Search

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

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

In recent years, microfluidic lab-on-paper devices have emerged as a rapid and low-cost alternative to traditional laboratory tests. Additionally, they were widely considered as a promising solution for point-of-care testing (POCT) at home or regions that lack medical infrastructure and resources. This review describes important advances in microfluidic lab-on-paper diagnostics for human health monitoring and disease diagnosis over the past five years. The review commenced by explaining the choice of paper, fabrication methods, and detection techniques to realize microfluidic lab-on-paper devices. Then, the sample pretreatment procedure used to improve the detection performance of lab-on-paper devices was introduced. Furthermore, an in-depth review of lab-on-paper devices for disease measurement based on an analysis of urine samples was presented. The review concludes with the potential challenges that the future development of commercial microfluidic lab-on-paper platforms for human disease detection would face.

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

Since the introduction of paper-based analytical devices as potential diagnostic platforms a few decades ago, huge efforts have been made in this field to develop systems suitable for meeting the requirements for the point-of-care (POC) approach. Considerable progress has been achieved in the adaptation of existing analysis methods to a paper-based format, especially considering the chemiluminescent (CL)-immunoassays-based techniques. The implementation of biospecific assays with CL detection and paper-based technology represents an ideal solution for the development of portable analytical devices for on-site applications, since the peculiarities of these features create a unique combination for fitting the POC purposes. Despite this, the scientific production is not paralleled by the diffusion of such devices into everyday life. This review aims to highlight the open issues that are responsible for this discrepancy and to find the aspects that require a focused and targeted research to make these methods really applicable in routine analysis.

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

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.

With the rapid development of the autonomous world, local decision making between devices is becoming important. This article provides a new paradigm (Rock-Paper-Scissors-Hammer: RPSH) that can reduce the number of conflicts or decision draws and thus increase the throughput of autonomous devices while reducing the kept number of records or transactions. The paradigm requires a sealed envelope protocol and sequential message passing between both parties to decide unanimously a winner between the two participants without a third-party mediation. The message passing proposes a detailed record in a blockchain-like format that is not corruptible and is verifiable for conflict resolution. A simulated IoT environment is created to show the advantage of the proposed protocol and it shows significant reduction in mean efforts due to the elimination of draws or undecided situations. Autonomous devices, such as cars, need to maintain meticulous, lightweight, but blockchain-like record keeping for insurance settlements or conflict resolutions, that archival data size is significantly reduced by the RPSH protocol.

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.

For this Special Issue “2020 Feature Papers by JDB’ Editorial Board Members,” we present a collection of studies, including original research papers, and review articles by our distinguished editorial board members that focus on advances in understanding multicellular organisms’ growth, differentiation, and remodeling [...]

Urbanization is accelerating due to economic and societal development. The accurate identification of urban functional zones is significant for urban structure optimization, urban planning, and resource allocation. This paper reviews the scholarly literature on urban functional zone identification. Based on the retrieval results of databases, we analyzed the overview and current status. The identification methods and classification schemes are summarized from the existing research. The following results were obtained: (1) point of interest (POI) data are widely used for functional zone identification; (2) the block is the most common unit for functional zone identification; (3) cluster analysis is the main approach for urban functional zone identification; (4) most of the classification schemes are based on the dominant land use and characteristics of data sources. We predict future trends of urban functional zone identification based on the reviewed literature. Our findings are expected to be valuable for urban studies.

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

In mobile systems, fog, rain, snow, haze, and sun glare are natural phenomena that can be very dangerous for drivers. In addition to the visibility problem, the driver must face also the choice of speed while driving. The main effects of fog are a decrease in contrast and a fade of color. Rain and snow cause also high perturbation for the driver while glare caused by the sun or by other traffic participants can be very dangerous even for a short period. In the field of autonomous vehicles, visibility is of the utmost importance. To solve this problem, different researchers have approached and offered varied solutions and methods. It is useful to focus on what has been presented in the scientific literature over the past ten years relative to these concerns. This synthesis and technological evolution in the field of sensors, in the field of communications, in data processing, can be the basis of new possibilities for approaching the problems. This paper summarizes the methods and systems found and considered relevant, which estimate or even improve visibility in adverse weather conditions. Searching in the scientific literature, in the last few years, for the preoccupations of the researchers for avoiding the problems of the mobile systems caused by the environmental factors, we found that the fog phenomenon is the most dangerous. Our focus is on the fog phenomenon, and here, we present published research about methods based on image processing, optical power measurement, systems of sensors, etc.

