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Mitigation of global warming, energy security and industrial competitiveness urge the energy-intensive pulp and paper industry (PPI) to transform energy use practices. This study investigates how the PPI has responded to the need for the energy transition in the 2000s. Finland and Sweden as forerunners of energy-efficient operation and decarbonization of the PPI are used as target countries. Understanding of changes in energy consumption is complemented using decomposition analysis (Logarithmic Mean Divisia Index Method) and the energy efficiency index approach. Analysis of companies’ investments in energy technologies is used for explaining changes in energy production. Evidence of significant development towards the more sustainable operation of the PPI was found. Energy consumption per produced unit has decreased, i.e., energy efficiency has improved. Fossil fuels have been partially replaced with bio-based alternatives. Thus, the CO2 intensity has decreased substantially. The generation of renewable electricity has increased in both countries. Examples of Finland and Sweden indicate that the PPI has great potential to contribute to CO2 emission reduction worldwide in the future as energy efficiency can be further improved, and the share of fossil fuels can be decreased increasing the use of biofuels and self-generated green electricity at least in kraft pulp mills.

A wide variety of biomass is available all around the world. Most of the biomass exists as a by-product from manufacturing industries. Pulp and paper mills contribute to a higher amount of these biomasses mostly discarded in the landfills creating an environmental burden. Biomasses from other sources have been used to produce different kinds and grades of biomaterials such as those used in industrial and medical applications. The present review aims to investigate the availability of biomass from pulp and paper mills and show sustainable routes for the production of high value-added biomaterials. The study reveals that using conventional and integrated biorefinery technology the ample variety and quantity of waste generated from pulp and paper mills can be converted into wealth. As per the findings of the current review, it is shown that high-performance carbon fiber and bioplastic can be manufactured from black liquor of pulping waste; the cellulosic waste from sawdust and sludge can be utilized for the synthesis of CNC and regenerated fibers such as viscose rayon and acetate; the mineral-based pulping wastes and fly ash can be used for manufacturing of different kinds of biocomposites. The different biomaterials obtained from the pulp and paper mill biomass can be used for versatile applications including conventional, high performance, and smart materials. Through customization and optimization of the conversion techniques and product manufacturing schemes, a variety of engineering materials can be obtained from pulp and paper mill wastes realizing the current global waste to wealth developmental approach.

Waste-to-energy or value-added products have been increasingly considered in many pulp and paper mills (PPMs) worldwide. However, developing appropriate conversion technologies is a major challenge in transforming PPMs wastes into biofuels or value-added biomaterials. In the present study, a long-term (320 d) anaerobic digestion of primary sludge of a thermomechanical pulp mill (TMP) was carried out for the first time in a thermophilic anaerobic membrane bioreactor (ThAnMBR). Effect of organic loading rate (OLR) in the range of 2.5–6.8 kg-COD/m3 d and hydraulic retention times (HRT) of 3–8 d on the process performance was investigated. Under various OLRs, stable biogas productions were obtained, and the best results were achieved with lower OLR (2.5 kg-COD/m3 d) and higher HRT (8 d), at biogas yields of 189 L biogas/kg MLSS fed. However, it was found that biogas production and sludge biomass degradation decrease when the organic loading rate increases. The proportion of sludge reduction ranged from 28.9 to 46.7% depending on the applied OLRs. Despite varying OLRs, stable membrane performance was obtained, where the required membrane flux was easily maintained during the reactor operation. In this study, also the properties of digestate and membrane permeates were studied under different operating conditions, and they fluctuated to some extent with OLR. ThAnMBR is a promising new technology for pulp and paper mill primary sludge treatment.

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

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.

Ample interest for more efficient utilization of bio-based residues has emerged in the Nordic pulp and paper (P&P) industry, which uses virgin wood as feedstock. Although different bioenergy retrofit technologies for production of liquid, solid, and gaseous bioenergy products have been applied in the existing P&P mills, the number of installations remains small. The lack of profound knowledge of existing bioenergy retrofits hinders the replication and market uptake of potential technologies. This review synthesises the existing knowledge of European installations and identifies the key drivers and barriers for implementation to foster the market uptake of potential technologies. The bioenergy retrofits were reviewed in terms of technical maturity, drivers, barriers and market potential. Based on this evaluation, common drivers and barriers towards wider market uptake were outlined from political, economic, social, technical, environmental, and legal perspective. Technologies already commercially applied include anaerobic fermentation of sludge, bark gasification, tall oil diesel and bioethanol production, whereas lignin extraction, biomethanol production, hydrothermal liquefaction and hydrothermal carbonization are being demonstrated or first applications are under construction. The findings of this review show that a stable flow of residues at P&P mills creates a solid base for retrofitting. New innovative bio-based products would allow widening the companies' product portfolios and creating new businesses. Also, European Union's (EU) legislation drives towards advanced biofuels production. Wider uptake of the retrofitting technologies requires overcoming the barriers related to uncertainty of economic feasibility and unestablished markets for new products rather than technical immaturity.

Activating flux is made up of inorganic material mixed with a volatile medium. The objective of this study is to identify and review the literature in activating flux, and different type of welding techniques. For that purpose, a structured review method is adopted and the Scopus database is used as a primary source for the selection of papers. The papers were identified using the string term of “Activating flux” OR “Activated flux” in the title of papers. And a total of 238 papers were identified for the study. Besides the above few other papers were also reviewed and the materials used, the type of flux used, and the findings of different combinations were highlighted in the form of tables. The findings of the study provide a comprehensive understanding of the different type of welding techniques focusing on TIG welding, and the different combination of flux used and their impact was summarized.

