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This work presents a unique approach to the design, fabrication, and characterization of paper-based origami robotic systems consisting of stackable pneumatic actuators. These paper-based actuators (PBAs) use materials with high elastic modulus-to-mass ratios, accordion-like structures, and direct coupling with pneumatic pressure for extension and bending. The study contributes to the scientific and engineering understanding of foldable components under applied pneumatic pressure by constructing stretchable and flexible structures with intrinsically nonstretchable materials. Experiments showed that a PBA possesses a power-to-mass ratio greater than 80 W/kg, which is more than four times that of human muscle. This work also illustrates the stackability and functionality of PBAs by two prototypes: a parallel manipulator and a legged locomotor. The manipulator consisting of an array of PBAs can bend in a specific direction with the corresponding actuator inflated. In addition, the stacked actuators in the manipulator can rotate in opposite directions to compensate for relative rotation at the ends of each actuator to work in parallel and manipulate the platform. The locomotor rotates the PBAs to apply and release contact between the feet and the ground. Furthermore, a numerical model developed in this work predicts the mechanical performance of these inflatable actuators as a function of dimensional specifications and folding patterns. Overall, we use stacked origami actuators to implement functionalities of manipulation, gripping, and locomotion as conventional robotic systems. Future origami robots made of paper-like materials may be suitable for single use in contaminated or unstructured environments or low-cost educational materials.

The International Conference on Cognitive Modeling brings together researchers from around the world whose main goal is to build computational systems that reflect the internal processes of the mind. In this issue, we present the five best representative papers on this work from our 19th meeting, ICCM 2021, which was held virtually from July 3 to July 9, 2021. Three of these papers provide new techniques for refining computational models, giving better methods for taking empirical data and producing accurate computational models of the cognitive systems that produce them. The other two papers focus on explanation: using models to elucidate the underlying processes affecting cognition in such diverse domains as logical reasoning and the effects of caffeine.

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

Artificial Intelligence research is presenting phenomenal progress in two directions: (i) new theories and methodologies, and (ii) applications that expand traditional domains with innovative interventions. As indicated by recent reports, this progress has created a disequilibrium, where demand for scientists with skills in Artificial Intelligence is not fulfilled, a trend that will intensify further in the years to come. A potential solution to this shortage of specialised workforce may come from encouraging more women to get educated and follow a career in one of the Artificial Intelligence areas. This special collection of invited papers is dedicated to all women researchers and practitioners in Artificial Intelligence and coincides with the March 8, 2023 International Women’s Day. Moreover, it has two specific goals: (i) to inspire more women to study and practice Artificial Intelligence through presentation of recognized women researchers who can act as role models, and (ii) to highlight some streamlined research areas of Artificial Intelligence.

In the tourism industry, the sales of local specialties is an important part, and the package design and integrity of the specialties are very important. This paper first introduced the support vector machine (SVM) algorithm that was used for detecting defects on the surface of paper packages. Then, the design of red wind packages was briefly described, and the simulation experiment was carried out on SVM algorithm using red wine packages with different degrees of surface defects. Proper parameters were tested using the k-fold cross-validation method. The results demonstrated that the properties of paper improved the value of packages and the SVM algorithm had better accuracy than artificial recognition in recognizing different degrees of defects on the surface of packages. In conclusion, this paper describes the application of paper in packages and provides an effective method for the defection of defects on the surface of packages. This study provides an effective references to the improvement of package values and the enhancement of package integrity.

The work aims to explore a microscopic observation system of paper surface and achieve high-precision stereoscopic observation with detail characterization of paper surface morphology. Based on the DT-400E precision program-controlled three-dimensional translation stage and KEYENCE LJV-7200 two-dimensional laser scanner, the hardware parts of our own system are developed to scan and transmit point cloud data of paper surface morphology to the computer. The corresponding system software will automatically process the point cloud data acquired from the laser scanner and generate the corresponding vivid 3D model and height histogram. This system scans and characterizes four different types of paper samples, allowing the human eye to visually distinguish the differences in surface morphology as well as automatically calculate the numerical differences in paper surface morphology parameters. The results of the applicability test show that the system is highly efficient in acquiring, observing, and evaluating the topography of the paper surface. The system can not only predict the paper surface quality of printed paper, but can also be extended to the evaluation of 3D printed surfaces.

