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Graphical abstract Enzyme-linked immunosorbent assay (ELISA) is widely used for the detection of disease biomarkers. However, it utilizes time-consuming procedures and expensive instruments, making it infeasible for point-of-care (POC) analysis especially in resource-limited settings. In this work, a multicolorimetric ELISA biosensor integrated on a paper/polymer hybrid microfluidic device was developed for rapid visual detection of disease biomarkers at point of care, without using costly equipment. This multicolormetric ELISA platform was built on multiple distinct color variants resulted from the catalytic oxidation of 3,3′,5,5′-tetramethylbenzidine (TMB) and the etching of gold nanorods (AuNRs). The vivid color changes could be easily distinguished by the naked eye, and their red mean values allowed quantitative biomarker detection, without using any sophisticated instruments. When this multicolorimetric ELISA was integrated on a paper/polymer hybrid analytical device, it not only provided integrated processing and high portability but also enabled fast assays in about 50 min due to the unique advantages of paper/polymer hybrid devices. The limit of detection of 9.1 ng/μL of the hepatitis C virus core antigen, a biomarker for hepatitis C, was achieved using this multicolorimetric ELISA platform. This multicolor ELISA analytical device provides a new versatile, user-friendly, affordable, and portable immunosensing platform with high potential for on-site detections of various viruses, proteins, and biomarkers for low-resource settings such as at home, public venues, rural areas, and developing nations. Supplementary Information The online version contains supplementary material available at 10.1007/s00216-021-03359-8.

Process-monitoring laboratories in the pulp and paper industry generally use a combination of wet chemical analyses and physical measurements to certify the fitness of a production pulp for a specific end-use. These laboratory tests require time and the effort of trained personnel, limiting their utility for real-time process control. Here we show that Raman probes of unrefined cellulosic pulps, well-suited to the online measurement of in-process materials, can predict the quality attributes of manufactured papers. The accuracy of prediction improves when the covariance is modelled in a reduced measurement space selected by a data-driven, feature-selection technique referred to as a Template Oriented Genetic Algorithm (TOGA). TOGA, combined with discrete wavelet transform (DWT), isolates functional-group features that correlate best with mechanical properties paper derived from refined pulp. Paper makers refine market pulps to build sheet strength using a beating process that decreases freeness as it increases fibre-fibre bonding. Methods demonstrated here predict manufactured sheet properties obtainable after any specified degree of refining from the Raman spectrum of an unrefined pulp. This analysis capacity will enable both vendors of market pulp and makers of sheet paper to specify in advance the amount of beating required to produce a desired product, thereby saving cost and conserving resources.

In order for enterprise imaging to be successful across a multitude of specialties, systems, and sites, standards are essential to categorize and classify imaging data. The HIMSS-SIIM Enterprise Imaging Community believes that the Digital Imaging Communications in Medicine (DICOM) Anatomic Region Sequence, or its equivalent in other data standards, is a vital data element for this role, when populated with standard coded values. We believe that labeling images with standard Anatomic Region Sequence codes will enhance the user’s ability to consume data, facilitate interoperability, and allow greater control of privacy. Image consumption—when a user views a patient’s images, he or she often wants to see relevant comparison images of the same lesion or anatomic region for the same patient automatically presented. Relevant comparison images may have been acquired from a variety of modalities and specialties. The Anatomic Region Sequence data element provides a basis to allow for efficient comparison in both instances. Interoperability—as patients move between health care systems, it is important to minimize friction for data transfer. Health care providers and facilities need to be able to consume and review the increasingly large and complex volume of data efficiently. The use of Anatomic Region Sequence, or its equivalent, populated with standard values enables seamless interoperability of imaging data regardless of whether images are used within a site or across different sites and systems. Privacy—as more visible light photographs are integrated into electronic systems, it becomes apparent that some images may need to be sequestered. Although additional work is needed to protect sensitive images, standard coded values in Anatomic Region Sequence support the identification of potentially sensitive images, enable facilities to create access control policies, and can be used as an interim surrogate for more sophisticated rule-based or attribute-based access control mechanisms. To satisfy such use cases, the HIMSS-SIIM Enterprise Imaging Community encourages the use of a pre-existing body part ontology. Through this white paper, we will identify potential challenges in employing this standard and provide potential solutions for these challenges.

Over the last few years, the microfluidics phenomenon coupled with the Internet of Things (IoT) using innovative nano-functional materials has been recognized as a sustainable and economical tool for point-of-care testing (POCT) of various pathogens influencing human health. The sensors based on these phenomena aim to be designed for cost-effectiveness, make it handy, environment-friendly, and get an accurate, easy, and rapid response. Considering the burgeoning importance of analytical devices in the healthcare domain, this review paper is based on the gist of sensing aspects of the microfabricated paper-based analytical devices (μPADs). The article discusses the various used design methodologies and fabrication approaches and elucidates the recently reported surface modification strategies, detection mechanisms viz., colorimetric, electrochemical, fluorescence, electrochemiluminescence, etc. In a nutshell, this article summarizes the state-of-the-art research work carried out over the nano functionalized paper-based analytical devices and associated challenges/solutions in the point of care testing domain.