The use of online learning and subsequent online testing has expanded rapidly in pharmacy programs across the United States. Numerous published studies have compared online learning with traditional methods in pharmacy. However, no such studies have been published in pharmacy. This study's objective is to compare first-professional year student preferences for computer-based vs. traditional paper-and-pencil testing for a required social and administrative pharmacy class.All students enrolled in the class were invited to complete one brief survey at the end of the semester to determine their testing preference.Of the 138 first-professional year students completing the survey, 79% (109 of 138) preferred computer-based testing, 9% had no preference, 6% (8 of 138) preferred paper-and-pencil testing, and the remainder (8 of 138) stated it depended on what was being tested. Ninety-one percent of students did not perceive the testing method to impact their grades. Students preferred computer-based testing over traditional paper-and-pencil as a convenient testing method (mean 4.73 vs. 3.4, P.001), providing immediate feedback (mean 4.87 vs. 1.91, P.001), and as a more effective testing method (mean 4.57 vs. 3.96, P.001).First-professional year students preferred computer-based over traditional paper-and-pencil testing for a social and administrative science class, with the method of testing not perceived to impact grade. This finding is timely, given the recent required transitioning of all pharmacy classes and subsequent testing to an online format due to the coronavirus disease of 2019 pandemic.

The authors of the title paper (Journal of Optics 49:181–186, 2020) claim to have grown a so-called urea potassium dichromate (UPDC) by slow evaporation of an aqueous solution containing equimolar amounts of urea and potassium dichromate and characterized it based on a unit cell study. A critical analysis reveals that the claims in the title paper are erroneous. Hence, we reinvestigated the crystal growth reaction and determined the structure of the product crystal. Based on these studies, we prove that due to no chemical reaction between the reagents fractional crystallization of potassium dichromate (K2Cr2O7) occurs and not formation of any so-called UPDC crystal. Taking the title crystal as an example we show the importance of reporting single-crystal structure for new compound characterization.

Membrane heat exchangers were used for moisture and heat recovery from a hot and humid natural gas stream in indirect contact with an air stream. A plate membrane module with spiral channels on each side was designed to configure gas and air streams in co-current and counter-current flow. Two types of membranes, a commercial kraft paper, and a laboratory-made thin-film composite membrane, were used as the module core. The thermal efficiency and the water recovery from the humid gas were calculated and compared under different operating conditions. At a unit flow ratio of air/gas, the greatest effectiveness was achieved at a methane flow of 100 cm3/min, but water recovery increased at higher gas flows. For the counterflow configuration, an air/methane flow ratio of 2 was sufficient to achieve the highest total effectiveness and water recovery; however, larger air/gas ratios were beneficial to improve heat and moisture transfer for the co-current configuration. The highest water recovery by the kraft paper and composite membrane was achieved (approximately 15 mgH2O/cm2.h) for the input gas with the highest humidity. The dehumidification performance of kraft paper as a porous and hydrophilic membrane was slightly better than that of the composite membrane containing a dense hydrophilic polymer thin film in most cases. A relatively low heat transfer coefficient was found for the module, which was increased by using higher gas flow rates at higher humidity conditions.

Background Display signage is ubiquitous and essential in hospitals to serve several clerical, operational, and clinical functions, including displaying notices, providing directions, and presenting clinical information. These functions improve efficiency and patient engagement, reduce errors, and enhance the continuity of care. Over time, signage has evolved from analog approaches such as whiteboards and handwritten notices to digital displays such as liquid crystal displays, light emitting diodes, and, now, electronic ink displays. Electronic ink displays are paper-like displays that are not backlit and show content by aligning microencapsulated color beads in response to an applied electric current. Power is only required to generate content and not to retain it. These displays are very readable, with low eye strain; minimize the emission of blue light; require minimal power; and can be driven by several data sources, ranging from virtual servers to electronic health record systems. These attributes make adapting electronic ink displays to hospitals an ideal use case. Objective In this paper, we aimed to outline the use of signage and displays in hospitals with a focus on electronic ink displays. We aimed to assess the advantages and limitations of using these displays in hospitals and outline the various public-facing and patient-facing applications of electronic ink displays. Finally, we aimed to discuss the technological considerations and an implementation framework that must be followed when adopting and deploying electronic ink displays. Methods The public-facing applications of electronic ink displays include signage and way-finders, timetables for shared workspaces, and noticeboards and bulletin boards. The clinical display applications may be smaller form factors such as door signs or bedside cards. The larger, ≥40-inch form factors may be used within patient rooms or at clinical command centers as a digital whiteboard to display general information, patient and clinician information, and care plans. In all these applications, such displays could replace analog whiteboards, noticeboards, and even other digital screens. Results We are conducting pilot research projects to delineate best use cases and practices in adopting electronic ink displays in clinical settings. This will entail liaising with key stakeholders, gathering objective logistical and feasibility data, and, ultimately, quantifying and describing the effect on clinical care and patient satisfaction. Conclusions There are several use cases in a clinical setting that may lend themselves perfectly to electronic ink display use. The main considerations to be studied in this adoption are network connectivity, content management, privacy and security robustness, and detailed comparison with existing modalities. Electronic ink displays offer a superior opportunity to future-proof existing practices. There is a need for theoretical considerations and real-world testing to determine if the advantages outweigh the limitations of electronic ink displays.

