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Standards are a set of guidelines or criteria used to ensure consistency, quality, safety, and compatibility in products, services, and processes [...]

A point-of-care (POC) can be defined as an in vitro diagnostic test that can provide results within minutes. It has gained enormous attention as a promising tool for biomarkers detection and diagnosis, as well as for screening of chronic noncommunicable diseases such as diabetes mellitus. Diabetes mellitus type 2 is one of the metabolic disorders that has grown exponentially in recent years, becoming one of the greatest challenges to health systems. Early detection and accurate diagnosis of this disorder are essential to provide adequate treatments. However, efforts to reduce incidence should remain not only in these stages but in developing continuous monitoring strategies. Diabetes-monitoring tools must be accessible and affordable; thus, POC platforms are attractive, especially paper-based ones. Paper-based POCs are simple and portable, can use different matrixes, do not require highly trained staff, and are less expensive than other platforms. These advantages enhance the viability of its application in low-income countries and hard-to-reach zones. This review aims to present a critical summary of the main components required to create a sensitive and affordable enzymatic paper-based POC, as well as an oriented analysis to highlight the main limitations and challenges of current POC devices for diabetes type 2 monitoring and future research opportunities in the field.

International audience; Despite predictions of the paperless office, global demand for printing and writing paper remains strong, and paper appears to be here to stay for some time. Not only firms, but also governments, libraries, and archives are in possession of large collections of legacy documents that still must be sorted and scanned. In this study, terahertz-based techniques are demonstrated to address several routine tasks related to the automated paper handling of unsorted legacy documents. Specifically, we demonstrate terahertz-based counting of the number of sheets in unconsolidated paper stacks, as well as locating stapled documents buried in paper stacks.

We present a rotational terahertz imaging system for inline nondestructive testing (NDT) of press sleeves for the paper industry during fabrication. Press sleeves often consist of polyurethane (PU) which is deposited by rotational molding on metal barrels and its outer surface mechanically processed in several milling steps afterwards. Due to a stabilizing polyester fiber mesh inlay, small defects can form on the sleeve’s backside already during the initial molding, however, they cannot be visually inspected until the whole production processes is completed. We have developed a fast-scanning frequenc-modulated continuous wave (FMCW) terahertz imaging system, which can be integrated into the manufacturing process to yield high resolution images of the press sleeves and therefore can help to visualize hidden structural defects at an early stage of fabrication. This can save valuable time and resources during the production process. Our terahertz system can record images at 0.3 and 0.5 THz and we achieve data acquisition rates of at least 20 kHz, exploiting the fast rotational speed of the barrels during production to yield sub-millimeter image resolution. The potential of automated defect recognition by a simple machine learning approach for anomaly detection is also demonstrated and discussed.

According to recent literature in the field of sustainability, the circular economy (CE) appears to be a thriving opportunity for creating new businesses, although less attention has been paid to the form in which its principles fit into a comprehensive framework that enables companies to design it in a practical way. This paper presents the methodology that has been adopted to pave the way to a coherent reference framework for circular business model innovation and its outstanding design and implementation, taking into consideration the entire value and supply chain. A unique analysis of recent innovations in circular economy models is provided herein, together with an exhaustive analysis of those elements that enable or hinder their implementation. The main interactions among all those critical elements influencing how organisations innovate and operate cooperatively within a CE ecosystem are also evaluated. In addition, a study of five industrial cases in the pulp and paper industry allowed searching for industrial insights and empirical evidence of the relevance of those elements, including observation, document analysis, and interviews. Lastly, the main outcomes of this research are illustrated using the CE reference framework designed when applied to the aforementioned industrial cases, and relevant insights into future improvements are also provided. This research was funded by European Union, grant number 730305, a collaborative research project entitled “New market niches for the pulp and paper industry waste based on circular economy approaches (paperChain)” from the European Union’s Horizon 2020 research and innovation programme.

Altres ajuts: Agència per a la Competitivitat de l'Empresa de la Generalitat de Catalunya by FEDER funds, Institució Catalana de Recerca i Estudis Avançats, and ERDF funds In this paper, an organic, fully recyclable and eco-friendly 20-bit inkjet-printed chipless RFID tag is presented. The tag operates in the near field and is implemented by means of chains of resonant elements. The characterization and manufacturing process of the tag, printed with a few layers of a commercial organic ink on conventional paper substrate (DIN A4), are presented, and tag functionality is demonstrated by reading it by means of a custom-designed reader. The tags are read by proximity (through the near field), by displacing them over a resonator-loaded transmission line, and each resonant element (bit) of the tag is interrogated by a harmonic signal tuned to the resonance frequency. The coupling between the reader line and the resonant elements of the tag produce and amplitude modulated (AM) signal containing the identification (ID) code of the tag.

Flying insect detection, identification, and counting are the key components of agricultural pest management. Insect identification is also one of the most challenging tasks in agricultural image processing. With the aid of machine vision and machine learning, traditional (manual) identification and counting can be automated. To achieve this goal, a particular data acquisition device and an accurate insect recognition algorithm (model) is necessary. In this work, we propose a new embedded system-based insect trap with an OpenMV Cam H7 microcontroller board, which can be used anywhere in the field without any restrictions (AC power supply, WIFI coverage, human interaction, etc.). In addition, we also propose a deep learning-based insect-counting method where we offer solutions for problems such as the “lack of data” and “false insect detection”. By means of the proposed trap and insect-counting method, spraying (pest swarming) could then be accurately scheduled.

The abstraction and implementation of plant movement principles into biomimetic compliant systems are of increasing interest for technical applications, e.g., in architecture, medicine, and soft robotics. Within the respective research and development approaches, advanced methods such as 4D printing or 3D-braiding pultrusion are typically used to generate proof-of-concept demonstrators at the laboratory or demonstrator scale. However, such techniques are generally time-consuming, complicated, and cost-intensive, which often impede the rapid realization of a sufficient number of demonstrators for testing or teaching. Therefore, we have produced comparable simple handcrafted compliant systems based on paper, wood, plastic foil, and/or glue as construction materials. A variety of complex plant movement principles have been transferred into these low-cost physical demonstrators, which are self-actuated by shrinking processes induced by the anisotropic hygroscopic properties of wood or paper. The developed systems have a high potential for fast, precise, and low-cost abstraction and transfer processes in biomimetic approaches and for the “hands-on understanding” of plant movements in applied university and school courses.