Behavioral diversity seen in biological systems is, at the most basic level, driven by interactions between physical materials and their environment. In this context we are interested in falling paper systems, specifically the V-shaped falling paper (VSFP) system that exhibits a set of discrete falling behaviors across the morphological parameter space. Our previous work has investigated how morphology influences dominant falling behaviors in the VSFP system. In this article we build on this analysis to investigate the nature of behavioral transitions in the same system. First, we investigate stochastic behavior transitions. We demonstrate how morphology influences the likelihood of different transitions, with certain morphologies leading to a wide range of possible paths through the behavior-space. Second, we investigate deterministic transitions. To investigate behaviors over longer time periods than available in falling experiments we introduce a new experimental platform. We demonstrate how we can induce behavior transitions by modulating the energy input to the system. Certain behavior transitions are found to be irreversible, exhibiting a form of hysteresis, while others are fully reversible. Certain morphologies are shown to behave like simplistic sequential logic circuits, indicating that the system has a form of memory encoded into the morphology–environment interactions. Investigating the limits of how morphology–environment interactions induce non-trivial behaviors is a key step for the design of embodied artificial life-forms.

Cocaine (COC) is one of the most widely consumed illegal drugs around the world. Street COC is commonly adulterated with pharmaceutical compounds that mimic or intensify the COC's sensory effect. Adulteration is performed to increase the profit of criminal organizations and each one has their own way of doing it. Therefore, determining the composition of seized COC samples (chemical profile) provides evidence for the police to track criminal organization networks and their activity patterns. Using filter paper as a substrate, we developed a multiple detection paper-based analytical device (PAD) that combines colorimetric and electrochemical measurements to discriminate COC samples according to adulterant's content. A regular graphite lead modified with a gold film made from Au leaf (graphite/Au) to improve electron transfer was used as a working electrode. Silver and Ag/AgCl were used as auxiliary and reference electrodes, respectively. The colorimetric device was patterned using a laser cutter and coupled to the electrochemical device using a double-sided tape, allowing simultaneous analysis to gather more analytical information about COC samples. Graphite/Au was characterized by scanning and transmission electron microscopies and electrochemical assays. The simultaneous colorimetric and electrochemical analyses combined to principal component analysis improved the analytical characterization of COC trial samples and provided a fast discrimination based on the assembled database.

The Artificial Intelligence Recommender System has emerged as a significant research interest. It aims at helping users find things online by offering recommendations that closely fit their interests. Recommenders for research papers have appeared over the last decade to make it easier to find publications associated with the field of researchers’ interests. However, due to several issues, such as copyright constraints, these methodologies assume that the recommended articles’ contents are entirely openly accessible, which is not necessarily the case. This work demonstrates an efficient model, known as RPRSCA: Research Paper Recommendation System Using Effective Collaborative Approach, to address these uncertain systems for the recommendation of quality research papers. We make use of contextual metadata that are publicly available to gather hidden relationships between research papers in order to personalize recommendations by exploiting the advantages of collaborative filtering. The proposed system, RPRSCA, is unique and gives personalized recommendations irrespective of the research subject. Thus, a novel collaborative approach is proposed that provides better performance. Using a publicly available dataset, we found that our proposed method outperformed previous uncertain methods in terms of overall performance and the capacity to return relevant, valuable, and quality publications at the top of the recommendation list. Furthermore, our proposed strategy includes personalized suggestions and customer expertise, in addition to addressing multi-disciplinary concerns.

We are pleased to announce that the JACIII Awards of 2022 have been decided by the JACIII editorial boards. This year, the award winning papers were severely and fairly selected among 321 papers published in JACIII Vols. 23 (2019) to 25 (2021) and there was no entries that deserved the Best Review Paper award. The award ceremony was held online in order to prevent spreading of COVID-19. JACIII BEST PAPER AWARD 2022 Katsuhiro Honda, Issei Hayashi, Seiki Ubukata, and Akira Notsu Three-Mode Fuzzy Co-Clustering Based on Probabilistic Conceptand Comparison with FCM-Type Algorithms JACIII Vol.25 No.4, pp. 478-488, 2021. JACIII YOUNG RESEARCHER AWARD 2022 Bijun Tang Path Planning Based on Improved Hybrid A* Algorithm By Bijun Tang, Kaoru Hirota, Xiangdong Wu, Yaping Dai, and Zhiyang Jia JACIII Vol.25 No.1, pp. 64-72, 2021. JACIII YOUNG RESEARCHER AWARD 2022 Takato Sasagawa Recommendation System Based on Generative Adversarial Network with Graph Convolutional Layers By Takato Sasagawa, Shin Kawai, and Hajime Nobuhara JACIII Vol.25 No.4, pp. 389-396, 2021.

