the places and positions of reading are viewed as more limited due to the material boundary conditions presented by devices. Students describe handwriting as a more flexible and relaxed bodily practice than typing, although the worsening skills of handwriting are recognized too. Skills and competences turned out to be less decisive factors than materials and images when trying to explain the differences in reading and writing practices when a paper/pen is compared with a keyboard/screen. Finally, the study argues that digital reading writing interfaces do not determine the ways we read and write as strongly as previously suggested.

White papers are likely the primary source of information provided to potential Initial Coin Offering (ICO) investors in platform-based ventures that may reduce information asymmetry between ICO issuers and investors. We use textual analysis to measure the information content of white paper documents. We examine how an informative white paper content signal and a concurrent potentially biased expert rating signal correlate with measures of ICO funding success, ICO underpricing, and post-ICO performance. Our empirical results suggest that high-quality ICO issuers signal their type by providing more informative white paper content, i.e. excess or new textual information not contained in recent and peer white papers. However, investors rely on the expert ratings signal in their decision to buy tokens that "jams" the white paper informative content signal. Once listed tokens receive a market valuation, white paper informative content is positively associated with our measures for underpricing, returns, and liquidity, while the expert ratings signal is rendered irrelevant.

Background Once a proposed topic has been identified for a systematic review and has undergone a question formulation stage, a protocol must be developed that specifies the scope and research questions in detail and outlines the methodology for conducting the systematic review. Rationale Framework modifications are often needed to accommodate increased complexity. We describe and give examples of adaptations and alternatives to traditional analytic frameworks. Discussion This article identifies and describes elements of frameworks and how they can be adapted to inform the protocol and conduct of systematic reviews of complex interventions. Modifications may be needed to adapt the population, intervention, comparators, and outcomes normally used in protocol development to successfully describe complex interventions; in some instances, alternative frameworks may be better suited. Possible approaches to analytic frameworks for complex interventions that illustrate causal and associative linkages are outlined, including time elements, which systematic reviews of complex interventions may need to address. The need for and specifics of the accommodations vary with details of a specific systematic review. This in turn helps determine whether traditional frameworks are sufficient, can be refined, or if alternate frameworks must be adopted.

Objective This article reviews the available evidence and guidance on methods to identify reports of quasi-experimental (QE) studies to inform systematic reviews of health care, public health, international development, education, crime and justice, and social welfare. Study Design and Setting Research, guidance, and examples of search strategies were identified by searching a range of databases, key guidance documents, selected reviews, conference proceedings, and personal communication. Current practice and research evidence were summarized. Results Four thousand nine hundred twenty-four records were retrieved by database searches, and additional documents were obtained by other searches. QE studies are challenging to identify efficiently because they have no standardized nomenclature and may be indexed in various ways. Reliable search filters are not available. There is a lack of specific resources devoted to collecting QE studies and little evidence on where best to search. Conclusion Searches to identify QE studies should search a range of resources and, until indexing improves, use strategies that focus on the topic rather than the study design. Better definitions, better indexing in databases, prospective registers, and reporting guidance are required to improve the retrieval of QE studies and promote systematic reviews of what works based on the evidence from such studies.

Dyslexia is a type of Specific Learning Disability (SLD) that affects the educational development of a student, as it is the reason of difficulties not only in writing or reading but also in other disciplines like Mathematics. Research has proven that Information and Communication Technologies (ICT) can have a positive effect to the educational development of a dyslexic student. In this paper, twenty 6th-grade primary-school students were observed while using computers during exams - ten with Dyslexia and ten with no SLDs. All students were called to solve a digital test and a paper test that were including some basic mathematic operations. The researcher recorded and examined the differences between the results from the two tests. Descriptive and deductive statistical analysis showed that students had better results when examined via a computer compared to a traditional written examination.