As a kind of industrial hazardous solid waste, paper mill sludge (PMS) was hard to treated by conventional process because of its high moisture, high organic content and and high environmental risk. The hydrothermal liquefaction (HTL) shows excellent potential for the conversion to a wide variety of organic matter with high water contents including various kinds sludge into fuels or value-added chemicals. In this paper, the liquefaction potential for PMS under different reaction temperatures were investigated, and the products were classified into several main components based on the results of GC-MS. The results showed that the highest yield of bio-oil was 44 % (wt/wt dry PMS without ash) at 340 °C with simply compound composition. And the bio-oil product has above 26 % long chain hydrocarbon and 22 % cycloketones. It has potential for conversion to biomass platform compounds. It can be used as bio-fuel for heat recovery of alkali recovery system of paper mill. So, adjusting different reaction temperatures of HTL, the properties of products can meet the different requirements for platform chemical and/or bio-oil fuel.

In this paper, we analyze the intelligent subdesign of the simulated marking system through an in-depth study of it. This paper proposes a correlation analysis-based quantification of N-element sense values and a rationality enhancement-based scoring fitting algorithm for English essays. This paper also extracts word features, sentence features, and chapter structure features in essays to fit English composition scores. Since not all students can complete the essays according to the topic requirements, a triage scoring model is used to separate the normal essays from the low-scoring essays. Statistically, it was found that the essay scores also showed a certain normal distribution. The standard support vector regression algorithm is prone to data skewing problems, so this paper addresses this problem by using a rationality enhancement method that gives a corresponding penalty factor according to the distribution of the dataset. The results show that the English essay scoring fitting algorithm proposed in this paper can well improve the prediction accuracy of some data and solve the problem of skewed data where the scores show a normal distribution. This paper designs and implements an online mock examination system that incorporates an intelligent scoring system for essays, enabling it to meet the needs of teachers and students for online examinations and intelligent scoring.

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

With the arrival of the Internet and social media, at the same time as masses of humans have benefitted from the full-size reassets of records available, there was an full-size boom with inside the upward push of cyber-crimes, mainly targeted closer to women. According to a 2019 file with inside the Economics Times, India has witnessed a 457% upward push in cybercrime with inside the 5 years span among 2011 and 2016. Most speculate that that is because of effect of social media inclusive of Facebook, Instagram and Twitter on our day by day lives. While those simply assist in growing a legitimate social network, advent of consumer debts in those websites normally desires simply an email-id. A actual lifestyles man or woman can create more than one fake IDs and for this reason impostors can effortlessly be made. Unlike the actual international state of affairs in which more than one policies and guidelines are imposed to become aware of oneself in a completely unique manner (as an instance at the same time as issuing one’s passport or driver’s license), with inside the digital international of social media, admission does now no longer require this kind of checks. In this paper, we study the one-of-a-kind debts of Instagram, specifically and try and verify an account as fake or actual the use of Machine Learning strategies specifically Logistic Regression and Random Forest Algorithm.

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.

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

Function-as-a-Service (FaaS) has raised a growing interest in how to "tame" serverless to enable domain-specific use cases such as data-intensive applications and machine learning (ML), to name a few. Recently, several systems have been implemented for training ML models. Certainly, these research articles are significant steps in the correct direction. However, they do not completely answer the nagging question of when serverless ML training can be more cost-effective compared to traditional "serverful" computing. To help in this task, we propose MLLess, a FaaS-based ML training prototype built atop IBM Cloud Functions. To boost cost-efficiency, MLLess implements two key optimizations: a significance filter and a scale-in auto-tuner, and leverages them to specialize model training to the FaaS model. Our results certify that MLLess can be 15X faster than serverful ML systems [24] at a lower cost for ML models (such as sparse logistic regression and matrix factorization) that exhibit fast convergence.

Despite the central importance of research papers to scientific progress, they can be difficult to read. Comprehension is often stymied when the information needed to understand a passage resides somewhere else: in another section, or in another paper. In this work, we envision how interfaces can bring definitions of technical terms and symbols to readers when and where they need them most. We introduce ScholarPhi, an augmented reading interface with four novel features: (1) tooltips that surface position-sensitive definitions from elsewhere in a paper, (2) a filter over the paper that "declutters" it to reveal how the term or symbol is used across the paper, (3) automatic equation diagrams that expose multiple definitions in parallel, and (4) an automatically generated glossary of important terms and symbols. A usability study showed that the tool helps researchers of all experience levels read papers. Furthermore, researchers were eager to have ScholarPhi's definitions available to support their everyday reading., 18 pages, 17 figures, 2 tables. To appear at the 2021 ACM CHI Conference on Human Factors in Computing Systems. For associated video, see https://youtu.be/yYcQf-Yq8B0. v2 changes: expanded discussion of design process and implementation; improved figure design. v3 changes: fixed typo in cell of Table 2; updated HEDDEx and Schwarz-Hearst accuracy in Section 5.3

In modern academic social network, it is very difficult for scholars to find academic papers consistent with their research direction. Time is a critical factor in paper recommendation. As time goes on, the impact of an academic paper would gradually fade. Likewise, the research interests of users may also change. Therefore, we propose a temporal perceptual neural collaborative filtering model that integrates the multi-dimensional features of papers. We conducted our experiments on the dataset from CiteULike, comparing the recommended results by using four time-decay functions and evaluating our model with multiple evaluation indicators. The satisfactory results show that our model is effective in filtering out the expired papers by considering the characteristics of papers and the changes of scholars' interests.

