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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.

Microfluidic paper-based analytical devices (μPADs) have developed rapidly in recent years, because of their advantages, such as small sample volume, rapid detection rates, low cost, and portability. Due to these characteristics, they can be used for in vitro diagnostics in the laboratory, or in the field, for a variety of applications, including food evaluation, disease screening, environmental monitoring, and drug testing. This review will present various detection methods employed by μPADs and their respective applications for the detection of target analytes. These include colorimetry, electrochemistry, chemiluminescence (CL), electrochemiluminescence (ECL), and fluorescence-based methodologies. At the same time, the choice of labeling material and the design of microfluidic channels are also important for detection results. The construction of novel nanocomponents and different smart structures of paper-based devices have improved the performance of μPADs and we will also highlight some of these in this manuscript. Additionally, some key challenges and future prospects for the use of μPADs are briefly discussed.

Compared with a single chemical indicator, the use of multivariate chemical indicators in aging status assessment of transformer cellulose paper insulation can improve the reliability of the evaluation. In this study, a non-destructive method based on multivariate chemical indicators was proposed to evaluate the remaining life of cellulosic paper insulation. The selected chemical indicators closely related to the aging of the insulating paper were analyzed. Additionally, by applying the subjective and objective weighting method, the evaluation information of each indicator was fully extracted to obtain reasonable comprehensive evaluation weights. Besides, the comprehensive evaluation index of multivariate chemical indicators was obtained by using the technique for order preference by similarity to ideal solution method, which is consistent with the deterioration law of paper insulation. Then, the correlation lifespan model between the comprehensive evaluation index of multivariate chemical indicators and the degree of polymerization of insulating paper under different aging stages was established. The effectiveness of the proposed model was verified by experiments. This promising model can increase the accuracy of the condition assessment for the transformer cellulosic paper insulation.

Papers published in top conferences or journals is an important measure of the innovation ability of institutions, and ranking paper acceptance rate can be helpful for evaluating affiliation potential in academic research. Most studies only focus on the paper quality itself, and apply simple statistical data to estimate the contribution of institutions. In this work, a novel method is proposed by combining different types of features of affiliation and author to predict the paper acceptance at the institutional level. Based on the history of the paper published, this work firstly calculates the affiliation scores, constructs an institutional collaboration network and analyzes the importance of the institutions using network centrality measures. Four measures about the authors’ influence and capability are then extracted to take the contributions of authors into consideration. Finally, a random forest algorithm is adopted to solve the prediction problem of paper acceptance. As a result, this paper improves the ranking of the paper acceptance rate NDCG@20 to 0.865, which is superior to other state-of-the-art approaches. The experimental results show the effectiveness of proposed method, and the information between different types of features can be complementary for predicting paper acceptance rate.

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.

Paper folding-and-punching tests are used to measure spatial abilities in humans. This paper presents a qualitative model for paper folding (QPF) and a computer game (Paper Folding Game) developed to apply and show the reasoning capabilities of the QPF model. This interactive game presents paper-folding activities intended to help users train and understand how to fold a paper to get a specific shape. Then, it presents paper-folding-and-punching tests to the players. The Paper Folding Game can automatically generate paper-folding-and-punching questions with varying degrees of difficulty depending on the number of folds and holes made, thus producing additional levels for training. The reasoning mechanisms in the QPF model are used by the Paper Folding Game to infer the right answer to each paper-folding-and-punching question. This reasoning capability allows the game to provide feedback to the players when they are wrong and also to create other plausible answers automatically, so that random question-answers are shown to the players in the master-mode. The Paper Folding Game has been implemented using Unity engine and it is available to download from GooglePlay and AppleStore for everyone to train their spatial reasoning skills.

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.