This article introduces a versatile functional form for Generalized Linear Models (GLMs) through a simple, yet effective, transformation of the current framework. The models are applied through a new hierarchical bayesian estimation procedure for logistic regression to highly-cited papers in the Management Information Systems (MIS) field. The results are uniformly better, in regards to model fit and inference for in-sample and out-of-sample data, for simulation studies and real-world data applications, requiring very little time to convergence to true population parameters. In simulation studies, I show that the method contains the true parameters nearly three times as often as widely used existing GLMs, and does so while having confidence intervals that are 54.50% smaller, while requiring around two-thirds the number of MCMC iterations as existing bayesian methods. In Scientometric applications the methodology is shown to be highly robust with predictive/classification accuracy, either equaling or exceeding existing methods for identifying highly-cited articles including Artificial Neural Networks (ANN). Thus, the method is shown to be robust to the amount of asymmetry (or symmetry) of the probability of success (or failure) and robust to unbalanced samples and varying Data Generating Processes. Further, the methodology is equivalent to current methods if the data support them and is therefore complementary to existing methods, without loss of interpretability of model parameters. For the MIS field it finds that Popularity Parameter (PP) of an article Keywords can predict whether a paper will be highly-cited (top 25% of highly-cited articles) between two to three years after publication and beyond. Furthermore, given the small number of iterations needed for convergence, the methodology can also be used as a baseline method in Big Data (BD) settings for both Artificial Intelligence (AI) and Machine Learning (ML) contexts as well.

For several decades, the fuzzy theory has been used to develop the so-called fuzzy control charts to analyze process data in a more reliable and flexible way than traditional control charts. Control charts are more commonly used in the papermaking processes to check the stability of the data source. These charts are conceived as devices that enhance routine time-by-time adjustments during the production process, so their value is incomparable. A critical variable that must be controlled during the papermaking process is the moisture content of the paper. However, due to the paper’s hygroscopic characteristics, its moisture content values may carry some uncertainty derived from measurement systems and the nature of the papermaking process. Therefore, an alternative approach that includes fuzz control charts to handle these uncertainties is proposed in this paper. The method to convert individual data to fuzzy numbers is based on the sigma level process as a first stage, and then, the fuzzy individual and moving range control charts are introduced using the α − cut fuzzy midrange approach. Data from the moisture content of a 240 ​g coated paper sample is used to prove the performance of the fuzzy individual control charts. According to the proposed methodology, findings show that fuzzy individual and moving range control charts have greater flexibility than traditional individual control charts by reflecting a larger amplitude of their control limits, in addition to a lower number of “out of control” values.

Paper currency recognition (PCR) is an important area of pattern recognition. A system for the recognition of paper currency is one kind of intelligent system which is a very important need of the current automation systems in the modern world of today. It has various potential applications including electronic banking, currency monitoring systems, money exchange machines, etc. This paper proposes an automatic paper currency recognition system for paper currency. A method of recognizing paper currencies has been introduced. This is based on interesting features and correlation between images. It uses Radial Basis Function Network for classification. The method uses the case of Saudi Arabian paper currency as a model. The method is quite reasonable in terms of accuracy. The system deals with 110 images, 10 of which are tilted with an angle less than 15o. The rest of the currency images consist of mixed including noisy and normal images 50 each. It uses fourth series (1984–2007) of currency issued by Saudi Arabian Monetary Agency (SAMA) as a model currency under consideration. The system produces accuracy of recognition as 95.37%, 91.65%, and 87.5%, for the Normal Non-Tilted Images, Noisy Non-Tilted Images, and Tilted Images respectively. The overall Average Recognition Rate for the data of 110 images is computed as 91.51%. The proposed algorithm is fully automatic and requires no human intervention. The proposed technique produces quite satisfactory results in terms of recognition and efficiency.

Academic research is crucial to the development of science and technology and is an important factor that affects national strength. When writing an academic research paper, a rhetorical structure is typically used to present the paper’s ideas, but this task is quite difficult for junior researchers. To solve this problem, some studies have adopted text mining to assist with the writing, but the existing methods still require human intervention to generate sentences. Recently, due to the increasing maturity of deep learning technology and the ability to address the problem of automatic text generation, progress has been made in this area. The highly complex deep learning operations can correctly generate sequences and find correlations between sequences. When a user provides a few keywords and key sentences, the proposed algorithm can generate an introduction section for the user. The results show that the generated introduction is more coherent, clearer, and more fluent than existing summarization methods. In addition, the method proposed in this study improves the accuracy compared with traditional text extraction methods. The manuscript produced by this study has been evaluated to show that the study can produce a comprehensive introduction compared with previous studies.