Automatically detecting the positions of key-points (e.g., facial key-points or finger key-points) in an image is an essential problem in many applications, such as driver's gaze detection and drowsiness detection in automated driving systems. With the recent advances of Deep Neural Networks (DNNs), Key-Points detection DNNs (KP-DNNs) have been increasingly employed for that purpose. Nevertheless, KP-DNN testing and validation have remained a challenging problem because KP-DNNs predict many independent key-points at the same time -- where each individual key-point may be critical in the targeted application -- and images can vary a great deal according to many factors. In this paper, we present an approach to automatically generate test data for KP-DNNs using many-objective search. In our experiments, focused on facial key-points detection DNNs developed for an industrial automotive application, we show that our approach can generate test suites to severely mispredict, on average, more than 93% of all key-points. In comparison, random search-based test data generation can only severely mispredict 41% of them. Many of these mispredictions, however, are not avoidable and should not therefore be considered failures. We also empirically compare state-of-the-art, many-objective search algorithms and their variants, tailored for test suite generation. Furthermore, we investigate and demonstrate how to learn specific conditions, based on image characteristics (e.g., head posture and skin color), that lead to severe mispredictions. Such conditions serve as a basis for risk analysis or DNN retraining., to appear in ISSTA 2021

FT-Matrix is a typical vector-SIMD architecture that refines the cooperation between scalar and vector units. This approach is widely used in digital signal processing, high-performance computing, and artificial intelligence, among other fields. FT-Matrix currently adopts C vector extension as the main programming model, improving the utilization efficiency of SIMD by providing explicit vector extension API. Moreover, it is difficult to efficiently transplant parallel programs (OpenCL, CUDA) adopted by users. This paper proposes an automatic mapping and code optimization method for OpenCL kernels on FT-Matrix architecture. The proposed approach solves these challenges by means of work item coalescing, slicing and rotation, and instruction-level code optimization. Preliminary results show that our method can achieve high performance and good hardware utilization for OpenCL kernels, as well as decreasing the programming difficulty on FT-Matrix.

This paper is review on image fusion for different technique i.e. Non-subsampled Contourlet Transform (NSCT) and Neural Network (NN). NSCT is the pyramid structure and gives the multiscale feature of the image. NSCT used to directional filtering structure and used to filter bank. Neural network is a progression of calculations that tries to perceive fundamental connections in a bunch of information through an interaction that impersonates the manner in which the human mind works. The implemented algorithm will applied to MRI image and with the help of NN to calculated accuracy. The previous algorithm was work of different technique i.e. principal component analysis, discrete wavelet transorm and curvelet transform. The implemented algorithm will implement MATLAB software and calculate mean square error and peak signal to noise ratio.

The objective of this research is to analyze how YouTube has been studied in the field of Internet and politics. The focus is received on two axes: i) as specificities of temporal and regional distribution and ii) as objects of study and as theoretical approaches most used in 90 articles and articles located in the field and published in Brazil, between 2005 and 2019, indexed to Google Scholar. The methodology consists in the combination of Bibliometry and Content Analysis. The data pointed that the production on YouTube at I&P grew from 2011 and that the southeast, south and northeast regions are the production centers on the analyzed theme. Also, election campaigns are found as the main object. Finally, political strategy / identity and citizenship and sociability are the two most widely used theoretical approaches in the analyzed universe.

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

Using machine learning (ML) services, both service customers and providers need to monitor complex contractual constraints of ML service that are strongly related to ML models and data. Therefore, establishing and monitoring comprehensive ML contracts are crucial in ML serving. This paper demonstrates a set of features and utilities of the QoA4ML framework for ML contracts.

In this invited paper, applications for Machine Learning (ML) in several areas of semiconductor manufacturing and test are reviewed and potential opportunities are discussed.

Applied Sciences has achieved a major milestone this year: it is the 10th anniversary of the journal [...]

This paper analyses over 89,000 documents between scientific journals and international patents in order to identify main technologies for material pyrolysis (i.e. fluidized bed, hot balls, microwave, plasma, and laser), compare them in terms of technical performances and discover future trends. The predictive model for future trends is based on the evolutionary model of the TRIZ theory, with particular attention to the evolutionary law, “from macro to micro”, which explains how technical systems evolve towards increasingly smaller, controlled, and resource-efficient interactions. In our case, we applied the focus to the interface between the heat source and the pyrolyzed raw material. The proposed evolutionary model showed a substantial alignment with chronological trends, and the model was consistent with both patent trends and those of the scientific literature. To make the analysis quantitatively more robust, a comparison of the different technologies has also been introduced in terms of percentage distribution and the number of patents/papers referring to the different classes of heating rates and reaction temperatures and types of flash/fast/intermediate/slow reaction. Finally, a quantitative metric based on the innovation index has been adopted, to take into account the number of citations normalized on the years of publication. The main outputs of this study identified that radiations-based pyrolysis, involving microscopic interaction, seem more interesting in term of technical performances (i.e. heating rate and reaction temperature), although their technological growth has yet to occur, unlike fluidized bed and hot balls, which typically work at a macroscopic level and seem to have already reached maturity. According to a bibliometric index, laser corpus resulted three time more innovative then fluidized bed reactors papers, especially for heating rates and reactions temperature.