An electrocardiogram (ECG) uses electrodes to monitor the heart rhythm and identify minute electrical changes that occur with each beat. It is employed to investigate particular varieties of aberrant heart activity, such as arrhythmias and conduction problems. One of the most essential tools for detecting heart problems is the electrocardiogram (ECG). The majority of ECG records are still on paper. Manual ECG paper record analysis can be difficult and time-consuming. It is possible to digitally digitize these paper ECG recordings for automated analysis and diagnosis. In this paper, we proposed a system to digitize the ECG paper, automatically detecting R peaks, calculating the average heart rate, and sending SMS to the doctor via cloud in the event of detection of abnormality. The method of the system is uploading an ECG image, then dimensionality reduction, feature extraction in the form of digital signals, and saving it in a CSV file format using the MATLAB programming language. After that, the system retrieves the signals for further processing of the raw signals. We used the fast Fourier transform (FFT) algorithm to calculate R peaks and calculate the heart rate. If the heart rate is abnormal, the system sends SMS messages to doctors via a technology platform (Twilio) using the Python programming language.

The number of scientific papers has been increasing ever more rapidly. Researchers have to spend a lot of time classifying papers relevant to their study, especially into fine-grained subfields. However, almost all existing paper classification models are coarse-grained, which can not meet the needs of researchers. Observing this, we propose a lightweight fine-grained classification model for scientific paper. Dynamic weighting coefficients on feature words are incorporated into the model to improve the classification accuracy. The feature word weight is optimized by the Mean Decrease Accuracy (MDA) algorithm. Considering applicability, the lightweight processing is conducted through algorithm pruning and training sample pruning. Comparison with mainstream models shows simultaneous improvement in accuracy and time efficiency by our model.

Abstract Artificial intelligence developments are essential to the successful deployment of community-wide, MRI-driven prostate cancer diagnosis. AI systems should ensure that the main benefits of biopsy avoidance are delivered while maintaining consistent high specificities, at a range of disease prevalences. Since all current artificial intelligence / computer-aided detection systems for prostate cancer detection are experimental, multiple developmental efforts are still needed to bring the vision to fruition. Initial work needs to focus on developing systems as diagnostic supporting aids so their results can be integrated into the radiologists’ workflow including gland and target outlining tasks for fusion biopsies. Developing AI systems as clinical decision-making tools will require greater efforts. The latter encompass larger multicentric, multivendor datasets where the different needs of patients stratified by diagnostic settings, disease prevalence, patient preference, and clinical setting are considered. AI-based, robust, standard operating procedures will increase the confidence of patients and payers, thus enabling the wider adoption of the MRI-directed approach for prostate cancer diagnosis. Key Points • AI systems need to ensure that the benefits of biopsy avoidance are delivered with consistent high specificities, at a range of disease prevalence. • Initial work has focused on developing systems as diagnostic supporting aids for outlining tasks, so they can be integrated into the radiologists’ workflow to support MRI-directed biopsies. • Decision support tools require a larger body of work including multicentric, multivendor studies where the clinical needs, disease prevalence, patient preferences, and clinical setting are additionally defined.

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

The spread of invasive alien plant species (IAPS) is a leading reason for worldwide environmental change due to their effects on biodiversity and humans. Some valued goods from IAPS have been produced, e.g. paper that consists of cellulose fibres from Japanese knotweed. Therefore, the aim of this study was to establish the usability of this paper grade as a printing substrate, since it does not have ideal optical properties as it is expected from commercial office paper. Because it is widely used, inkjet printing technology was employed. Print permanence is essential, especially when printing documents. However, typographic characteristics must be considered to make a text more legible. Two widely used typefaces (Arial and Times) were tested in three commonly used type sizes (8 pt, 10 pt, and 12 pt). The results showed that the paper made from Japanese knotweed could have valuable properties and suitable legibility, especially when using typefaces with a moderate counter size, high x-height, and minimal differences in the letter stroke width to obtain an appropriate typographic tonal density with an adequate type size. Even after exposure to light, the texts printed in a proper type size and stroke width remained visible.