Shared reading interactions with electronic books (ebooks) versus paper books were compared, using a randomized controlled crossover design. The ebooks were designed to encourage an interactive reading style by the adult reader and to highlight text-meaning correspondences for the child participants. Twenty-eight children from kindergarten classes situated in a low income community participated in the study, sharing books with an adult reader. Each child shared the same paper book three times in a single week and an ebook three times in a second week, with story and book presented in counterbalanced order. At the end of each week the childs learning with respect to story comprehension and emergent literacy was assessed by research personnel. All outcomes were significantly related to the childs letter knowledge. However, emergent literacy knowledge was greatest in the ebook condition compared to the print book condition, especially for those children with poor letter knowledge. Therefore, ebooks designed to facilitate supportive adult reading strategies may improve emergent literacy skills, especially in children with deficient skills at school entry.

The purpose of this study was to compare student performance on two proctored quizzes and one practice test taken in paper-based or computer-based testing mode using an equal number of algorithmic, conceptual, and definition questions to examine the effect of testing mode when students take multiple tests in class. Data were collected from 221 students enrolled in a general chemistry course at a Midwestern university. After learning the material in lecture, students completed two quizzes: Quiz 1 and Quiz 2. One quiz was delivered on paper and another quiz was taken on computer. Each quiz tested student understanding of different chemistry concepts and the quizzes were proctored on different days. Several days after Quiz 2, students took a practice test that tested the knowledge of Quiz 1 and Quiz 2 with different yet similar questions, and again, the testing mode of the practice test was either paper-and-pencil or computer. After each quiz and the practice test, students received feedback on their performance. Differences in performance between the quizzes and the practice test were measured with normalized gains and the differences between the normalized gains for each condition were analyzed using an Analysis of Variance. The results showed no significant testing mode effects among the four conditions overall, or for algorithmic, conceptual, or definition type of questions. Altogether, the results of the present study do not provide evidence to suggest that instructors need to be concerned about testing mode (paper versus computer) when designing and administering chemistry tests.

This research is concerned with the detection of similar academic papers. Given a tested paper from a given corpus of 10,099 peer-reviewed scientific papers, a two-stage process was activated. During the first stage, most of the papers were filtered out using a fast filter method. In the second stage, in order to detect similar papers we applied 23 heuristic variants derived from 3 novel prototype methods using various parameter settings. The three novel prototype methods are: CT-TR Constant Number of randomized T fingerprints, compared to each one-third of R (first/middle/last) fingerprints, CT-AR: Constant Number of randomized T fingerprints, compared to all R fingerprints, and CDT-AR: Constant Number of divided randomized T fingerprints compared, to all R fingerprints. Results achieved by the new methods are superior to those of previous heuristic methods, which were approximations of the Full Fingerprint (FF) method, currently considered the best heuristic method. The order of this new methods' run-time, (n), is far more efficient than the order of the FF method run-time, (n2) (after removing short documents from the corpus).

BACKGROUND Gaining access to educational resources supports knowledge development for all individuals across the continuum of care. A dedicated multimedia education center (MEC) serves inpatients, outpatients, and the surrounding community at an acute inpatient rehabilitation facility by lending paper-based resources such as books. Resultantly, inpatients and their caregivers can take the paper-based resources back to inpatient rooms for review. Leadership from the MEC sought guidance from the infection prevention team on best practices for lending paper-based resources to all inpatients as the resources could serve as potential sources for infection transmission when handled by inpatients on isolation precautions or when handled by the caregivers of such patients. METHODS The infection prevention team and medical librarian conducted a literature search to determine the risk of organism transmission on paper and the feasibility of lending paper-based resources to inpatients on isolation precautions. The medical librarian also consulted several health science libraries for comparison and guidance of library circulation policies with patients. RESULTS It was determined all inpatients and their caregivers could continue to bring paper-based resources back to inpatient rooms. Additional interventions to reduce infection transmission risk included education of the MEC's staff on low level disinfection, the creation of a bookmark with infection prevention messaging, the application of polyester film covers to hardcover books, and the application of polypropylene covers to softcover books. The MEC also provided plastic carriers, which displayed messaging promoting hand hygiene, for each paper-based resource to serve as a barrier in areas of potential surface contact contamination. CONCLUSIONS This collaboration between the infection prevention team and the MEC's team supports the continued lending of paper-based resources to all inpatients and their caregivers within the facility. Being able to continue to lend books improves patient satisfaction with a normalcy that reinforces condition information, health literacy, and holistic well-being.