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Teaching about biodiversity and its conservation could be an effective way to teach the importance of different species and human dependence on ecological support systems. This study compares the effectiveness of a digital and a paper-based dichotomous identification key for teaching pre-service teachers about plantspecies diversity. Twenty-four Slovenian teachers in pre-service training used a digital version of the key on tablet computers and twenty-nine used a paper-based version to identify woody species in the garden near their university faculty. Both keys contained the same species, identical photographs and the same sequence of steps to identify the species. The accuracy and time taken to identify the five species were measured. The participants also completed a questionnaire about the usability of the identification key. Overall, both versions of the key were equally good for determining species names. The digital version of the key was found to be more time-effective than the paper version only when multiple identification steps were required to identify species. The results confirm that those who scored better on the identification tasks have a better general opinion about the usability of the key especially students using digital version of the key. Implications for teachers using or creating identification keys are discussed in the conclusion.

In this paper, we present a system for automatic evaluation of the quality of a question paper. Question paper plays a major role in educational assessment. The quality of a question paper is crucial to fulfilling the purpose of the assessment. In many education sectors, question papers are prepared manually. A prior analysis of a question paper might help in finding the errors in the question paper, and better achieving the goals of the assessment. In this experiment, we focus on higher education in the technical domain. First, we conducted a student survey to identify the key factors that affect the quality of a question paper. The top factors we identified are question relevance, question difficulty, and time requirement. We explored the strategies to handle these factors and implemented them. We employ various concepts and techniques for the implementation. The system finally assigns a numerical quality score against these factors. The system is evaluated using a set of question papers collected from various sources. The experimental results show that the proposed system is quite promising.

Background The outpatient setting is a key scenario for the implementation of antimicrobial stewardship (AMS) activities, considering that overconsumption of antibiotics occurs mainly outside hospitals. This publication is the result of a joint initiative by the JPIAMR ARCH and COMBACTE-MAGNET EPI-Net networks, which is aimed at formulating a set of target actions for linking surveillance data with AMS activities in the outpatient setting. Methods A scoping review of the literature was carried out in three research areas: AMS leadership and accountability; antimicrobial usage and AMS; antimicrobial resistance and AMS. Consensus on the actions was reached through a RAND-modified Delphi process involving over 40 experts in infectious diseases, clinical microbiology, AMS, veterinary medicine or public health, from 18 low-, middle- and high-income countries. Results Evidence was retrieved from 38 documents, and an initial 25 target actions were proposed, differentiating between essential or desirable targets according to clinical relevance, feasibility and applicability to settings and resources. In the first consultation round, preliminary agreement was reached for all targets. Further to a second review, 6 statements were re-considered and 3 were deleted, leading to a final list of 22 target actions in the form of a practical checklist. Conclusions This White Paper is a pragmatic and flexible tool to guide the development of calibrated surveillance-based AMS interventions specific to the outpatient setting, which is characterized by substantial inter- and intra-country variability in the organization of healthcare structures, maintaining a global perspective and taking into account the feasibility of the target actions in low-resource settings.

A good quality control system is important to be implemented to increase productivity and minimize defects in products. One of the quality control methods is failure mode and effect analysis (FMEA). This study uses the FMEA to identify the causes of the defects and recommend the prevention methods to overcome the causes of the defects in an Indonesian paper mill. The risk priority number (RPN) is calculated by multiplying the severity, occurrence, and detection of the failures that have been determined. Unsymmetrical and tainted products are the most dominant defects in the paper mill. An inappropriate machine setting is the cause of unsymmetrical products with the highest RPN of 343. The second highest RPN is problems with bleaching machines that caused tainted products with an RPN value of 216. This study offers suggestions to Indonesian paper mill to prevent and minimize defective products.

With characters of low cost, portability, easy disposal, and high accuracy, as well as bulky reduced laboratory equipment, paper-based sensors are getting increasing attention for reliable indoor/outdoor onsite detection with nonexpert operation. They have become powerful analysis tools in trace detection with ultra-low detection limits and extremely high accuracy, resulting in their great popularity in medical detection, environmental inspection, and other applications. Herein, we summarize and generalize the recently reported paper-based sensors based on their application for mechanics, biomolecules, food safety, and environmental inspection. Based on the biological, physical, and chemical analytes-sensitive electrical or optical signals, extensive detections of a large number of factors such as humidity, pressure, nucleic acid, protein, sugar, biomarkers, metal ions, and organic/inorganic chemical substances have been reported via paper-based sensors. Challenges faced by the current paper-based sensors from the fundamental problems and practical applications are subsequently analyzed; thus, the future directions of paper-based sensors are specified for their rapid handheld testing.

Development of paper-based microfluidic devices that perform colorimetric measurements requires quantitative image analysis. Because the design geometries of paper-based microfluidic devices are not standardized, conventional methods for performing batch measurements of regularly spaced areas of signal intensity, such as those for well plates, cannot be used to quantify signal from most of these devices. To streamline the device development process, we have developed an open-source program called ColorScan that can automatically recognize and measure signal-containing zones from images of devices, regardless of output zone geometry or spatial arrangement. This program, which measures color intensity with the same accuracy as standard manual approaches, can rapidly process scanned device images, simultaneously measure identified output zones, and effectively manage measurement results to eliminate requirements for time-consuming and user-dependent image processing procedures.

