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The use of nanocelluloses as strength-enhancing additives in papermaking is widely known since both cellulose nanofibers (CNF) and nanocrystals (CNC) present similar composition than paper but their exceptional properties in the nanometer scale confers a paper quality enhancement. However, some agglomeration problems in CNF and CNC through hydrogen bonding cause a lower improvement of mechanical properties of paper. Therefore, a better dispersion of both nanocelluloses can maximize their effect on paper properties, thus reducing the needed dose to get the same increment in tensile strength and then reducing material costs. To ease the implementation of these nanocelluloses in the production process of recycled paper, typically used operations of these industries have been used. Among them, those devoted to improve the homogeneous mixture of nanocellulose in the pulp suspension have been assessed. Firstly, pulping conditions were studied, including pulping time, temperature and need for soaking as variables. Secondly, some dispersing agents used in papermaking were considered, studying the effect of different types and doses. The highest tensile strength of paper was achieved by applying long pulping times (60 min), getting increments up to 30% with the use of soaking and polyacrylamide as retention system. However, with the use of a low dose of a dispersing agent (0.003%), tensile index can be still increased up to 20.6% avoiding these long times. This study can be of great interest of those researchers trying to implement the use of nanocelluloses as strength additive in papermaking., Economy and Competitiveness Ministry of Spain (MINECO), Depto. de Ingeniería Química y de Materiales, Fac. de Ciencias Químicas, TRUE, pub

Context and motivation: AI-based systems (i.e., systems integrating some AI model or component) are becoming pervasive in society. A number of characteristics of AI-based systems challenge classical requirements engineering (RE) and raise questions yet to be answered. Question: This vision paper inquires the role that RE should play in the development of AI-based systems with a focus on three areas: roles involved, requirements’ scope and non-functional requirements. Principal Ideas: The paper builds upon the vision that RE shall become the cornerstone in AI-based system development and proposes some initial ideas and roadmap for these three areas. Contribution: Our vision is a step towards clarifying the role of RE in the context of AI-based systems development. The different research lines outlined in the paper call for further research in this area., This paper is part of the project TED2021-130923B-I00, funded by MCIN/AEI/10.13039/501100011033 and the European Union “NextGenera-tionEU”/PRTR., Peer Reviewed, Postprint (author's final draft)

Cellulose nanofibers (CNF) have increasing relevance in different applications, for instance, in the paper industry as a sustainable strength additive. This application is especially beneficial for recycled paper, which reaches higher product quality despite its limitations. CNF change paper properties and also can affect the production process, especially the drainage stage, in which retention additives (RA) are commonly used to promote interaction of cellulose fibers. CNF probably interact with fibers and RA, affecting the drainage stage. However, these interactions vary depending on the type and flocculation mechanism of RA. This research is aimed at establishing possible synergies between CNF and RA to improve paper strength, avoiding negative effects on the drainage process. No further RA were used to retain CNF, taking advantage of the RA already used in the process. Polyvinylamine, chitosan, cationic starch, C-PAM, and C-PAM-B were selected as RA. CNF from eucalyptus kraft pulp and corn stalk organosolv pulp were tested. Strength properties of laboratory sheets were studied, and interactions were assessed by measuring Z-potential. Synergies between PVA, chitosan, C-PAM, and C-PAM-B with CNF were found. Drainage time decreased ranging between 30 and more than 40% using CNF. Strength depended on RA and formation quality. Among the studied options, CNF with C-PAM-B or chitosan resulted in the best formation and higher strengths with a significant drainage time reduction., Economy and Competitiveness Ministry of Spain (MINECO), Depto. de Ingeniería Química y de Materiales, Fac. de Ciencias Químicas, TRUE, pub

Intelligent Tutoring Systems (ITS) can only respond adaptively to the digital learning activities of the students. If students are learning offline without any digital devices, they have little or no means to receive personalized learning materials with the help of intelligent systems. This paper proposes a Paper-Digital Integration System that can provide offline learners equitable access to ITS capabilities by looking at their work on paper and giving personalized printable feedback. We analyzed data from a paper algebra assessment of N = 17 students and found mistakes that may generalize and help us offer adaptive paper-based recommendations to students. Our analysis showed us some specific algebra mistakes that may help in providing intelligent feedback., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Design Aesthetics

Research on text comprehension in relation to the reading medium (paper or screen) has mainly involved undergraduate or high school students. To advance current knowledge on the effects of reading medium, this longitudinal study focused on beginner readers, specifically, the role of precursors in first graders’ comprehension of narrative and expository linear texts from reading on paper and computer screen. Working memory and inference skills as cognitive precursors and basic digital skills were measured at the end of preschool (T1); reading text comprehension and word reading, as a control variable, were measured at the end of the first grade (T2). Sixty-three children participated in total. The first graders read four texts, one narrative and one expository, on both paper and computer screen, in a counterbalanced order. Results showed no main effects of the reading medium or text genre, but the interactive effect of these variables was significant. At T2, the children had higher comprehension scores after reading narrative than descriptive texts from paper. In addition, reading from the screen was preferred at post-test, after all texts were presented. As precursors, working memory and inference skills predicted both printed and digital text comprehension. In contrast, basic digital skills predicted only digital text comprehension after controlling for medium, text genre, and word reading.

