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

Advancements in technology have led to the use of smartphones as biosensor detectors. However, it is difficult to obtain stable color information of colorimetric sensors using multiple smartphones owing to different light conditions and image correction operations. In this study, we attempted color detection of the colorimetric paper chip using smartphone-embedded light-emitting diode (LED) and simple transformation calculation. This method enables the acquisition of stable color information using different smartphones by reducing the influence of external light sources. Moreover, we studied characteristics such as convergence and distinction of detection result details by the manufacturers and smartphones. These findings suggested the necessity of classification criteria for the use of smartphones to achieve optimal detection. In addition, white and black references were used to set the standard for color correction of various light sources. Based on our results, we conclude that this approach presents the possibility of smartphone-based colorimetric detection in practical applications.

The present paper was prepared and submitted as part of the European Commission's public consultation to give stakeholders the opportunity to express their views on the questions raised and policy options proposed in the White Paper on Artificial Intelligence.

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

Thermal papers are replacing the conventional form of printing and are being extensively used across the globe. This study encompasses a non-destructive approach to examine thermal papers by using ATR-FTIR spectroscopy and Video Spectral Comparator (VSC), where the former technique helps in characterizing and discriminating different samples and the latter helps in deciphering the faded prints on thermal paper. The qualitative analysis of the spectroscopic data based on peak to peak comparison and quantitative analysis using chemometrics has been done to obtain high discriminating power. Multivariate analysis using HCA gave a discriminating power of 83.82% and PCA showed a variance of 95.64%. The strength of the study is portrayed through the decipherment of artificially and naturally faded thermal papers using VSC and analyzing the effect of different storing conditions on their rate of fading.

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

Purpose Electrocardiogram (ECG) is one of the most essential tools for detecting heart problems. Till today most of the ECG records are available in paper form. It can be challenging and time-consuming to manually assess the ECG paper records. Hence, automated diagnosis and analysis are possible if we digitize such paper ECG records. Methods The proposed work aims to convert ECG paper records into a 1-D signal and generate an accurate diagnosis of heart-related problems using deep learning. Camera-captured ECG images or scanned ECG paper records are used for the proposed work. Effective pre-processing techniques are used for the removal of shadow from the images. A deep learning model is used to get a threshold value that separates ECG signal from its background and after applying various image processing techniques threshold ECG image gets converted into digital ECG. These digitized 1-D ECG signals are then passed to another deep learning model for the automated diagnosis of heart diseases into different classes such as ST-segment elevation myocardial infarction (STEMI), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), and T-wave abnormality. Results The accuracy of deep learning-based binarization is 97%. Further deep learning-based diagnosis approach of such digitized paper ECG records was having an accuracy of 94.4%. Conclusions The digitized ECG signals can be useful to various research organizations because the trends in heart problems can be determined and diagnosed from preserved paper ECG records. This approach can be easily implemented in areas where such expertise is not available. Supplementary Information The online version contains supplementary material available at 10.1007/s40846-021-00632-0.

In February 2020 the EU published its white paper on ‘Artificial Intelligence: A European approach to excellence and trust’. This is likely to form the core of future policy and legislation within the EU and as such will have global impact on standards and norms. In this comment piece we survey the five sections of the white paper and then critically examine three themes, namely, i. regulatory signalling, ii. the risk-based approach, and, iii. the auditing styles. The key takeaway is that the white paper, and the EU’s strategy at large, is ambiguous and lacks vision, which, if unchecked, is likely to have a negative impact on EU competitiveness in the development of AI solutions and services.