The presence of microplastics (MPs) in the environment is becoming a problem for soils and seas, as well as for the food chain of animals and humans. The scientific community has been called upon to contribute to solving the problem and several papers have been published, especially in the last decade. The aim of this work is to carry out a bibliometric analysis of the scientific literature dedicated to the problem of MPs, highlighting its course over the years, and to identify the sectors to which the research could be profitably addressed. The VOSviewer software has been used to perform the analysis of the data in which specific maps were used to represent the network of the relationships among countries, journals, organizations, authors, and keywords related to the investigated topic and subtopics. The results of the survey demonstrated that during the investigated range of time, most attention has been paid to the individuation of the MPs, and to marine pollution, while a gap seems to exist in the possible advanced oxidation processes specifically addressing the degradation of MPs and their derivates.

The goal of this Special Issue is to highlight, through selected works, frontier research in basic to applied horticulture among those published in Horticulturae in 2020 [...]

Intelligent packaging is an emerging technology, aiming to improve the standard communication function of packaging. Radio frequency identification (RFID) assisted smart packaging is of high interest, but the uptake is limited as the market needs cost-efficient and sustainable applications. The integration of screen printed antennas and RFID chips as smart labels in reusable cardboard packaging could offer a solution. Although paper is an interesting and recyclable material, printing on this substrate is challenging as the ink conductivity is highly influenced by the paper properties. In this study, the best paper/functional silver ink combinations were first selected out of 76 paper substrates based on the paper surface roughness, air permeance, sheet resistance and SEM characterization. Next, a flexible high frequency RFID chip (13.56 MHz) was connected on top of screen printed antennas with a conductive adhesive. Functional RFID labels were integrated in cardboard packaging and its potential application as reusable smart box for third party logistics was tested. In parallel, a web-based software application mimicking its functional abilities in the logistic cycle was developed. This multidisciplinary approach to developing an easy-scalable screen printed antenna and RFID-assisted smart packaging application is a good example for future implementation of hybrid electronics in sustainable smart packaging. ispartof: MATERIALS vol:14 issue:19 ispartof: location:Switzerland status: published

Falls are unusual actions that cause a significant health risk among older people. The growing percentage of people of old age requires urgent development of fall detection and prevention systems. The emerging technology focuses on developing such systems to improve quality of life, especially for the elderly. A fall prevention system tries to predict and reduce the risk of falls. In contrast, a fall detection system observes the fall and generates a help notification to minimize the consequences of falls. A plethora of technical and review papers exist in the literature with a primary focus on fall detection. Similarly, several studies are relatively old, with a focus on wearables only, and use statistical and threshold-based approaches with a high false alarm rate. Therefore, this paper presents the latest research trends in fall detection and prevention systems using Machine Learning (ML) algorithms. It uses recent studies and analyzes datasets, age groups, ML algorithms, sensors, and location. Additionally, it provides a detailed discussion of the current trends of fall detection and prevention systems with possible future directions. This overview can help researchers understand the current systems and propose new methodologies by improving the highlighted issues. ispartof: SENSORS vol:21 issue:15 ispartof: location:Switzerland status: published

The increasing demand for smart vehicles with many sensing capabilities will escalate data traffic in vehicular networks. Meanwhile, available network resources are limited. The emergence of AI implementation in vehicular network resource allocation opens the opportunity to improve resource utilization to provide more reliable services. Accordingly, many resource allocation schemes with various machine learning algorithms have been proposed to dynamically manage and allocate network resources. This survey paper presents how machine learning is leveraged in the vehicular network resource allocation strategy. We focus our study on determining its role in the mechanism. First, we provide an analysis of how authors designed their scenarios to orchestrate the resource allocation strategy. Secondly, we classify the mechanisms based on the parameters they chose when designing the algorithms. Finally, we analyze the challenges in designing a resource allocation strategy in vehicular networks using machine learning. Therefore, a thorough understanding of how machine learning algorithms are utilized to offer a dynamic resource allocation in vehicular networks is provided in this study.

Microcapsule electrophoretic display (MED) is a kind of display with the properties of reflectivity and low power consumption. It is widely used in electronic book readers, but some new applications have appeared in the Internet of Things (IoT) products. Long working time is required in IoT products because it is not easy to replace or install such displays. The main failure phenomenon is the mura that will appear after about 1 to 2 years of use. The root cause of the failure is analyzed, and the lifetime prediction models for MED are introduced. The high temperature and high humidity (HTHH) test is used to evaluate the protective effect of the packaging structure. The HTHH test result is reported for the new MED structure, it shows that the MED with the new structure involves a longer working time.

Accurate and timely traffic forecasting is an important task for the realization of urban smart traffic. The random occurrence of social events such as traffic accidents will make traffic prediction particularly difficult. At the same time, most of the existing prediction methods rely on prior knowledge to obtain traffic maps and the obtained map structure cannot be guaranteed to be accurate for the current learning task. In addition, traffic data is highly non-linear and long-term dependent, so it is more difficult to achieve accurate prediction. In response to the above problems, this paper proposes a new integrated unified architecture for traffic prediction based on heterogeneous graph attention network combined with residual-time-series convolutional network, which is called HGA-ResTCN. First, the heterogeneous graph attention is used to capture the changes in the relationship between the traffic graph nodes caused by social events, so as to learn the link weights between the target node and its neighbor nodes; at the same time, by introducing the timing of residual links convolutional network to capture the long-term dependence of complex traffic data. These two models are integrated into a unified framework to learn in an end-to-end manner. Through testing on real-world data sets, the results show that the accuracy of the model in this paper is better than other proposed baselines.