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 [...]

V tomto článku testujeme hypotézu, že informatické články kladoucí otázky (tj. s otazníkem na konci názvu) se citují častěji než ty, které tuto vlastnost nemají. Za tímto účelem analyzujeme datovou sadu s téměř dvěma miliony záznamů o odborných článcích v počítačových oborech, jež jsou indexovány v databázi Web of Science, a zaměřujeme svou pozornost na průměrnou citovanost příspěvků v jejích specifických podmnožinách. Hlavním poznatkem je, že průměrná citovanost tzv. „dotazujících se článků“ je o téměř 20 % vyšší než u jiných příspěvků a že tento rozdíl je statisticky významný. In this article, we test the hypothesis that computer science papers asking questions (i.e., those with a question mark at the end of their title) are cited more frequently than those that do not have this property. To this end, we analyze a data set of almost two million records on computer science papers indexed in the Web of Science database and focus our investigation on the mean number of citations per paper of its specific subsets. The main finding is that the average number of citations per paper of the so-called “asking papers” is greater by almost 20% than that of other papers, and that this difference is statistically significant.

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

This short paper concerns the analysis of the M/M/k queueing system with customer abandonment. In this system, service managers provide a finite buffer space, which is a waiting area that prevents customers from abandoning the system. Abandonment of the system can occur from reneging (exiting from the queue while waiting), and/or balking (leaving the system without waiting). We derive an analytical expression to represent the impact of the buffer space capacity on the delay probability and the abandonment probability for a system with deferred abandonment. The result indicates the provision of the buffer space in a large system could only increase the delay probability while the abandonment probability remains unchanged. Despite the benevolent intentions of service managers, providing a buffer space may exacerbate the performance of larger systems.

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

International audience; We introduce a blind spot method to create image changes contingent on eye movements. One challenge of eye movement research is triggering display changes contingent on gaze. The eye-tracking system must capture the image of the eye, discover and track the pupil and corneal reflections to estimate the gaze position, and then transfer this data to the computer that updates the display. All of these steps introduce delays that are often difficult to predict. To avoid these issues, we describe a simple blind spot method to generate gaze contingent display manipulations without any eye-tracking system and/or display controls.

This article introduces a 6P framework for understanding intelligence, as well as the theories and tests that are derived from it. The 6Ps in the framework are purpose, press, problems, persons, processes, and products underlying intelligence. Each of the 6Ps is considered in turn. We argue that although the purpose of intelligence is culturally universal, the other Ps can vary at least somewhat over time and space. A single theory or test of intelligence represents a particular configuration of the 6Ps, but other configurations of the 6Ps might yield different theories and different tests.

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.

Drilling and blasting remain the preferred technique used for rock mass breaking in mining and construction projects compared to other methods from an economic and productivity point of view. However, rock mass breaking utilizes only a maximum of 30% of the blast explosive energy, and around 70% is lost as waste, thus creating negative impacts on the safety and surrounding environment. Blast-induced impact prediction has become very demonstrated in recent research as a recommended solution to optimize blasting operation, increase efficiency, and mitigate safety and environmental concerns. Artificial neural networks (ANN) were recently introduced as a computing approach to design the computational model of blast-induced fragmentation and other impacts with proven superior capability. This paper highlights and discusses the research articles conducted and published in this field among the literature. The prediction models of rock fragmentation and some blast-induced effects, including flyrock, ground vibration, and back-break, were detailed investigated in this review. The literature showed that applying the artificial neural network for blast events prediction is a practical way to achieve optimized blasting operation with reduced undesirable effects. At the same time, the examined papers indicate a lack of articles focused on blast-induced fragmentation prediction using the ANN technique despite its significant importance in the overall economy of whole mining operations. As well, the investigation revealed some lack of research that predicted more than one blast-induced impact.