Summary Skin-interfaced wearable electronics can find a broad spectrum of applications in healthcare, human-machine interface, robotics, and others. The state-of-the-art wearable electronics usually suffer from costly and complex fabrication procedures and nonbiodegradable polymer substrates. Paper, comprising entangled micro- or nano-scale cellulose fibers, is compatible with scalable fabrication techniques and emerges as a sustainable, inexpensive, disposable, and biocompatible substrate for wearable electronics. Given various attractive properties (e.g., breathability, flexibility, biocompatibility, and biodegradability) and rich tunability of surface chemistry and porous structures, paper offers many exciting opportunities for wearable electronics. In this review, we first introduce the intriguing properties of paper-based wearable electronics and strategies for cellulose modifications to satisfy specific demands. We then overview the applications of paper-based devices in biosensing, energy storage and generation, optoelectronics, soft actuators, and several others. Finally, we discuss some challenges that need to be addressed before practical uses and wide implementation of paper-based wearable electronics., Graphical abstract, Biodevices; Bioelectronics; Electronic materials

This short position paper was presented at the Meeting of the OECD Network of Experts on Artificial Intelligence (ONE AI) held in Paris on 26-27 February 2020. It argues that public and collective governance, including through collective bargaining, is urgently needed to prevent the most invasive uses of Artificial Intelligence and algorithmic management and surveillance over workers. Tools such as GPS-based wearable devices that monitor workers' position at the workplace and collect data on particularly private information, including workers' heartbeats and emotional status call for immediate action from regulators and social partners. Surveillance practices should be limited to indispensable cases and transparency of data collection and processes should be safeguarded, also to rule out the many possible discriminatory consequences of management-by-algorithm.

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

The main objective of this project was to assess an inkless method to collect reference (exemplar) fingerprints as a low-cost, portable, and hygienic technique; for temporary storage and as an alternative to the conventional black ink method. The tested inkless approach requires alcohol-based hand sanitizer gel as the medium and thermal paper as the substrate. The final quality and clarity of fingerprints were examined and compared between the two methods, especially on the basis of medium/substrate and biological sex effects. Two different evaluation systems of visual quality (clarity) were conducted to provide more reliable results: a quality score scheme (QS) and metrics from the FBI's Universal Latent Workstation (ULW). Fingerprints were obtained from 20 individuals (11 males and 9 females) between the ages of 20 and 40; a total of 240 impressions were evaluated. The results provide evidence that the inkless method produces identifiable fingerprints of equivalent quality to the standard ink method, although significantly better for males. The potential of the proposed technique as an alternative to the conventional ink method is proven and corroborated by the two independent systems of visual quality assessments.

A non-destructive method based on Fourier Transformed Infrared Spectroscopy (FT-IR) was proposed to estimate the date of paper from different years in this article. For the paper samples, dated from 1940 to 1980, naturally aged and conserved in library. Partial least squares-discriminate analysis (PLS-DA), Logistic regression and convolutional neural network (CNN), were employed to evaluate the date of paper, with the accuracy 60.74%, 95.31% and 98.77%, respectively. Based on the characteristics of CNN model and with the help of network localization, active variables could be recognized in the whole spectrum. Although the localization of active variables showed a discriminative pattern, the selected spectral regions were similar. Most important variables focused on the 1700–1400 cm−1, were corresponding to cellulose crystallinity, which was consisted with the ageing processing. The present work gave the potential of FT-IR combined with chemometric techniques could estimate the dating of unknown paper. Meanwhile, the analysis of active variables obtained further indicated the worthy of CNN model for document dating.

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

Highlights • In this position paper, we provide a collection of views on the role of AI in the COVID-19 pandemic, from the clinical needs to the design of AI-based systems, to the translation of the developed tools to the clinic. We highlight key factors in designing system solutions - per specific task; as well as design issues in managing the disease on the national level. We focus on three specific use-cases for which AI systems can be built: from the early disease detection, the management of the disease in a hospital setting, and building patient-specific predictive models that require the combination of imaging with additional clinical features. Infrastructure considerations and population modeling in two European countries will be described. This pandemic has made the practical and scientific challenges of making AI solutions very explicit. A discussion concludes this paper, with a list of challenges facing the community in the AI road ahead., In this position paper, we provide a collection of views on the role of AI in the COVID-19 pandemic, from clinical requirements to the design of AI-based systems, to the translation of the developed tools to the clinic. We highlight key factors in designing system solutions - per specific task; as well as design issues in managing the disease at the national level. We focus on three specific use-cases for which AI systems can be built: early disease detection, management in a hospital setting, and building patient-specific predictive models that require the combination of imaging with additional clinical data. Infrastructure considerations and population modeling in two European countries will be described. This pandemic has made the practical and scientific challenges of making AI solutions very explicit. A discussion concludes this paper, with a list of challenges facing the community in the AI road ahead.