This white paper explores the considerations of standards-based interoperability of medical images between organizations, patients, and providers. In this paper, we will look at three different standards-based image exchange implementations that have been deployed to facilitate exchange of images between provider organizations. The paper will describe how each implementation uses applicable technology and standards; the image types that are included; and the governance policies that define participation, access, and trust. Limitations of the solution or non-standard approaches to solve challenges will also be identified. Much can be learned from successes elsewhere, and those learnings will point to recommendations of best practices to facilitate the adoption of image exchange.

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.

Despite substantial advances in sensing technologies, the development, preparation, and use of self-testing devices is still confined to specialist laboratories and users. Decentralized analytical devices will enormously impact daily lives, enabling people to analyze diverse clinical, environmental, and food samples, evaluate them and make predictions to improve quality of life, particularly in remote, resource-scarce areas. In recent years, paper-based analytical tools have attracted a great deal of attention; the well-known properties of paper, such as abundance, affordability, lightness, and biodegradability, combined with features of printed electrochemical sensors, have enabled the development of sustainable devices that drive (bio)sensors beyond the state of the art. Their blindness toward colored/turbid matrices (i.e., blood, soil), their portability, and the capacity of paper to autonomously filter/purge/react with target species make such devices powerful in establishing point-of-need tools for use by non-specialists. This protocol describes the preparation of a voltammetric phosphate sensor and an amperometric nerve agent biosensor; both platforms produce quantitative measurements with currents in the range of microamperes. These printed strips comprise three electrodes (graphite for working and counter electrodes and silver/silver chloride (Ag/AgCl) for the reference electrode) and nanomodifiers (carbon black and Prussian blue) to improve their performance and specificity. Depending on analytical need, different types of paper (filter, office) and configurations (1D, 2D, 3D) can be adopted. The protocol, based on the use of cost-effective manufacturing techniques such as drop casting (to chemically modify the substrate surface) and wax/screen printing (for creating the channels and electrodes), can be completed in

Electronic data capture is fast becoming the preferred method of collecting patient-reported outcome (PRO) data in clinical trials. Data collection can be site-based (clinical study site), and typically collected on a tablet, or field-based (subject's typical environment such as home, school, or workplace), and most often accomplished with handheld devices, such as a smartphone. While site and study subject compliance with protocol-specific data collection procedures using these devices is critical to trial success, so is the robustness of the device hardware and the software these devices use to capture the trial data. Technology failures and/or site or subject resistance to the electronic data capture protocol may lead a subject to record data on paper, which can result in undesirable data challenges. As such, both site and subject compliance issues and technology-related factors must be anticipated to adhere to the ePRO data collection plan. The objective of this paper is to provide the technology industry's best practice recommendations for optimizing ePRO data collection in clinical trials by proposing the inclusion of a planned approach to data collection that includes viable electronic backup strategies so that defaulting to a paper-based backup becomes unnecessary.

Performance/security trade-off is widely noticed in CFI research, however, we observe that not every CFI scheme is subject to the trade-off. Motivated by the key observation, we ask three questions: ➊ does trade-off really exist in different CFI schemes? ➋ if trade-off do exist, how do previous works comply with it? ➌ how can it inspire future research? Although the three questions probably cannot be directly answered, they are inspiring. We find that a deeper understanding of the nature of the trade-off will help answer the three questions. Accordingly, we proposed theGPTconjecture to pinpoint the trade-off in designing CFI schemes, which says that at most two out of three properties (fine granularity, acceptable performance, and preventive protection) could be achieved.

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.

Analytical strategies to ensure the quality and safety of food products without the need for skilled personnel are highly required. The simplicity of glucose strips for diabetes monitoring should be translated to the agri-food sector, ensuring easy evaluation of certain molecules and/or the freshness of a beverage/foodstuff. In addition to the well-known advantages that are characteristic of electroanalytical methods over other methods, paper-based materials are being used to further reduce the gap between complex laboratory testing and simple point-of-need testing. This article highlights some of the most recent advances in the development of paper-based electrochemical approaches for food surveillance, specifically focusing on the use of novel paper-based materials. Two examples are discussed: the development of a miniaturized biosensor realized on copy paper for the quantification of ethanol in commercial beers, and the measurement of ascorbic acid in food supplements within printed electronics supports. Paper-based materials have the potential to lower economic costs, simplify the operative tasks, and most importantly reduce waste generation. The continued combination of manufacturing methods and functional (smart) materials will facilitate the implementation of food analysis at the point of need.