This work describes the log-MAP (BCJR) algorithm implementation of a close to capacity dirty paper coding CODEC. The CODEC consists of eight deep pipeline processors. It decodes blocks of 975 bits in 26.9 ms using less than 9.7% of low-cost FPGA (and no DSP blocks). Two pipelines, for alpha and beta, calculate the values of gamma (of the BCJR) to reduce the storage requirements. The final log-likelihood ratio (LLR) is calculated together with alpha, reusing intermediate results. The number of bits used by the different signals of the processor is easily configurable. It was set to six bits to the channel measure signals and eight bits to log of probability signals like alpha, beta, and others. The CODEC clock was 100 MHz. The achieved bit rate is 36.2 Kbps per CODEC, but multiple CODECs can be fit into a single chip. The CODEC is 3.49 dB from the channel capacity.

Directed evolution has become an important method to unleash the latent potential of enzymes to make them uniquely suited for human purposes. However, the need for a large reagent volume and sophisticated instrumentation hampers its broad implementation. In an attempt to address this problem, here we report a paper-based high-throughput screening approach that should find broad application in generating desired enzymes. As an example case, the dehalogenation reaction of the halohydrin dehalogenase was adopted for assay development. In addition to visual detection, quantitative measurements were performed by measuring the color intensity of an image that was photographed by a smartphone and processed using ImageJ free software. The proposed method was first validated using a gold standard method and then applied to mutagenesis library screening with reduced consumption of reagents (i.e., ≤ 10 μl per assay) and a shorter assay time. We identified two active mutants (P135A and G137A) with improved activities toward four tested substrates. The assay not only consumes less reagents but also eliminates the need for expensive instrumentation. The proposed method demonstrates the potential of paper-based whole-cell screening coupled with digital image colorimetry as a promising approach for the discovery of industrially important enzymes.Key Points• A frugal method was developed for directed enzyme evolution.• Mutagenesis libraries were successfully screened on a paper platform.• Smartphone imaging was efficiently used to measure enzyme activities.

The paper discusses current research efforts focusing on security and privacy for 5G cellular networks. It then outlines research directions.

Diagnostic and evidential static image, video clip, and sound multimedia are captured during routine clinical care in cardiology, dermatology, ophthalmology, pathology, physiatry, radiation oncology, radiology, endoscopic procedural specialties, and other medical disciplines. Providers typically describe the multimedia findings in contemporaneous electronic health record clinical notes or associate a textual interpretative report. Visual communication aids commonly used to connect, synthesize, and supplement multimedia and descriptive text outside medicine remain technically challenging to integrate into patient care. Such beneficial interactive elements may include hyperlinks between text, multimedia elements, alphanumeric and geometric annotations, tables, graphs, timelines, diagrams, anatomic maps, and hyperlinks to external educational references that patients or provider consumers may find valuable. This HIMSS-SIIM Enterprise Imaging Community workgroup white paper outlines the current and desired clinical future state of interactive multimedia reporting (IMR). The workgroup adopted a consensus definition of IMR as “interactive medical documentation that combines clinical images, videos, sound, imaging metadata, and/or image annotations with text, typographic emphases, tables, graphs, event timelines, anatomic maps, hyperlinks, and/or educational resources to optimize communication between medical professionals, and between medical professionals and their patients.” This white paper also serves as a precursor for future efforts toward solving technical issues impeding routine interactive multimedia report creation and ingestion into electronic health records.

Paper-based microfluidic analytical devices (μPADs) have shown great potential in the field of analysis due to their advantages of rapid analysis, environmental friendliness and the ability to realize the flow of fluid without external power. Saliva is an emerging biofluid which is used in diseases diagnostic and screening for the easy collection and the reflection of the physiological state. This review focuses on the fabrication methods for two-dimensional (2D) and three-dimensional (3D) μPADs and their applications on the saliva analysis. In the first part, the flow mechanism in μPADs is discussed. The second part mainly introduces the fabrication methods for the μPADs and compares the different methods. The third part presents the application of μPADs in the detection of biomarkers such as nitrite, glucose, and thiocyanate in saliva. Finally, the research directions of saliva analysis are discussed in the conclusion. There have been a lot of researches on μPADs, but the fabrication methods and applications need to be further studied to meet the commercial needs.

Biologically inspired or mimetic approaches have been traditionally widely adopted in robotic creative design. Generally, the most considered source for biorobotics and soft robotics are animals. Rarely, plants have been considered as a model of inspiration for innovative engineering solutions owing to their inconspicuous motion principles. In this work, a new type of plant-inspired soft robotic gripper was created by reconstructing and simulating the leaf structure and head formation mechanisms of cabbage. Firstly, according to the research reports of biological morphology and curling development mechanism of cabbage leaves, we determined that the flexible leaf with special hierarchical veins is the biological structure basis of curling formation. In the light of this observation, we designed a cabbage-inspired leaf bionic structure using paper substrate as a leaf body and using polylactic acid (PLA) polymer as leaf veins. This bionic design schema of cabbage leaves first has the advantage of ease of manufacture using 3D printing technology. In addition, due to the shape memory effect of PLA, we can control the reciprocal deformation of bionic leaves by controlling the temperature field. Undoubtedly, when two or more pieces of such bionic leaves are assembled together, they can be considered as a soft robotic gripper. Furthermore, we thoroughly studied the embodiment design of this bionic design concept as well as its 3D printing process. Finally, we built a prototype of this cabbage-inspired soft robotic gripper and successfully performed small objects (e.g., spitballs and cylinders) grasping experiments. Obviously, this research achievement first successfully integrated the bionic structure of plant deformation, direct digital manufacturing, and the control of shape memory materials. Secondly, this work realized the unified design of the structure, function, driving, and fabrication of robotics, and further expanded the family of the bionic robot, especially plant-like or plant-inspired robotic solutions.