Eco-design initiatives are gaining importance due to changing environmental conditions and the industry is developing different solutions. The purpose of this paper is the identification and evaluation of barriers related to the implementation of eco-design initiatives in pulp and paper industry. This study identifies the key barriers through literature research and provides information flow dependencies using design structure matrix through a case study of a company in Finland. This method differs from traditional management tools because it focuses on representing information flow rather than workflow. The findings provide policy recommendations to policy makers for managing eco-design implementation.

In production, poor ergonomic environments not only lead to increased workloads and health hazards for employees but also tend to reduce efficiency and quality. Recently, the human-cyber-physical (HCP) system has been proposed and widely studies to meet human capabilities and limitations. However, most existing frameworks are still not adaptive enough to integrate humans into a smart production environment due to a lack of real-time individual human factors digitalization. This research proposes an HCP workstation model for comprehensive assembly resources digitalization and autonomous interaction by CPS enabling technologies. Based on this, an adaptive control system has been developed for proactive ergonomic risk mitigation. Computer vision is deployed for real-time individual ergonomic evaluation and a prototype has been set up for functional validation.

This document seeks to respond to the questions raised by the Australian Human Rights Commission and World Economic Forum in their ‘Artificial Intelligence: governance and leadership’ White Paper consultation. It draws upon some of the research conducted at the Allens Hub to make suggestions about if and how systems involving some component of artificial intelligence or automation should be regulated. It also seeks to create further clarity regarding the potential structure and functions of the proposed Responsible Innovation Organisation. We note that our research does not relate to all the questions asked in the White Paper consultation, and so we only set out answers in relation to those matters where our research may be relevant. We are otherwise grateful for the opportunity to present our views and hope this submission will help the Australian Human Rights Commission and World Economic Forum in their consideration.

During the folding process substrates are exposed to high-localized stresses, which in the case of coated papers and boards, can lead to decreased aesthetic features or complete loss of functionality. Production efficiency of the folding process could be improved by an automated, computer vision-based inspection system. For such a task, different existing computer-aided fold-crack evaluation approaches were analyzed. A detailed research was conducted to propose an image processing based fold cracking assessment via finding optimal sample preparation and digitization techniques and developing an algorithm for the digital image analysis and feature extraction. The analysis of the applicability of different sample preparation and digitization parameters, as well as the proposed digital image feature, was done by correlation evaluation, one-way analysis of variance (ANOVA) and corresponding post hoc tests. The results indicated that the developed algorithm fulfils the set requirements and the proposed feature of digitized samples faithfully describes the analyzed fold-cracks.