Nowadays, multi-functional systems driven by solar energy have been explored to alleviate the crisis of energy and freshwater scarcity. However, it remains a great challenge to integrate multi-functions into one hybrid system with a monolithic material, which can harvest, convert and store solar energy synergistically. Here, we firstly propose a multi-functional carbon nanotube paper, which starts from a material level to solve these problems. The carbon nanotube paper is fabricated by vacuum filtration. It exhibits high efficiency of solar-thermal conversion. Secondly, three types of energy conversion/storage systems (e.g., solar-thermal evaporation system, solar-thermoelectric generation system, and supercapacitor) are constructed based on the carbon nanotube paper, which extend the research to a device level. The solar-thermal evaporation system shows an evaporation rate of 1.28 kg m-2h−1 under simulated sunlight irradiation of 1 kW m−2. The solar-thermoelectric generation system demonstrates a stable electric power generation with an output voltage of 100 mV under light irradiation of 1 kW m−2. The supercapacitors perform the energy storage function with an areal specific capacitance of 19.6 mF cm−2. These devices and systems can be used in freshwater generation (0.84 kg m-2h−1, sunlight irradiation of 1 kW m−2), water purification/desalination (inorganic ions rejection larger than 99.9%) and portable solar charging system (maximum output voltage of 5 V). Finally, a hybrid energy system is innovatively proposed from the system level to simultaneously produce freshwater, generate and store electric power. Through the synergetic coupling of solar conversion and storage technologies, the hybrid energy system can supply sustainable freshwater and electricity for remote areas in daily life, highlighting the superiority of multi-energy (thermal energy, electrical energy, and electrochemical energy) complementary.

Considering the large amount of cement consumed in construction and that straw and other renewable resources are discarded in considerable amounts every year, the heat transfer mechanism of solid-core functional paper honeycomb plates (FHPs) was investigated by calculating and analyzing the equivalent thermal conductivity λE of honeycomb plates (HPs) to develop functional sandwich plates and realize the utilization of straw as a resource. The results led to the following conclusions: (1) Under normal temperatures and hot-above/cold-below conditions, radiative heat transfer, which is usually neglected under normal temperatures, was the primary heat transfer mode in HPs. (2) Local convection occurred in HPs due to weak disturbances when the plate thickness h was greater than 15–20 mm. Accordingly, a technical approach was proposed for improving the heat insulation performance of HPs through multilayer substitution. (3) The filling materials could effectively prevent radiative and local convective heat transfer by drastically reducing the cavity size in the honeycomb structure. The heat conduction among solid particles had little influence on the λE of the FHPs. This paper lays a theoretical foundation for studying the heat transfer mechanism of FHPs at normal temperatures.

Developing monitoring system for paper industrial wastewater treatment system is an important route for wastewater reuse and recycling from wastewater, which are regarded as effective way for cleaner production. A novel hybrid deep leaning CLSTMA model, which based on sequential fusion convolutional neural network (CNN), long short term memory (LSTM) and attention mechanism (AM), was developed to monitor the water quality in a full-scale paper industrial wastewater treatment system for energy conservation and emissions reduction. The hybrid CLSTMA model for predicting water quality of paper industrial wastewater treatment system was divided into three steps: spatial information fusion by using CNN module, temporal information fusion by using LSTM module and variable weighted calculation by using AM module. Compare with other models (CNN, LSTM and CLSTM models), RMSE of CLSTMA model for the effluent chemical oxygen demand (CODeff) reduced by 23.3–31.55%, MAE of CLSTMA model reduced by 38.89–74.50%, R of CLSTMA model increased by 8.29–11.86%. For the effluent suspended solids (SSeff), compared with CNN and LSTM models, RMSE of CLSTMA model reduced by 10.26% and 9.92%, MAE of CLSTMA model reduced by 5.37% and 3.44%, R of CLSTMA model increased by 15.13% and 37.21%, respectively. While, R of CLSTMA was consistent with CLSTM model, but RMSE and MAE of CLSTMA model reduced by 16.07% and 7.49% than the CLSTM model. Simulation results demonstrate that the proposed CLSTMA model has a great potential in monitoring paper industrial wastewater treatment system for cleaner production.

To develop functional sandwich plates (FHPs) and identify new ways to use straw, straw-core paper honeycomb plates (SHPs) were prepared. The mechanisms of the influence of the spatial distribution of various filling systems on the heat transfer performance of SHPs were investigated, and the following results were obtained. 1) The equivalent thermal conductivity (λE) of the three FHPs were significantly lower than that of the honeycomb plate (HP), and the λE of all the FHPs met or approached the requirements of high-efficiency heat insulation materials for envelope structures. Moreover, the λE values of the three FHPs were closest to each other at the upper and lower limits of the tested thickness. 2) The spatial distribution characteristics of the spherical particle and slender filling systems under natural filling conditions, their corresponding simple heat transfer models and the concept of divisional local radiative (DLR) heat transfer were presented for the first time. 3) The fundamental factor determining the magnitudes of the λE of the HP and FHPs was the presence of DLR in the core layer structures, and the heat transfer characteristics of the FHPs were the result of the combined actions of the DLR and filling material properties.

In a circular economy, extended producer responsibility (EPR) is a regulatory approach that accounts for all environmental costs associated with a product throughout its life cycle. When considering the EPR, the regulator must (1) design an integrated framework for different stakeholders and (2) provide effective policies to create financial incentives to accelerate the circular system. Recycling funds and subsidies are the most prominent financial strategies that can either encourage or curtail the flows of recycled products. In Indonesia, a few companies provided recycling bonus s to their customers as incentives to increase the recycling rate. However, the recycling rate was not as effective as expected by the enterprise because recycling was practiced by a limited number of companies, rendering it economically unviable. The enterprises hope the government could develop a recycling fund system. In order to develop the recycling fund system, a system dynamic model was proposed and simulated to optimize the recycling funds and subsidies based on decentralized reverse supply chains. Although it is not a novelty model; however, the research show a very significant results for the government in Indonesia. This enabled a more precise, well-planned, and sustainable decision-making process for the EPR. In addition, by utilizing the system dynamic model with different scenarios in a real case study (aseptic paper packaging waste), we found that the capacity of the recycler could become the reference for the funds to be paid by the importer-producer group, wherein the recycling rate will increase with an increase in the recycling fund.