Renewable paper reusing plays a significant role in the sustainable environment under the background of the shortage in forest resources and the pollution from the paper industry. The conventional reusing stream of waste office paper appears to have low reusing rates while consuming massive amounts of energy in intermediate steps. In this study, we developed a novel portable renewable desktop paper reusing system based on font area detection and greyscale sensor. The proposed system consists of two main parts, namely, a greyscale sensor and font area detection model and a polishing mechanism. Acting as an ink mark detector for waste desktop paper, the greyscale sensor and font area detection model can detect the font in the waste desktop paper using an adaptive dynamic compensation schematic. The polishing mechanism will grind the font area of the wasted desktop paper, and this paper reusing processing is non-chemical, energy saving and environmentally friendly. The proposed system is demonstrated through simulations and experimental results, which show that the proposed renewable desktop paper reusing system is portable and is effective for reusing waste office paper in the office. An accuracy of 99.78% is demonstrated in the greyscale sensor and font area detection model, and the average reuse rate of one piece of paper is 2.52 times, verifying that the proposed portable system is effective and practical in renewable desktop paper reusing applications.

The accuracy of a predictive system is critical for predictive maintenance and to support the right decisions at the right times. Statistical models, such as ARIMA and SARIMA, are unable to describe the stochastic nature of the data. Neural networks, such as long short-term memory (LSTM) and the gated recurrent unit (GRU), are good predictors for univariate and multivariate data. The present paper describes a case study where the performances of long short-term memory and gated recurrent units are compared, based on different hyperparameters. In general, gated recurrent units exhibit better performance, based on a case study on pulp paper presses. The final result demonstrates that, to maximize the equipment availability, gated recurrent units, as demonstrated in the paper, are the best options.

In simple dyadic games such as rock, paper, scissors (RPS), people exhibit peculiar sequential dependencies across repeated interactions with a stable opponent. These regularities seem to arise from a mutually adversarial process of trying to outwit their opponent. What underlies this process, and what are its limits? Here, we offer a novel framework for formally describing and quantifying human adversarial reasoning in the rock, paper, scissors game. We first show that this framework enables a precise characterization of the complexity of patterned behaviors that people exhibit themselves, and appear to exploit in others. This combination allows for a quantitative understanding of human opponent modeling abilities. We apply these tools to an experiment in which people played 300 rounds of RPS in stable dyads. We find that although people exhibit very complex move dependencies, they cannot exploit these dependencies in their opponents, indicating a fundamental limitation in people’s capacity for adversarial reasoning. Taken together, the results presented here show how the rock, paper, scissors game allows for precise formalization of human adaptive reasoning abilities.

Researchers face difficulties in finding relevant papers to their research interest as the number of scientific publication is rapidly increasing on the web. Scientific paper recommenders have emerged as a leading solution to help researchers by automatically suggesting relevant and useful publications. Several approaches have been proposed on improving recommender systems. However, most existing approaches depend on priori user profiles, and thus they cannot recommend papers to new user. Furthermore, the existing approaches utilize non-public contextual information, and thus it cannot adequately find similarities between papers due to copyright restrictions. Also, the existing approaches consider only single level paper-citation relation to identify similarities between papers. Considering the above challenges, this paper presents a collaborative filtering based recommendation approach for scientific papers that does not depend on priori user profiles and which utilizes only public contextual information. Using citation context, we utilized 2-level paper-citation relations to find hidden associations between papers. The rational underlying this approach is that, two papers are co-occurred with same cited paper(s) and two papers are co-occurring with same citing paper(s) are significantly similar to some extent. To evaluate the performance of the proposed approach, publicly available datasets are used to conduct extensive experiments. The experimental results demonstrate that the proposed approach has significantly outperforms the baseline approaches in terms of precision, recall, F1, mean average precision, and mean reciprocal rank, which are commonly used information retrieval metrics. The novelty of this study is that, with the proposed approach, researchers are able to find relevant and useful publications over the internet regardless of their previous research experiences and research area.

Background In recent times, the use of thermal papers has increased exponentially and the fingerprints on thermal papers are frequently encountered. Although fingerprint development on paper has been standardized, that on thermal paper is complicated. Thermal papers turn black on the application of heat which poses a problem in the visualization of fingerprints which makes it difficult to visualize. Main body One solution to this problem is to use heat or steam for the development of white fingerprints on darker backgrounds. Second solution might be to dissolve the chemicals present on the surface of the thermal papers using acetone. Many studies to develop fingerprints on thermal papers using iodine fuming, ninhydrin, 1,8-diazofluorene-9-one, and p-dimethylaminocinnamaldehyde have been performed. In the present review, an attempt has been made to summarize the problem posed to fingerprint development on thermal papers and the probable solutions attempted by different authors. Conclusion Although the development of fingerprints is difficult on thermal papers, it can be achieved either by subjecting the thermal paper to controlled heat or by removing the temperature-sensitive layer before treating it with other fingerprint development methods.

The development of diagnostic tools for measuring a wide spectrum of target analytes, from biomarkers to other biochemical parameters in biological fluids, has experienced a significant growth in the last decades, with a good number of such tools entering the market. Recently, a clear focus has been put on miniaturized wearable devices, which offer powerful capabilities for real-time and continuous analysis of biofluids, mainly sweat, and can be used in athletics, consumer wellness, military, and healthcare applications. Sweat is an attractive biofluid in which different biomarkers could be noninvasively measured to provide rapid information about the physical state of an individual. Wearable devices reported so far often provide discrete (single) measurements of the target analytes, most of them in the form of a yes/no qualitative response. However, quantitative biomarker analysis over certain periods of time is highly demanded for many applications such as the practice of sports or the precise control of the patient status in hospital settings. For this, a feasible combination of fluidic elements and sensor architectures has been sought. In this regard, this paper shows a concise overview of analytical tools based on the use of capillary-driven fluidics taking place on paper or fabric devices integrated with solid-state sensors fabricated by thick film technologies. The main advantages and limitations of the current technologies are pointed out together with the progress towards the development of functional devices. Those approaches reported in the last decade are examined in detail.

