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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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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 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 White Paper summarises currently available empirical data about attitudes and opinions of citizens and state actors regarding cybersecurity. The data emerges from reports of EU projects, Eurobarometer surveys, policy documents of state actors and additional scientific papers. It describes what these stakeholders generally think, what they feel, and what they do about cyber threats and security (counter)measures. For citizens’ perspectives, three social spheres of particular interest are examined: 1) health, 2) business, 3) police and national security.

This White Paper summarizes the current state of discussion regarding the main technological challenges in cybersecurity and impact of those, including ways and approaches to addressing them, on key fundamental values. It provides an overview on current cybersecurity threads and countermeasures and focuses on ethical dilemmas that emerge when counteracting those threads. It also points to the fact that the cybersecurity community relies much more on interpersonal relations when sharing intelligence and data than in explicit national or supranational regulations. Furthermore, the White Paper presents advanced cryptographic techniques and data anonymization techniques that may help to solve or minimize some of the ethical dilemmas.

This White Paper outlines how the ethical discourse on cybersecurity has developed in the scientific literature, which ethical issues gained interest, which value conflicts are discussed, and where the “blind spots” in the current ethical discourse on cybersecurity are located. The White Paper is based on an extensive literature with a focus on three reference domains with unique types of value conflicts: health, business/finance and national security. For each domain, a systematic literature search has been performed and the identified papers have been analysed using qualitative and quantitative methods. An important observation is that the ethics of cybersecurity not an established subject. In all domains, cybersecurity is recognized as being an instrumental value, not an end in itself, which opens up the possibility of trade-offs with different values in different spheres. The most prominent common theme is the existence of trade-offs and even conflicts between reasonable goals, for example between usability and security, accessibility and security, privacy and convenience. Other prominent common themes are the importance of cybersecurity to sustain trust (in institutions), and the harmful effect of any loss of control over data.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

Architectures, networks, and systems and middleware technologies have been advancing.These advances lead to new concepts and platforms for computing, ranging from clusters and grids to clouds and datacenters.The CCGrid 2015 Conference discussed research and results on topics related to these concepts and platforms, and their applications.This special section presents five high-quality papers, extended from CCGrid 2015 papers.

This paper is intended to address the economic and legal challenges of regulating digital "platforms, and leads to the following conclusions. The concept of platform covers broad range of services and business models that have very little in common. Before considering regulatory measures targeting platforms, further work is required to define exactly what services or activities should be covered by regulation, and why. If a new regulatory category is created, it should be done at a European level in order to avoid fragmentation within Europe.Economic literature shows that classic indicia of market power do not necessarily apply to multi-sided business models. There is no consensus as to whether open or closed ecosystems maximize social welfare. Economists disagree as to whether the accumulation of user data creates barriers to entry that require regulatory intervention. Caution is therefore required before concluding that platforms create a market failure requiring special regulation.We have seen no evidence of harmful conduct by platforms that is not already covered by existing law. Obligations of fairness already exist in data protection, consumer protection and commercial law, and those principles have been applied by courts to online platforms. Competition law has been used successfully to address abuses of dominant position or anticompetitive agreements imposed by platforms. Emergency procedures exist, and have been used, to address immediate competitive harms.Further work on platform regulation should include evidence-based analysis of the harms actually created by platforms and what legal tools already exist to deal with them. Analysis should also include consideration of self- or co-regulatory solutions, and an evaluation of the potential harms created by new regulatory measures, including the risk of error, and adverse effects on the Internet ecosystem.

The advancement of electric grid into smart grid has become possible by integrating communication network with the component of power system for free disturbance and effective power distribution. Advanced metering infra-structure (AMI) is used for measurement, collection, analysis and the electricity consumption of the customer. This AMI has variety of sensors for the consumption of electricity usage, smart meter, data concentrators, various server and different communication network. The implementation of net-work for making information available all the time introduces the cyber security concern on metering data of the customer. The modification or adulteration on metering data may lead to Financial loss during billing, false pre-diction of required energy and physical damage to the equipment present in the entire infrastructure. Hence it become essential to ensure confidentiality and integrity of metering data. In this paper various security algorithm has been surveyed to understand the factor for achieving the confidentiality and integrity of smart metering data. At last comparative work and future scope is discussed to identify factors and their importance for cyber security on metering data of the customer has been identified.