IntroductionHealth Data Research UK designated seven UK-based Hubs to facilitate health data use for research. PIONEER is the Hub in Acute Care. PIONEER delivered workshops where patients/public citizens agreed key principles to guide access to unconsented, anonymised, routinely collected health data. These were used to inform the protocol.MethodsThis paper describes the PIONEER infrastructure and data access processes. PIONEER is a research database and analytical environment that links routinely collected health data across community, ambulance and hospital healthcare providers. PIONEER aims ultimately to improve patient health and care, by making health data discoverable and accessible for research by National Health Service, academic and commercial organisations. The PIONEER protocol incorporates principles identified in the public/patient workshops. This includes all data access requests being reviewed by the Data Trust Committee, a group of public citizens who advise on whether requests should be supported prior to licensed access.Ethics and disseminationEast Midlands–Derby REC (20/EM/0158): Confidentiality Advisory Group (20/CAG/0084). www.PIONEERdatahub.co.uk

Energy management is the most prominent means of improving energy efficiency, and improved energy efficiency constitutes the cornerstone in decarbonization. For successful industrial energy management, defining accurate energy performance indicators (EnPIs) is essential. Energy-intensive industries have previously been found to have an improvement potential regarding the current monitoring of EnPIs, especially at process level. While general models for developing and implementing EnPIs exist, manufacturing industries are diverse in terms of their production processes, which is why industry-tailored models for EnPI development are needed. One major outcome of this paper is a unique model specifically tailored for kraft pulp mills. The model derives from a practice-based approach for EnPI development, building on real-life experiences from a Swedish group of companies. This paper’s developed model, and the validation of the EnPIs, further increase the understanding of the kraft pulp industry’s processes and how to apply descriptive and explanatory indicators. The developed model can potentially be generalized to other sectors. Funding: Swedish Environmental Protection Agency; Swedish Agency for Marine and Water Management [802-0082-17]

Robotics and artificial intelligence (AI) are revolutionizing all spheres of human life. From industrial processes to graphic design, the implementation of automated intelligent systems is changing how industries work. The spread of robots and AI systems has triggered academic institutions to closely examine how these technologies may affect the humanity—this is how the fields of roboethics and AI ethics have been born. The identification of ethical issues for robotics and AI and creation of ethical frameworks were the first steps to creating a regulatory environment for these technologies. In this paper, we focus on regulatory efforts in Europe and North America to create enforceable regulation for AI and robotics. We describe and compare ethical principles, policies, and regulations that have been proposed by government organizations for the design and use of robots and AI. We also discuss proposed international regulation for robotics and AI. This paper tries to highlight the need for a comprehensive, enforceable, and agile policy to ethically regulate technology today and in the future. Through reviewing existing policies, we conclude that the European Unition currently leads the way in defining roboethics and AI ethical principles and implementing them into policy. Our findings suggest that governments in Europe and North America are aware of the ethical risks that robotics and AI pose, and are engaged in policymaking to create regulatory policies for these new technologies.

Coal mining and its unfavourable consequences have generated global attention. It has further stimulated the paradoxical debate of development and desolation in the contemporary scenario. Since the coal resources are profusely available in India and it is comparatively less expensive, the country has been largely relying on it for its essential and non-essential consumption needs. Coal resources indirectly fulfil many of the modern requirements and comforts. Consequently, mining of minerals in general and coal in particular has become the prime focus. It helps to generate revenue, augment industrialisation and promote employment opportunities in India. Despite these progressive features, the opencast coal mining activities impose serious threats to the livelihood of the local communities. Based on critical review of the relevant literature, the article seeks to exhibit the mineral reserves and coal mining activities at global, national and regional context. The paper has incorporated a special focus on Odisha—a resource-rich state located in eastern part of India. Particularly, the two existing major coalfields—Talcher and Ib valley, situated in western part of the state—have been emphasised. The manifold adverse externalities of the coal mining practices on the livelihood pattern among the affected people have been assessed. Furthermore, the paper aims to examine the mining-induced threats on different livelihood capitals which has given rise to multiple risks and challenges.

Due to the highly infectious nature of the novel coronavirus (COVID‐19) disease, excessive number of patients waits in the line for chest X‐ray examination, which overloads the clinicians and radiologists and negatively affects the patient's treatment, prognosis and control of the pandemic. Now that the clinical facilities such as the intensive care units and the mechanical ventilators are very limited in the face of this highly contagious disease, it becomes quite important to classify the patients according to their severity levels. This paper presents a novel implementation of convolutional neural network (CNN) approach for COVID‐19 disease severity classification (assessment). An automated CNN model is designed and proposed to divide COVID‐19 patients into four severity classes as mild, moderate, severe, and critical with an average accuracy of 95.52% using chest X‐ray images as input. Experimental results on a sufficiently large number of chest X‐ray images demonstrate the effectiveness of CNN model produced with the proposed framework. To the best of the author's knowledge, this is the first COVID‐19 disease severity assessment study with four stages (mild vs. moderate vs. severe vs. critical) using a sufficiently large number of X‐ray images dataset and CNN whose almost all hyper‐parameters are automatically tuned by the grid search optimiser.