Text classification is a fundamental language task in Natural Language Processing. A variety of sequential models are capable of making good predictions, yet there is a lack of connection between language semantics and prediction results. This paper proposes a novel influence score (I-score), a greedy search algorithm, called Backward Dropping Algorithm (BDA), and a novel feature engineering technique called the “dagger technique”. First, the paper proposes to use the novel influence score (I-score) to detect and search for the important language semantics in text documents that are useful for making good predictions in text classification tasks. Next, a greedy search algorithm, called the Backward Dropping Algorithm, is proposed to handle long-term dependencies in the dataset. Moreover, the paper proposes a novel engineering technique called the “dagger technique” that fully preserves the relationship between the explanatory variable and the response variable. The proposed techniques can be further generalized into any feed-forward Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs), and any neural network. A real-world application on the Internet Movie Database (IMDB) is used and the proposed methods are applied to improve prediction performance with an 81% error reduction compared to other popular peers if I-score and “dagger technique” are not implemented.

The automated fault detection and diagnostics (AFDD) of heating, ventilation, and air conditioning (HVAC) using data mining and machine learning models have recently received substantial attention from researchers and practitioners. Various models have been developed over the years for AFDD of complete HVAC or its sub-systems. However, HVAC complexities, which partly have roots in its close coupling nature and interrelated dependencies, mean that understanding the relationship between faults and the suitability of the techniques remains an unanswered question. The literature analysis and interactive visualization of the data collected from the past implementation of AFDD models can provide useful insight to further explore this question by applying artificial intelligence (AI). Association rule mining (ARM) is deployed by this paper, using the frequent pattern (FP) growth algorithm to generate frequent fault sets for most common HVAC faults from the body of AFDD models developed in the literature to represent the status quo. A new model is developed for common HVAC faults and the techniques most frequently used to detect and diagnose them. A recommender system is developed using the ARM model to extract knowledge from the body of knowledge of HVAC data-driven AFDD in the form of rule-sets that reflect the associations. Findings of this review paper can significantly help civil and building engineers, as well as facility managers, in better management of building HVAC systems.

Assistive technologies (ATs) often have a high-dimensionality of possible movements (e.g., assistive robot with several degrees of freedom or a computer), but the users have to control them with low-dimensionality sensors and interfaces (e.g., switches). This paper presents the development of an open-source interface based on a sequence-matching algorithm for the control of ATs. Sequence matching allows the user to input several different commands with low-dimensionality sensors by not only recognizing their output, but also their sequential pattern through time, similarly to Morse code. In this paper, the algorithm is applied to the recognition of hand gestures, inputted using an inertial measurement unit worn by the user. An SVM-based algorithm, that is aimed to be robust, with small training sets (e.g., five examples per class) is developed to recognize gestures in real-time. Finally, the interface is applied to control a computer’s mouse and keyboard. The interface was compared against (and combined with) the head movement-based AssystMouse software. The hand gesture interface showed encouraging results for this application but could also be used with other body parts (e.g., head and feet) and could control various ATs (e.g., assistive robotic arm and prosthesis).

The purpose of this paper is to study and analyze the concept of fractional-order complex-valued chaotic networks with external bounded disturbances and uncertainties. The synchronization problem and parameter identification of fractional-order complex-valued chaotic neural networks (FOCVCNNs) with time-delay and unknown parameters are investigated. Synchronization between a driving FOCVCNN and a response FOCVCNN, as well as the identification of unknown parameters are implemented. Based on fractional complex-valued inequalities and stability theory of fractional-order chaotic complex-valued systems, the paper designs suitable adaptive controllers and complex update laws. Moreover, it scientifically estimates the uncertainties and external disturbances to establish the stability of controlled systems. The computer simulation results verify the correctness of the proposed method. Not only a new method for analyzing FOCVCNNs with time-delay and unknown complex parameters is provided, but also a sensitive decrease of the computational and analytical complexity.

This paper presents an investigation of the capacity of machine learning methods (ML) to localize leakage in water distribution systems (WDS). This issue is critical because water leakage causes economic losses, damages to the surrounding infrastructures, and soil contamination. Progress in real-time monitoring of WDS and ML has created new opportunities to develop data-based methods for water leak localization. However, the managers of WDS need recommendations for the selection of the appropriate ML methods as well their practical use for leakage localization. This paper contributes to this issue through an investigation of the capacity of ML methods to localize leakage in WDS. The campus of Lille University was used as support for this research. The paper is presented as follows: First, flow and pressure data were determined using EPANET software, then, the generated data were used to investigate the capacity of six ML methods to localize water leakage. Finally, the results of the investigations were used for leakage localization from offline water flow data. The results showed excellent performance for leakage localization by the artificial neural network, logistic regression, and random forest, but there were low performances for the unsupervised methods because of overlapping clusters.

The concept of measurement uncertainty was introduced in the 1990s by the “Guide to the expression of uncertainty in measurement”, known as GUM. The word uncertainty has a lexical meaning and reflects the lack of exact knowledge or lack of complete knowledge about the value of the measurand. Thanks to the suggestions in the GUM and following the mathematical probabilistic approaches therein proposed, an uncertainty value can be found and be associated to the measured value. In the last decades, however, other methods have been proposed in the literature, which try to encompass the definitions of the GUM, thus overcoming its limitations. Some of these methods are based on the possibility theory, such as the one known as the RFV method. The aim of this paper is to briefly recall the RFV method, starting from the very beginning and the initial motivations, and summarize in a unique paper the most relevant obtained results.

In this paper, I propose a conceptual framework for law and artificial intelligence (AI) that is based on ideas derived from systems and control theory. The approach considers the relationship between the input to an AI-controlled system and the system’s output, which may affect events in the real-world. The approach aims to add to the current discussion among legal scholars and legislators on how to regulate AI, which focuses primarily on how the output, or external behavior of a system, leads to actions that may implicate the law. The goal of this paper is to show that not only is the systems output an important consideration for law and AI but so too is the relationship between the systems input to its desired output, as mediated through a feedback loop (and other control variables). In this paper, I argue that ideas derived from systems and control theory can be used to provide a conceptual framework to help understand how the law applies to AI, and particularly, to algorithmically based systems.