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.

In heterogeneous networks where macro cells and metro cells use the same frequency band to communicate with their respective users, the major problem limiting performance is the interference caused by macro cells to metro cells. The information theoretic result known as dirty paper coding provides a way to address this problem and significantly improve the performance of heterogeneous networks with co-channel deployment. In this paper, we show how a simple dirty paper coding scheme employing Tomlinson-Harashima pre-coding with partial interference pre-subtraction can be employed by metro cells to mitigate the interference caused by macro cells. A performance study included in this paper shows that the proposed dirty paper coding scheme can lead to significant improvement in user rate statistics.

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.

Neurotransmitters are important biological molecules related to several nervous system diseases (NSDs). Point-of-care testing (POCT) of neurotransmitters is of great importance in preclinical research and early diagnosis of NSDs. Among various POCT platforms, paper-based electrochemical biosensors have achieved great advances in detection of neurotransmitters, thus taking a significant role in POCT of neurotransmitters nowadays. This review gives an overview of the recent advances of paper-based electrochemical biosensors for POCT of neurotransmitters. We first introduce the types of neurotransmitters and biological sample sources mainly used for neurotransmitter detection. Second, we review the components and the traditional fabrication technologies for paper-based electrochemical POCT biosensors, and then the functional modification methods of biosensor surfaces and three-dimensional fabrication methods for further enhancement of their detection performance. Then, we list examples of paper-based electrochemical biosensors used for detecting different neurotransmitters in biological samples. Last, we give a conclusion and promising development direction of paper-based electrochemical biosensors for neurotransmitters detection. The purpose of this review is to introduce the paper-based electrochemical biosensors as an emerging technology for POCT of neurotransmitters, offering a reference for readers and researchers for early diagnosis of NSDs using POCT technologies.

Paper-based microfluidic devices offer unparalleled adaptability for the development of low-cost, point-of-care analytical tests. The potential for these devices to drastically improve access to healthcare around the globe is obvious, but very few tests have found success in clinical environments. Here, we identify manufacturing—specifically, methods to pattern paper devices at large scales—as a major barrier to translating prototype paper-based devices from the academic benchtop to the field. We introduce current methods used to pattern papers and discuss their utility as means to prototype and manufacture paper-based devices.

Nowadays, recommender system has become one of the main tools to search for users’ interested papers. Since one paper often contains only a part of keywords that a user is interested in, recommender system returns a set of papers that satisfy the user’s need of keywords. Besides, to satisfy the users’ requirements of further research on a certain domain, the recommended papers must be correlated. However, each paper of an existing paper citation network hardly has cited relationships with others, so the correlated links among papers are very sparse. In addition, while a mass of research approaches have been put forward in terms of link prediction to address the network sparsity problems, these approaches have no relationship with the effect of self-citations and the potential correlations among papers (i.e., these correlated relationships are not included in the paper citation network as their published time is close). Therefore, we propose a link prediction approach that combines time, keywords, and authors’ information and optimizes the existing paper citation network. Finally, a number of experiments are performed on the real-world Hep-Th datasets. The experimental results demonstrate the feasibility of our proposal and achieve good performance.

Proportional–Integral–Derivative controller technique continues to provide the easiest and effective solutions to most of the industrial applications in recent years. However PID controller is poorly tuned in practice compared to most other tuning methods and is complicated with poor performance. This research presents a multi objective optimization approach involving Genetic Algorithm, Evolutionary Programming, Particle Swarm Optimization and Bacterial foraging optimization. The proposed multi objective optimization algorithm is used to tune the PID controller parameters and their performances have been compared with the conventional methodologies like Ziegler Nichols method. The results proved that the use of multi objective optimization approach based controller tuning improves the performance of process in terms of time domain specifications and performance index, set point tracking and regulatory changes and also provides stability. This paper describes the various multi objective optimization algorithms and its implementation to tune the PID Controller used in paper machine DCS as real time processing of a Pulp and paper industry processes.