Adenosine-5'-triphosphate (ATP) is the primary energy carrier in all living organisms, and its detection in living cells represents a well-established approach. ATP-driven bioluminescence (BL) relying on the D-luciferin-luciferase reaction is a bioanalytical tool widely employed for monitoring hygiene and microbial contamination of foods.Here, we report a straightforward method for ATP BL detection using an ATP sensing paper fabricated with an alternative freeze-dry procedure. The assay can be easily implemented in laboratories equipped with (i) freeze-drying, wax printing, and 3D printing technologies and (ii) instrumentation for BL detection such as benchtop luminometers and portable light detectors including a smartphone camera without the need for additional equipment.

This position paper summarizes key results from the workshop “Professional Ethics at the End of Life” held from 27 May to 1 June 2019 in Greifswald (Germany). An international group of 13 young scholars from six countries and various healthcare-related disciplines (e.g. nursing, medicine, philosophy, theology, gerontology, psychology) met to discuss ethical issues in patient care at the end of life. A special focus of the workshop was on international comparisons and the roles and moral obligations of health-care professionals. The need for interprofessional dialogue and family involvement emerged as key factors for patient-centred, holistic care at the end of life. Further main results were collected from the participants subsequent to the workshop, arranged regarding content and further circulated and revised within the group. The position paper, therefore, represents the views of young health-care researchers and practitioners with respect to the current status and further perspectives of professionalism and interprofessional care at the end of life. It can serve as a starting point for scientific and political debates on moral obligations, professional practice and health-care structures.

Research on academic paper ranking has received great attention in recent years, and many algorithms have been proposed to automatically assess a large number of papers for this purpose. How to evaluate or analyse the performance of these ranking algorithms becomes an open research question. Theoretically, evaluation of an algorithm requires to compare its ranking result against a ground truth paper list. However, such ground truth does not exist in the field of scholarly ranking due to the fact that there does not and will not exist an absolutely unbiased, objective, and unified standard to formulate the impact of papers. Therefore, in practice researchers evaluate or analyse their proposed ranking algorithms by different methods, such as using domain expert decisions (test data) and comparing against predefined ranking benchmarks. The question is whether using different methods leads to different analysis results, and if so, how should we analyse the performance of the ranking algorithms? To answer these questions, this study compares among test data and different citation-based benchmarks by examining their relationships and assessing the effect of the method choices on their analysis results. The results of our experiments show that there does exist difference in analysis results when employing test data and different benchmarks, and relying exclusively on one benchmark or test data may bring inadequate analysis results. In addition, a guideline on how to conduct a comprehensive analysis using multiple benchmarks from different perspectives is summarised, which can help provide a systematic understanding and profile of the analysed algorithms.

This chapter examines the short term production, transportation and inventory planning problems encountered in the fine-paper industry. After positioning the problems in the context of a general supply chain planning system for the pulp and paper industry, a comprehensive synchronized production-distribution model is gradually developed. First, a model for the dynamic lot-sizing of intermediate products on a single paper machine with a predetermined production cycle is proposed. The model also plans the production and inventory of finished products. Then, we consider the lot-sizing of intermediate products on multiple parallel paper machines with a predetermined production sequence. Finally, simultaneous production and distribution planning for a single mill multiple distribution centers network is studied by considering different transportation modes between the mill and its Distribution Centers (DCs).

Under current economic conditions, several companies have decided to implement collaborations with their suppliers, distributors, and retailers, in order to share products and information efficiently as well as reduce their operational costs. Even traditional industries such as the forest industry are now seeking for new business relationships to outperform the competition. In this article, we propose a case study of collaboration between a pulp and paper producer and a wholesaler. In particular, we describe a methodology used to compare different collaborative approaches. We show that this methodology is useful to take multiple operational parameters and constraints into account, as well as to update planning decisions over time. We also discuss the different results obtained for the case study. We demonstrate that collaborative approaches such as CPFR and VMI may reduce network costs, but the savings obtained with these strategies must be distributed fairly. Furthermore, we analyze the use of several incentives to increase the value and strength of the collaboration., 0

This chapter examines the short term production, transportation and inventory planning problems encountered in the fine-paper industry. After positioning the problems in the context of a general supply chain planning system for the pulp and paper industry, a comprehensive synchronized production-distribution model is gradually developed. First, a model for the dynamic lot-sizing of intermediate products on a single paper machine with a predetermined production cycle is proposed. The model also plans the production and inventory of finished products. Then, we consider the lot-sizing of intermediate products on multiple parallel paper machines with a predetermined production sequence. Finally, simultaneous production and distribution planning for a single mill multiple distribution centers network is studied by considering different transportation modes between the mill and its Distribution Centers (DCs).

