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The rapid economic growth and environmental concerns have led to high demands on paper and paper-based products in terms of variety, quantity, quality, and specialty. Enhancement and functionalization with additives are constantly required. Moving away from traditional petroleum-based additives, researchers have attempted to use “green” nanoadditives by introducing renewable environmentally friendly nanocellulose. This article studies the functions of nanocellulose as bio-additives (enhancer, retention and filtration reagent, and coating aid) in paper and paper products, and overviews the research development of nanocellulose-based additives and their applications in the paper industry for both efficient production and paper functionalization. The review shows that (1) a variety of nanocellulose-based bioadditives have been reported for various applications in paper and paper-based products, while commercially viable developments are to be advanced; (2) nanocellulose was mostly formulated with other polymer and particles as additives to achieve their synergistic effects; (3) major interests have concentrated on the nanocellulose in the specialty papers as representing more value added products and in the efficient utilization of recycled fibers, which remains most attractive and promising for future development. This report shall provide most useful database information for researchers and industries for paper recycling and enhancement, and paper-based products innovation and application.

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.

The fabrication of low-cost, flexible, and recyclable electronic devices has been the focus of many research groups, particularly for integration in wearable technology and the Internet of Things (IoT). In this work, porous zinc oxide (ZnO) nanostructures are incorporated as a UV sensing material into the composition of a sustainable water-based screen-printable ink composed of car-boxymethyl cellulose (CMC). The formulated ink is used to fabricate flexible and foldable UV sensors on ubiquitous office paper. The screen-printed CMC/ZnO UV sensors operate under low voltage (≤2 V) and reveal a stable response over several on/off cycles of UV light exposure. The devices reach a response current of 1.34 ± 0.15 mA and a rise and fall time of 8.2 ± 1.0 and 22.0 ± 2.3 s, respectively. The responsivity of the sensor is 432 ± 48 mA W−1, which is the highest value reported in the literature for ZnO-based UV sensors on paper substrates. The UV-responsive devices display impressive mechanical endurance under folding, showing a decrease in responsivity of only 21% after being folded 1000 times. Their low-voltage operation and extreme folding stability indicate a bright future for low-cost and sustainable flexible electronics, showing potential for low-power wearable applications and smart packaging. publishersversion published

This study investigates the efficacy of using discarded textile (cotton and polycotton) and paper waste (newspaper and corrugated cardboard) as substrates to form sheets with optimum tensile strength. The effect of alkali treatment (sodium hydroxide (NaOH) and sodium bicarbonate (NaHCO3)), compressive loads (200 N and 500 N), and the use of binding agents (blackstrap molasses, sodium alginate, and cornstarch) were studied to optimize the tensile strength of homogeneous sheets. The alkali treatment using 5% NaOH for 5 h of soaking demonstrated the highest increase in tensile strength of 21% and 19% for cotton and newspaper, respectively. Increasing compressive load from 200 N to 500 N showed the highest increase in tensile strength of 37% and 42% for cotton and newspaper, respectively. Remarkably, among the binders, cornstarch at 20% concentration obtained an increase in tensile strength of 395%, 320%, 310%, and 185% for cotton, polycotton, corrugated cardboard, and newspaper sheets, respectively. The optimum results obtained from this study will be utilized to develop biodegradable seedling pots using discarded textile and paper waste.

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.

1/f noise represents the dominant source of noise in the low-frequency range in several physical systems, including field-effect transistors. Its investigation can provide very important information on the fabrication process, highlighting the steps that are more prone to the introduction of defects. Here, 1/f noise in bilayer MoS2 transistors on paper with inkjet-printed Ag contacts and hBN die- lectric is investigated. These devices are promising building blocks for future low-cost, flexible, and easily recyclable disposable electronics. The analysis of 1/f noise, performed following Hooge’s empirical approach, results in a Hooge parameter ≈1–10, which is comparable to those reported for bilayer MoS2 transistors on SiO2. The present results indicate that the noise properties of the investigated devices are stable against substrate bending and are mainly determined by the printing of the dielectric, while not being sensibly affected by the use of the paper substrate. These results are promising for the further development of low noise 2D material-based flexible electronics on paper.