This paper presents a new approach towards the design of paper based autonomous microfluidic devices. Autonomy in the device operation is achieved through the incorporation of mechanically actuated microfluidic switches that are versatile in their design and may be configured to be simple time triggered ON or OFF switches or more complex switches that can be timed to be in multiple states (timed ON, followed by timed OFF). These switches are self-contained and require no external power for their operation, deriving their functionality solely through stored elastic energy. This paper presents the design and fabrication of these switches as fluidic analogs of electronic transistors, and their integration into microfluidic paper based circuit demonstrating their operation as a programmable paper-based microfluidic device.

One way of exposing predatory journals is to submit a spoof article to see if it gets accepted. We investigate one journal, looking at how it has performed since a spoof paper it accepted came to light. We find that it has published almost 20% more papers following the acceptance of the spoof paper. The journal has a new web site, which appears to show that it did not publish its first paper until 2018. However, there is an old web site that lists 321 papers dating back to 2012. The editors said that this is due to a new workflow system, which meant that previously published papers could not be listed on the new web site. Moreover, the number of papers could be not accommodated, as a high configuration server would be required to provide a responsive web site. We found some plagiarism, which looks quite serious. We conclude that the spoof paper had little effect on the journal, which continues to operate, seven years after the spoof paper was accepted.

Headline generation is a challenging subtask of abstractive text summarization, which its output should be a summary, shorter than one sentence. It would be precious to develop a dataset for the evaluation of abstractive summarization methods on this task in the Persian language. There are several datasets for headline generation in Persian, most of which are not large enough to be used by more sophisticated methods of text summarization, such as deep learning models. Moreover, all of these datasets are focused on daily news and there is no dataset for summarizing scientific Persian papers. In this article, we present “ElmNet,” a headline generation dataset of about 400,000 abstract/headline pairs of scientific papers, gathered from six major publishers for scientific articles in Persian. We, moreover, evaluate the performance of the most important deep learning-based headline generation methods, on the proposed dataset. The results prove the comparability of the performance of the state-of-the-art methods on this task, to their results on the existing English datasets.

Development of wearable devices for in situ monitoring biological analytes in sweat has been fueled up in the past few years. Although microfluidic thread/paper-based analytical device (μTPAD) fulfills the requirements of wearable systems on flexibility and biocompatibility, it has not been employed as a sensing system for the in situ sweat analysis. In this work, we developed a wearable μTPAD containing a cotton thread and a functionalized filter paper for non-invasive, quantitative and in situ monitoring of human sweat glucose with the assistance of a smartphone. The oxygen plasma was applied to tailor the wicking property of the cotton thread. Amounts of enzymes and reagents on the filter papers were optimized to achieve the high-performance colorimetric sensing of glucose. The as-prepared device possesses a dynamic range of 50–250 μΜ and a detection limit of ~ 35 μΜ. Because of its great wearability and compatibility with conventional textile industry, the μTPAD was integrated with an arm guard to sensitively detect glucose in human sweat. This work may provide a low-cost, easy to use wearable device based on the cotton thread and filter paper for human sweat analysis.

Zone identification is a topic in the area of text mining which helps researchers be benefited by the content of scientific papers in a satisfactory manner. The major aim of zone identification is to classify the sentences of scientific texts into some predefined zone categories which can be useful for summarization as well as information extraction. In this paper, we propose a two-level approach to zone identification within which the first level is in charge of classifying the sentences in a given paper based on some semantic and lexical features. In this respect, several machine learning algorithms such as Simple Logistics, Logistic Model Trees and Sequential Minimal Optimization are applied. The second level is responsible for applying fusion to the classification results obtained for consecutive sentences of the first level in order to make the final decision. The proposed method is evaluated on ART and DRI corpora as two well-known data sets. Results obtained for the accuracy of zone identification for these corpora are respectively 65.75% and 84.15%, which seem to be quite promising compared to those obtained by previous approaches.