Under current economic conditions, several companies have decided to implement collaborations with their suppliers, distributors, and retailers, in order to share products and information efficiently as well as reduce their operational costs. Even traditional industries such as the forest industry are now seeking for new business relationships to outperform the competition. In this article, we propose a case study of collaboration between a pulp and paper producer and a wholesaler. In particular, we describe a methodology used to compare different collaborative approaches. We show that this methodology is useful to take multiple operational parameters and constraints into account, as well as to update planning decisions over time. We also discuss the different results obtained for the case study. We demonstrate that collaborative approaches such as CPFR and VMI may reduce network costs, but the savings obtained with these strategies must be distributed fairly. Furthermore, we analyze the use of several incentives to increase the value and strength of the collaboration., 0

Despite technological advances in the analysis of digital images for medical consultations, many health information systems lack the ability to correlate textual descriptions of image findings linked to the actual images. Images and reports often reside in separate silos in the medical record throughout the process of image viewing, report authoring, and report consumption. Forward-thinking centers and early adopters have created interactive reports with multimedia elements and embedded hyperlinks in reports that connect the narrative text with the related source images and measurements. Most of these solutions rely on proprietary single-vendor systems for viewing and reporting in the absence of any encompassing industry standards to facilitate interoperability with the electronic health record (EHR) and other systems. International standards have enabled the digitization of image acquisition, storage, viewing, and structured reporting. These provide the foundation to discuss enhanced reporting. Lessons learned in the digital transformation of radiology and pathology can serve as a basis for interactive multimedia reporting (IMR) across image-centric medical specialties. This paper describes the standard-based infrastructure and communications to fulfill recently defined clinical requirements through a consensus from an international workgroup of multidisciplinary medical specialists, informaticists, and industry participants. These efforts have led toward the development of an Integrating the Healthcare Enterprise (IHE) profile that will serve as a foundation for interoperable interactive multimedia reporting.

This issue of Reaction Kinetics, Mechanisms and Catalysis hosts two collections of articles with special topics. In one way, or another, both of them are connected to the XV International Conference on Fundamental and Applied Aspects of Physical Chemistry—PHYSICAL CHEMISTRY 2021, which was held in Belgrade, Serbia by an online method between September 20 and 24, 2021.

Context: Peer review is a key activity intended to preserve the quality and integrity of scientific publications. However, in practice it is far from perfect. Objective: We aim at understanding how reviewers, including those who have won awards for reviewing, perform their reviews of software engineering papers to identify both what makes a good reviewing approach and what makes a good paper. Method: We first conducted a series of interviews with recognised reviewers in the software engineering field. Then, we used the results of those interviews to develop a questionnaire used in an online survey and sent out to reviewers from well-respected venues covering a number of software engineering disciplines, some of whom had won awards for their reviewing efforts. Results: We analyzed the responses from the interviews and from 175 reviewers who completed the online survey (including both reviewers who had won awards and those who had not). We report on several descriptive results, including: Nearly half of award-winners (45%) are reviewing 20+ conference papers a year, while 28% of non-award winners conduct that many. The majority of reviewers (88%) are taking more than two hours on journal reviews. We also report on qualitative results. Our findings suggest that the most important criteria of a good review is that it should be factual and helpful, which ranked above others such as being detailed or kind. The most important features of papers that result in positive reviews are a clear and supported validation, an interesting problem, and novelty. Conversely, negative reviews tend to result from papers that have a mismatch between the method and the claims and from papers with overly grandiose claims. Further insights include, if not limited to, that reviewers view data availability and its consistency as being important or that authors need to make their contribution of the work very clear in their paper. Conclusions: Based on the insights we gained through our study, w, open access