This is the world’s first tutorial article on Python Packaging for beginners and practitioners for translational medicine or medicine in general. This tutorial will allow researchers to demonstrate and showcase their tools on PyPI packages around the world. Nowadays, for translational medicine, researchers need to deal with big data. This paper describes how to build an executable Python Package Index (PyPI) code and package. PyPI is a repository of software for the Python programming language with 5,019,737 files and 544,359 users (programmers) as of 19 October 2021. First, programmers must understand how to scrape a dataset over the Internet; second, they must read the dataset file in csv format; third, build a program to compute the target values; fourth, convert the Python program to the PyPI package.; and fifth, upload the PyPI package. This paper depicts a covidlag executable package as an example for calculating the accurate case fatality rate (CFR) and the lag time from infection to death. You can install the covidlag by pip terminal command and test it. This paper also introduces deathdaily and scorecovid packages on PyPI Stats, which can inform how many users have downloaded the specified PyPI package. The usefulness and applicability of a developed tool can be verified by PyPI Stats with the number of downloaded users.

Precise environmental modelling of pollutants distributions represents a key factor for addresing the issue of urban air pollution. Nowadays, urban air pollution monitoring is primarily carried out by employing sparse networks of spatially distributed fixed stations. The work in this paper aims at improving the situation by utilizing machine learning models to process the outputs of multi-sensor devices that are small, cheap, albeit less reliable, thus a massive urban deployment of those devices is possible. The main contribution of the paper is the design of a mathematical model providing sensor fusion to extract the information and transform it into the desired pollutant concentrations. Multi-sensor outputs are used as input information for a particular machine learning model trained to produce the CO, NO2, and NOx concentration estimates. Several state-of-the-art machine learning methods, including original algorithms proposed by the authors, are utilized in this study: kernel methods, regularization networks, regularization networks with composite kernels, and deep neural networks. All methods are augmented with a proper hyper-parameter search to achieve the optimal performance for each model. All the methods considered achieved vital results, deep neural networks exhibited the best generalization ability, and regularization networks with product kernels achieved the best fitting of the training set.

Metamaterials (MTMs) based on a periodic array of resonant coils have been shown to behave as μ-negative (MNG), enabling the focusing of magnetic flux. The phenomenon has been deployed by designers to boost the efficiency of many inductively coupled systems, such as magnetic resonance imaging, underwater and underground communications, and charging base stations (CBS) for consumer electronics and implanted devices. However, due to their dependency on high-Q unit cells, linear MNG-like MTMs have limited bandwidth, restricting their use in many applications, notably in near-field simultaneous wireless information and power transmission (NF-SWIPT) systems. To improve the tight constraints of the amplitude-bandwidth trade-off of artificial magnetic lenses, this paper presents a theoretical analysis of nonlinear MTMs based on a lattice of Duffing resonators (DRs). Additionally, it introduces a criterium for the quantification and evaluation of the amplitude-bandwidth enhancement. The analytical results are based on a circuit model and further verified by numerical simulations using commercial software. The preliminary findings in this paper open up possibilities for nonlinear MTM lenses and can be applied to enhance the linear amplitude-bandwidth limit.

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’s primary objective is to segment the online marketplace of the Canary Islands’ museums by using different conversion funnel metrics. Little systematic research exists on digital user behaviour, and much less is known about how to segment cultural users with structured data from manually extracted and SEO software sources. With this aim in mind, we built a database with data related to the different phases of the conversion funnel of the museums to segment this online museum marketplace. In the findings, not only do we acknowledge the existence of different segments, but we also provide insight into the user’s digital behaviour by considering different metrics from the different phases of the conversion model process (awareness, consideration, conversion and loyalty). The originality of this paper is multifold. Firstly, it estimates the potential optimisation of these websites to improve the digital marketing implemented by the museum sector of the Canary Islands. Secondly, it sheds light on what benchmarking tactics and statistics procedures can be followed to carry out a non-hierarchical segmentation with standardised and comparable data. Thirdly, it contributes to the literature of digital marketing by eclectically combining the conversion funnel model, benchmarking techniques and non-hierarchical segmentation procedures.