Paper-based microfluidics is a rapidly developing field with applications for point-of-care disease and environmental diagnostics. In parallel, printed electronics has grown swiftly, particularly for wearable technologies. By combining these fields, fluidic sample processing and control, as well as automated sensing and readout can be integrated on a single device. Towards this goal, this work highlights the design, manufacture, and testing of paper-based electrochemical sensors, with focus on photo paper and chromatography paper substrates. These substrates are typically used for printed electronics and paper-based fluidics, respectively. The electrochemical sensors were screen printed using manual techniques. For chromatography paper sensors, wax-printed fluidic barriers were used to illustrate the potential integration of the sensors with typical paper-based microfluidic device formats. As an initial example, the detection of heavy metals (Cd(II) and Pb(II)) in buffer solution was demonstrated. Commercial DropSens sensors were used as reference with the limit of detection (LOD) of Cd(II) and Pb(II) on chromatography sensors showing comparable results to commercial DropSens sensors. It is worth noting that the chromatography paper sensors showed a higher repeatability than the commercial DropSens sensors. Tap water samples spiked with Cd(II) and Pb(II) were also tested and showed promising results. Future work will include sensor optimization and exploration of scale-up to provide low-cost solutions for effective point-of-need diagnostics—ranging from environmental monitoring to healthcare applications.

The printing effect of paper packaging has an impact on the circulation effect of products. Only when the printing effect is good can it attract consumers’ purchasing desire. In this study, based on color management technology, the printing of the designed Bazhong specialty paper packaging was studied. Firstly, the content and process of color management were analyzed, and equipment correction and characterization were introduced. Then, the printing process of Bazhong specialty paper packaging was explored, and the printing equipment and the establishment of the profile file were analyzed. The application of color management achieves a high-quality printing effect, which provides a theoretical reference for its further application in the printing industry.

Scientific research cooperation and co-authored papers are becoming increasingly popular in the era of big science. However, allocating appropriate credit to each co-author of papers remains a challenge. We consider author contribution declarations according to the contributor roles taxonomy (CRediT) scheme (assigning each co-author to 14 contributor roles) and propose a new method of author contribution to allocate co-authors’ credits reasonably by converting the 14 contributor roles in an article into a binary author-role matrix. Based on the data of PLOS ONE, we further explore the new method’s advantages by comparing with other representative methods: It normalizes the total credits of different articles to 1, avoiding the inflationary bias caused by the increasing number of co-authors; awards different credits per co-author based on the participation rate of contributor roles to avoid the equalization bias; reduces the impact of the increasing number of co-authors on the credit of the first co-author.

Predicting the impact of academic papers can help scholars quickly identify the high-quality papers in the field. How to develop efficient predictive model for evaluating potential papers has attracted increasing attention in academia. Many studies have shown that early citations contribute to improving the performance of predicting the long-term impact of a paper. Besides early citations, some bibliometric features and altmetric features have also been explored for predicting the impact of academic papers. Furthermore, paper metadata text such as title, abstract and keyword contains valuable information which has effect on its citation count. However, present studies ignore the semantic information contained in the metadata text. In this paper, we propose a novel citation prediction model based on paper metadata text to predict the long-term citation count, and the core of our model is to obtain the semantic information from the metadata text. We use deep learning techniques to encode the metadata text, and then further extract high-level semantic features for learning the citation prediction task. We also integrate early citations for improving the prediction performance of the model. We show that our proposed model outperforms the state-of-the-art models in predicting the long-term citation count of the papers, and metadata semantic features are effective for improving the accuracy of the citation prediction models.

Along with the unceasing growth of worldwide economic and the associated issues on resources, energy and environment, clean energy generating technologies that are based on recyclable materials, if possible, may become the future trend of development. Here, we report the design of a cheap, lightweight, and recyclable single-electrode triboelectric nanogenerator (TENG) that utilizes waste paper as the triboelectric material. Under the current strategy, we successfully developed green energy machines without vastly increasing the mining of various critical minerals around the world. The as-designed TENG could not only collect and convert mechanical energy into electricity with sound efficiency, but also has the merit for continuous reuse and quick construction. The maximum output power density is as high as 171 mW·m−2 at a resistance of 130 MΩ and could be integrated into a book for monitoring reading actions, thus providing a new approach to the low-cost, green and sustainable self-powered electronic systems.

The governing international standard for the development of prosthetic heart valves is International Organization for Standardization (ISO) 5840. This standard requires the assessment of the thrombus potential of transcatheter heart valve substitutes using an integrated thrombus evaluation. Besides experimental flow field assessment and ex vivo flow testing, computational fluid dynamics is a critical component of this integrated approach. This position paper is intended to provide and discuss best practices for the setup of a computational model, numerical solving, post-processing, data evaluation and reporting, as it relates to transcatheter heart valve substitutes. This paper is not intended to be a review of current computational technology; instead, it represents the position of the ISO working group consisting of experts from academia and industry with regards to considerations for computational fluid dynamic assessment of transcatheter heart valve substitutes.

The urgent demand for portable electronics has promoted the development of high-efficiency, sustainable, and even stretchable self-charging power sources. In this work, we propose a flexible self-charging power unit based on folded carbon (FC) paper for harvesting mechanical energy from human motion and power portable electronics. The present unit mainly consists of a triboelectric nanogenerator (FC-TENG) and a supercapacitor (FC-SC), both based on folded carbon paper, as energy harvester and storage device, respectively. This favorable geometric design provides the high Young’s modulus carbon paper with excellent stretchability and enables the power unit to work even under severe deformations, such as bending, twisting, and rolling. In addition, the tensile strain can be maximized by tuning the folding angle of the triangle-folded carbon paper. Moreover, the waterproof property of the packaged device make it washable, protect it from human sweat, and enable it to work in harsh environments. Finally, the as-prepared self-charging power unit was tested by placing it on the human body to harvest mechanical energy from hand tapping, foot treading, and arm touching, successfully powering an electronic watch. This work demonstrates the impressive potential of stretchable self-charging power units, which will further promote the development of high Young’s modulus materials for wearable/portable electronics.