The purpose of this submission is to address the list of questions posed by the Australian Human Rights Commissioner on the White Paper Artificial Intelligence: Governance and Leadership (2019). It is an interim submission, and a final submission will be made later. Some main points of the interim submission, stated in summary, are: 1) The main goals of government regulation in the area of artificial intelligence include: a. ‘Regulation’ must not be confused with economic enablement and stimulation. Both may be legitimate in relation to AI, but the same organisation must not be given responsibility for both, as they involve an inherent potential for conflict of interests. b. Attempts to ‘pick winners’ or to encourage governments to do so should be avoided. Focus on principles, not products. c. Encourage Australian input into the global development of standards for, ethics of, and treaties concerning, AI development and use. d. Legislate where necessary to prevent the most harmful uses of AI. e. It should be assumed that some regulation (possibly including co-regulation) will be necessary. f. The general objectives of the regulation of AI are primarily the protection of human rights and the protecting of competition. 2) There is no Australian policy-oriented organisation with a permanent high level of expertise in AI-related technologies, and certainly no such organisation which does not have potentially conflicting industry development objectives. 3) There would be value in Australia creating such a new policy-oriented organisation, with a limited life-span, but ‘Responsible Innovation Organisation’ (RIO) is a completely inappropriate title, as it suggests that the body has a role in promoting innovation (ie industry development), either by itself or as part of some larger economically-oriented organisation, which will inevitably dominate and distort its views. We should consider approaches taken by other national governments. 4) Many Australian organisations would find it valuable to have an Australian policy-oriented organisation recommending international standards for Australian adoption, or assisting in the developing of Australian standards. 5) Strictly econometric measures of the ‘business case’ for such policy-oriented organisations are not possible or desirable. 6) The desirable goals for such an organisation are set out in (i) above. a. It should primarily encourage and assist other Australian organisations to provide inputs into development of standards and ethics (internationally and in Australia), including by funding assistance where necessary. It should have the capacity to make such inputs itself, when needed. b. A board of part-time Commissioners, with one full-time Commissioner, is desirable. It should be a stand-alone organisation, as it would not fit comfortably within other organisations c. Public funding is more desirable, as the costs will be modest. Joint funding by industry prevents very grave risks of regulatory capture. d. It should provide an annual report to Parliament, and communicate its work between reports via a variety of Internet channels.

The paper relic identification is a pending issue to be resolved in the field of cultural heritage. As we all known, heritage paper has significant importance in archaeological research. Nowadays, there are a variety of research methodologies focuses on the analysis of inks for dating documents. While the paper analysis attained little attention. This work is to explore the non-destructive application of ATR-FTIR technique in discrimination of paper relics. 15 types of paper spectra were collected by ATR-FTIR, which wavenumber range were range from 4000 to 650 cm−1. And the moving average smoothing and normalization was used for pretreatment analysis. Five different classification algorithms, principal component analysis-linear discriminant analysis (PCA-LDA), partial least squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), least squares-support vector machine (LS-SVM), partial least squares-linear discriminant analysis (PLS-LDA) were selected to classify the types of paper. PLS-LDA and LS-SVM are effective techniques with 100% classification accuracy. PCA-LDA, PLS-DA and SIMCA give accuracy of 98.67%, 97.33% and 95.56%, respectively. The present experiment suggested that ATR-FTIR combining with chemometrics will be highly useful in paper identification of cultural heritage.

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

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

This paper describes a simple design for increasing the tolerance of reagent dislocation on a paper-based platform using a combination of wax-treated paper and a vortex mixer. To date, massive budgetary funds are required in the biotechnological industry to develop new applications; a large part of that cost is attributable to the screening of specific chemical compounds. Here, we propose using a liquid-handling robot to automatically deposit selected reagents on a paper-based platform. We also present a preliminary concept approach for developing a reagent placing device with simple and inexpensive features. A defect of inaccuracy was observed between droplet location and test well location after viewing the performance of the liquid-handling robot on our paper-based platform. Because of dislocation error resulting from robotic reagent placement, we decided to apply an external, rotational force following droplet placement in order to compensate for the distance of reagent dislocation. Note, the largest distance of reagent dislocation was determined by examining the results of altering applied reagent volume, but not concentration, in volumes from 5 µL to 30 µL in a series of experiments. As a result of these experiments, we observed that dislocation was positively affected by an increase in applied volume. A colorimetric assay for nitrite detection was also performed to confirm the feasibility of this method. This work, we believe, can minimize the cost of chemical compound screening for the biotechnological industry.