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.

Social media platforms have had an enormous impact on the dissemination of scientific work and have fared well in covering scientific papers. However, little is known about the general dissemination process from academia to social media and how various factors affect the dissemination of scientific papers at different stages. In this paper, we proposed a two-staged dissemination process to profile the diffusion of scientific papers from academia to social media. A two-step simultaneous equation modeling–artificial neural network approach was adopted to predict the retweet scale of scientific papers on Twitter by combining source-related and content-related factors. The analysis in the field of oncology suggests that the artificial neural network algorithm (ANN) with the input units generated from the simultaneous equation model (3SLS) can predict the retweet scale of scientific papers on Twitter with an accuracy of 78.05%. According to the normalized importance obtained from the ANN, we found that most factors related to the information source play critical roles in promoting the dissemination of scientific papers. The number of first-generation tweets has the most remarkable impact on subsequent dissemination. As for the content-related predictors, tweets attached with more URLs can provide richer information for audiences, thereby increasing the retweet scale of scientific papers. Besides, the influence of research topics on dissemination varies with different audiences. The findings of this study contribute to the literature on the dissemination of scientific papers beyond academia and provide practical implications for scholarly communication.

Text representation is the preliminary work for in-depth analysis and mining of information in scientific papers. It directly affects the effects of downstream tasks such as, scientific papers classification, clustering, and similarity calculation. However, recent researches mainly considered citation network and partial structural information, which is insufficient when representing scientific papers. Therefore, in order to improve the performance of text representation model, this paper proposed MV-HATrans, a text representation model that combines multi-viewpoint information, such as the semantic information of knowledge graph and structural information. This model extracts word information from three aspects, including contextual content, part of speech, and word meaning of WordNet. Based on combination of hierarchical attention mechanism and transformer, the model achieves the full text representation of scientific papers. Finally, this paper uses the binary experimental dataset AAPR, which indicates whether scientific papers are accepted or not, and applies the proposed model of text representation to achieve the goal of automatic quality assessment. Results show that in the quality classification of scientific papers, adopting part-of-speech information and semantic information based on WordNet definitions can effectively achieve the accuracy of prediction as 70.14%. Among all the structural modules, authors and abstracts contributes the most to the quality classification of scientific papers, especially authors as 9.51%.

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.

We present a mixed reality-based assistive system for shading paper sketches. Given a paper sketch made by an artist, our interface helps inexperienced users to shade it appropriately. Initially, using a simple Delaunay-triangulation based inflation algorithm, an approximate depth map is computed. The system then highlights areas (to assist shading) based on a rendering of the 2.5-dimensional inflated model of the input contour. With the help of a mixed reality system, we project the highlighted areas back to aid users. The hints given by the system are used for shading and are smudged appropriately to apply an artistic shading to the sketch. The user is given flexibility at various levels to simulate conditions such as height and light position. Experiments show that the proposed system aids novice users in creating sketches with impressive shading.

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.