Educational evaluation helps to improve the learning and teaching process. The learning process can be assessed by the examination system. In the examination system, the learners have to answer a set of questions. The learning assessment ensures improved performance in the educational system. The success of the learning assessment is highly dependent on the listening and understanding ability of each student. The assessment of the question paper can be at two levels—cognitive level assessment and difficulty level. The cognitive level assessment is purely based on Bloom’s Taxonomy and the difficulty level assessment is purely based on the examination marks of each student. This article presents a technical method called Question Paper’s Difficulty Level (QPDL) that helps to identify the difficulty level of each question in the question paper. The QPDL method is incorporate in the implementation of ‘QAUDIT’ web application to assess the difficulty level of the question paper systematically. The systematic analysis to identify the difficulty level of the question paper is made for 239 question papers. Each question paper can be recognized based on three difficulty levels such as High, Moderate and Low.

Fast and simultaneous identification of multiple viable pathogens on food is critical to public health. Here we report a pathogen identification system using a paper chromogenic array (PCA) enabled by machine learning. The PCA consists of a paper substrate impregnated with 23 chromogenic dyes and dye combinations, which undergo colour changes on exposure to volatile organic compounds emitted by pathogens of interest. These colour changes are digitized and used to train a multi-layer neural network (NN), endowing it with high-accuracy (91–95%) strain-specific pathogen identification and quantification capabilities. The trained PCA–NN system can distinguish between viable Escherichia coli, E. coli O157:H7 and other viable pathogens, and can simultaneously identify both E. coli O157:H7 and Listeria monocytogenes on fresh-cut romaine lettuce, which represents a realistic and complex environment. This approach has the potential to advance non-destructive pathogen detection and identification on food, without enrichment, culturing, incubation or other sample preparation steps. Fast and simultaneous identification of multiple viable pathogens on food is critical to public health. By integrating paper chromogenic arrays (PCAs) and machine learning, a system was developed to automatically recognize PCA patterns on multiplexed viable pathogens with strain-level specificity.

Cancer is characterized as a heterogeneous disease of various types. The early detection and prognosis of a cancer type have turned into a major requirement, as it facilitates successive medical treatment of patients. The research team has classified the cancer patients into high or low-risk groups. This makes it a significant task for the medical teams to study the application of deep learning and machine learning models. As a result, such techniques have been employed for modeling the development and treatment of cancer conditions. Additionally, the machine learning tools can have the ability the significant detection features from complex datasets. Numerous techniques like Support Vector Machines (SVM), Bayesian Networks (BN), Decision Trees (DT), Artificial Neural Networks (ANN), Recurrent Neural Network (RNN), and Deep Neural Network (DNN) has been broadly utilized in cancer research. As per the current survey, the detection rate is about 99.89%, which shows the prediction models' efficiency and precise decision making. However, it is proven that deep learning and machine learning approaches can enhance cancer progression. An adequate level of estimation is required for such approaches for considering the daily medical practice. This survey analyzes and learns the diverse contributions of cancer prediction models using intelligent approaches. Further, the paper tries to categorize the different algorithms, the utilized datasets, and utilized environments. Along with this, various performance measures evaluated in each contribution is sorted out. An extensive search is conducted relevant to machine learning and deep learning methods in cancer susceptibility, recurrence, and survivability prediction, and the existing challenges in this area are clearly described. However, ML models are still in the testing as well as the experimentation phase for cancer prognoses. As the datasets are getting larger with higher quality, researchers are building increasingly accurate models. Moreover, ML models have a long way to go, and most of the models still lack sufficient data and suffer from bias.

Despite the increasing number of potential biomarkers identified in laboratories and reported in much literature, the adoption of biomarkers routinely available in clinical practice to inform treatment decisions is very limited. Reimbursement decisions for new health technologies are often informed by economic evaluations; however, economic evaluations of diagnostics/testing technologies, such as companion biomarker tests, are far less frequently reported than drugs. Furthermore, few countries provide the health economic evaluation methods guide specific to co-dependent technologies such as companion diagnostics or precision medicines. Therefore, this paper aims to guide the process of the development of cost-effectiveness models of cancer biomarkers for targeted therapies, focusing on companion diagnostics. This tutorial paper provides practical guidance on how to conduct economic evaluations of cancer biomarkers and how to model the characteristics of the biomarker tests as part of the value for money of corresponding targeted therapies. This paper presents a brief introduction to the methods and data requirements, a step-by-step guide to constructing a health economic model of companion cancer biomarkers, and a discussion of issues that arise in their application to healthcare decision making. This practical guidance is provided in R, and worked examples are provided in this paper with R codes in the accompanying electronic supplementary material. Supplementary Information The online version contains supplementary material available at 10.1007/s40273-021-01069-8.