Summary Coronavirus disease (COVID‐19) has spread all across the world. Low‐ and medium‐income countries are more affected economically and socially compared to developed countries due to the lack of a rapid, robust, and affordable testing infrastructure. Furthermore, the high cost of real‐time polymerase chain reaction (PCR) system, sophisticated user‐handling procedure, and high expense of the conventional clinical tests are the root causes of the less accessibility of the testing systems to the users. In this study, a COVID‐19 Point‐of‐Care (POC) ecosystem model is proposed for the low‐ and medium‐income countries (or energy deprived countries) that will facilitate the technological development with locally available fabrication components. In addition, the nontechnological development phases have also been discussed, which encompasses the collaboration among academia, local as well as government bodies, and entrepreneurial ventures. In addition, a hypothetical design of a microfluidic paper‐based analytical (μPADs) POC platform is proposed to detect COVID‐19 analyte using unprocessed patient‐derived saliva, which is a miniaturized form‐factor of conventional real‐time polymerase chain reaction (PCR) technique. The device contains four major reaction zones, which are sample zone, buffer zone, loop‐mediated isothermal amplification (LAMP) Master Mix zone, Ethylenediamine tetraacetic acid (EDTA) zone, and sensor zone. To obtain quicker test results and easier operation, a handheld image acquisition technique is introduced in this study. It is hypothesized that in a remote setting, the proposed design could be used as an initial guideline to develop a POC COVID‐19 testing system, which may be simple, easy‐to‐use, and cost‐effective., A COVID‐19 Point‐of‐Care (POC) model is proposed that will facilitate the technological development with locally available fabrication components. To obtain quicker test results and easier operation, a handheld image acquisition technique is introduced. The proposed design could be used as an initial guideline to develop a POC COVID‐19 system, which may be simple, easy‐to‐use, and cost‐effective.

Examination is a vital role to measure the capabilities of students in their learning. Hence, generating question paper in an effective way is a decisive job for educators in educational institution. Using traditional method, it is monotonous and time consuming. Today, Autonomous Examination Paper (AEP) is used to produce exam paper. Many researchers have proposed effective AEPs to be used by educators. This paper aims to investigate about AEP development and to construct AEP in UiTM Cawangan Perlis. As a result, Ad-Hoc Question Paper Application (AQPA) has been developed using Fisher-Yates algorithm to generate questions for exam paper in the university. Evaluation based on Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) reveal that lecturers in the university manage to interact with AQPA and willing to use it as a tool to minimize their workload. However, more improvement must be done on AQPA to be an effective AEP. To conclude, AEP brings significance to educators and can be improved with the latest technology.

With the increasing advance of computer and information technologies, numerous research papers have been published online as well as offline, and as new research fields have been continuingly created, users have a lot of trouble in finding and categorizing their interesting research papers. In order to overcome the limitations, this paper proposes a research paper classification system that can cluster research papers into the meaningful class in which papers are very likely to have similar subjects. The proposed system extracts representative keywords from the abstracts of each paper and topics by Latent Dirichlet allocation (LDA) scheme. Then, the K-means clustering algorithm is applied to classify the whole papers into research papers with similar subjects, based on the Term frequency-inverse document frequency (TF-IDF) values of each paper.

There is a lot of investment in research on displaying texture through a stylus pen. However, little is known about the effect of a stylus pen that can provide the texture of pen and paper on user experience. This study investigated whether there is a change in user experience when using a stylus pen that can provide pen-and-paper texture compared with a typical stylus pen. Experiments involving writing and drawing tasks using three tools (a stylus pen that can provide pen-and-paper texture, a typical stylus pen, and analogue pen and paper) were conducted, and user experiences were compared. After a literature review that examined the factors affecting the experience of using a pen, the experiments were conducted to measure usability and emotions. We found little difference between the typical stylus pen and the stylus pen in providing texture in respect of completion time and amount of error. However, the stylus pen that can provide texture showed results closer to those of the analogue pen and paper, and better results than those of the typical stylus pen in respect of familiarity and overall satisfaction of the usability and emotional variables. Therefore, this study confirmed that user experience is improved when using a stylus pen that can provide the texture of pen and paper. Daar is al baie belê en nagevors in die nabootsing van die pen-op-papier gevoel van 'n elektroniese stilus. Daar is egter weinige inligting beskikbaar oor die effek wat tekstuur op gebruikers-ervaring het. Hierdie studie ondersoek of daar n verandering in gebruikerservaring is wanneer n stilus met pen-op-papier terugvoer gebruik word teenoor n tipiese stilus wat nie terugvoer aan die gebruiker lewer nie. Eksperimente wat skryf en teken-take insluit is met drie gereedskapstukke (n stilus met pen-op-papier terugvoer, n tipiese stilus, en n konvensionele pen op papier) uitgevoer. Na n literatuurstudie van stapel gestuur is, is die eksperimente uitgevoer om die bruikbaarheid en subjektiewe terugvoer van die gebruiker te bepaal. Die resultate toon min verskil tussen n tipiese stilus en een met die pen-op-papier gevoel, beide in terme van die tyd om die take te voltooi en die aantal foute begaan. Die stilus met pen-op-papier terugvoer se vertoning was nader aan die van n tradisionele pen, asook beter subjektiewe terugvoer van die gebruiker in terme van vertroudheid en algehele bevrediging. Die gevolgtrekking is dus dat n stilus met pen-op-papier terugvoer gebruikerservaring verbeter.