Cognitive deficits are a core feature of mental and behavioral disorders, leading to poor treatment adherence and functionality. Virtual reality (VR) methodologies are promising solutions for cognitive interventions in psychiatry once they provide greater ecological validity. This study assessed and compared two content-equivalent cognitive training (CT) interventions, delivered in desktop VR (Reh@City v2.0) and paper-and-pencil (Task Generator (TG)) formats, in patients with mental and behavioral disorders. 30 patients were randomly assigned to the Reh@City v2.0 group and the TG group. Both groups of patients underwent a time-matched 24-sessions intervention. Neuropsychological assessments were performed at baseline, post-intervention, and follow-up. A within-groups analysis revealed significant improvements in visual memory and depressive symptomatology after the Reh@City intervention. The TG group improved in processing speed, verbal memory, and quality of life (social relationships and environmental domains). Between groups, Reh@City led to a greater reduction in depressive symptomatology, whereas the TG group showed higher improvements in social relationships aspects of quality of life. At follow-up, previous gains were maintained and new improvements found in the Reh@City (global cognitive function, language, visuospatial and executive functions) and the TG groups (attention). The Reh@City significantly reduced depressive symptomatology, and the TG led to greater improvements in processing speed, abstraction, and social relationships domain of quality of life at follow-up. Both interventions were associated with important cognitive, emotional, and quality of life benefits, which were maintained after two months. Reh@City and TG should be considered as complementary CT methods for patients with mental and behavioral disorders. Trial registration The trial is registered at ClinicalTrials.gov, number NCT04291586.

Paper is a widely used support for use in devices for point-of-care testing (POCT) in clinical diagnosis, food safety monitoring and environmental pollution. Paper is inexpensive, biocompatible, biodegradable and allows a sample fluid to flow by capillary force. Numerous method have been developed recently for chemical modification of papers in order to introduce different functionalities. This review (with 148 refs.) summarizes the recent progress in paper-based POCT devices. The introduction summarizes the state of the art of paper-based POCT devices and the physicochemical properties of existing unmodified materials (including cellulose, cellulose-based composites, cotton fibers, glass fibers, nitrocellulose, thread). Methods for paper modification for sample pretreatment are summarized next, with subsections on sample storage and collection, sample separation, nucleic acid extraction and sample preconcentration. Another main section covers approaches for paper modification for improving POCTs, with subsections on assays for proteins, nucleic acids, drugs, ion and organic molecules. The advantages and disadvantages of these approaches are compared. Several tables are presented that summarize the various modification techniques. A concluding section summarizes the current status, addresses challenges and gives an outlook on future perspectives of POCTs. Graphical abstract This review summarizes the progress that has been made in paper based point-of-care testing (POCT) and lateral flow assays (LFAs), quite often by using advanced nanomaterials for paper modification.

A hot topic in paper-based microfluidics is the achievement of capillary-driven flows much faster than the ones theoretically allowed by the hydrodynamic permeability of common filter papers. Recent works have experimentally shown that the flow rates can be substantially improved by using multilayer paper systems instead of single-layer papers. The present work discusses a theoretical assessment of fluid transport in multilayer paper-based microfluidic devices. The proposed model effectively predicts a series of experimental observations, namely: the occurrence of fluid velocities 2 orders of magnitudelarger than those measured in single-layer paper; the variation of fluid velocity as a function of the gap thickness, whichpresents a particular maximum; the effects of gravity on the filling dynamics, which is unexpected for single-layer paper; and the effect of the sample volume size, which leads to non-trivial filling dynamics. It is worth mentioning that these effects cannot be described by Lucas?Washburn model for single layers, because they arise from the interplay among the coupled flow domains in the multilayer system, together with the action of the gravitational force. The proposed model provides clues to understand the fundamentals behind the capillary-driven flow in multilayer systems, and hence to better design paper-based microfluidics devices for specified flow rates and time-stepped functions. Fil: Schaumburg, Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina Fil: Berli, Claudio Luis Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina

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.