Due to the exponential increase in research papers on a daily basis, finding and accessing related academic documents over the Internet is monotonous. One of the leading approaches was the use of recommendation systems to proactively recommend scholarly papers to individual researchers. The primary drawback to these methods, however, is that their success depends on user profile information and is therefore unable to provide useful suggestions to the new user. In addition, both the public and the non-public used descriptive metadata are used. The scope of the recommendation is therefore limited to a number of documents which are either publicly available or which are granted copyright permits. In alleviating the above problems, we proposed an alternative approach using public contextual metadata for an independent framework that customizes scholarly papers, regardless of the research field and user expertise. Experimental tests have shown significant improvements over other baseline methods.

The medical field has witnessed an exponential growth of wearable devices mainly due to the advancement in wireless communication and antenna technology. There is a demand for wearable antennas, which are lightweight, flexible, and ease of integration into the fabric which suits on-body applications. Therefore, this work presents the design, fabrication, and analysis of two microstrip antennas using denim cloth and stacked photo paper as substrate material. The novelty of these antennas is the use of silver fabric as ground and radiating patch-conducting layer, which has resulted in significant improvement in overall antenna performance. It is observed that our fabricated antennas have exhibited a gain of 8.71 dB with VSWR of 1.32 for denim and gain of 2.45 dB with VSWR of 1.03 for photo paper substrates respectively.

Augmented reality technology is attracting increased attention, given the popularity of the smartphone. The utilisation of the smartphone with AR technology is also enabling mobile augmented reality (MAR) to become accessible. Stereoscopic vision offers users the benefit of depth perception, which can help improve user memory. Therefore, this study aims to investigate the effect on knowledge retention using MAR with stereoscopic vision. An experiment to compare a stereoscopic-based MAR application and a paper-based manual was designed to test the participants’ knowledge retention on a product part and disassembly process. The developed MAR adopted a smartphone and headset case to provide a stereoscopic view to the user. The experiment consisted of both pre-test and post-test phases where the pre-test phase examined the participant’s knowledge about the product, and the post-test phase focused on how much information the participants could recall. The post-test phase was conducted after 48 h following the pre-test phase since long-term memory required several hours in which to stabilise. The results showed that the MAR group had an advantage over the paper-based manual group for both information retentions having a mean score of 8.89 from the MAR group, compared with 6.33 for the paper-based manual group. Moreover, the MAR group was observed to have fewer errors in the post-test with a mean score of 0.53, whereas the paper-based achieved a score of 2.2. This result indicated that the MAR group and the paper-based manual group had equivalent performance in the completion time without the assistance tool. In addition, MAR had a better mean score for the subjective feedback (usefulness = 4.47, ease of use 4.18 and satisfaction = 4.07).

Portable communication devices such as WLAN, WiMAX, LTE, ISM, and 5G utilize one or more of the triple bands at (2.3–2.7 GHz, 3.4–3.6 GHz, and 5–6 GHz) and suffer from the effect of multipath problems because they are used in urban regions. To date, no one has performed a review of the antennas used for these types of wireless communications. This study reviewed two types of microstrip antennas (slot and fractal) that have been reported by researchers (as a single element) using a survey that included the evaluation of several important specifications of the antennas in previous research, such as operating bandwidth, gain, efficiency, axial ratio bandwidth (ARBW), and size. The weaknesses in the design of all antennas were carefully identified to determine the most important challenges in the design of these antennas and name the most important limits in the design to be overcome in future research. This study also indicated some antennas that have circular polarization characteristics, the techniques used to generate the circular polarization characteristics, and the challenges. Finally, several suggestions as a guideline for antenna design and future work in designing antennas for Wi-Fi, LTE, and WiMAX communications, according to the market demands, and methods for overcoming the identified limits are presented.

Catalogue of billions of phrases from 107 million papers could ease computerized searching of the literature. Catalogue of billions of phrases from 107 million papers could ease computerized searching of the literature.

The confluence of significant computational power and inexpensive sensors provides the opportunity to reliably collect large volumes of information from the world and extract humanly useful information resources. This paper reviews a coherent body of work over the last 20+ years focused on development of advanced bio-inspired computing techniques, and their applications primarily for human related data in behaviour and human centered computing. We close with a synthesis proposing an experiment analysis methodology combining these tools.

Elisabeth Bik quit her job to spot errors in research papers — and has become the public face of image sleuthing. Elisabeth Bik quit her job to spot errors in research papers — and has become the public face of image sleuthing.