Efficient and safe detection of Bacillus anthracis spores (BAS) is a challenging task especially in bio-terror scenarios where the agent is concealed. We provide a proof-of-concept for the identification of concealed BAS inside mail envelopes using short-wave infrared hyperspectral imaging (SWIR-HSI). The spores and two other benign materials are identified according to their typical absorption spectrum. The identification process is based on the removal of the envelope signal using a new automatic new algorithm. This method may serve as a fast screening tool prior to using classical bioanalytical techniques.

Nitrite is one of the most common carcinogens in daily food. Its simple, rapid, inexpensive, and in-field measurement is important for food safety, based on the requirements of the standard from Codex Alimentarius Commission and China. Using polyacrylonitrile (PAN) and thin layer silica gel (SG), p-aminophenylcyclic acid (SA) and naphthalene ethylenediamine hydrochloride (NEH), as carriers and chromogenic agents, respectively, PAN-NSS as nitrite color sensor is proposed. After fixing and protecting of SA and NEH with layer-upon-layer PAN, the validity period of the test paper can be prolonged from 7 days to more than 30 days. The reproducibility of PAN-NSS preparation is ensured by electrospinning. Combined with PAN-NSS, deep convolutional neural network (DCNN) and APP as a visual monitoring platform, which has the functions of rapid sampling, data processing and transmission, intuitive feedback, etc., and provides a fully integrated detection system for field detection.

Paper is non-uniform; thus, it is difficult to ascertain whether the slight differences in near-infrared absorption spectra are caused by the difference in paper type or the paper’s non-uniformity. We used near-infrared hyperspectral imaging to measure the spectra of many different points quickly for eight different paper samples. Next, we analysed the data using machine learning and constructed a model to ascertain whether the differences in the spectra are due to the difference in paper type or the paper’s non-uniformity. Even papers with very similar spectral shapes were distinguished with high accuracy with this method. As the number of spectra used to construct the classification model increased, the accuracy of the classification model increased. With 6840 spectra per sample, the accuracy exceeded 85%, and with 68,400 spectra per sample, the accuracy for identification was 87.8%.

With the emergence of artesunate antimalarial counterfeiting in Southeast Asia and sub-Saharan Africa, we present the production of a rapid, inexpensive and simple colorimetric-based testing kit for the detection of counterfeit artesunate in order to preserve life and prevent the development of multi-drug resistant malaria. The kit works based on paper microfluidics which offer several advantages over conventional microfluidics, and has great potential to generate inexpensive, easy-to-use, rapid and disposable diagnostic devices. Here, we have developed a colorimetric assay that is specific to artesunate and turns yellow upon addition of the sample. The test can be done within minutes, and allows for a semi-quantitative analysis of the artesunate tablets by comparing the developed yellow color on the paper test to a color-coded key chart that comes with the kit. A more accurate and precise analysis is done by utilizing a color analyzer on an iPhone camera that measures the color intensity of the developed color on the paper chip. A digital image of the chip was taken and analyzed by measuring the average gray intensity of the color developed on the paper circle. A plot of the artesunate concentration versus the average gray scale intensity was generated. Results show that the intensity of the yellow color developed on the paper test was consistent and proportional to the amount of artesunate present in the sample. With artesunate concentrations ranging from 0.0 to 20 mg/mL, a linear calibration plot was obtained with a detection limit of 0.98 mg/mL.

Sewer networks play a pivotal role in our everyday lives by transporting the stormwater and urban sewage away from the urban areas. In this regard, Sewer Overflow (SO) has been considered as a detrimental threat to our environment and health, which results from the wastewater discharge into the environment. In order to grapple with such deleterious phenomenon, numerous studies have been conducted; however, there has not been any review paper that provides the researchers undertaking research in this area with the following inclusive picture: (1) detailed-scientometric analysis of the research undertaken hitherto, (2) the types of methodologies used in the previous studies, (3) the aspects of environment impacted by the SO occurrence, and (4) the gaps existing in the relative literature together with the potential future works to be undertaken. Based on the comprehensive review undertaken, it is observed that simulation and artificial intelligence-based methods have been the most popular approaches. In addition, it has come to the attention that the detrimental impacts associated with the SO are fourfold as follows: air, quality of water, soil, and business and structure. Among these, the majority of the studies' focus have been tilted towards the impact of SO on the quality of ground water. The outcomes of this state-of-the-art review provides the researchers and environmental engineers with inclusive hindsight in dealing with such serious issue, which in turn, this culminates in a significant improvement in our environment as well as humans’ well-beings.