International audience; This paper focuses on the problem of the hierarchical multi‐label classification of research papers, which is the task of assigning the set of relevant labels for a paper from a hierarchy, using reduced amounts of labelled training data. Specifically, we study leveraging unlabelled data, which are usually plentiful and easy to collect, in addition to the few available labelled ones in a semi‐supervised learning framework for achieving better performance results. Thus, in this paper, we propose a semi‐supervised approach for the hierarchical multi‐label classification task of research papers based on the well‐known Co‐training algorithm, which exploit content and bibliographic coupling information as two distinct papers' views. In our approach, two hierarchical multi‐label classifiers, are learnt on different views of the labelled data, and iteratively select their most confident unlabelled samples, which are further added to the labelled set. The success of our suggested Co‐training‐based approach lies in two main components. The first is the use of two suggested selection criteria (i.e., Maximum Agreement and Labels Cardinality Consistency) that enforce selecting confident unlabelled samples. The second is the appliance of an oversampling method that rebalances the labels distribution of the initial labelled set, which reduces the reinforcement of the label imbalance issue during the Co‐training learning. The proposed approach is evaluated using a collection of scientific papers extracted from the ACM digital library. Performed experiments show the effectiveness of our approach with regards to several baseline methods.

This research studied the strategies that players use in sequential adversarial games. We took the Rock-Paper-Scissors (RPS) game as an example and ran players in two experiments. The first experiment involved two humans, who played the RPS together for 100 times. Importantly, our payoff design in the RPS allowed us to differentiate between participants who used a random strategy from those who used a Nash strategy. We found that participants did not play in agreement with the Nash strategy, but rather, their behavior was closer to random. Moreover, the analyses of the participants’ sequential actions indicated heterogeneous cycle-based behaviors: some participants’ actions were independent of their past outcomes, some followed a well-known win-stay/lose-change strategy, and others exhibited the win-change/lose-stay behavior. To understand the sequential patterns of outcome-dependent actions, we designed probabilistic computer algorithms involving specific change actions (i.e., to downgrade or upgrade according to the immediate past outcome): the Win-Downgrade/Lose-Stay (WDLS) or Win-Stay/Lose-Upgrade (WSLU) strategies. Experiment 2 used these strategies against a human player. Our findings show that participants followed a win-stay strategy against the WDLS algorithm and a lose-change strategy against the WSLU algorithm, while they had difficulty in using an upgrade/downgrade direction, suggesting humans’ limited ability to detect and counter the actions of the algorithm. Taken together, our two experiments showed a large diversity of sequential strategies, where the win-stay/lose-change strategy did not describe the majority of human players’ dynamic behaviors in this adversarial situation.

Foam forming technology has attracted much attention during the past few years in the paper industry. Its advantages compared to conventional water forming are a new product portfolio and increased process efficiency. To support the paper industry in pushing foam forming technology forward, process simulation is needed to provide supporting data for strategic decision-making and as a basis for equipment dimensioning. This study examined the conversion of an existing wallpaper machine from water to foam forming technology using process simulation. To determine the required process configuration and parameter changes in the existing process, both published and unpublished data on the foam forming process were collected. This paper also describes modeling of the foam phase in the selected simulation software. The suitability of existing paper process equipment for foam was analyzed. Simulations revealed that undisturbed operation with foam requires some equipment modifications and re-arrangements in water circuits. With foam forming, the water balance in both short and long circulation changes remarkably compared to conventional water forming, leading to a large increase in the long circulation volume flows.

To explore whether the brain contains pattern differences in the rock–paper–scissors (RPS) imagery task, this paper attempts to classify this task using fNIRS and deep learning. In this study, we designed an RPS task with a total duration of 25 min and 40 s, and recruited 22 volunteers for the experiment. We used the fNIRS acquisition device (FOIRE-3000) to record the cerebral neural activities of these participants in the RPS task. The time series classification (TSC) algorithm was introduced into the time-domain fNIRS signal classification. Experiments show that CNN-based TSC methods can achieve 97% accuracy in RPS classification. CNN-based TSC method is suitable for the classification of fNIRS signals in RPS motor imagery tasks, and may find new application directions for the development of brain–computer interfaces (BCI).

Paper waste in the mockups design with regular, irregular, and amorphous patterns is a critical problem in digital printing presses. Paper waste reduction directly impacts production costs, generating business and environmental benefits. This problem can be mapped to the two-dimensional irregular bin-packing problem. In this paper, an iterated local search algorithm using a novel neighborhood structure to detect overlaps between amorphous shapes is introduced. This algorithm is used to solve the paper waste problem, modeled as one 2D irregular bin-packing problem. The experimental results show that this approach works efficiently and effectively to detect and correct the overlaps between regular, irregular, and amorphous figures.

Paper-based analytical devices (PADs) and Electrospun Fiber-Based Biosensors (EFBs) have aroused the interest of the academy and industry due to their affordability, sensitivity, ease of use, robustness, being equipment-free, and deliverability to end-users. These features make them suitable to face the need for point-of-care (POC) diagnostics, monitoring, environmental, and quality food control applications. Our work introduces new and experienced researchers in the field to a practical guide for fibrous-based biosensors fabrication with insight into the chemical and physical interaction of fibrous materials with a wide variety of materials for functionalization and biofunctionalization purposes. This research also allows readers to compare classical and novel materials, fabrication techniques, immobilization methods, signal transduction, and readout. Moreover, the examined classical and alternative mathematical models provide a powerful tool for bioanalytical device designing for the multiple steps required in biosensing platforms. Finally, we aimed this research to comprise the current state of PADs and EFBs research and their future direction to offer the reader a full insight on this topic.