This paper presents a study and proposes a new methodology to analyze, evaluate and reduce the overall uncertainty of instrumentations for EMC measurements. For the scope of this work, the front end of a commercial EMI receiver is chosen and variations due to tolerances, temperature and frequency response of the system are evaluated. This paper illustrates in detail how to treat each block composing the model by analyzing each discrete component, and how to evaluate their influence on the measurand. Since a model can have hundreds or even thousands of parameters, the probability distribution functions (PDFs) of some variable might be unknown. So, a method that allows to obtain in a fast and easy way the uncertainty of the measurement despite having so many variables, to then being able to evaluate the influence of each component on the measurand, is necessary for a correct design. In this way, it will be possible to indicate which discrete components have the most influence on the measurand and thus set the maximum tolerances allowed and being able to design a cost-effective solution. Furthermore, this works presents a methodology which can easily be extended and applied to estimate and compute the uncertainty for electromagnetic interferences, energy storage systems (ESS), energy production, electric machines, electric transports and power plants in general.

The critical state theory is a robust conceptual framework for the characterisation of soil behaviour. In the laboratory, triaxial tests are used to assess the critical state locus. In the last decades, the equipment and testing procedures for soil characterisation, within the critical state framework, have advanced to obtain accurate and reliable results. This review paper summarises and describes a series of recommended laboratory procedures to assess the critical state locus in cohesionless soils. For this purpose, results obtained in the laboratory from different cohesionless soils and triaxial equipment configurations are compiled, analysed and discussed in detail. The procedures presented in this paper reinforce the use of triaxial cells with lubricated end platens and an embedded connection piston into the top-cap, together with the verification of the full saturation condition and the measurement end-of-test water content—preferable using the soil freezing technique. The experimental evidence and comparison between equipment configurations provide relevant insights about the laboratory procedures for obtaining a reliable characterisation of the critical state locus of cohesionless geomaterials. All the procedures recommended herein can be easily implemented in academic and commercial geotechnical laboratories.

Recent technological advancements in immersive reality technologies have become a focus area in the virtual heritage (VH) domain. In this regard, this paper attempts to design and implement clouds-based collaborative and multi-modal MR application aiming at enhancing cultural learning in VH. The design and implementation can be adopted by the VH domain for various application themes. The application utilises cloud computing and immersive reality technologies. The use of cloud computing, collaborative, and multi-modal interaction methods is influenced by the following three issues. First, studies show that users’ interaction with immersive reality technologies and virtual environments determines their learning outcome and the overall experience. Second, studies also demonstrate that collaborative and multi-modal interaction methods enable engagement in immersive reality environments. Third, the integration of immersive reality technologies with traditional museums and cultural heritage sites is getting significant attention in the domain. However, a robust approach, development platforms (frameworks) and easily adopted design and implementation approaches, or guidelines are not commonly available to the VH community. This paper, therefore, will attempt to achieve two major goals. First, it attempts to design and implement a novel application that integrates cloud computing, immersive reality technology and VH. Second, it attempts to apply the proposed application to enhance cultural learning. From the perspective of cultural learning and users’ experience, the assumption is that the proposed approach (clouds-based collaborative and multi-modal MR) can enhance cultural learning by (1) establishing a contextual relationship and engagement between users, virtual environments and cultural context in museums and heritage sites, and (2) by enabling collaboration between users.

Innovative manufacturers have used Integrated Business Solutions (IBSs) as a means to co-create products and services to solve diverse business problems and more effectively compete in their field of endeavour. However, the efficacy and benefits of IBSs have been diminished due to the rigid method in which project management has been applied. This paper provides a conceptual approach for manufacturers to create new revenue sources in collaboration with their customers by adopting an agile project methodology that accommodates the interactive and iterative nature of IBS development. The research findings highlight the lack of success in IBSs using traditional project management as the delivery method. It provides an alternative solution in the use of an agile project management approach with its customer-centred and iterative mindset. This paper provides a conceptual model of the agile method known as Scrum and describes how it better aligns with innovative IBS development. Though both IBSs and agile have been around for several decades, their development is still in a state of infancy. This research adds to the body of literature on the application of agile in IBSs and presents an argument for converting its conceptual model into a practice delivery.

Recently, electric vehicles (EV) have gained much attention as a potential enabling technology to support CO2 emissions reduction targets. Relative to their internal combustion vehicle counterparts, EVs consume less energy per unit distance, and add the benefit of not emitting any carbon dioxide in operation and instead shift their emissions to the existing local fleet of power generation. However, the true success of EVs depends on their successful integration with the supporting infrastructure systems. Building upon the recently published methodology for the same purpose, this paper presents a “systems-of-systems” case study assessing the impacts of EVs on these three systems in the context of Abu Dhabi. For the physical transportation system, a microscopic discrete-time traffic operations simulator is used to predict the kinematic state of the EV fleet over the duration of one day. For the impact on the intelligent transportation system (ITS), the integration of EVs into Abu Dhabi is studied using a multi-domain matrix (MDM) of the Abu Dhabi Department of Transportation ITS. Finally, for the impact on the electric power system, the EV traffic flow patterns from the CMS are used to calculate the timing and magnitude of charging loads. The paper concludes with the need for an intelligent transportation-energy system (ITES) which would coordinate traffic and energy management functionality.

This paper examines the computational feasibility of the standard model of learning in economic theory. It is shown that the information update technique at the heart of this model is impossible to compute in all but the simplest scenarios. Specifically, using tools from theoretical machine learning, the paper first demonstrates that there is no polynomial implementation of the model unless the independence structure of variables in the data is publicly known. Next, it is shown that there cannot exist a polynomial algorithm to infer the independence structure, consequently, the overall learning problem does not have a polynomial implementation. Using the learning model when it is computationally infeasible carries risks, and some of these are explored in the latter part of the paper in the context of financial markets. Especially in rich, high-frequency environments, it implies discarding a lot of useful information, and this can lead to paradoxical outcomes in interactive game-theoretic situations. This is illustrated in a trading example where market prices can never reflect an informed trader’s information, no matter how many rounds of trade. The paper provides new theoretical motivation for the use of bounded rationality models in the study of financial asset pricing—the bound on rationality arising from the computational hardness in learning.