In recent years renewable and cleaner fuel for diesel engines are compulsory due to depletion of fossil fuel. Various types of bio-based fuels are investigated by the researchers. Biodiesel is anticipated as potential contenders of diesel fuel. Though it is possible to utilize pure biodiesel in diesel engines, some burdens like higher density, lower cetane number and lesser calorific value hinder it from replacing conventional diesel completely. Therefore, using blends with biofuels in diesel engines has a preference. Thus, this paper reviews two different approaches on the role of nanoparticles on biofuel production and effects of nanoparticles in biodiesel–diesel fuel blends on performance, combustion analysis and emission characteristics of diesel engines. Wide range of results from previous research studies with potential and application of nanoparticles in bioethanol production, the effect of the addition of nanoparticles into diesel fuel with different biofuels ratios are collected in this review study. There are different engine performances enhancing methods surveyed. Nanoparticles can be utilized in the production of biofuels from feedstock pre-treatment to chemical reaction as catalysts. It was observed from the overall results that by adding nanoparticles, there was a significant reduction in the brake specific fuel consumption about 20% to 23% as compared with biodiesel–diesel blends with and without alcohol as additives. Besides as nanoparticles possess high thermal conductivity, the addition of nanoparticles enhanced the process of combustion and increases the brake power about 2.5% to 4%. Emission results showed that in most reviews, NO x emission is increased by up to 55%, while HC, CO and PM are decreased significantly. It was concluded from the study that a diesel engine could be effectively run and give better performance and effective regulated emissions on the application of added nanoparticles with biodiesel and their blends as fuel in a CI engine.

COVID - 19 affected severely worldwide. The pandemic has caused many causalities in a very short span. The IoT-cloud-based healthcare model requirement is utmost in this situation to provide a better decision in the covid-19 pandemic. In this paper, an attempt has been made to perform predictive analytics regarding the disease using a machine learning classifier. This research proposed an enhanced KNN (k NearestNeighbor) algorithm eKNN, which did not randomly choose the value of k. However, it used a mathematical function of the dataset’s sample size while determining the k value. The enhanced KNN algorithm eKNN has experimented on 7 benchmark COVID-19 datasets of different size, which has been gathered from standard data cloud of different countries (Brazil, Mexico, etc.). It appeared that the enhanced KNN classifier performs significantly better than ordinary KNN. The second research question augmented the enhanced KNN algorithm with feature selection using ACO (Ant Colony Optimization). Results indicated that the enhanced KNN classifier along with the feature selection mechanism performed way better than enhanced KNN without feature selection. This paper involves proposing an improved KNN attempting to find an optimal value of k and studying IoT-cloud-based COVID - 19 detection.

Contains fulltext : 222324.pdf (Publisher’s version ) (Open Access) In the early months of 2020, the deadly Covid-19 disease spread rapidly around the world. In response, national and regional governments implemented a range of emergency lockdown measures, curtailing citizens' movements and greatly limiting economic activity. More recently, as restrictions begin to be loosened or lifted entirely, the use of so-called contact tracing apps has figured prominently in many jurisdictions' plans to reopen society. Critics have questioned the utility of such technologies on a number of fronts, both practical and ethical. However, little has been said about the ways in which the normative design choices of app developers, and the products that result therefrom, might contribute to ethical reflection and wider political debate. Drawing from scholarship in critical design and human–computer interaction, this paper examines the development of a QR code-based tracking app called Zwaai ('Wave' in Dutch), where its designers explicitly positioned the app as an alternative to the predominant Bluetooth and GPS-based approaches. Through analyzing these designers' choices, this paper argues that QR code infrastructures can work to surface a set of ethical–political seams, two of which are discussed here - responsibilization and networked (im)permanence - that more 'seamless' protocols like Bluetooth actively aim to bypass, and which may go otherwise unnoticed by existing ethical frameworks. 28 september 2020 11 p.

Background Accelerated by the coronavirus disease 2019 (Covid-19) pandemic, major and lasting changes are occuring in healthcare structures, impacting people's experiences and value creation in all aspects of their lives. Information systems (IS) research can support analysing and anticipating resulting effects. Aim The purpose of this study is to examine in what areas health information systems (HIS) researchers can assess changes in healthcare structures and, thus, be prepared to shape future developments. Method A hermeneutic framework is applied to conduct a literature review and to identify the contributions that IS research makes in analysing and advancing the healthcare industry. Results We draw an complexity theory by borrowing the concept of 'zooming-in and out', which provides us with a overview of the current, broad body of research in the HIS field. As a result of analysing almost 500 papers, we discovered various shortcomings of current HIS research. Contribution We derive future pathways and develop a research agenda that realigns IS research with the transformation of the healthcare industry already under way.

Abundant alcohols, including ethanol, propanol, butanol, and pentanol, are expected to be used in compression ignition engines to ease the shortages of fossil fuel. The various alcohols have quite a different combustion and emission characteristics in the engine due to the changes in molecular structures. In this paper, a series of experiments were conducted on a modified common-rail diesel engine fueled with diesel/alcohols blended fuels in a wide operating range. The effects of alcohol chain length, oxygen content, and molecular structure on engine combustion and emission characteristics are studied systematically. The experimental results show that the blending of alcohols increases the peak values of in-cylinder pressure and maximum pressure rising rate. Besides, the combustion duration and ignition delay are mainly affected by oxygen content and isomer structure. The addition of short-chain alcohols will significantly reduce the total mass of particulate matter (PM) emissions, while the CO and HC emissions increase appropriately. The CO, HC, aldehydes, and ethylene emissions are mainly affected by carbon chain length and isomer structure. For PM emissions, the carbon chain length, molecular structure, and oxygen content of alcohol fuels have different influences on PM number, PM mass, and particle size distributions. The shorter carbon chain of alcohol leads to smaller particle size, and higher oxygen content leads to lower total particle mass.