To explore whether the brain contains pattern differences in the rock–paper–scissors (RPS) imagery task, this paper attempts to classify this task using fNIRS and deep learning. In this study, we designed an RPS task with a total duration of 25 min and 40 s, and recruited 22 volunteers for the experiment. We used the fNIRS acquisition device (FOIRE-3000) to record the cerebral neural activities of these participants in the RPS task. The time series classification (TSC) algorithm was introduced into the time-domain fNIRS signal classification. Experiments show that CNN-based TSC methods can achieve 97% accuracy in RPS classification. CNN-based TSC method is suitable for the classification of fNIRS signals in RPS motor imagery tasks, and may find new application directions for the development of brain–computer interfaces (BCI).

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.

Despite the potential in fabrication of microfluidic paper-based analytical devices (μPADs) for point-of-care testing (POCT) kits, the development of simple, accurate, and rapid devices with higher sensitivity remains challenging. Here, we report a novel method for 3D-μPAD fabrication with enclosed channels using vat photopolymerization to avoid fluid evaporation. In detail, height of the enclosed channels was adjusted from 0.3 to 0.17 mm by varying the UV exposure time from 1 to 4 s for the top barrier, whereas the exposure time for the bottom and side barriers was fixed. As a result, sample flow in the enclosed channels of 3D-μPADs showed lesser wicking speed with very scant evaporation compared to that in the hemi channels in the 3D-μPADs. The stoppage of evaporation in the enclosed channels significantly improved the gray intensity and uniformity in the detection zone of the 3D-μPADs, resulting in as low as 0.3 mM glucose detection. Thus 3D-μPADs with enclosed channels showed enhanced sensitivity compared to the 3D-μPADs with hemi channels when dealing with a small volume sample. Our work provides a new insight into 3D-μPAD design with enclosed channels, which redefines the methodology in 3D printing.

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.

This paper presents a critical review of laser pyrolysis. Although this technology is almost 60 years old, in literature many researchers, both from academia and industry, are still developing and improving it. On the contrary industrial applications are struggling to take off, if not in very restricted areas, although the technology has undoubted advantages that justify future development. The aim of this work consists in analysing a representative pool of scientific papers (230) and patents (121), from the last 20 years, to have an overview about the evolution of the method and try to understand the efforts spent to improve this technology effectively in academia and in industry. This study is important to provide a complete review about the argument, still missing in the literature. The objective is to provide an overview sufficiently broad and representative in the sources and to capture all the main ways in which laser pyrolysis has been used and with what distribution. The main focuses of the study are the analyses of the functions carried out by laser technologies, the application fields, and the types of used laser (i.e. models, power and fluence). Among the main results, the study showed that the main use of laser pyrolysis is to produce nanoparticles and coatings, the main materials worked by laser pyrolysis are silicon and carbon dioxide and the main searched properties in the products of laser pyrolysis are catalysts activity and electrical conductivity. CO2 lasers are the most used and the have high versatility compared to others. In conclusion, the study showed that laser pyrolysis is a consolidated technology within its main application fields (nanoparticles and coatings) for several years. Within this context, the technology has been developed on very different sizes and processes, obtaining a very wide range of results. Finally, these results may also have stimulated new areas of experimentation that emerged mainly in recent years and which concern biomedical applications, additive manufacturing, and waste disposal. Graphical abstract

Despite years of study, controversy remains regarding the optimal graft for anterior cruciate ligament reconstruction (ACLR), suggesting that a single graft type is not ideal for all patients. A large community based ACLR Registry that collects prospective data is a powerful tool that captures information and can be analyzed to optimize surgery for individual patients. The studies highlighted in this paper were designed to optimize and individualize ACLR surgery and have led to changes in surgeon behavior and improvements in patient outcomes. Kaiser Permanente (KP) is an integrated health care system with 10.6 million members and more than 50 hospitals. Every KP member who undergoes an ACLR is entered into the Registry, and prospectively monitored. The Registry uses a variety of feedback mechanisms to disseminate Registry findings to the ACLRR surgeons and appropriately influence clinical practices and enhance quality of care. Allografts were found to have a 3.0 times higher risk of revision than bone-patellar tendon-bone (BPTB) autografts. Allograft irradiation >1.8 Mrad, chemical graft processing, younger patients, BPTB allograft, and male patients were all associated with a higher risk of revision surgery. By providing feedback to surgeons, overall allograft use has decreased by 27% and allograft use in high-risk patients ≤21 years of age decreased 68%. We have identified factors that influence the outcomes of ACLR. Statement of Clinical Significance: We found that information derived from an ACLR Registry and shared with the participating surgeons directly decreased the use of specific procedures and implants associated with poor outcomes.

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.