Zemblys et al. (Behavior Research Methods, 51(2), 840–864, 2019) reported on a method for the classification of eye-movements (“gazeNet”). I have found three errors and two problems with that paper that are explained herein. Error 1: The gazeNet classification method was built assuming that a hand-scored dataset from Lund University was all collected at 500 Hz, but in fact, six of the 34 recording files were actually collected at 200 Hz. Of the six datasets that were used as the training set for the gazeNet algorithm, two were actually collected at 200 Hz. Problem 1 has to do with the fact that even among the 500 Hz data, the inter-timestamp intervals varied widely. Problem 2 is that there are many unusual discontinuities in the saccade trajectories from the Lund University dataset that make it a very poor choice for the construction of an automatic classification method. Error 2 The gazeNet algorithm was trained on the Lund dataset, and then compared to other methods, not trained on this dataset, in terms of performance on this dataset. This is an inherently unfair comparison, and yet nowhere in the gazeNet paper is this unfairness mentioned. Error 3 arises out of the novel event-related agreement analysis employed by the gazeNet authors. Although the authors intended to classify unmatched events as either false positives or false negatives, many are actually being classified as true negatives. True negatives are not errors, and any unmatched event misclassified as a true negative is actually driving kappa higher, whereas unmatched events should be driving kappa lower.

In this short note, we offer a suggestion that we believe may be ethically sound for research. Requiring co-authors to be able to deliver a reasonable presentation of a paper may lead to co-authors being fewer in number and more representative in nature. A research market involving research papers with hundreds of authors, single authors who have been credited with several hundred articles, or strict definitions of who has done what can be troublesome for public faith in research. To some extent, the public view of research as very trustworthy has changed and academic society should consider radical propositions to regain its position as solid and credible.

Science funding plays a guiding role in the development direction of scientific innovation. As one of research funding providers, private companies influence the development of science and technology (S&T) through their selective support. Thus, strategy and layout of enterprises in S&T can be revealed by analysing their funded papers. Taking Google as the example, the paper proposes an analytical method of funded papers by the combination of co-word analysis, clusters analysis and social network analysis, so as to explore the scientific strategy and collaboration preference. The total 2162 valid bibliographic records of papers supported by Google from the Thomson Reuters Web of Science are divided into four groups according to discipline clusters using Hierarchical Clustering Algorithm. Social network analysis is conducted to detect communities among keywords and institutions. The results demonstrate that Google shows different funding patterns between traditional research fields and emerging industries. Famous universities are the main funding targets of Google, and the important institutions can be divided into two groups.

Eight major publishers have issued joint guidelines for how journal editors can spot and deal with suspicious images or data. Eight major publishers have issued joint guidelines for how journal editors can spot and deal with suspicious images or data.

Physical paper, in its various forms (e.g. books, leaflets, catalogues), is extensively used in everyday activities, despite any advancements in digital technology and ICT. Inspired by the popularity of this medium, several research efforts have envisioned and pursued a new era of interactive paper, however several challenges still remain to be addressed. On the other hand, recent advancements towards Ambient Intelligence (AmI) and Smart environments, bear the promise of seamless integration of the physical and digital worlds in an intuitive and user-friendly manner. This paper presents InPrinted, a systematic and generic framework supporting physical paper augmentation and user interaction in Ambient intelligence environments. InPrinted enables natural multimodal user interaction with any kind of printed matter in smart environments, providing context aware and anticipation mechanisms, as well as tools and interaction techniques that support the development of applications incorporating printed matter augmentation. InPrinted has been put to practice in the development of various systems, including the Interactive Maps system, facilitating interaction with printed maps as well as their augmentation with digital information. The results of an in-situ observation experiment of the Interactive Maps system are reported, highlighting that interacting with augmented paper is quite easy and natural, while the overall User Experience is positive.

With the speedy progress of network technology and internet applications, protection of digitized data against unauthorized access and modification has become a paramount issue. In order to face this challenge, numerous secret image sharing schemes have been introduced. Secret image sharing scheme is a method used for safeguarding the sensitive digitized image against illegal copying and tempering. The secret image is divided into many random shares in such a way that each share does not reveal any information to the intruders. In this paper, we present a comprehensive survey from over 100 papers which explains the new approaches and challenges. This paper also provides a comparative analysis of different methods based on different properties.

An automated, portable, and integrated paper-based microfluidic system has been developed for influenza A detection with smartphone at point-of-care (POC) settings. The low-cost paper-based microfluidic chip consists of a reagent storage and reaction modules. The storage module, which consists of a couple of reagent chambers with dispensation channels, is responsible for reagent storage and release. The reaction module consists of an absorbent pad and a nitrocellulose (NC) membrane which is functionalized with specific monoclonal antibodies on a test and control spots for immunoassay detection. Microfluidic Dot-ELISA is performed when the dispensed reagent flows through the NC membrane at a controllable speed and reaches the absorbent pad because of the gravity and capillary force without active pumping. A smartphone is used to capture image from the NC membrane with its own camera and process the image with an intelligent algorithm of custom application software which is developed with Java. With a smartphone, the detection result can be displayed and transmitted to other medical agencies if necessary. Experimental results show that, compared with the traditional methods, more convenient and efficient influenza A detection can be achieved with the developed paper-based POC microfluidic chip with the assistance of smartphone.