In this modern age, Internet of Things (IoT) and Wireless Sensor Network (WSN) as its derivatives have become one of the most popular and important technological advancements. In IoT, all things and services in the real world are digitalized and it continues to grow exponentially every year. This growth in number of IoT device in the end has created a tremendous amount of data and new data services such as big data systems. These new technologies can be managed to produce additional value to the existing business model. It also can provide a forecasting service and is capable to produce decision-making support using computational intelligence methods. In this survey paper, we provide detailed research activities concerning Computational Intelligence methods application in IoT WSN. To build a good understanding, in this paper we also present various challenges and issues for Computational Intelligence in IoT WSN. In the last presentation, we discuss the future direction of Computational Intelligence applications in IoT WSN such as Self-Organizing Network (dynamic network) concept.

This study supports the labour-friendly nature of product innovation among developers of artificial intelligence (AI) technologies. GMM-SYS estimates on a worldwide longitudinal dataset covering 3,500 companies that patented inventions related to AI technologies over the period 2000-2016 show a positive and significant impact of AI patent families on employment. The effect is small in magnitude and limited to service sectors and younger firms, which are front-runners of the AI revolution. We also detect some evidence of increasing returns suggesting that innovative companies more focused on AI technologies are those obtaining larger impacts in terms of job creation. ispartof: FEB Research Report MSI_2306 pages:1-36 status: published

Paper spray ionization has been used as a fast sampling/ionization method for the direct mass spectrometric analysis of biological samples at ambient conditions. Here, we demonstrated that by utilizing paper spray ionization-mass spectrometry (PSI-MS) coupled with field asymmetric waveform ion mobility spectrometry (FAIMS), predictive metabolic and lipidomic profiles of routine breast core needle biopsies could be obtained effectively. By the combination of machine learning algorithms and pathological examination reports, we developed a classification model, which has an overall accuracy of 87.5% for an instantaneous differentiation between cancerous and noncancerous breast tissues utilizing metabolic and lipidomic profiles. Our results suggested that paper spray ionization-ion mobility spectrometry-mass spectrometry (PSI-IMS-MS) is a powerful approach for rapid breast cancer diagnosis based on altered metabolic and lipidomic profiles.

Paper prototypes have been successful for gathering concrete, grounded user feedback. Most of these prototypes are used to elicit feedback during bounded design sessions. However, for systems that support new social practices these bounded sessions do not fully materialize the interactions the prototype supports. This paper presents a case study of a six-month paper prototype deployment that materialized collaborative interactions in care retirement community. Throughout this deployment the prototype was situated within the lives of participants, where they shaped the interactions and usage of the prototype over time. As these interactions materialized, the evoked community practice was unlike what participants envisioned during our first, more traditional paper prototyping and design sessions. This article shows how our interactive, paper prototype provided critical guidance in how realistic our imagined practice would be.