Objective: We propose a MATLAB-based tool to convert electrocardiography (ECG) waveforms from paper-based ECG records into digitized ECG signals that is vendor-agnostic. The tool is packaged as an open source standalone graphical user interface (GUI) based application. Methods and procedures: To reach this objective we: (1) preprocess the ECG records, which includes skew correction, background grid removal and linear filtering; (2) segment ECG signals using Connected Components Analysis (CCA); (3) implement Optical Character Recognition (OCR) for removal of overlapping ECG lead characters and for interfacing of patients’ demographic information with their research records or their electronic medical record (EMR). The ECG digitization results are validated through a reader study where clinically salient features, such as intervals of QRST complex, between the paper ECG records and the digitized ECG records are compared. Results: Comparison of clinically important features between the paper-based ECG records and the digitized ECG signals, reveals intra- and inter-observer correlations of 0.86–0.99 and 0.79–0.94, respectively. The kappa statistic was found to average at 0.86 and 0.72 for intra- and inter-observer correlations, respectively. Conclusion: The clinically salient features of the ECG waveforms such as the intervals of QRST complex, are preserved during the digitization procedure. Clinical and Healthcare Impact: This open-source digitization tool can be used as a research resource to digitize paper ECG records thereby enabling development of new prediction algorithms to risk stratify individuals with cardiovascular disease, and/or allow for development of ECG-based cardiovascular diagnoses relying upon automated digital algorithms.

The fabrication of underwater devices is necessary for the exploration of water environments and interactions in the Human–Computer Interaction (HCI) field. However, there are fewer approaches to support prototyping used in water environments. The existing prototype methods lack systematic waterproof treatments and provide insufficient software for balance and buoyancy analysis. To address these limitations, we present ElectroPaper, a new approach for the design and fabrication of prototypes used in water environments (surface or beneath) with paper-based electronic interfaces with a crease layer, hardware distribution layer, and hollow-out layer to support physical properties, such as waterproofing, foldability, and conformability. The approach includes a computational design tool for assisting in balance analysis, three-dimensional (3D) model unfolding, and circuit drawing. We describe the design and fabrication process and provide several example applications to illustrate the feasibility and utility of our approach. ElectroPaper provides an inexpensive and effective medium for the fabrication of customized digital prototypes for water environment use.

Escherichia coli has been known to cause a variety of infectious diseases. The conventional enzyme-linked immunosorbent assay (ELISA) is a well-known method widely used to diagnose a variety of infectious diseases. This method is expensive and requires considerable time and effort to conduct and complete multiple integral steps. We previously proposed the use of paper-based ELISA to rapidly detect the presence of E. coli. This approach has demonstrated utility for point-of-care (POC) urinary tract infection diagnoses. Paper-based ELISA, while advantageous, still requires the execution of several procedural steps. Here, we discuss the design and experimental implementation of a turntable paper-based device to simplify the paper-based ELISA protocols for the detection of E. coli. In this process, antibodies or reagents are preloaded onto zones of a paper-based device and allowed to dry before use. We successfully used this device to detect E. coli with a detection limit of 105 colony-forming units (colony-forming unit [CFU])/mL.

Paper mills are among the most polluting industries, responsible for many organic and inorganic compounds emissions. The fibres electro-kinetic features strongly affect the ability to retain fillers since the fillers–fibres interactions are charge induced. The control and the prediction of these parameters would represent a precious aid for process management, allowing the fillers retention enhancement, a lower environmental impact and the paper sheet properties streamlining. The work presented deals with the implementation and training of four artificial neural networks (ANNs) for the prediction of the main electrochemical and physical features of cellulose pulp and paper. First, two ANNs predict the electrochemical parameters. Following, they were applied to predict the paper sheet properties and fillers retention. The neural models implemented showed outstanding prediction performance, with R2 in the order of 0.999 and a low mean error. The results demonstrate how Artificial Neural Networks may be a valuable instrument for paper mill pollutant reduction. However, they suggest a more inclusive investigation for a better fibres behaviour representation.

The final quality of tissue paper depends on the parameters of the continuous transformation process and maintenance of rolls of a tissue paper machine. For a better quality, outer surfaces of the rolls must always be clean, and their roughness should be maintained in the same way. A Doctor Blade provides for such requirements. Our work is focused on the development of a pneumatic oscillator that moves a Doctor Blade in order to scrape the outer surfaces of the rolls. It is based on a low-friction double-effect cylinder with a through-rod. The integration of two limit switch valves, one for each head of the cylinder, and a smart pneumatic circuit to obtain a very low speed of the piston at constant load, represents the novelty of the proposed device. The piston of the cylinder allows for alternatively switching the limit switches that command the 5/2 air-operated pilot valve of the cylinder. The oscillator’s design, pneumatic circuit, and working principle are detailed. Experimental tests on the conceived limit switch and on the first prototype of the pneumatic oscillator validated the effectiveness of the proposed solution, the technical feasibility of the device, and the absence of stick-slip during the oscillatory motion at low speed.

Formulaic expressions, such as 'in this paper we propose', are helpful for authors of scholarly papers because they convey communicative functions; in the above, it is showing the aim of this paper'. Thus, resources of formulaic expressions, such as a dictionary, that could be looked up easily would be useful. However, forms of formulaic expressions can often vary to a great extent. For example, 'in this paper we propose', 'in this study we propose' and 'in this paper we propose a new method to' are all regarded as formulaic expressions. Such a diversity of spans and forms causes problems in both extraction and evaluation of formulaic expressions. In this paper, we propose a new approach that is robust to variation of spans and forms of formulaic expressions. Our approach regards a sentence as consisting of a formulaic part and non-formulaic part. Then, instead of trying to extract formulaic expressions from a whole corpus, by extracting them from each sentence, different forms can be dealt with at once. Based on this formulation, to avoid the diversity problem, we propose evaluating extraction methods by how much they convey specific communicative functions rather than by comparing extracted expressions to an existing lexicon. We also propose a new extraction method that utilises named entities and dependency structures to remove the non-formulaic part from a sentence. Experimental results show that the proposed extraction method achieved the best performance compared to other existing methods., 21 pages, 11 figures