Presently, adsorption/absorption is one of the most efficient and cost-effective methods to clean oil spill up. In this work, self-supporting paper-like fibrous membranes were prepared via electrospinning and carbonisation at different temperatures (500, 650 or 800 °C) by using polyacrylonitrile/polymethylmethacrylate blends with a different mass ratio of the two polymers (1:0, 6:1 or 2:1). After morphological and microstructural characterisation, the as-produced membranes were evaluated as sorbents by immersion in vegetable (sunflower seed or olive) and mineral (motor) oil or in 1:4 (v:v) oil/water mixture. Nitrogen-rich membrane carbonised at the lowest temperature behaves differently from the others, whose sorption capacity by immersion in oil, despite the great number of sorbent and oil properties involved, is mainly controlled by the fraction of micropores. The encapsulation of water nanodroplets by the oil occurring during the immersion in oil/water mixture causes the oil-from-water separation ability to show an opposite behaviour compared to the sorption capacity. Overall, among the investigated membranes, the support produced with 2:1 mass ratio of the polymers and carbonisation at 650 °C exhibits the best performance both in terms of sorption capacity (73.5, 54.8 and 12.5 g g−1 for olive, sunflower seed and motor oil, respectively) and oil-from-water separation ability (74, 69 and 16 for olive, sunflower seed and motor oil, respectively).

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.

Low energy surface coatings have found wide range of applications for generating hydrophobic and superhydrophobic surfaces. Most of the studies have been related to use of a single coating material over a single substrate or using a single technique. The degree of hydrophobicity is highly dependent on fabrication processes as well as materials being coated and as such warrants a high-level study using experimental optimization leading to the evaluation of the parametric behavior of coatings and their application techniques. Also, a single platform or system which can predict the required set of parameters for generating hydrophobic surface of required nature for given substrate is of requirement. This work applies the powerful machine learning algorithms (Levenberg Marquardt using Gauss Newton and Gradient methods) to evaluate the various processes affecting the anti-wetting behavior of coated printable paper substrates with the capability to predict the most optimized method of coating and materials that may lead to a desirable surface contact angle. The major application techniques used for this study pertain to dip coating, spray coating, spin coating and inkjet printing and silane and sol–gel base coating materials.

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

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.

This paper reflects on what materialised during recovery operations following the earthquake in L'Aquila, Italy, on 6 April 2009. Previous critiques have focused on the actions of the Government of Italy and the Department of Civil Protection (Protezione Civile), with little attention paid to the role of local authorities. This analysis sheds light on how the latter used emergency powers, the command-and-control approach, and top-down planning to manage the disaster context, especially in terms of removal of rubble, implementing safety measures, and allocating temporary accommodation. It discusses how these arrangements constituted the mechanism via which ‘disaster capitalism’ took hold at the local and national level, and how it violated human rights, produced environmental and social impacts, hindered local communities from learning, transforming, and building resilience, and facilitated disaster capitalism and corruption. To make the disaster risk reduction and resilience paradigm more effective, a shift from centralised civil protection to decentralised, inclusive community empowerment systems is needed.

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.

Coronavirus disease 2019 (COVID-19) pandemic is currently a serious global health threat. While conventional laboratory tests such as quantitative real-time polymerase chain reaction (qPCR), serology tests, and chest computerized tomography (CT) scan allow diagnosis of COVID-19, these tests are time-consuming and laborious, and are limited in resource-limited settings or developing countries. Point-of-care (POC) biosensors such as chip-based and paper-based biosensors are typically rapid, portable, cost-effective, and user-friendly, which can be used for COVID-19 in remote settings. The escalating demand for rapid diagnosis of COVID-19 presents a strong need for a timely and comprehensive review on the POC biosensors for COVID-19 that meet ASSURED criteria: Affordable, Sensitive, Specific, User-friendly, Rapid and Robust, Equipment-free, and Deliverable to end users. In the present review, we discuss the importance of rapid and early diagnosis of COVID-19 and pathogenesis of COVID-19 along with the key diagnostic biomarkers. We critically review the most recent advances in POC biosensors which show great promise for the detection of COVID-19 based on three main categories: chip-based biosensors, paper-based biosensors, and other biosensors. We subsequently discuss the key benefits of these biosensors and their use for the detection of antigen, antibody, and viral nucleic acids. The commercial POC biosensors for COVID-19 are critically compared. Finally, we discuss the key challenges and future perspectives of developing emerging POC biosensors for COVID-19. This review would be very useful for guiding strategies for developing and commercializing rapid POC tests to manage the spread of infections.Graphical abstract.