The clinical importance of blood group (BG) antigens is related to their ability to induce immune antibodies that can cause hemolysis. Yet, ABO and D (Rh) are still considered to be the key antigens for healthy blood transfusion and secondary antigens are the next priority. Serological typing is the most widely used typing method. Rapid and accurate blood grouping plays an important role in some clinical conditions, rather than conventional techniques. Hence, developing a simple and economical model for rapid blood grouping would facilitate these tests. In recent decades, paper-based microfluidics such as μPADs has gained much interest in wide application areas such as point-of-care diagnostic. In this study, we evaluated μPADs that are performed for blood grouping and its recent progress. A comprehensive literature search was performed using databases including PUBMED, SCOPUS, Web of Science and Google Scholar. Keywords were blood grouping or typing, paper analytical device, rapid test, etc. After investigation of search results, 16 papers from 2010 to 2020 were included. Further information in detail was classified in Table 1. Generally, two principles for blood typing μPADs are introduced. The lateral chromatographic flow method and the vertical flow-through method that detects BG in a visual-based manner. To detect results with acceptable clarity many factors and challenges like paper, blood sample, buffer, Ab and RBC interaction and also μPADs stability need to be considered, which are discussed. In conclusion, the simplicity, stability, cheapness, portability and biocompatibility of μPADs for blood grouping confirming its utility and also they have the capability to robust, universal blood-grouping platform. Table 1 Summary of blood grouping tests using paper-based analytical devices Antigens Type of diagnosis Validation method Sample No Accuracy Action time Paper type Stability Sample dilution Buffer Ref A, B, Rh Forward volunteers records 5 - - Whatman No. 4 - 1/2 PBS* (Khan et al. 2010) A, B, Rh Forward gel assay test and conventional slide test 100 100% 1 min Whatman No. 4 and Kleeenex paper towel 7 Days in 4 °C 1/1 NSS (Al-Tamimi et al. 2012) A, B, Rh Forward gel card assay 99 100% 20 Sec + Washing Kleeenex paper towel - 1/1 NSS (Li et al. 2012) A, B, Rh Forward - - - - Kleeenex paper towel - 45/100 PSS (Li et al. 2013) A, B, Rh Forward gel card assay 98 100% 1.5 min Kleeenex paper towel - 85/100 PBS (Guan et al. 2014b) C, E, c, e, K, Jka, Jkb, M, N, S, P1, and Lea Forward gel card assay 266 100% - Kleeenex paper towel - 1/1 NSS (Li et al. 2014b) A, B, Rh Forward and Reverse conventional slide test 96 ≈ 91% 10 min Whatman No. 1 21 Days in 4 °C 1/2 NSS (Noiphung et al. 2015) C, c, E, e, K, k, Fya, Fyb, Jka, Jkb, M, N, S and s, P1, Lea and Leb Forward - 478 - - Kleeenex paper towel - 1/1 NSS, PBS (Then et al. 2015) A, B Forward and Reverse conventional slide test 76 100% 5-8 min Whatman No. 4 38 Days in 4 °C 1/4, 1/1 NSS (Songjaroen and Laiwattanapaisal 2016) D, K Forward volunteers records 210 - 7.5 min Kleenex paper towel - 1/1 NSS (Yeow et al. 2016) A, B, c, e, D, C, E, M, N, S, s, P1, Jka, Jkb, Lea, Leb, Fya, and Fyb Forward and Reverse gel card assay 3550 ≈100% 30 s Fiber glass and cotton linter 180 Days in 25 °C 45/100, 1/1 PBS (Zhang et al. 2017) A, B Forward conventional slide test 598 100% 3 min Whatman No. 113 14 Day in 4 °C 1/1 NSS (Songjaroen et al. 2018) A, B, Rh Forward conventional slide test - - 30 Sec + Washing Unrefined sisal paper - 1/2 NSS (Casals-Terré et al. 2019) A, B, Rh Forward - - - - Whatman No.1 - 1/1 NSS (Ansari et al. 2020) ABORh Forward and Reverse conventional slide test - 100% Unrefined Eucalyptus papers - 1/2 NSS, PBS (Casals-Terré et al. 2020) A, B, Rh Forward - - - 30 Sec + Washing Whatman No. 4 modified with chitosan ≥ 100 days in 25 °C 1/1 NSS (Parween et al. 2020)

Visual individual recognition requires animals to distinguish among conspecifics based on appearance. Though visual individual recognition has been reported in a range of taxa, the features that animals rely on to discriminate between individuals are often not well understood. Northern paper wasp females, Polistes fuscatus, possess individually distinctive color patterns on their faces, which mediate individual recognition. It is currently unclear what facial features P. fuscatus use to distinguish individuals. The anterior optic tubercle, a chromatic processing brain region, is especially sensitive to social experience during development, suggesting that color may be important for recognition in this species. We sought to test the roles of color in wasp facial recognition. Color may be important simply because it creates a pattern. If this is the case, then wasps should perform similarly when discriminating color or grayscale images of the same faces. Alternatively, color itself may be important for recognition, which would predict poorer performance on grayscale image discrimination relative to color images. We found wasps trained on grayscale faces, unlike those trained on color images, did not perform better than chance. Suggesting that color is necessary for the recognition of an image as a face by the wasp visual system.

Due to the increasing use of the Internet and other online resources, there is tremendous growth in the data of text documents. It is not possible to manage this huge data manually. This has led to the growth of fields like text mining and text summarization. This paper presents a title prediction model for research papers using Recursive Recurrent Neural Network (Recursive RNN) and evaluates its performance by comparing it with sequence-to-sequence models. Research papers published between 2018 and 2020 were obtained from a standard repository, viz. Kaggle, to train the title prediction model. The performance of different versions of Recursive RNN and Seq2Seq was evaluated in terms of training and hold-out loss. The experimental results show that Recursive RNN models perform significantly better than the other models.