This paper presents a critical review of laser pyrolysis. Although this technology is almost 60 years old, in literature many researchers, both from academia and industry, are still developing and improving it. On the contrary industrial applications are struggling to take off, if not in very restricted areas, although the technology has undoubted advantages that justify future development. The aim of this work consists in analysing a representative pool of scientific papers (230) and patents (121), from the last 20 years, to have an overview about the evolution of the method and try to understand the efforts spent to improve this technology effectively in academia and in industry. This study is important to provide a complete review about the argument, still missing in the literature. The objective is to provide an overview sufficiently broad and representative in the sources and to capture all the main ways in which laser pyrolysis has been used and with what distribution. The main focuses of the study are the analyses of the functions carried out by laser technologies, the application fields, and the types of used laser (i.e. models, power and fluence). Among the main results, the study showed that the main use of laser pyrolysis is to produce nanoparticles and coatings, the main materials worked by laser pyrolysis are silicon and carbon dioxide and the main searched properties in the products of laser pyrolysis are catalysts activity and electrical conductivity. CO2 lasers are the most used and the have high versatility compared to others. In conclusion, the study showed that laser pyrolysis is a consolidated technology within its main application fields (nanoparticles and coatings) for several years. Within this context, the technology has been developed on very different sizes and processes, obtaining a very wide range of results. Finally, these results may also have stimulated new areas of experimentation that emerged mainly in recent years and which concern biomedical applications, additive manufacturing, and waste disposal. Graphical abstract

This work was supported by the research group "Ciencia y Cultura del Patrimonio"(HUM-1057) and the Ministerio de Economia y Competitividad in the framework of the I + D research project "Nuevas alternativas al conocimiento de los materiales y los procesos de conservacion y restauracion de obra grafica y patrimonio documental"(REF.MAT2014-58659-P). Funding for open access charge: Universidad de Granada/CBUA., This study offers a new tool to restorers to bleach paper documents: artificial light radiation (ALR). It is a controlled method that is an alternative to other highly aggressive procedures that have been applied to cellulose based media. Its main novelty is its intrinsic lighting characteristics which guarantee uniformity and avoid heating the paper. The results of testing this new tool were compared to those of bleaching with hydrogen peroxide (H2O2). Each of the two methods was applied to five different types of widely used paper: Arches and Somerset (brands serving for engraving and digital printing), Ingres (drawing), press-type (magazines and newspapers) and Bible-type (historical documents). Once characterised by physico-mechanical, chemical and optical parameters (grammage, thickness, specular gloss, optical properties, air permeance, tensile properties, determining the pH of both surface and aqueous extracts), the different papers were bleached by the two methods according to pre-established criteria. Finally, to determine the evolution and effects of the two treatments, the papers were subjected to accelerated ageing., Research group "Ciencia y Cultura del Patrimonio" HUM-1057, Spanish Government MAT2014-58659-P, Universidad de Granada/CBUA

PurposeThis position paper urges a drive towards clarity in the key definitions, terminologies and habits of speech associated with digital engineering and building information modelling (BIM). The ultimate goal of the paper is to facilitate the move towards arriving at an ideal definition for both concepts.Design/methodology/approachThis paper takes the “explanation building” review approach in providing prescriptive guidelines to researchers and industry practitioners. The aim of the review is to draw upon existing studies to identify, describe and find application of principles in a real-world context.FindingsThe paper highlights the definitional challenges surrounding digital engineering and BIM in Australia, to evoke a debate on BIM and digital engineering boundaries, how and why these two concepts may be linked, and how they relate to emerging concepts.Originality/valueThis is the first scholarly attempt to clarify the definition of digital engineering and address the confusion between the concepts of BIM and digital engineering.

The handling of cultural heritage objects has become a highly debated topic in the last decade. The work and outcomes described in this paper are aimed to provide objective data to assist in making appropriate decisions as to whether or not wearing gloves is appropriate in a given situation. The forensic fingermark development techniques of 1,2-indandione and single metal deposition II were used to investigate the efficacy of handwashing and glove use to improve the information available when deciding whether to use gloves when handling paper objects. It was found that fingermarks did not permeate through polymer glove types but could through cotton gloves. It was also shown that the amounts of observable fingermark residues were greater 5 min after handwashing than if handwashing had not occurred, undermining previous arguments for not wearing gloves if hands could be washed before object handling., Graphical abstract Image 1, Highlights • Fingermarks can be transferred onto paper even after washing hand. • Developed fingermark residues initially increase after handwashing. • Handwashing is not sufficient to prevent fingermark contamination of artifacts. • Fingermark residues can be transferred from surfaces to gloves to paper. • Some antibacterial gels can increase the amount of fingermark residue deposited.

3D city models are mainly viewed on computer screens, but many municipalities also use 3D printing to make urban planning tangible. Since 3D color printing is still comparatively expensive and the colors often fade over time, many of these models are monochrome. Here, color textured paper models offer an inexpensive and under-appreciated alternative. In this paper, a greedy algorithm adapted to CityGML building models is presented, which creates print templates for such paper models. These 2D layouts consist of cut edges and fold edges that bound polygons of a building. The polygons can be textured or left blank depending on the existence of CityGML textures. Glue tabs are attached to cut edges. In addition to the haptic 3D visualization, the quality of the 3D models can sometimes be better assessed on the basis of the print templates than from a perspective projection. The unfolding procedure was applied to parts of the freely available CityGML model of Berlin as well as to parts of models of the cities of Dortmund and Krefeld.