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.

Rheumatoid arthritis (RA) is a common autoimmune disease characterized by joint inflammation and immune dysfunction. Although various therapeutic approaches have been utilized for the treatment of RA in clinical applications, the low responsiveness of RA patients and undesired systemic toxicity are still unresolved problems. Targeting the resolution pathway of inflammation with pro-resolving mediators would evoke the protective actions of patient for combating the inflammation. Ac2–26, a 25-amino acid peptide derived from Annexin A (a pro-resolving mediator), has shown good efficacy in the treatment of inflammatory disorders. However, the low bioavailability of Ac2–26 peptides hinders their efficacy in vivo. In this paper, we formed PEGylated lipid nanoparticles (LDNPs) by the co-assembly of l-ascorbyl palmitate (L-AP) and N-(carbonyl methoxypolyethylene glycol-2000)-1,2-distearoyl-sn‑glycero-3-phosphoethanolamine (DSPE-PEG2k) to encapsulate and deliver Ac2–26 peptides to the arthritic rats. They showed good stability and biocompatibility. After being intravenously administrated, Ac2–26 peptide-loaded PEGylated lipid nanoparticles (ADNPs) showed the prolonged in vivo circulation time and enhanced accumulation in inflamed sites. In vivo therapeutic evaluations revealed that ADNPs could attenuate synovial inflammation and improve joint pathology. Therefore, the pro-resolving therapeutic strategy using ADNPs is effective in RA treatment., Graphical abstract The fabrication of ADNPs and their in vivo performances in arthritic rats.Image, graphical abstract

Atomic force microscopy (AFM) has been an important tool for nanoscale imaging and characterization with atomic and subatomic resolution. Theoretical investigations are getting highly important for the interpretation of AFM images. Researchers have used molecular simulation to examine the AFM imaging mechanism. With a recent flurry of researches applying machine learning to AFM, AFM images obtained from molecular simulation have also been used as training data. However, the simulation is incredibly time consuming. In this paper, we apply super-resolution methods, including compressed sensing and deep learning methods, to reconstruct simulated images and to reduce simulation time. Several molecular simulation energy maps under different conditions are presented to demonstrate the performance of reconstruction algorithms. Through the analysis of reconstructed results, we find that both presented algorithms could complete the reconstruction with good quality and greatly reduce simulation time. Moreover, the super-resolution methods can be used to speed up the generation of training data and vary simulation resolution for AFM machine learning.

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.

The paper contributes to unravelling the perceptions of urban commercial drivers regarding their vulnerability to catching the SARS-CoV-2 virus while at work. It further examines how the perception of vulnerability influences personal use of face masks by drivers, as well as on their insistence on appropriate masking behaviour by other persons on-board public transport. Overcrowding and unsanitary conditions in informal public transport in Africa could facilitate the spread of the corona virus. However, the use of face masks, among other enhanced mitigation measures significantly contain and minimize the spread of the virus. Primary data, obtained through surveys at five major public transport terminals in Accra, was analysed and interpreted using the Health Belief Model as an explanatory framework. Results indicate that most drivers have a high vulnerability perception to Covid-19. It further emerged that older drivers, in particular, consistently wore face masks and insisted on other persons in their commercial vehicles to follow suit. Socio-demographic factors, and the need to ensure one’s personal safety and those of loved ones were critical determinants of face mask use among surveyed drivers. The study thus recommends that public awareness campaigns should strategically focus attention on the younger generation of drivers (i.e. 18–39 years) who perceived themselves to be immune to the SARS-CoV-2 virus. Also, the collaborative efforts of state and non-state actors, like the transport operator unions, must be further strengthened if the gains made so far against Covid-19 is to be sustained.

Diabetes is a metabolic disease that affects millions of people each year. It is associated with an increased likelihood of vital organ failures and decreased quality of life. Early detection and regular monitoring are crucial for managing diabetes. Remote patient monitoring can facilitate effective intervention and treatment paradigms using current technology. This work proposes an end‐to‐end remote monitoring framework for automated diabetes risk prediction and management, using personal health devices, smart wearables and smartphones. A support vector machine was developed for diabetes risk prediction using the Pima Indian Diabetes Database, after feature scaling, imputation, selection and augmentation. This work achieved the performance metrics of accuracy, sensitivity and specificity scores at 83.20%, 87.20% and 79% respectively through the tenfold stratified cross validation method, which is competitive with existing methods. Patients can use multiple healthcare devices, smartphones and smartwatches to measure vital parameters, curb the progression of diabetes and close the communication loop with medical professionals. The proposed framework enables medical professionals to make informed decisions based on the latest diabetes risk predictions and lifestyle insights while attaining unobtrusiveness, reduced cost, and vendor interoperability.