Nowadays, due to modernization or advancement in the Internet of Things (IoT) especially in the Healthcare area, we want to take care of our elders with some monitoring equipment, and the Internet of Things can play a significant role in it. The motivation of writing this paper is to collect the information of various existing Internet of Things Architecture and Communication Techniques used in Healthcare Monitoring System to observe that how efficiently, different researchers have used it. So we have studied different real-time health monitoring system based on diseases which are common in elderly people like diabetes, blood pressure, heart disease, sleep apnea, and cancer, etc. In this real-time health monitoring system, researchers introduced many new measures, communication techniques like ZigBee, Long-Range Wide Area Network (LoRawan), Radio Frequency Identification (RFID). Apart from this, it was also observed that remote monitoring system in Healthcare is incomplete without data processing and early prediction in such diseases. Though, Machine learning provides efficient techniques to extract knowledge from diagnostic medical datasets collected from the patients. That is why we highlighted the current role of various Machine Learning algorithms like Support Vector Machine, K-Nearest Neighbor, Random Forest, etc., for processing of Healthcare data and also helpful to predict the output more precisely.

Recently, virus diseases, such as SARS-CoV, MERS-CoV, and COVID-19, continue to emerge and pose a severe public health problem. These diseases threaten the lives of many people and cause serious social and economic losses. Recent developments in information technology (IT) and connectivity have led to the emergence of Internet of Things (IoT) and Artificial Intelligence (AI) applications in many industries. These industries, where IoT and AI together are making significant impacts, are the healthcare and the diagnosis department. In addition, by actively communicating with smart devices and various biometric sensors, it is expanding its application fields to telemedicine, healthcare, and disease prevention. Even though existing IoT and AI technologies can enhance disease detection, monitoring, and quarantine, their impact is very limited because they are not integrated or applied rapidly to the emergence of a sudden epidemic. Especially in the situation where infectious diseases are rapidly spreading, the conventional methods fail to prevent large-scale infections and block global spreads through prediction, resulting in great loss of lives. Therefore, in this paper, various sources of infection information with local limitations are collected through virus disease information collector, and AI analysis and severity matching are performed through AI broker. Finally, through the Integrated Disease Control Center, risk alerts are issued, proliferation block letters are sent, and post-response services are provided quickly. Suppose we further develop the proposed integrated virus disease control model. In that case, it will be possible to proactively detect and warn of risk factors in response to infectious diseases that are rapidly spreading worldwide and strengthen measures to prevent spreading of infection in no time.

A prediction model is an indispensable tool in business, helping to make decisions, whether in the short, medium, or long term. In this context, the implementation of machine learning techniques in time series forecasting models has a notorious relevance, as information processing and efficient and dynamic knowledge uncovering are increasingly demanded. This paper develops a model called Variable step-size evolving Participatory Learning with Kernel Recursive Least Squares, VS-ePL-KRLS, applied to the forecast of weekly prices for S500 and S10 diesel oil, at the Brazilian level, for biweekly and monthly horizons. The presented model demonstrates a better accuracy compared with analogous models in the literature, without loss of computational performance for all time series analyzed.

Nagahara M., Krishnamachari B., Ogura M., et al. Control, intervention, and behavioral economics over human social networks against COVID-19. Advanced Robotics, 35(11), 733-739, May 19, 2021 © 2021 Informa UK Limited, trading as Taylor & Francis Group and The Robotics Society of Japan. https://doi.org/10.1080/01691864.2021.1928553., In this short paper, we propose a new direction of cross-cutting research for prediction and control of spreading COVID-19 viruses over a human social network. Such a network consists of human agents whose behaviors are highly uncertain and biased. To predict and control such an uncertain network, we need to employ various researches such as control theory, signal processing, machine learning, and behavioral economics. In this article, we introduce our recent research results and propose future research topics to overcome the COVID-19 pandemic.

In the era of autonomous robots, multi-targets search methods inspired researchers to develop adapted algorithms to robot constraints, and with the rising of Swarm Intelligence (SI) approaches, Swarm Robotics (SRs) became a very popular topic. In this paper, the problem of searching for an exponentially increasing number of targets in a complex and unknown environment is addressed. Our main objective is to propose a Robotic target search strategy based on the EHO (Elephants Herding Optimization) algorithm, namely Robotic-EHO (REHO). The main additions were the collision-free path planning strategy, the velocity limitation, and the extension to the multi-target version in discrete environments. The proposed method has been the subject of many experiments, emulating the search of infected individuals by COVID-19 in a context of containment within complex and unknown random environments, as well as in the real case study of USA. The particularity of these environments is their increasing targets' number and the dynamic Containment Rate (CR) that we propose. The experimental results show that REHO reacts much better in high Containment Rate, early start search mission, and where the robots' speed is higher than the virus spread speed.

COVID-19 is an acronym for coronavirus disease 2019. Initially, it was called 2019-nCoV, and later International Committee on Taxonomy of Viruses (ICTV) termed it SARS-CoV-2. On 30th January 2020, the World Health Organization (WHO) declared it a pandemic. With an increasing number of COVID-19 cases, the available medical infrastructure is essential to detect the suspected cases. Medical imaging techniques such as Computed Tomography (CT), chest radiography can play an important role in the early screening and detection of COVID-19 cases. It is important to identify and separate the cases to stop the further spread of the virus. Artificial Intelligence can play an important role in COVID-19 detection and decreases the workload on collapsing medical infrastructure. In this paper, a deep convolutional neural network-based architecture is proposed for the COVID-19 detection using chest radiographs. The dataset used to train and test the model is available on different public repositories. Despite having the high accuracy of the model, the decision on COVID-19 should be made in consultation with the trained medical clinician.