In many manufacturing industries, the production process involves the production of objects and the cutting of such objects into smaller pieces in order to meet a specific demand. In the optimization of these processes, one can identify the lot sizing and the cutting stock problems. In the literature, these problems are mostly separately dealt. However, treating these two problems in an integrated approach can decrease overall costs. In this research, we deal with the coupled lot sizing and cutting stock problem. It is proposed a mathematical formulation for the production and cutting of paper based on a real case, which considers setup costs and limited machine capacity in the production process of the objects. For the solution of the proposed model, we used a column generation approach and a relax-and-fix heuristic. Computational tests were carried out in order to analyze the methodology used in the resolution of the model. The results showed to be competitive in a reasonable computational time.

This work reports on fully integrated “sample‐in‐signal‐out” microfluidic paper‐based analytical devices (μPADs) relying on bioluminescence resonance energy transfer (BRET) switches for analyte recognition and colorimetric signal generation. The devices use BRET‐based antibody sensing proteins integrated into vertically assembled layers of functionalized paper, and their design enables sample volume‐independent and fully reagent‐free operation, including on‐device blood plasma separation. User operation is limited to the application of a single drop (20–30 μL) of sample (serum, whole blood) and the acquisition of a photograph 20 min after sample introduction, with no requirement for precise pipetting, liquid handling, or analytical equipment except for a camera. Simultaneous detection of three different antibodies (anti‐HIV1, anti‐HA, and anti‐DEN1) in whole blood was achieved. Given its simplicity, this type of device is ideally suited for user‐friendly point‐of‐care testing in low‐resource environments.

The symbol error rate (SER) performance analysis of a broadcast underlay cognitive radio (CR) network, under Nakagami-m fading channels is studied in this paper. Particularly, the underlay CR network is studied as a closed loop multiple antenna system, presented with dirty paper coding (DPC) approach with the aim to allowing the secondary user (SU) transmission to utilize the spectrum resources efficiently and avoid interference to the primary user (PU) receiver. The proposed approach is capable of achieving the same performance as that of the zero-forcing (ZF) algorithm over Nakagami-m fading channels at the SU receiver. We further show with the simulation results that the SER and bit error rate (BER) performances of the PU under Nakagami-m and Rician fading channels are significantly improved for the proposed study. Finally, we optimize the power allocation of the PU transmitter and approximately achieve 3 dB performance gain over Nakagami-m fading for the SU receiver.

Due to the increasing size of s (s), their design and optimization phases (i.e., ) grow increasingly complex. At design time, a large design space needs to be explored to find an implementation that fulfills all specifications and then optimizes metrics like energy, area, delay, reliability, etc. At run time, a large configuration space needs to be searched to find the best set of parameters (e.g., voltage/frequency) to further optimize the system. Both spaces are infeasible for exhaustive search typically leading to heuristic optimization algorithms that find some trade-off between design quality and computational overhead. ML can build powerful models that have successfully been employed in related domains. In this survey, we categorize how () may be used and is used for design-time and run-time optimization and exploration strategies of s. A meta-study of published techniques unveils areas in that are well-explored and underexplored with, as well as trends in the employed algorithms. We present a comprehensive categorization and summary of the state of the art on for. Finally, we summarize remaining challenges and promising open research directions.

Our life depends more and more on intelligent machines that help us carry out our work or have completely taken our place in tasks which used to be wholly human. This also applies to the field of care and assistance for the sick and fragile: indeed, carebots are increasingly present by the patient’s bed and can collaborate in care work. However carebots are not objects like others that we use in our everyday life: unlike the tools through which we do things, robots have (and will more and more have) some level of autonomy. We intend to consider the type of relationship it is right to build with these devices and ask whether intelligent robots deserve some moral and legal relevance. The robot may be seen as a slave or an entity with which we can form friendships or loving relationships: we will maintain that it is our responsibility to prepare for future scenarios in which increasingly intelligent, autonomous machines will be not mere tools, but significant life companions.

Internet of Things (IoT) devices are increasingly being found in civilian and military contexts, ranging from smart cities and smart grids to Internet-of-Medical-Things, Internet-of-Vehicles, Internet-of-Military-Things, Internet-of-Battlefield-Things, etc. In this paper, we survey articles presenting IoT security solutions published in English since January 2016. We make a number of observations, including the lack of publicly available IoT datasets that can be used by the research and practitioner communities. Given the potentially sensitive nature of IoT datasets, there is a need to develop a standard for sharing IoT datasets among the research and practitioner communities and other relevant stakeholders. Thus, we posit the potential for blockchain technology in facilitating secure sharing of IoT datasets (e.g., using blockchain to ensure the integrity of shared datasets) and securing IoT systems, before presenting two conceptual blockchain-based approaches. We then conclude this paper with nine potential research questions. Keywords: Blockchain, Blockchain security, Collaborative security, Internet-of-military things, IoT dataset, IoT self-healing, IoT security, Intrusion-prevention system, Predictive IoT security, Predictive security

Tissue paper is defined by its physical and mechanical properties, namely: high softness, low grammage, high bulk and high liquid absorption capacity. It is expected that the production of tissue paper will continue to grow, which increases the importance of better understanding the processes involved in its production as well as its optimization [1] . The experimental data presented in this article, are the physical-mechanical characterization of a group of 13 industrial base tissue papers, which were collected at the end of the tissue paper machine on Portuguese factories. These samples vary in grammage, composition and creping [2] , enabling a later evaluation of the crepe type [3] and its relationship with the final properties of the tissue paper.