Over the past few years, researchers have focused on stimulus-responsive soft materials and their applications in the design of soft actuators. The key factor of the soft actuators to achieve various applications is the programmable and controllable shape deformations. Here, a versatile and simple surface patterning method consisted of pencil drawing and pasting polymer films is proposed to design the soft actuators with programmable deformations. Due to the thermal expansion effect and the hygroexpansion effect, the graphite paper/polymer actuator performs a bending motion with a curvature of 1.4 cm−1 under light irradiation and 1.2 cm−1 under high humidity. Owing to the convenience of the patterning method, different patterns on both two surfaces of the paper substrate are designed without effort. Thus, through tuning the grayscale of the graphite on one surface of the paper, three types of T-shaped actuators can be obtained with different deformations under light irradiation. In addition, by pencil drawing or hydrophobically modifying specific patterns on the two surfaces of the paper, three types of strip-shaped actuators are also proposed to demonstrate that different 3D shape deformations can be obtained under different stimuli. Finally, a helix-shaped actuator inspired by the natural plant tendrils is fabricated by the thermal-induced shaping method. The helix-shaped actuator performs twisting/untwisting motion under different stimuli and can be used as a grasping robot. These results demonstrate the diversified programmability of the graphite paper/polymer actuator. Hence, the simple and versatile programming methods of soft actuators have great potential to be used in the field of biomimetic applications, intelligent robotics, and lab-on-paper devices.

The optical model for reflectance lightness of stereo paper product surface is proposed, which could promote color reproduction study of paper model products and improving color reproduction quality. The reflectance lightness was measured using non-contact measurement method with spectroradiometer, CS2000A, that can measure stereo paper product surface lightness and tristimulus. Once inputting the paper product parameters of surface angle and tone, the model will predict the reflectance lightness for stereo paper product surface. After analyzing statistical information of light transmission, paper product surface reflectance lightness can be obtained. The results showed that prediction model is versatile and easy to use, besides the model quite approached to realistic paper product surface. The optical model simulation system could sufficiently accomplish reflectance lightness, which provides a novel way to predict and reproduce printing quality.

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

International audience; Planning is motivated by acting. Most of the existing work on automated planning underestimates the reasoning and deliberation needed for acting; it is instead biased towards path-finding methods in a compactly specified state-transition system. Researchers in this AI field have developed many planners, but very few actors. We believe this is one of the main causes of the relatively low deployment of automated planning applications. In this paper, we advocate a change in focus to actors as the primary topic of investigation. Actors are not mere plan executors: they may use planning and other deliberation tools, before and during acting. This change in focus entails two interconnected principles: a hierarchical structure to integrate the actor's deliberation functions, and continual online planning and reasoning throughout the acting process. In the paper, we discuss open problems and research directions toward that objective in knowledge representations, model acquisition and verification, synthesis and refinement, monitoring, goal reasoning, and integration.

In this study, the effects of ambient illuminance and light source on participants’ reading performance and visual fatigue during a long reading task were investigated using three electronic paper displays. Reading on electronic paper displays was also compared with reading on paper. In Experiment 1, 100 participants performed a reading task where the display area for the text was equated for the displays. The results indicated that participants’ visual performance and visual fatigue did not differ significantly among different electronic paper displays, ambient illuminance conditions, or light sources. In Experiment 2, another 60 participants performed the same reading task where the full screen of each electronic paper display was used to present the text. The results showed that reading speed differed significantly across different electronic paper displays and ambient illuminance levels. The reading speed was slower for displays with smaller screens and increased as the ambient illuminance increased. Changes in the critical flicker fusion frequency significantly differed across ambient illuminance levels. Implications of the results for the use of electronic paper displays are discussed.