Among the most commonly specified tests in the geotechnical engineering industry, the liquid limit and plastic limit tests are principally used for (i) deducing useful design parameter values from existing correlations with these consistency limits and (ii) for classifying fine-grained soils, typically employing the Casagrande-style plasticity chart. This updated state-of-the-art review paper gives a comprehensive presentation of salient latest research and understanding of soil consistency limits determinations/measurement, elaborating concisely on the many standardized and proposed experimental testing approaches, their various fundamental aspects and possibly pitfalls, as well as some very recent alternative proposals for consistency limits determinations. Specific attention is given to fall cone testing methods advocated (but totally unsuitable) for plastic limit determination; that is, the water content at the plastic–brittle transition point, as defined using the hand rolling of threads method. A framework (utilizing strength-based fall cone-derived parameters) appropriate for correlating shear strength variation with water content over the conventional plastic range is presented. This paper then describes two new fine-grained soil classification system advancements (charts) that do not rely on the thread-rolling plastic limit test, known to have high operator variability, and concludes by discussing alternative and emerging proposals for consistency limits determinations and fine-grained soil classification.

In this paper, we describe two related scripting methods of cartographic data processing and visualization that provide 2D and 3D mapping of Japan with different algorithm complexity. The first algorithm utilizes Generic Mapping Toolset (GMT), which is known as an advanced console-based program for spatial data processing. The modules of GMT combine the functionality of scripting with the aspects of geoinformatics, which is especially effective for the rapid analysis of large geospatial datasets, multi-format data processing, and mapping in 2D and 3D modes. The second algorithm presents the use of the R programming language for cartographic visualization and spatial analysis. This R method utilizes the packages ‘tmap’, ‘raster’, ‘maps’, and ‘mapdata’ to model the morphometric elements of the Japanese archipelago, such as slope, aspect, hillshade and elevation. The general purpose graphical package ‘ggplot2’ of R was used for mapping the prefectures of Japan. The two scripting approaches demonstrated an established correspondence between the programming languages and cartography determined with the use of scripts for data processing. They outperform several well-known and state-of-the-art GIS methods for mapping due to their high automation of data processing. Cartography has largely reflected recent advances in data science, the rapid development of scripting languages, and transfer in the approaches of data processing. This extends to the shift from the traditional GIS to programming languages. As a response to these new challenges, we demonstrated in this paper the advantages of using scripts in mapping, which consist of repeatability and the flexible applicability of scripts in similar works.

Aim of this research paper is to introduce a novel method of hybrid orthodontic tooth-borne distalizer treatment of class II malocclusion by using 3D printed biocompatible personalized distalizer. Explains 3D designing, printing and clinical application of individualized biocompatible medical device dedicated for orthodontic teeth distalization. Compares such distalizer manufactured from two different biocompatible photopolymers (white and transparent). Evaluates their clinical performance and also patients’ aesthetical perception. Clinical part includes comparison of treatment debonding on the set of 12 complete orthodontic treatments with uni-lateral class II malocclusion managed with hybrid approach (CAT-Invisalign with 3D printed distalizer). Paper offers an evaluation of the personalized distalizer functioning in regard to current publications and comparison to conventional prefabricated alternatives like Carriere® Distalizer™ appliance. Results showed no significance of material differences on clinical performance of such individualized distalizers. Research showed preference of patients towards transparent biocompatible photopolymer instead of white A2 shade.

English based datasets are commonly available from Kaggle, GitHub, or recently published papers. Although benchmark tests with English datasets are sufficient to show off the performances of new models and methods, still a researcher need to train and validate the models on Korean based datasets to produce a technology or product, suitable for Korean processing. This paper introduces 15 popular Korean based NLP datasets with summarized details such as volume, license, repositories, and other research results inspired by the datasets. Also, I provide high-resolution instructions with sample or statistics of datasets. The main characteristics of datasets are presented on a single table to provide a rapid summarization of datasets for researchers., 11 pages, 1 horizontal page for large table

The Industrial Internet of things (IIoT) enabled smart system has entered into a golden era of rapid technology growth. IIoT is a concept to make every system interrelated such that they are able to collect and transfer data over a wireless network without human intervention. In this paper, we discuss the development of an IoT enabled system to monitor the vibration signature of equipment as part of prognosis and availability management system (P&AM) that serves to prevent unplanned operation downtime and catastrophic failure of a whole system. In order to simply the complexity of processing video content and performing inference, the Intel OpenVINO platform was selected because of it’s simplicity, portability across Intel AI processors, performance and comprehensiveness of it’s analytical and diagnostics capabilities that can be tested in Intel’s DevCloud. The IIoT system consists of a High Performance Computing (HPC) platform based on Intel’s Xeon processors and Movidius AI accelerator, Intel’s OpenVINO toolkit for AI, a Regul high performance programmable controller capturing vibration data through sensors and a low-latency network connection. Notifications of anomalies are sent to a smartphone. This paper reveals an approach for the features extraction and selection, known as feature engineering, of the equipment component we want to protect. Feature engineering is the first step for the P&AM of these components and extends to the whole system. The broader aim of this paper is to help technical leaders at the exploring or experimenting stages of their AI framework to learn the concepts of implementing algorithms using datasets that have real value to their companies. Datasets generated and referred to in this paper were generated by simulation under various material failure scenarios.