In this work, a fine correlation among destructive and diagnostic parameters of transformer’s paper insulation is presented. The degree of polymerization (DP) directly assesses the condition of the paper insulation strength and is measured through destructive procedure. Generally, 2-FAL, CO2 and CO are the aging products of paper decomposition and referred as diagnostic parameters. An Adaptive Neuro Fuzzy Inference System (ANFIS) is proposed and developed to estimate the value of DP as the function of amount of diagnostic parameters. The proposed system has an advantage of diagnosing the health of solid insulation without performing destructive tests. The diagnostic parameters are taken as inputs to the system to determine the value of DP. The system uses 630 data points for training the ANFIS model and follows a ten-fold cross-validation approach. The average validation error has been determined to be. 0.0029. Further, the model’s performance has been assessed using experimental data. The optimal ANFIS model has been achieved by suitably selecting the number and type of membership function. The estimated value of DP has been found to conform to the experimental measurements for every case under test. The performance of this model has also been compared with a fuzzy inference system (FIS) model in the reported literature. The comparison shows that the ANFIS model determines the DP values with a greater degree of accuracy than the FIS model.

In traditional research, the application of quantitative measurement on the value and influence of uncited papers is not feasible owing to the lack of data. The rapid advancement of social media has offered a new path for the dissemination of scientific papers and gain influence. In this thesis, the uncited papers published during 2006–2014 with a 5-year window on PLOS ONE journals from the Web of Science core collection was selected. A measurement model was built for the influence of uncited papers on social media dimension based on its Discussed, Saved, and Viewed data to explore the dynamic evolution track of the influence and determine the patterns of interaction. We found that uncited papers have a universal and significant influence on social media platforms. Besides, the evolution pattern of influence of the uncited papers can be illustrated by the layer-to-layer aggregation model of “document properties → three indicators → influence” Overall, the findings of this thesis are referential to the quantitative measurement of the influence of uncited papers.

I will comment on the comments by the groups of three reviewers separately. First of all, I will thank all three groups providing a first round of reports in order for me to get rid of obvious mistakes. In the second round the reviewers were free to comment on the qualities of my revised version. I am not to change my revised paper when giving my comments on what would be honest reports on the quality of my final version. However, the reviewers will not have a go at my rejoinders to comments according to the symposium rules. I keep the section numbering of the authors in order to make it easier to identify the arguments.

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.

Herein we report a quantitative, multiplex assay for disease markers in plasma based on an integrated setup of a portable scanner and a disposable paper-based analytical device (PAD). The quantitative analysis relies on the digital colorimetric reading of the three-layer PAD with 30 assay sites for performing respective chromogenic reactions for plasma uric acid, glucose, and triglyceride, which are considered as important risk factors for cardiovascular diseases. A portable scanner with WiFi transmission capability was used to produce high-quality color images of the PADs and wirelessly transfer them to a smartphone or other mobile devices for data processing. The concentrations of biomarkers in both standard solutions and plasma samples can be directly obtained using a custom-designed smartphone app that is also capable of constructing calibration curves. The detection limits of uric acid, glucose, and triglyceride were determined to be 0.50 mg/dL, 0.84 mmol/L, and 14 mg/dL, respectively, which are below the normal limits and adequate for clinical validation. Owing to the distinct advantages—simple, portable, and cost-effective—this mobile assay protocol can be used for point-of-care (POC) settings or resource-limited situations, and potentially for the diagnosis and prevention of infectious diseases.

Three-dimensional visualization of cellular and subcellular-structures in histological-tissues is essential for understanding the complexities of biological-phenomena, especially with regards structural and spatial relationships and pathologlical-diagnosis. Recent advancements in tissue-clearing technology, such as Magnified Analysis of Proteome (MAP), have significantly improved our ability to study biological-structures in three-dimensional space; however, their wide applicability to a variety of tissues is limited by long incubation-times and a need for advanced imaging-systems that are not readily available in most-laboratories. Here, we present optimized MAP-based method for paper-thin samples, Paper-MAP, which allow for rapid clearing and subsequent imaging of three-dimensional sections derived from various tissues using conventional confocal-microscopy. Paper-MAP successfully clear tissues within 1-day, compared to the original-MAP, without significant differences in achieved optical-transparency. As a proof-of-concept, we investigated the vasculature and neuronal-networks of a variety of human and rodent tissues processed via Paper-MAP, in both healthy and diseased contexts, including Alzheimer’s disease and glioma.