The use of waste from the regional production of waste wood, waste paper, and cardboard in the form of briquettes may be causing an increase in local emissions, both of major elements and trace elements. When burning paper and cardboard briquettes, more than 70% of Mn, Zn, As, and Pb is released into the air from the total content of trace elements in the fuel. The largest amounts of major and trace elements are released when burning paper briquettes (56 g/kg of fuel), half of these amounts are released from burning briquettes from waste wood and coal (23 g/kg of fuel). The pursuit of alternative uses for those cardboard components that are not suitable for recycling cannot be directed to the production of briquettes for residential combustion in the framework of the application of the principles of the circular economy. In particular, the high concentrations of undesirable elements in the emissions released in the gas phase into the atmosphere are an obstacle existing even when the parameters of the PM10 emissions of a boiler are met. This is related to the high ash content of the cardboard (13.5%). Waste paper or cardboard could be added to waste wood at a maximum of 10% to make the pollution produced comparable to the burning of coal briquettes.

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.

Achieving long‐term device stability is one of the most challenging issues that impede the commercialization of perovskite solar cells (PSCs). Recent studies have emphasized the significant role of the cathode interfacial layer (CIL) in determining the stability of inverted p‐i‐n PSCs. However, experimental investigations focusing on the influence of the CIL on PSC degradation have not been systematically carried out to date. In this study, a comparative analysis was performed on the PSC device stability by using four different CILs including practical oxides like ZnO and TiOx. A new implemented co‐doping approach was found to results in high device performance and enhanced device stability. The PSC with a thick film configuration of chemically modified TiOx CIL preserves over 77 % of its initial efficiencies of 17.24 % for 300 h under operational conditions without any encapsulation. The PSCs developed are among the most stable reported for methylammonium lead iodide (MAPbI3) perovskite compositions., In for the CIL: Highly stable inverted perovskite solar cells (PSCs) are assembled by using chemically modified titanium suboxide (TiOx) as the cathode interfacial layer (CIL). Codoping enhances the electrical properties of the TiOx CIL, providing better inner encapsulation with thick film configuration. The PSCs exhibit significantly improved operational stability, maintaining >77 % of their initial efficiencies for up to 300 h without encapsulation.

In this paper, linear regression (LR), multi-linear regression (MLR) and polynomial regression (PR) techniques are applied to propose a model Li-MuLi-Poly. The model predicts COVID-19 deaths happening in the United States of America. The experiment was carried out on machine learning model, minimum mean square error model, and maximum likelihood ratio model. The best-fitting model was selected according to the measures of mean square error, adjusted mean square error, mean square error, root mean square error (RMSE) and maximum likelihood ratio, and the statistical t-test was used to verify the results. Data sets are analyzed, cleaned up and debated before being applied to the proposed regression model. The correlation of the selected independent parameters was determined by the heat map and the Carl Pearson correlation matrix. It was found that the accuracy of the LR model best-fits the dataset when all the independent parameters are used in modeling, however, RMSE and mean absolute error (MAE) are high as compared to PR models. The PR models of a high degree are required to best-fit the dataset when not much independent parameter is considered in modeling. However, the PR models of low degree best-fits the dataset when independent parameters from all dimensions are considered in modeling.

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.

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causing the COVID-19 pandemic, has had an enormous effect on conventional clinical practice. Telemedicine has emerged as critical to the provision of healthcare services when reducing the transmission of COVID-19 among patients, families, and clinicians. It has been an essential tool for continuing care for patients with lower urinary tract symptoms (LUTS) during the COVID-19 pandemic and has been the link between socially distant patient contact. The aim of this perspective paper was to identify the strengths and limitations of technology-based care focusing on literature linked to patients with lower urinary tract symptoms (LUTS). We search PubMed and CINHAL Plus for grey literature and secondary research on LUTS and telemedicine during the COVID-19 pandemic. Publications dated between the year March 2020 and March 2021were searched. We gathered key specialist opinions in the field of LUTS from several countries around the world, including the countries that had been hit significantly with COVID-19. This perspective paper proposes that there is evidence to support the use of modern technology to facilitate continued healthcare services for patients with LUTS during the COVID-19 pandemic. Telemedicine has been recognised a crucial digital tool for diagnosis, treatment and follow-up appointments during a time of social distancing. Although there are many advantages of telemedicine, the older adult population and those economically disadvantaged with technology may not benefit from technology-based healthcare. The available literature on telemedicine during the COVID-19 pandemic has proven to be successful in the management of some patients with LUTS. It is certain that the COVID-19 pandemic has given telemedicine a significant drive for implementation now and in the immediate future. Robust data on long-term efficacy and safety of telemedicine is required to ensure there are governance protocols embedded when looking after patients with LUTS.