The shift to e-commerce has changed many business areas. Real estate is one of the applications that has been affected by this modern technological wave. Recommender systems are intelligent models that assist users of real estate platforms in finding the best possible properties that fulfill their needs. However, the recommendation task is substantially more challenging in the real estate domain due to the many domain-specific limitations that impair typical recommender systems. For instance, real estate recommender systems usually face the clod-start problem where there are no historical logs for new users or new items, and the recommender system should provide recommendations for these new entities. Therefore, the recommender systems in the real estate market are different and substantially less studied than in other domains. In this article, we aim at providing a comprehensive and systematic literature review on applications of recommender systems in the real estate market. We evaluate a set of research articles (13 journal and 13 conference papers) which represent the majority of research and commercial solutions proposed in the field of real estate recommender systems. These papers have been reviewed and categorized based on their methodological approaches, the main challenges that they addressed, and their evaluation procedures. Based on these categorizations, we outlined some possible directions for future research.

With the automotive industry moving towards automated driving, sensing is becoming an increasingly important part of enabling technology. The virtual sensors allow data fusion from various vehicle sensors and provide a prediction for measurement that is hard or too expensive to measure in another way or in the case of demand on continuous detection. In this paper, virtual sensing is discussed for the case of vehicle suspension control, where information about the relative velocity of the unsprung mass for each vehicle corner is required. The corresponding goal can be identified as a regression task with multi-input sequence input. The hypothesis is that the state-of-art method of Bidirectional Long-Short Term Memory (BiLSMT) can solve it. In this paper, a virtual sensor has been proposed and developed by training a neural network model. The simulations have been performed using an experimentally validated full vehicle model in IPG Carmaker. Simulations provided the reference data which was used for Neural Network (NN) training. The extensive dataset covering 26 scenarios has been used to obtain training, validation and testing data. The Bayesian Search was used to select the best neural network structure using root mean square error as a metric. The best network is made of 167 BiLSTM, 256 fully connected hidden units and 4 output units. Error histograms and spectral analysis of the predicted signal compared to the reference signal are presented. The results demonstrate the good applicability of neural network-based virtual sensors for the estimation of vehicle unsprung mass relative velocity.

This paper presents a novel architecture for detecting mathematical formulas in document images, which is an important step for reliable information extraction in several domains. Recently, Cascade Mask R-CNN networks have been introduced to solve object detection in computer vision. In this paper, we suggest a couple of modifications to the existing Cascade Mask R-CNN architecture: First, the proposed network uses deformable convolutions instead of conventional convolutions in the backbone network to spot areas of interest better. Second, it uses a dual backbone of ResNeXt-101, having composite connections at the parallel stages. Finally, our proposed network is end-to-end trainable. We evaluate the proposed approach on the ICDAR-2017 POD and Marmot datasets. The proposed approach demonstrates state-of-the-art performance on ICDAR-2017 POD at a higher IoU threshold with an f1-score of 0.917, reducing the relative error by 7.8%. Moreover, we accomplished correct detection accuracy of 81.3% on embedded formulas on the Marmot dataset, which results in a relative error reduction of 30%. Validerad;2021;Nivå 2;2021-09-01 (alebob);Forskningsfinansiär: European project INFINITY (883293)

The paper describes problems with the current additive manufacturing chain before considering additive manufacturing as part of a modern manufacturing chain. Additive manufacturing can be used for near net-shape for finishing, for repair or for adding special features which cannot be made with traditional manufacturing. This paper describes how STEP-NC deals with these different scenarios in terms of accuracy, multi-material and variation of slice direction. The possibilities of multi-material objects also raises questions about the design of such objects and how these need to be handled by an advanced controller. The paper also describes non-planar slicing. Curved direction and cylindrical direction are shown to improve the accuracy of curved structure additive manufacturing. STEP-NC using boundary representation has better capability of depicting complex internal structures for additive processes. By using exact model of the final product represented by STEP-NC, the paper demonstrates improvements in data size reduction, slicing accuracy, and precise manipulation of internal structure.

This paper presents the super-twisting algorithm (STA) direct power control (DPC) scheme for the control of active and reactive powers of grid-connected DFIG. Simulations of 5 KW DFIG has been presented to validate the effectiveness and robustness of the proposed approach in the presence of uncertainties with respect to vector control (VC). The proposed controller schemes with fixed gains are effective in reducing the ripple of active and reactive powers, effectively suppress sliding-mode chattering and the effe This paper presents a comparative study of two approaches for the direct power control (DPC) of doubly-fed induction generator (DFIG) based on wind energy conversion system (WECS). Vector Control (VC) and Sliding Mode Control (SMC). The simulation results of the DFIG of 5 KW in the presence of various uncertainties were carried out to evaluate the capability and robustness of the proposed control scheme. The (SMC) strategy is the most appropriate scheme with the best combination such as reducing high powers ripple, diminishing steady-state error in addition to the fact that the impact of machine parameter variations does not change the system performance. cts of parametric uncertainties not affecting system performance.

In this paper, we propose methods for brain tumor detection in MRI images based on ensemble learning. We build upon prior research on ensemble methods by testing the concatenation of pre-trained models: features extracted via transfer learning are merged and segmented by classification algorithms or a stacked ensemble of those algorithms. The proposed approach achieved accuracy scores of 0.98 , outperforming a benchmark VGG-16 model. Considerations to granular computing are given in the paper as well.