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

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

Creatinine is a clinically significant analyte used to diagnose kidney condition. However, the literature still lacks in creatinine sensors fulfilling point-of-care testing requirements. In this paper, we have developed colorimetric paper-based creatinine sensors adhering to point-of-care testing principles. The signal readout is accomplished with a smartphone modified with 3D-printed elements and processed with a self-written application compromising computer vision algorithm for automatic detection of the colored zone. Two colorimetric methods – routinely used Jaffe method and an alternative one with 3,5-dinitrobenzoic acid were both tested and compared. Hue channel intensity from HSV color space and green channel from RGB color space was used as the analytical signal in Jaffe and 3,5-dinitrobenzoate method, respectively. For both kinds of sensors the linear range of the response covered the range significant for urinary analysis, with precision, expressed as RSD, below 5%. Limit of quantification for Jaffe method was 1.05 mmol·L−1 whereas it was 0.82 mmol·L−1 for 3,5-dinitrobenzoate method. The utility of the developed sensors to selectively quantify creatinine in undiluted urine was proved using artificial urine samples and the obtained recoveries were in the range from 70 to 129 %.

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

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

In this paper we describe a decentralized protocol that is applied for scheduling and management of energy sharing in heterogeneous systems. Particular system we have in mind comprises of agents with different mobility, energy harvesting and energy sharing capabilities herein called aPads, aFish and aMussels. aPads are surface vehicles that harvest energy and are able to transmit it to both aFishes and aMussels. aFish are underwater vehicles that can transmit energy to aMussels or to other aFish, and receive energy from aPads. aMussels, that can either be attached to the sea-ground or float on the surface, can only receive energy, either from aFish or aPads. For such a system to be able to function for an extended period of time, a decentralized energy management protocol should be designed, whose purpose is to schedule and coordinate charging of agents over time. These decisions depend on the current battery levels of different agents, as well as their positions, and should be done in a decentralized manner. In this paper we apply an energy sharing protocol based on TAEMS framework, originally applied to planning and coordination in aerial-ground robotics systems for search and rescue missions. We refine the obtained plans using the coalition-based metaheuristic for vehicle routing problems. We show execution schedules achieved using the proposed protocol and highlight its advantages and flaws when applied to our underwater scenario.

Paper being the basis of different documents is often the subject of forensic examination. Growing number of bogus or in other manner fraudulently alternated documents causes necessity of identification of individual paper sheets and discrimination between sheets being the parts of analyzed questioned document. Frequently it is necessary to distinguish between paper of the same type but of a different age. Thus, it is essential to know whether the degradation process of paper influences the possibility of differentiation between paper samples. Samples of five types of office paper from different manufacturers were artificially aged in a climatic chamber under 65% relative humidity in air at 90 °C for various periods of time up to 35 days. The conditioned samples were examined by the use of infrared and Raman spectroscopy. Three cards of each paper type were chosen for the experiment. Three different spots on each paper card were measured to assure reproducibility of the experiment in both spectroscopic methods. The possibility of differentiation between aged samples was evaluated. The 2D correlation analysis based on the Noda’s method was carried out using ATR FTIR spectra as an input data for generating the correlation maps. It was found that pattern of 2D maps allow to distinguish tested paper samples, identified its components and get insight into paper degradation mechanism.

There are a number of issues which are involved with organizing a conference. Among these issues, assigning conference-papers to reviewers is one of the most difficult tasks. Assigning conference-papers to reviewers is automatically the most crucial part. In this paper, we address this issue of paper-to-reviewer assignment, and we propose a method to model the reviewers, based on the matching degree between the reviewers and the papers by combining a preference-based approach and a topic-based approach. We explain the assignment algorithm and show the evaluation results in comparison with the Hungarian algorithm.