Purpose A large percentage of anterior cruciate ligament (ACL) surgical reconstructions experience sub-optimal outcomes within 2 years. A potential factor contributing to poor outcomes is an incomplete understanding of micro-level, regional ACL biomechanics. This research aimed to demonstrate a minimally invasive method that uses micro-CT imaging to quantify regional ACL strains under clinically relevant joint loading. Methods A pattern of 0.8 mm diameter zirconium dioxide beads were arthroscopically inserted into four regions of the ACL of four cadaveric knee specimens (mean [SD] age = 59 [9] years). A custom micro-CT compatible joint motion simulator then applied clinically relevant joint loading conditions, while an image was acquired at each condition. From the resulting images, strains within each region were calculated using the centroid coordinates of each tissue-embedded bead. Strain repeatability was assessed using the mean intra-specimen standard deviation across repeated load applications. A one-way repeated measures ANOVA (α = 0.05) was used to determine regional strain variations. Results The mean intra-specimen standard deviation across repeated load application was ±0.003 strain for all specimens. No statistically significant differences were found between tissue regions, although medium and large effect sizes (0.095–0.450) suggest that these differences may be clinically relevant. Conclusions The method presented here demonstrates a minimally invasive measurement of regional ACL strain under clinically relevant joint loads using micro-CT imaging. The strain measurements demonstrated excellent reliability across the five repeated load applications and suggest a non-homogenous distribution of strain through the ACL.

A combined system of an anaerobic baffled reactor (ABR), a down-flow hanging sponge (DHS) reactor, an aquarium tank (AT), and a constructed wetland (CWL) was proposed as a new concept for sewage treatment. The ABR and DHS reactor, AT, and CWL were applied for biological sewage treatment, bioassay, and nutrient removal with food production, respectively. Killifishes and tomatoes were cultivated in the AT and CWL, respectively. In the ABR, 81.3% of total chemical oxygen demand and 76.5% of total biochemical oxygen demand were removed at 5.1 h of hydraulic retention time (HRT). Most remaining organic matter and 47.1% of ammonia were removed in the DHS reactor. In the CWL, 97.0% of total inorganic nitrogen and 78.6% of phosphate were removed with 3.87 kg/m2 of tomatoes producing yield at 4.4 days of HRT. In addition, anaerobic ammonium-oxidizing bacteria Candidatus Scalindua and ammonia-oxidizing bacteria Nitrospira and Nitorosococcus were considered as contributors to nitrogen removal in the CWL. The final effluent's water can be utilized as recycled water by installation of sand filtration and disinfection processes. Therefore, the proposed system can be applied as a low-energy, low-cost sewage treatment system with direct resource recovery. HIGHLIGHTS Anaerobic baffled reactor removed most of organic matters in sewage directly.; Foods for killifishes were automatically fed by sewage treatment.; Nutrients in sewage were consumed by tomatoes in constructed wetland.; Proposed system achieved direct resource recovery from sewage.

The release of high amount of nitrogen and phosphorous from agricultural, municipal and industrial sewage into natural water body is the major source of eutrophication in aquatic ecosystems. This leads to the depletion of oxygen, cyanobacteria bloom and proliferation of aquatic weed plants, and destruction of water ecosystem. Therefore, effective treatment of municipal wastewater is crucial prior to discharge into natural water bodies in order to prevent pollution, and to meet the wastewater acceptable limits set by government and Environmental Protection Agencies. The present study evaluates the performance of varying weight of S. molesta plants in phytoremediation of treated domestic wastewater samples for 14 days at 24 h retention time. Physicochemical analysis such as turbidity, phosphate, ammoniacal nitrogen and nitrate tests were conducted on the wastewater samples according to spectrophotometric methods. Also, S. molesta plants were harvested once in a week and the relative growth rate (RGR) was calculated. The outcome of the study indicated that the treated domestic wastewater from the sewage treatment plant contains pollutants that stimulates the growth of S. molesta plants. Similarly, the highest reduction efficiency of up to 97.7%, 99.7%, 99% and 90.6% was observed for turbidity, phosphate, ammoniacal nitrogen and nitrate, respectively. In addition, the outcome of the study also demonstrated that S. molesta plants with the highest weight (280 g) were more efficient in removal of the excess nutrient present in the influent samples. Furthermore, the free energy content in the wastewater provided a suitable consortium for the plant cultivation and growth. Therefore, this method can be used for lab-scale production of wastewater-based biomass for the generation of biofuels.