PURPOSE Generalizability, reproducibility and objectivity are critical elements that need to be considered when translating machine learning models into clinical practice. While a large body of literature has been published on machine learning methods for segmentation of brain tumors, a systematic evaluation of paper quality and reproducibility has not been done. We investigated the use of “Transparent Reporting of studies on prediction models for Individual Prognosis Or Diagnosis” (TRIPOD) items, among papers published in this relatively new and growing field. METHODS According to PRISMA a literature review was performed on four databases, Ovid Embase, Ovid MEDLINE, Cochrane trials (CENTRAL) and Web of science core-collection first in October 2020 and a second time in February 2021. Keywords and controlled vocabulary included artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, and glioblastoma. The publications were assessed in order to the TRIPOD items. RESULTS 37 publications from our database search were screened in TRIPOD and yielded an average score of 12.08 with the maximum score being 16 and the minimum score 7. The best scoring item was interpretation (item 19) where all papers scored a point. The lowest scoring items were the title, the abstract, risk groups and the model performance (items number 1, 2, 11 and 16), where no paper scored a point. Less than 1% of the papers discussed the problem of missing data (item 9) and the funding of research (item 22). CONCLUSION TRIPOD analysis showed that a majority of the papers do not score high on critical elements that allow reproducibility, translation, and objectivity of research. An average score of 12.08 (40%) indicates that the publications usually achieve a relatively low score. The categories that were consistently poorly described include the ML network description, measuring model performance, title details and inclusion of information into the abstract.

We present a rapid (

In the age of Evidence-Based Medicine, Clinical Guidelines (CGs) are recognized to be an indispensable tool to support physicians in their daily clinical practice. Medical Informatics is expected to play a relevant role in facilitating diffusion and adoption of CGs. However, the past pioneering approaches, often fragmented in many disciplines, did not lead to solutions that are actually exploited in hospitals. Process Mining for Healthcare (PM4HC) is an emerging discipline gaining the interest of healthcare experts, and seems able to deal with many important issues in representing CGs. In this position paper, we briefly describe the story and the state-of-the-art of CGs, and the efforts and results of the past approaches of medical informatics. Then, we describe PM4HC, and we answer questions like how can PM4HC cope with this challenge? Which role does PM4HC play and which rules should be employed for the PM4HC scientific community? This research received no external funding

VLDB is a premier annual international forum for data management, and it targets database researchers, users, vendors, and application developers. Each year the conference covers current issues in data management and database systems, which remain key technological cornerstones of emerging applications of the twenty-first century. VLDB 2013 took place at the picturesque town of Riva del Garda in Italy. We received a total of 559 research paper submissions, and of these 127 submissions were accepted

To improve the accuracy of user implicit rating prediction, we combine the traditional latent factor model (LFM) and bidirectional gated recurrent unit neural network (BiGRU) model to propose a hybrid model that deeply mines the latent semantics in the unstructured content of the text and generates a more accurate rating matrix. First, we utilize the user’s historical behavior (favorites records) to build a user rating matrix and decompose the matrix to obtain the latent factor vectors of users and literature. We also apply the BERT model for word embedding of the research papers to obtain the sequence of word vectors. Then, we apply the BiGRU with the user attention mechanism to mine the research paper textual content and to generate the new literature latent feature vectors that are used to replace the original literature latent factor vectors decomposed from the rating matrix. Finally, a new rating matrix is generated to obtain users’ ratings of noninteractive research papers and to generate the recommendation list according to the user latent factor vector. We design experiments on the real datasets and verify that the research paper recommendation model is superior to traditional recommendation models in terms of precision, recall, F1-value, coverage, popularity and diversity.

Automatic data processing represents the future for the development of any system, especially in scientific research. In this paper, we describe one of the automatic classification methods applied to scientific research as a supervised learning task. Throughout the process, we identify the main features that are used as keys to play a significant role in terms of predicting the new rank under the supervised learning setup. First, we propose an overview of the work that has been realized in ranking scientific research papers. Second, we evaluate and compare some of state-of-the-art for the classification by supervised learning, semi-supervised learning and non-supervised learning. During the preliminary tests, we have obtained good results for performance on realistic corpus then we have compared performance metrics, such as NDCG, MAP, GMAP, F-Measure, Precision and Recall in order to define the influential features in our work. Keywords: Scientific research, Ranking scientific research papers, Data mining, Supervised learning, Multilayer perceptron algorithm

Attention to the environment and its problems has undergone unprecedented growth in recent years [...]

Over the past few years, what might not unreasonably be described as a true revolution has taken place in the field of machine vision, radically altering the way many things had previously been done and offering new and exciting opportunities for those able to quickly embrace and master the new techniques. Rapid developments in machine learning, largely enabled by faster GPU-equipped computing hardware, has facilitated an explosion of machine vision applications into hitherto extremely challenging or, in many cases, previously impossible to automate industrial tasks. Together with developments towards an internet of things and the availability of big data, these form key components of what many consider to be the fourth industrial revolution. This transformation has dramatically improved the efficacy of some existing machine vision activities, such as in manufacturing (e.g. inspection for quality control and quality assurance), security (e.g. facial biometrics) and in medicine (e.g. detecting cancers), while in other cases has opened up completely new areas of use, such as in agriculture and construction (as well as in the existing domains of manufacturing and medicine). Here we will explore the history and nature of this change, what underlies it, what enables it, and the impact it has had - the latter by reviewing several recent indicative applications described in the research literature. We will also consider the continuing role that traditional or classical machine vision might still play. Finally, the key future challenges and developing opportunities in machine vision will also be discussed.

This paper proposes an efficient decision support tool for the optimal production scheduling of a variety of paper grades in a paper machine. The tool is based on a continuous-time scheduling model and generalized disjunctive programming. As the full-space scheduling model corresponds to a large-scale mixed integer linear programming model, we apply data analytics techniques to reduce the size of the decision space, which has a profound impact on the computational efficiency of the model and enables us to support the solution of large-scale problems. The data-driven model is based on an automated method of identifying the forbidden and recommended paper grade sequences, as well as the changeover durations between two paper grades. The results from a real industrial case study show that the data-driven model leads to good results in terms of both solution quality and CPU time in comparison to the full-space model.