The8th InternationalConference onAdvancedNetworks and Telecommunication Systems—IEEE ANTS 2014 (http:// www.ieee-comsoc-ants.org/2014/)—was held inNewDelhi, India, during December 14–17, 2014. Over the past few years, ANTS has been a premier forum onnetworking and telecommunication related topics in India. It is the only conference in India financially sponsored by IEEE Communications Society. A key feature of the IEEE ANTS is that it is aimed at promoting intense dialogue between academia and industry to bridge the gap between academic research, industry initiatives, and governmental policies. Besides the technical paper and poster presentations, this is fostered through panel discussions, keynotes, invited talks, and industry exhibits. IEEE ANTS 2014 was a four day event organized by the Centre for Development of Telematics (C-DOT), New Delhi, India. IEEEANTS2014was co-sponsored byC-DOT, Tata Consultancy Services, Infinera, Cisco, IP Junction, and Avaya. We received 135 long paper submissions and 17 short paper submissions from over 15 countries. Each paper was reviewed by at least 2 (and often 3 or 4 or even 5) members of the technical program committee, and we accepted 42 long papers and 6 short papers for oral presentation. The acceptance rate for long papers was about 31%. The high

Papers are getting more rigorous, but progress is slower than some researchers would like. Papers are getting more rigorous, but progress is slower than some researchers would like.

Free-falling paper shapes exhibit rich, complex and varied behaviours that are extremely challenging to model analytically. Physical experimentation aids in system understanding, but is time-consuming, sensitive to initial conditions and reliant on subjective visual behavioural classification. In this study, robotics, computer vision and machine learning are used to autonomously fabricate, drop, analyse and classify the behaviours of hundreds of shapes. The system is validated by reproducing results for falling discs, which exhibit four falling styles: tumbling, chaotic, steady and periodic. A previously determined mapping from a non-dimensional parameter space to behaviour groups is shown to be consistent with these new experiments for tumbling and chaotic behaviours. However, steady or periodic behaviours are observed in previously unseen areas of the parameter space. More complex hexagon, square and cross shapes are investigated, showing that the non-dimensional parameter space generalizes to these shapes. The system highlights the potential of robotics for the investigation of complex physical systems, of which falling paper is one example, and provides a template for future investigation of such systems. The dynamics of paper shapes in free fall are still not fully understood, despite being discussed for more than 150 years. Collecting large amounts of data has the potential to give us new insights and a robotics system could generate and analyse data in large quantities.

While the extraction paper chromatography (EPC) technique constitutes a novel paradigm for the determination of few Becquerels of 90Sr in MBq quantities of 90Y obtained from 90Sr/90Y generator, validation of the technique is essential to ensure its usefulness as a real time analytical tool. With a view to explore the relevance and applicability of EPC technique as a real time quality control (QC) technique for the routine estimation of 90Sr content in generator produced 90Y, a systematic validation study was carried out diligently not only to establish its worthiness but also to broaden its horizon. The ability of the EPC technique to separate trace amounts of Sr2+ in the presence of large amounts of Y3+ was verified. The specificity of the technique for Y3+ was demonstrated with 90Y obtained by neutron irradiation. The method was validated under real experimental conditions and compared with a QC method described in US Pharmacopeia for detection of 90Sr levels in 90Y radiopharmaceuticals.

Search engine’s tool for summarizing studies promises easier skim-reading. Search engine’s tool for summarizing studies promises easier skim-reading.

Scientific retrieval systems need to be given domain search terms for searching publications, however, as natural language, search terms provided by users are often fuzzy and limited and some relevant terms are always overlooked in searching. Meanwhile, users always desire to be given domain related keywords to enlighten themselves what other terms can be used for their searching. This paper presents a concepts recommendation model in scientific paper retrieval, in which concepts are extracted from keyword in scientific papers, and some data mining algorithms are used to calculate the similarity between search terms and concepts and do recommendation for users. This model is simple and can be used with small dataset, in which all training data used is from meta data of papers that is easy to acquired. Experimental result hold good precision, which shows that this research not only simplifies searching step and improves the searching quality for users, but also lays the foundation for semantic search.

Scientific Reports 6: Article number: 38171; published online: 02 December 2016; updated: 16 March 2017 The original version of this Article contained errors in the spelling of the authors Chung-Yuh Tzeng and Hsueh-Yao Chu, which were incorrectly given as Chung-Yuh Tseng and Shueh-Yao Chu respectively.

Clarivate Analytics's Web of Science (WoS) is the world's leading scientific citation search and analytical information platform. It is used as both a research tool supporting a broad array of scientific tasks across diverse knowledge domains as well as a dataset for large-scale data-intensive studies. WoS has been used in thousands of published academic studies over the past 20 years. It is also the most enduring commercial legacy of Eugene Garfield. Despite the central position WoS holds in contemporary research, the quantitative impact of WoS has not been previously examined by rigorous scientific studies. To better understand how this key piece of Eugene Garfield's heritage has contributed to science, we investigated the ways in which WoS (and associated products and features) is mentioned in a sample of 19,478 English-language research and review papers published between 1997 and 2017, as indexed in WoS databases. We offered descriptive analyses of the distribution of the papers across countries, institutions and knowledge domains. We also used natural language processingtechniques to identify the verbs and nouns in the abstracts of these papers that are grammatically connected to WoS-related phrases. This is the first study to empirically investigate the documentation of the use of the WoS platform in published academic papers in both scientometric and linguistic terms.