For the detection of product color uniformity and seam texture defects in the production process of test paper, detection methods are proposed respectively. For the detection of the color uniformity, a uniformity detection method based on dynamic light correction is proposed. The illumination correction image is used for color dynamic compensation of the test paper image to solve the influence of uneven illumination on the test paper. For the detection of texture defects such as seams, a texture defect detection method based on gray gradient aggregation intensity is proposed. This method uses one-way Gaussian convolution to strengthen features, and then calculates the gradient, divides the region and calculates the gradient extremum aggregation strength to judge texture defects, which can quickly and efficiently find seam defects. The experimental results show that the above methods have obvious effects on the color uniformity and seam detection of the test paper. The color uniformity accuracy rate is 96%, the seam detection accuracy rate is 97%. The total detection time of a single test paper is about 90ms. The algorithm is real-time and accurate, and can be used in actual production detection.

배경 및 목적: 본 연구는 언어발달지연 아동의 한국판 수용 · 표현 어휘력 검사에서 컴퓨터 기반(태블릿 PC) 매체와 종이 기반 매체를 이용하여 매체의 변화가 아동의 언어 수행력에 차이를 보이는지 알아보고자 하였다. 방법: 연구의 대상은 생활 연령이 만 3-7세의 언어발달지연 아동 27명을 대상으로 하였다. 언어발달지연 정도와 통합언어연령에 따라 각각 두...

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

AimTo discuss ethical issues related to a complex study (PROFID) involving the development of a new, partly artificial intelligence-based, prediction model to enable personalised decision-making about the implantation of an implantable cardioverter defibrillator (ICD) in postmyocardial infarction patients, and a parallel non-inferiority and superiority trial to test decision-making informed by that model.MethodThe position expressed in this paper is based on an analysis of the PROFID trials using concepts from high-profile publications in the ethical literature.ResultsWe identify ethical issues related to the testing of the model in the treatment setting, and to both the superiority and the non-inferiority trial. We underline the need for ethical-empirical studies about these issues, also among patients, as a parallel to the actual trials. The number of ethics committees involved is an organisational, but also an ethical challenge.ConclusionThe PROFID trials, and probably other studies of similar scale and complexity, raise questions that deserve dedicated parallel ethics and social science research, but do not constitute a generic obstacle. A harmonisation procedure, comparable to the Voluntary Harmonization Procedure (VHP) for medication trials, could be needed for this type of trials.

Cellulose is an important component of green plants cell wall and other organisms. Cellulose from plants is mainly used to produce paper. Because of the widespread use of paper throughout the world, it causes the use of wood in large quantities that involves cutting down trees which can lead to negative impacts on ecosystems. Many research studied to find other raw materials that could replace wood and some focused on coconut coir because it contains high amount of cellulose which is enough to use in manufacturing. Coconut pulp is also the residue from coconut. It has 59 % cellulose. This makes it possible to use coconut pulp in paper industry. Paper for chromatography uses fiber from wood as an important component too. So, the purpose of this study was to identify whether cellulose from coconut pulp can replace the original material of chromatography paper. The procedure started from extracted impurity from coconut pulp and used the remaining for making chromatographic paper. Washed coconut pulp and removed residue coconut milk. Boiled clean coconut pulp for 10 minutes and dried in oven at 60 degree Celsius. Remove the residue fat with hexane. Drained hexane and dried in oven at 60 degree Celsius. Extracted cellulose by the soda process. Cellulose yield was 50.85%. Used extracted cellulose as raw material for making chromatographic paper. Tried different amount of cellulose per area of sieve to find the suitable for making paper. The appropriate paper area per extracted cellulose weight was about 81.25 cm2/g. Then trailed the paper used as stationary phase for color separation. The appropriate condition of mobile phase in chromatographic technique (ethyl-methyl ketone, acetone and water) to separate standard colors was also studied. The appropriate ratio of mobile phase which could separate standard solutions was 7:3:3, respectively. The results show that this paper was effective in serving as a stationary phase. This paper has the potential to be developed to use as a substitute material in Bio manufacturing.

The International Conference on Cognitive Modeling brings together researchers from around the world whose main goal is to build computational systems that reflect the internal processes of the mind. In this issue, we present the four best representative papers on this work from our 18th meeting, ICCM 2020, which was also the first meeting to be held virtually. Two of these papers develop novel techniques for building larger and more complex models using Reinforcement Learning and Learning By Instruction, respectively. The other two show how cognitive models connect to neuroscience, drawing on details of the hippocampus and cerebellum to constrain and explain the cognitive processes involved in memory and conditioning.