Electrical energy is a vital part of modern life, and expectations for grid resilience to allow a continuous and reliable energy supply has tremendously increased even during adverse events (e.g. Ukraine cyberattack, Hurricane Maria). The global pandemic COVID‐19 has raised the electric energy reliability risk due to potential workforce disruptions, supply chain interruptions, and increased possible cybersecurity threats. Additionally, the pandemic introduces a significant degree of uncertainty to the grid operation in the presence of other challenges including aging power grids, high proliferation of distributed generation, market mechanism, and active distribution network. This situation increases the need for measures for the resiliency of power grids to mitigate the impact of the pandemic as well as simultaneous extreme events including cyberattacks and adverse weather events. Solutions to manage such an adverse scenario will be multi‐fold: (a) emergency planning and organisational support, (b) following safety protocol, (c) utilising enhanced automation and sensing for situational awareness, and (d) integration of advanced technologies and data points for ML‐driven enhanced decision support. Enhanced digitalisation and automation resulted in better network visibility at various levels, including generation, transmission, and distribution. These data or information can be employed to take advantage of advanced machine learning techniques for automation and increased power grid resilience. In this paper, the resilience of power grids in the face of pandemics is explored and various machine learning tools that can be helpful to augment human operators are discused by: (a) reviewing the impact of COVID‐19 on power grid operations and actions taken by operators/organisations to minimise the impact of COVID‐19, and (b) presenting recently developed tools and concepts of machine learning and artificial intelligence that can be applied to increase the resiliency of power systems in normal and extreme scenarios such as the COVID‐19 pandemic.

The paper structure of historical prints is sort of a unique fingerprint. Paper with the same origin shows similar chain line distances. As the manual measurement of chain line distances is time consuming, the automatic detection of chain lines is beneficial. We propose an end-to-end trainable deep learning method for segmentation and parameterization of chain lines in transmitted light images of German prints from the 16th Century. We trained a conditional generative adversarial network with a multitask loss for line segmentation and line parameterization. We formulated a fully differentiable pipeline for line coordinates’ estimation that consists of line segmentation, horizontal line alignment, and 2D Fourier filtering of line segments, line region proposals, and differentiable line fitting. We created a dataset of high-resolution transmitted light images of historical prints with manual line coordinate annotations. Our method shows superior qualitative and quantitative chain line detection results with high accuracy and reliability on our historical dataset in comparison to competing methods. Further, we demonstrated that our method achieves a low error of less than 0.7 mm in comparison to manually measured chain line distances.

Background All aging societies face the challenge of allocating limited resources for the highest value of use. The sharing economy provides one method to address the imbalance between the demand and supply of health services to the older adult population. With a substantial aging population, China’s practices in the sharing aging industry may set examples for other “getting old before getting rich” countries. Objective There is a gap in both the data and research on China’s aging industry sharing economy. This paper addresses these data and research lacunae by constructing a framework for the application of a sharing model in China’s aging industry, by assessing the current state of the aging industry sharing economy, by setting out the challenges to the sharing aging health care and service economy, and by making recommendations for the development of the aging industry sharing economy. Methods This paper constructs a sharing economy framework in the aging industry covering four aspects (people, facilities, capital, and information) to test the current state and future prospects of China’s aging industry sharing economy. Results In people sharing, we analyzed the sharing of emotional companionship, doctors, nurses, nursing attendants, and domestic helpers. We discussed facility sharing models from the point of land and housing, medical devices, and other items such as pensioner meals and shared medicine bins. We acknowledge that crowdfunding platforms have developed fast in China, but many older adult users faced problems in their operation. Information sharing is a developing field, which can optimize users’ experiences and should help older adults filter out misinformation, but China currently does not have adequate sharing information platforms for older adults. Conclusions We identified four major challenges in China’s aging industry sharing economy: poor adaptability to technology for older adults, mediocre quality of shared services, one-size-fits-all and the concept of the useless elderly, and shortage of qualified practitioners. We make recommendations for specific measures by governments, communities, and enterprises to improve the sharing economy in the aging industry.