This paper presents a demonstration of our PAAMS 2021 paper using data-driven analysis of airport terminal operations and An Agent-based Airport Terminal Operations Model Simulator (AATOM). The goal of this paper is to demonstrate and analyze the impact of the current COVID-19 and future pandemic-related measures on airport terminal operations and to identify plans that airport management agents can take into account to control the flow of passengers in a safe, efficient, secure and resilient way. To analyze the impact of the identified COVID-19 measures on the airport operations, the existing agent-based AATOM model was need to be modified in order to implement these measures. In this paper, we illustrate a demo of a developed simulator tool by investigating the effects of different degrees of physical distancing rules among agents on the performances of the airport. In the demo session the attendees will have the possibility to (i) work with the simulator tool on different relevant parameters regarding different sections and agents in the airport; (ii) view and analyze different performance indicator analyzers of the simulator., Air Transport & Operations

In the Diagrams 2020 conference, we (Mikkel Willum Johansen and myself) reported on our first successes with machine-learning agents identifying and counting diagrams in mathematical papers. One year later, we have progressed, and in this paper I present and discuss ways of creating evidence on the use of diagrams in mathematical publications. Studying a corpus of mathematical journals from the early 21st century and focusing on the intensity of their reliance on diagrams, I explore different means of measuring the use of diagrams as a precursor to further studying their integration into the mathematical argument.

This paper presents a demonstration of our PAAMS 2021 paper using data-driven analysis of airport terminal operations and An Agent-based Airport Terminal Operations Model Simulator (AATOM). The goal of this paper is to demonstrate and analyze the impact of the current COVID-19 and future pandemic-related measures on airport terminal operations and to identify plans that airport management agents can take into account to control the flow of passengers in a safe, efficient, secure and resilient way. To analyze the impact of the identified COVID-19 measures on the airport operations, the existing agent-based AATOM model was need to be modified in order to implement these measures. In this paper, we illustrate a demo of a developed simulator tool by investigating the effects of different degrees of physical distancing rules among agents on the performances of the airport. In the demo session the attendees will have the possibility to (i) work with the simulator tool on different relevant parameters regarding different sections and agents in the airport; (ii) view and analyze different performance indicator analyzers of the simulator., Aerospace Transport & Operations

This position paper focuses on the problem of building dialogue systems for people who have lost the ability to communicate via speech, e.g., patients of locked-in syndrome or severely disabled people. In order for such people to communicate to other people and computers, dialogue systems that are based on brain responses to (imagined) speech are needed. A speech-based dialogue system typically consists of an automatic speech recognition module and a speech synthesis module. In order to build a dialogue system that is able to work on the basis of brain signals, a system needs to be developed that is able to recognize speech imagined by a person and can synthesize speech from imagined speech. This paper proposes combining new and emerging technology on neural speech recognition and auditory stimulus construction from brain signals to build brain signal-based dialogue systems. Such systems have a potentially large impact on society., Multimedia Computing

In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.

This paper presents the ESA Voyage 2050 White Paper for a concept of TeraHertz Exploration and Zooming-in for Astrophysics (THEZA). It addresses the science case and some implementation issues of a space-borne radio interferometric system for ultra-sharp imaging of celestial radio sources at the level of angular resolution down to (sub-) microarcseconds. THEZA focuses at millimetre and sub-millimetre wavelengths (frequencies above ∼ 300 GHz), but allows for science operations at longer wavelengths too. The THEZA concept science rationale is focused on the physics of spacetime in the vicinity of supermassive black holes as the leading science driver. The main aim of the concept is to facilitate a major leap by providing researchers with orders of magnitude improvements in the resolution and dynamic range in direct imaging studies of the most exotic objects in the Universe, black holes. The concept will open up a sizeable range of hitherto unreachable parameters of observational astrophysics. It unifies two major lines of development of space-borne radio astronomy of the past decades: Space VLBI (Very Long Baseline Interferometry) and mm- and sub-mm astrophysical studies with “single dish” instruments. It also builds upon the recent success of the Earth-based Event Horizon Telescope (EHT) – the first-ever direct image of a shadow of the super-massive black hole in the centre of the galaxy M87. As an amalgam of these three major areas of modern observational astrophysics, THEZA aims at facilitating a breakthrough in high-resolution high image quality studies in the millimetre and sub-millimetre domain of the electromagnetic spectrum. © 2021, The Author(s).

In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.

This position paper focuses on the problem of building dialogue systems for people who have lost the ability to communicate via speech, e.g., patients of locked-in syndrome or severely disabled people. In order for such people to communicate to other people and computers, dialogue systems that are based on brain responses to (imagined) speech are needed. A speech-based dialogue system typically consists of an automatic speech recognition module and a speech synthesis module. In order to build a dialogue system that is able to work on the basis of brain signals, a system needs to be developed that is able to recognize speech imagined by a person and can synthesize speech from imagined speech. This paper proposes combining new and emerging technology on neural speech recognition and auditory stimulus construction from brain signals to build brain signal-based dialogue systems. Such systems have a potentially large impact on society., Multimedia Computing

In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.

In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.

In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.