Around the world, every year, about 17 million people death cause happen due to CardioVascular Diseases (CVD). As per clinical records, primarily sufferers exhibit myocardial infarctions and Heart Failures (HF). Creatinine is a Musculo - skeletal waste product. The kidneys filter creatinine from the blood and excrete it through the urine in a healthy body. High creatinine levels can suggest renal problems. Elevated Serum Creatinine (SC) has been well established in the HF. Patients’ electronic medical records can be used to quantify symptoms and other related clinical laboratory test values, which would then be utilized to direct biostatistics exploration to uncover patterns and associations that doctors would otherwise miss. The latest American Heart Association guidelines for 1500 mg/d sodium tend to be sufficiently relevant for patients with stage A and B with HF. In this article, we used a dataset of the year 2015 of heart patients records of 299 patients. The present paper used the data analytic and statistical tools to verify the significant differences between alive and dead patients’ SC and Serum Sodium (SS). It also demonstrates the impact of significant features on abnormal SC and SS on the Survival-Status levels. The Age-Group feature, which is derived from age attribute and, Ejection Fraction (EF), anemia, platelets, Creatinine Phosphokinase (CPK), Blood-Pressure (BP), gender, diabetes, and smoking-status were utilized to determine the potential contributing features to mortality with Cox regression model. The Kaplan Meier plot was used to investigate the overall pattern of survival concerning age-group. During pre-processing of the dataset, Age and SS were removed due to multicollinear features during performing machine learning algorithms experiments. This paper also predicted patients’ survival, age group, and gender using supervised machine learning classifiers. Detection of significant features would help in making informed decisions to balance the lifestyle of heart patients. The author revealed that the patient’s follow-up months, as well as SC, EF, CPK, and platelets, are sufficient key features to predict heart patient survival using Random Forest (RF) stratified 10-fold CV method with accuracy (96%) with 5% Standard Deviation (SD) from medical records dataset. We identified the age-group and gender of the patient, and the RF model outperformed others with the best accuracy 96% and 94% in both cases having 11% SD. Also, prominent features such as CPK, SC, follow-up month, platelets, and ejection were found to be significant factors in predicting the patient’s age-group. Smoking habits, CPK, platelets, follow-up month, and SC of each patient were discovered to be significant predictors of patient gender. The hypothetical study proved that SC and SS making substantial differences in the survival of patients (p < 0.05) and failed to reject that anemia, diabetes, and BP making a significant impact on the creatinine and sodium of each patient (p > 0.05). With χ2(1) = 8.565, the Kaplan Meier plot revealed that mortality was high in the extremely elder age-group. The finding has possible effects on clinical practice and becomes a new medical support system when predicting whether a patient can survive a heart attack or not. The doctor should primarily concentrate on follow-up month, SC and EF, CPK, and platelet count since the aim is to understand whether a patient survives after HF.

In this paper, the author utilizes Saddle Theorem and variational methods to deduce existence of at least six stationary solutions for reaction-diffusion Gilpin-Ayala competition model (RDGACM). To obtain the global stabilization of the positive stationary solution of the RDGACM, the author designs a suitable impulsive event triggered mechanism (IETM) to derive the global exponential stability of the the positive stationary solution. It is worth mentioning that the new mechanism can exclude Zeno behavior and effectively reduce the cost of impulse control through event triggering mechanism. Besides, compared with existing literature, the restrictions on the parameters of the RDGACM are relaxed so that the methods used in existing literature can not be applied to the relaxed case of this paper, and so the author makes comprehensive use of Saddle Theorem, orthogonal decomposition of Sobolev space $H_0^1(\Omega)$ and variational methods to overcome the mathematical difficulty. Numerical examples show the effectiveness of the methods proposed in this paper.

The main purpose of this paper is to present a comprehensive view of the application mathematical models in the designing and implementing SSCM beside to solving problems and making decision. The research questions are: what mathematical models are used for designing and implementing sustainable supply chain management, how to use them, which industries implemented in, what modules of SSCM depth in and finally finding the gaps of researches. The methodology of research is Systematic Literature review through peer review papers which are published in high ranking journals. In this paper, First, we search all papers through scientific data bases like Scopus, science direct, MDPI, Springer, Google Scholar, then, screening papers based on the criteria such as subject of paper, journals impact factor which is published in-should be peer review journal- and relative content of the papers. Finally, we selected 245 papers with three steps screening through 2806 papers that they have enough quality and relative to our research goals for context analysis. Through context analysis, first we categorized the information of the papers and drew the current situation of researches in the framework of our topic. Then, we evaluate and compare the goals of sustainability and current situation and found the gapes, then, offered new suggestions like implementing SSCMs models in pollutant industries like casting industry, Heavy industry, coal Industry and so on. On the other hand, there are gaps in researches in some modules of SSCM such as packaging, designing products, etc.

As RPG has high sales and profits, lots of developers have supplied various RPG to market but it changed to mass production type with sensational advertising, low quality and excessive charging and similar contents which affects game market and users’ game play experience. The author of this paper studied ways to improve mobile RPG by collecting and analyzing users’ reviews using crawling on Google Play Store. The author of this paper used topic modeling that uses text mining technique and LDA (Latent Dirichlet Allocation) to extract meaningful information from collected big data and visualized it. Inferring users’ reviews, figuring out opinions objectively and seeking ways to improve games are helpful in improving mobile RPG that can be played continuously.

This is break direction on oxygen sources and conveyance methodologies for COVID-19 treatment. It has been adjusted from WHO and UNICEF's specialized details and direction for oxygen treatment gadgets, which is important for the WHO clinical gadget specialized arrangement, 1 and depends on current information on the circumstance in China and different nations where cases have been distinguished. This direction is proposed for wellbeing office chairmen, clinical leaders, acquisition officials, arranging officials, biomedical architects, foundation engineers and strategy producers. It portrays how to: measure oxygen interest, to distinguish oxygen sources that are accessible, and select suitable flood sources to best react to COVID-19 patients' requirements, particularly in low-and-center pay nations. WHO will refresh these suggestions as new data opens up. Coronavirus pandemic spurred fake interest for oxygen gas chambers for clinical use - both at emergency clinics and inquisitively, for home use by patients. A few patients and surprisingly sound people investigate the conceivable outcomes and likely benefits of utilizing oxygen from chambers for private utilization. Be that as it may, this isn't continuously protected, and sufficient safety measures are to be taken, bombing which there can be fatalities. This paper investigates the significance of keeping up satisfactory degrees of oxygen levels appropriate for human utilization. It advises the clinical use and the advantages and disadvantages of putting away oxygen chambers at home. The investigation likewise addresses lawful and administrative perspectives. The investigation's discoveries can help people settle on an educated choice on the protected use regarding oxygen gas. Further, it cautions on the expanded significance of guidelines and limiting access and use. This paper aims at designing an oxygen level monitoring technique in an oxygen cylinder. The amount of oxygen present inside the oxygen cylinder is very vital information when such cylinder is in use for supply of oxygen to a critical patient. The amount of oxygen present inside the cylinder is measured by the pressure at the outlet nozzle. The pressure is measured using a high precision MEMS Pressure Sensor. The output of the MEMS pressure sensor is voltage of the order milli. An amplifier is used to amplify this milli volt signal. A microcontroller is used in cascade to process the signal and display the pressure of oxygen cylinder.