The Australian Treasury has identified a number of challenges in FinTech, especially around Initial Coin Offerings (ICOs). In this paper we examine four types of tokens within ICOs: pure utility tokens, security tokens, stored value tokens, and hybrid tokens. There is considerable confusion surrounding what each means and signifies, and how each should be treated by both consumers and regulators. We explore the differences, noting what promoters should do to avoid falling into regulatory traps, and the consequences for selecting particular types of token. Because of the confusion around ICO tokens, we suggest there should be a new asset class of token recognised in the Australian Corporations Act. It would have its own funding limits and regulatory framework. Finally, we outline a new code of conduct for ICOs.

Intrusion Detection System (IDS) has been an effective way to achieve higher security in detecting malicious activities for the past couple of years. Anomaly detection is an intrusion detection system. Current anomaly detection is often associated with high false alarm rates and only moderate accuracy and detection rates because it’s unable to detect all types of attacks correctly. An experiment is carried out to evaluate the performance of the different machine learning algorithms using KDD-99 Cup and NSL-KDD datasets. Results show which approach has performed better in term of accuracy, detection rate with reasonable false alarm rate.

Section identification is an important task for library science, especially knowledge management. Identifying the sections of a paper would help filter noise in entity and relation extraction. In this research, we studied the paper section identification problem in the context of Chinese medical literature analysis, where the subjects, methods, and results are more valuable from a physician's perspective. Based on previous studies on English literature section identification, we experiment with the effective features to use with classic machine learning algorithms to tackle the problem. It is found that Conditional Random Fields, which consider sentence interdependency, is more effective in combining different feature sets, such as bag-of-words, part-of-speech, and headings, for Chinese literature section identification. Moreover, we find that classic machine learning algorithms are more effective than generic deep learning models for this problem. Based on these observations, we design a novel deep learning model, the Structural Bidirectional Long Short-Term Memory (SLSTM) model, which models word and sentence interdependency together with the contextual information. Experiments on a human-curated asthma literature dataset show that our approach outperforms the traditional machine learning methods and other deep learning methods and achieves close to 90% precision and recall in the task. The model shows good potential for use in other text mining tasks. The research has significant methodological and practical implications.

Although software has helped researchers conduct research, little is known of the impact of software on science. To fill this gap, this article proposes an improved bootstrapping method to extract software entities from full-text papers and assess their impact on science. Evaluation results show that the proposed entity extraction system outperforms three baseline methods on extracting software entities from full-text papers. The proposed method is then used to learn software entities from all papers published in PLoS ONE in 2014. More than 2000 unique software entities are obtained which accounted for more than 20,000 mentions and more than 7000 citations. The paper finds that software is commonly used in the scientific community along with a substantial uncitedness.

There have been several cases in the news regarding the presence of synthetic cannabinoids on paper being smuggled into jails and prisons in the United States. The Allegheny County Office of the Medical Examiner has been involved in the analysis of multiple cases. For the first time the laboratory received an analysis request for not only identification of controlled substances, but also for the presence of latent fingerprints. It was unknown if the processing methods used by the latent print unit would interfere with the identification of the controlled substances. This paper shows that DFO and ninhydrin processing will remove some of the synthetic cannabinoids, possibly effecting concentration on the paper, but the substances were still able to be positively identified after processing.