In this paper, we obtain samples of thermal aging transformer insulation paper at 6 time points through an accelerated thermal aging test that simulates the actual operating state of the transformer. The Raman spectroscopy diagnosis model of transformer insulating paper aging is proposed. The model combines an autoencoder and a linear discriminant analysis algorithm to extract the characteristics of the Raman spectrum, and uses a Naive Bayes classification method to divide the aging stage of the insulating paper. Through the Raman spectroscopy diagnosis model of transformer insulation paper aging designed in this paper, the Raman spectroscopy characteristic quantity extracted by the AE-LDA method can effectively reflect the aging characteristic information of the insulating paper, and the Naive Bayes classification algorithm is used to predict. Research shows that the model has a high predictive accuracy for the assessment of the aging state of insulating paper, and can effectively diagnose the aging state of transformer oil-paper insulation. The Raman spectroscopy used in the diagnosis of the aging of insulating paper has good aging discrimination ability and good application prospects, and provides a new idea for the diagnosis of the aging state of transformer oil-paper insulation.

Because the traditional insulation paper state detection method has some shortcomings, this paper proposes a non-destructive detection method, which uses the texture feature value of insulation paper image to establish a relationship with the degree of polymerization (DP) of insulation paper state, and then indirectly estimates the aging state of insulation paper. Firstly, the insulation paper images in different aging states are collected, and the texture feature values of the images are extracted by gray histogram, gray level co-occurrence matrix and gray level run-length matrix; then, the texture feature values are selected by correlation; then, the validity of the selected texture feature values to represent the insulation paper state is verified by support vector machine (SVM); finally, the multiple linear regression method is used. The relationship between the degree of polymerization and the texture feature value is established and verified by the actual results. The results show that the texture feature value has a good fitting relationship with the degree of polymerization, which verifies the effectiveness of this method in evaluating the state of insulating paper.

The ageing status of the oil-paper insulation is closely related to the operation of the transformers. Recently, near infrared spectroscopy (NIRS) is used to identify the ageing condition of oil-paper insulation for it is a more rapid and nondestructive detection than the traditional viscometry method. However, the identification accuracy of existing quantitative analysis method of NIRS are still insufficient. This paper proposed an improved Linear discriminant analysis (LDA) method to classify the spectra and to identify the ageing condition of the insulating paper. Based on DP measurement, the insulating paper specimens prepared in laboratory are divided into varying ageing groups. The principle of LDA method is introduced. The classic LDA method has been applied to identify the ageing status of specimens respectively from 4 ageing groups and 3 ageing groups. The results show that LDA has a better performance in classifying four groups than three groups. To achieve an accurate ageing condition identification with balanced accuracy of the model and enough categories of ageing status, the improved LDA method is proposed. The identification results show that the improved method has satisfactory performance and indicates great application potentials in the ageing assessment of the oil-paper impregnated insulation.

The articles in this special section are based on selected papers presented at the 2021 ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS 2021), a premier single-track conference that promotes development of fundamental principles that underpin the integration of cyber and physical elements, as well as the development of technologies, tools, architectures, and infrastructure for the design and implementation of CPS. ICCPS 2021 focused on contributions related to smart and connected cities, autonomous CPS, verification and control, security and privacy, and human health and biomedical CPS.

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

This paper aims at defining, analyzing and evaluating barriers and drivers for eco-design implementation to the Finnish pulp and paper industry by using multi-criteria decision-making methods and consolidated ranking methodology. The drivers and barriers are identified through literature research and a survey is conducted for their evaluation. The results show that barriers more commonly present and interconnected in companies originate from internal management, while drivers often originate from external stakeholders. The paper concludes with policy recommendation, which will facilitate the adoption of eco-deign practices for sustainable manufacturing in the Finnish pulp and paper industry. The ranking of the barriers and drivers from different point of view will help the decision-makers and managers in government and industry to mitigate these barriers in an effective manner.

The folder contains the data and codes used in the analysis described in the paper: Parteka A., Kordalska A. (2023) Artificial intelligence and productivity: global evidence from AI patent and bibliometric data. Technovation, Volume 125, July 2023, 102764