This paper discussed the application of health monitoring systems to 20th-century historic buildings. Natural disasters are major threats to monuments. They are often seismically vulnerable and require interventions. However, taking into account their historic and cultural values, it is appropriate to observe long-term behaviour before making a decision on intervention schemes. To this aim, health monitoring is considered an effective approach. In recent years, MEMS (micro-electromechanical systems) accelerometers have been attracting attention for their convenience and efficacy. Nonetheless, the reliability of MEMS accelerometers still needs to be examined for the monitoring of monuments as sufficient research contributions have not been made. This paper presented two case studies that were monitored by means of MEMS accelerometers. They were masonry structures positioned in seismic-prone regions in Japan. A number of earthquakes were detected by the accelerometers during one year of monitoring. To examine the accuracy of the adopted MEMS accelerometers, dynamic identification tests were conducted using high-sensitivity strain-gauge accelerometers and servo velocity meters. Based on responses obtained from the tests, numerical simulation was performed. Nonlinear static analysis was performed. The numerical simulation permitted the comparison of reliability among sensors and test types. This paper provided suggestions for the dynamic identification tests of heritage structures.

Cryptocurrency is a new sort of asset that has emerged as a result of the advancement of financial technology and it has created a big opportunity for researches. Cryptocurrency price forecasting is difficult due to price volatility and dynamism. Around the world, there are hundreds of cryptocurrencies that are used. This paper proposes three types of recurrent neural network (RNN) algorithms used to predict the prices of three types of cryptocurrencies, namely Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH). The models show excellent predictions depending on the mean absolute percentage error (MAPE). Results obtained from these models show that the gated recurrent unit (GRU) performed better in prediction for all types of cryptocurrency than the long short-term memory (LSTM) and bidirectional LSTM (bi-LSTM) models. Therefore, it can be considered the best algorithm. GRU presents the most accurate prediction for LTC with MAPE percentages of 0.2454%, 0.8267%, and 0.2116% for BTC, ETH, and LTC, respectively. The bi-LSTM algorithm presents the lowest prediction result compared with the other two algorithms as the MAPE percentages are: 5.990%, 6.85%, and 2.332% for BTC, ETH, and LTC, respectively. Overall, the prediction models in this paper represent accurate results close to the actual prices of cryptocurrencies. The importance of having these models is that they can have significant economic ramifications by helping investors and traders to pinpoint cryptocurrency sales and purchasing. As a plan for future work, a recommendation is made to investigate other factors that might affect the prices of cryptocurrency market such as social media, tweets, and trading volume.

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.

Both historically and in terms of practiced academic organization, the anticipation should be that a flourishing synergistic interface exists between statistics and operations research in general, and between spatial statistics/econometrics and spatial optimization in particular. Unfortunately, for the most part, this expectation is false. The purpose of this paper is to address this existential missing link by focusing on the beneficial contributions of spatial statistics to spatial optimization, via spatial autocorrelation (i.e., dis/similar attribute values tend to cluster together on a map), in order to encourage considerably more future collaboration and interaction between contributors to their two parent bodies of knowledge. The key basic statistical concept in this pursuit is the median in its bivariate form, with special reference to the global and to sets of regional spatial medians. One-dimensional examples illustrate situations that the narrative then extends to two-dimensional illustrations, which, in turn, connects these treatments to the spatial statistics centrography theme. Because of computational time constraints (reported results include some for timing experiments), the summarized analysis restricts attention to problems involving one global and two or three regional spatial medians. The fundamental and foundational spatial, statistical, conceptual tool employed here is spatial autocorrelation: geographically informed sampling designs—which acknowledge a non-random mixture of geographic demand weight values that manifests itself as local, homogeneous, spatial clusters of these values—can help spatial optimization techniques determine the spatial optima, at least for location-allocation problems. A valuable discovery by this study is that existing but ignored spatial autocorrelation latent in georeferenced demand point weights undermines spatial optimization algorithms. All in all, this paper should help initiate a dissipation of the existing isolation between statistics and operations research, hopefully inspiring substantially more collaborative work by their professionals in the future.

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.