Previously, plant-wide disturbance analysis has looked into the propagation of faults through an industrial production process by investigating process measurements. However, the extent of the analysis has mostly been limited to a section of a plant. In this work, we propose a top-down approach which investigates measurements of the complete plant and identifies a section where the disturbance originates. Root cause analysis is carried out thereafter to pinpoint the faulty asset. The proposed approach has three novel elements: Using key performance indicators (KPI) as reference and starting point of the analysis, restricting measurements to a measurement type (e.g. flow) thus focusing on a section and applying the novel method of contribution plots of spectral PCA T 2 statistic to find the contribution of each measurement towards the disturbance observed in the KPI. The approach is described and carried out on a paper machine where a quality KPI showed an established oscillation.

This paper concerns the global cooperative output regulation problem for a class of nonlinear multi-agent systems with unknown control directions and unknown exosystem. The communication among agents makes it difficult to implement the Nussbaum gain technique to handle the non-identical unknown control directions. Moreover, the unknown parameter in the exosystem, which cannot be easily handled by the estimators in existing results, makes our problem more intricate. To overcome these challenges, we employ a novel internal model candidate to develop a two-layer control scheme for an adaptive distributed controller design. Theoretic stability analysis is presented and a numerical example is given to show the effectiveness of the proposed controller.

Many studies analysing generic structures and linguistic features in academic settings have focussed on a single genre, while the structures of inter-related genres are relatively understudied. ‘Calls for papers’ (CFPs), as one example of the ignition of a genre chain of academic activities, could play a rather crucial role in attracting prospective submitters' attention and also determining the quality of contributions. Yet, compared with the research on other academic texts, CFPs have received little attention from analysts. Thus, in this study 40 “Calls for papers” from various language and education-related conferences announced on the Internet were collected and their schematic structure and lexico-grammatical features were analysed. A manual multi-level move analysis as well as a computerised analysis of the textual elements was performed, followed by qualitative interviews with several conference organisers. Six major moves together with their sub-steps were identified, namely: Drawing attention, Identifying the discourse community coverage, Soliciting contributions, Presenting incentives for participation, Clarifying miscellanea, and Signing off.

As authors of a paper about the optimal performance of myocardial strain for the assessment of cardiotoxicity [(1)][1], we were excited to learn that our work had been the subject of an editorial comment [(2)][2]. Our work seeks to determine whether left ventricular strain obtained from a single

The calculation of various test characteristics may be required as part of a data analysis exercise. This paper explains how to set up these calculations in Microsoft Excel in order to obtain sensitivity, specificity, positive and negative predictive values, diagnostic accuracy and prevalence.

Every product that exists, ranging from a toothbrush to a car, has first been conceived as a mental concept. Due to its efficacy in rapidly externalizing concepts, paper-based sketching is still extensively used by practising designers to gradually develop the three-dimensional (3D) geometric form of a concept. It is a common practice that form concepts are sketched on paper prior to generating 3D virtual models in commercial Computer-Aided Design (CAD) systems. However, the user-interface of such systems does not support automatic generation of 3D models from sketches. Furthermore, the inherent characteristics of form sketching (e.g. idiosyncrasy) pose a challenge to computer-based understanding of the form concept semantics expressed on paper. To address these issues, this paper is therefore concerned with the development of a visual language that is prescribed and to be used by product designers to annotate paper-based sketches such that the form geometry semantics can be formally represented; parsing the annotated sketch allows for the automatic generation of 3D virtual models in CAD. Inspired by re-usable 3D CAD modelling functions and the related environmental constraints and requirements, a prescribed sketching language, PSL, has been developed to annotate paper-based form sketches. The framework architecture which parses the annotated sketch and subsequently extracts the form concept semantics is described. Based on this framework, a prototype computer tool has been implemented and evaluated. Evaluation results provide a degree of evidence, first on the suitability of PSL in representing the semantics of a range of forms, and secondly on the designers? acceptance of taking up this annotated sketching approach in practice. The key contribution is a visual language to formally represent form geometry semantics on paper.Parsing the language allows for the automatic generation of 3D virtual models.A proof-of-concept prototype tool was implemented.The language is capable to roughly model forms with linear topological ordering.Evaluation results show that practising designers would use the language.