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

This study investigated the effects of bending curvature of simulated e-paper displays (−10 cm, plane and 10 cm) and ambient illuminance (50, 500, 6000 and 12,000 lx) on the visual performance of young and elderly users for simulated electronic-paper displays. In total, 24 people comprising 12 young and 12 elderly users participated in this study. Young users demonstrated significantly enhanced visual performance using the simulated electrophoretic ink (E ink) e-paper display with ambient illuminance set at 50 and 500 lx; however, participants did not demonstrate significantly different visual performance using the 2 simulated e-paper displays with ambient illuminance set at 6000 and 12,000 lx. The bending curvature did not exhibit significant effects on the visual performance of young participants under various ambient illuminances. The elderly participants exhibited significantly enhanced visual performance using the simulated E ink e-paper display only when ambient illuminance was set at 50 lx; however, participants did not exhibit significantly different visual performance using the two simulated e-paper displays with ambient illuminance set at other levels. Elderly users exhibited significantly enhanced visual performance when the bending curvature was set at 10 cm, with ambient illuminance set at 50 lx. Elderly users also demonstrated significantly enhanced visual performance under bending curvature settings of 10 cm and plane, with ambient illuminance set at 500 lx. However, the bending curvature of the simulated e-paper did not exhibit significant effects on the visual performance of elderly participants under ambient illuminance settings of 6000 and 12,000 lx.

A novel system based on Bernoulli Theorem of Large Number Law and the genetic algorithms was designed and realized in this paper, which had many advantages such as self-adaptive study for difficulty coefficient of item pool and intelligent test paper construction etc. At present, the system has applied in the examination of paperless computer tests of Jinlin university of finance and economics and some satisfactory results have been also obtained.

The question of whether digitally captured signatures and conventional signatures executed with a pen on paper differ in their characteristics is of practical relevance for forensic handwriting examiners. Due to gaps in the current literature, the present research is dedicated to this issue. Eighty persons signed in three conditions: a) with a stylus on a pad, b) with an inking pen on a sticky note attached to a signature pad allowing to obtain a digital and an analogue version on paper of one and the same writing simultaneously, and c) with a pen on paper. The first step was to investigate to what extent the character shape and number of pen lifts differ between the digital and analogue representation of one and the same signature. This revealed minor differences which are due to technical characteristics of the devices used. The observed distortions are of minor practical relevance according to ratings by eight participating forensic handwriting examiners. Subsequently, signature characteristics were compared between the three different writing conditions in a casework-oriented way. Statistical multi-level models indicate significant differences between the three signature types, but minor effect sizes in most of the examined characteristics. From the point of view of the participating handwriting examiners, these factors do not fundamentally restrict the comparability between digitally captured and conventional signatures in practice. However, caution should be exercised when generalising the results, as several factors, such as the usage of different signature pads as well as signatures made with the finger instead of a stylus, could result in more important differences compared to pen and paper signatures.

For centuries, the research paper have been the main vehicle for scientific progress. From the paper, readers in the scientific community are expected to extract all the relevant information necessary to reproduce and validate the results presented by the paper's authors. However, the increased use of computer software in science makes reproducing scientific results increasingly difficult. The research paper in its current state is no longer sufficient to fully reproduce, validate, or review a paper's experimental results and conclusions. This impedes scientific progress. To remedy these concerns, we introduce PaperMâche, a new system for creating dynamic, executable research papers. The key novelty of PaperMâche is its use of virtual machines, which lets readers and reviewers easily view and interact with a paper, and reproduce key experimental results. For authors, the Paper Mâche workbench provides an easy-touse interface to build an executable paper. By transforming the static research paper into a dynamic and interactive entity, Paper Mâche brings the presentation of scientific results into the 21st century. We believe that Paper Mâche will become indispensable to the scientific process, and increase the visibility of key findings among members and non-members of the scientific community.