This paper presents an innovative concept for the online application of Frequency Response Analysis (FRA). FRA is a well known technique that is applied to detect damage in electric machinery. As an offline technique, the machine under testing has to be removed from service—which may cause loss of production. Experimental adaptations of FRA to online operation are usually based on the use of passive high pass coupling—which, ideally, should provide attenuation to the grid voltage, and at the same time, allow the high frequency FRA signals to be injected at the machine. In practice, however, the passive coupling results in a trade-off between the required attenuation and the useful area obtained at the FRA spectra. This paper proposes the use of an active coupling system, based on power electronics, in order to cancel the grid voltage at the terminals of FRA equipment and allow its safe connection to an energized machine. The paper presents the basic concepts of FRA and the issue of online measurements. It also presents basic concepts about power electronics converters and the operating principles of the Modular Multilevel Converter, which enables the generation of an output voltage with low THD, which is important for tracking the grid voltage with minimum error.

Offline handwritten text recognition (HTR) for historical documents aims for effective transcription by addressing challenges that originate from the low quality of manuscripts under study as well as from several particularities which are related to the historical period of writing. In this paper, the challenge in HTR is related to a focused goal of the transcription of Greek historical manuscripts that contain several particularities. To this end, in this paper, a convolutional recurrent neural network architecture is proposed that comprises octave convolution and recurrent units which use effective gated mechanisms. The proposed architecture has been evaluated on three newly created collections from Greek historical handwritten documents that will be made publicly available for research purposes as well as on standard datasets like IAM and RIMES. For evaluation we perform a concise study which shows that compared to state of the art architectures, the proposed one deals effectively with the challenging Greek historical manuscripts.

This paper describes the matching of various loads to sources (including nonlinear ones). The purpose of matching is to extract the maximum available power from the source. This has particular importance for renewable sources and energy-harvesting devices, in which unused energy is just wasted. The main innovations in this paper include (and followed by examples) simplified calculation of the matching parameter for a controllable load and matching by means of a family of power-conservative two-port networks, denoted POPI (Pin = Pout), such as transformers, gyrators, loss-free resistors (LFRs) and series LFRs (SLFRs). An additional innovation described in this paper is a new, simplified model of an HF power amplifier based on the series LFR concept. This model predicts that the efficiency of the HF power amplifier operated under the matched-mode condition can significantly exceed the 50% efficiency limit that is predicted by the conventional model. As HF power amplifiers drive antennas in transmission and some wireless power transfer (which uses radiative techniques) systems, it is clear that the operation of such systems in the matched-mode condition is not restricted to a 50% efficiency limit.

In recent times, significant research has been carried out into developing and applying soft computing techniques for modeling hydro-climatic processes such as seepage modeling. It is necessary to properly model seepage, which creates groundwater sources, to ensure adequate management of scarce water resources. On the other hand, excessive seepage can threaten the stability of earthfill dams and infrastructures. Furthermore, it could result in severe soil erosion and consequently cause environmental damage. Considering the complex and nonlinear nature of the seepage process, employing soft computing techniques, especially applying pre-post processing techniques as hybrid methods, such as wavelet analysis, could be appropriate to enhance modeling efficiency. This review paper summarizes standard soft computing techniques and reviews their seepage modeling and simulation applications in the last two decades. Accordingly, 48 research papers from 2002 to 2021 were reviewed. According to the reviewed papers, it could be understood that regardless of some limitations, soft computing techniques could simulate the seepage successfully either through groundwater or earthfill dam and hydraulic structures. Moreover, some suggestions for future research are presented. This review was conducted employing preferred reporting items for systematic reviews and meta-analyses (PRISMA) method.

Wideband radar has high-range directional resolution, which can effectively reduce the fluctuation of echo and improve the detection probability of a target under the same detection probability requirement. In this paper, a unified wideband radar χ2 distribution target model with more practical significance is innovatively established, on which the probability density function and detection probability function of Swerling 0, Swerling II and Swerling IV targets are analyzed, respectively. A generalized “frequency diversity gain” of wideband radar is proposed and defined based on the contradiction between suppression of fluctuation and accumulation loss, which represents the ratio of Signal-to-Noise Ratio (SNR) gain between broadband signal and reference bandwidth signal under the same condition (when the reference bandwidth is used, the radar target has only one range unit), and the mathematical relation equation of the target detection performance and signal bandwidth (equivalent to the number of distinguishable range elements of the target) is given. A Monte Carlo simulation experiment is designed. Based on the target model established in this paper, the optimal number of target range units corresponding to different detection probability requirements is obtained, which verifies the correctness of the concept proposed in this paper.

A digital-correlation full-polarized microwave radiometer is an important passive remote sensor, as it can obtain the amplitude and phase information of an electromagnetic wave at the same time. It is widely used in the measurement of sea surface wind speed and direction. Its configuration is complicated, so the error analysis of the instrument is often difficult. This paper presents a full-polarized radiometer system model that can be used to analyze various errors, which include input signal models and a full-polarized radiometer (receiver) model. The input signal models are generated by WGN (white Gaussian noise), and the full-polarized radiometer model consists of an RF front-end model and digital back-end model. The calibration matrix is obtained by solving the overdetermined equations, and the output voltage is converted into Stokes brightness temperature through the calibration matrix. Then, we use the four Stokes parameters to analyze the sensitivity, linearity, and calibration residuals, from which the simulation model is validated. Finally, two examples of error analysis, including gain imbalance and quantization error, are given through a simulation model. In general, the simulation model proposed in this paper has good accuracy and can play an important role in the error analysis and pre-development of the fully polarized radiometer.