Since the end of 2019, computed tomography (CT) images have been used as an important substitute for the time‐consuming Reverse Transcriptase polymerase chain reaction (RT‐PCR) test; a new coronavirus 2019 (COVID‐19) disease has been detected and has quickly spread through many countries across the world. Medical imaging such as computed tomography provides great potential due to growing skepticism toward the sensitivity of RT‐PCR as a screening tool. For this purpose, automated image segmentation is highly desired for a clinical decision aid and disease monitoring. However, there is limited publicly accessible COVID‐19 image knowledge, leading to the overfitting of conventional approaches. To address this issue, the present paper focuses on data augmentation techniques to create synthetic data. Further, a framework has been proposed using WoT and traditional U‐Net with EfficientNet B0 to segment the COVID Radiopedia and Medseg datasets automatically. The framework achieves an F‐score of 0.96, which is best among state‐of‐the‐art methods. The performance of the proposed framework also computed using Sensitivity, Specificity, and Dice‐coefficient, achieves 84.5%, 93.9%, and 65.0%, respectively. Finally, the proposed work is validated using three quality of service (QoS) parameters such as server latency, response time, and network latency which improves the performance by 8%, 7%, and 10%, respectively.

The state of cellulosic solid kraft paper (CSKP) insulation, to a large extent, is an indication of a transformer’s health. It not only reflects the condition of transformer but also diagnose its residual life. The quantity of 2-furfuraldehyde (2-FAL), carbon dioxide (CO2), and carbon monoxide (CO) dissolved in the transformer oil are useful diagnostic indicators to predict the state of the CSKP insulation. In this work, the current physical state of the CSKP is determined with the help of easily measurable parameters, like temperature, moisture, and the aging time. Here, the degree of deterioration of CSKP insulation has been determined using an integrated insulation health assessment system. This technique integrates a two-stage system comprising of a neural network (NN) model followed by a Smart Life Prediction Approach (SLPA). A thermo-moisture-aging multi-layer feed-forward NN model has been developed to predict the concentrations of 2-FAL, CO2, and CO, which are further correlated to estimate the Degree of Polymerization (DP) values adopting an SLPA. The advantage of the proposed integrated system is that it provides an alternative means of paper health assessment based on Dissolved Gas Analysis (DGA) without estimating dissolved gas concentrations in oil, thereby avoiding the use of sophisticated measuring instruments. The optimal configuration of the NN model has been achieved at minimum iterations with an average cross-validation mean square error of 3.78 × 10−7. The proposed system thereby avoids destructive and offline measurement of DP and facilitates real-time condition monitoring of oil-immersed transformers. The test results of the developed system show considerable reliability in determining insulation health using easily measurable parameters. Furthermore, the system’s performance is compared with reported work and has been found to provide encouraging outcomes.

Over the last decades, microalgal biomass has gained a significant role in the development of different high-end (nutraceuticals, colorants, food supplements, and pharmaceuticals) and low-end products (biodiesel, bioethanol, and biogas) due to its rapid growth and high carbon-fixing efficiency. Therefore, microalgae are considered a useful and sustainable resource to attain energy security while reducing our current reliance on fossil fuels. From the technologies available for obtaining biofuels using microalgae biomass, thermochemical processes (pyrolysis, Hydrothermal Liquefaction (HTL), gasification) have proven to be processed with higher viability, because they use all biomass. However, due to the complex structure of the biomass (lipids, carbohydrates, and proteins), the obtained biofuels from direct thermochemical conversion have large amounts of heteroatoms (oxygen, nitrogen, and sulfur). As a solution, catalyst-based processes have emerged as a sustainable solution for the increase in biocrude production. This paper’s objective is to present a comprehensive review of recent developments on the catalyst-mediated conversion of algal biomass. Special attention will be given to operating conditions, strains evaluated, and challenges for the optimal yield of algal-based biofuels through pyrolysis and HTL.

Performance of green treatment systems such as adsorption to treat textile effluents often suffers lack of longevity and efficiency due to the presence of complex compounds of varying reactivity. There is scope for improving the operational efficiency of such processes using real-time monitoring systems. The present study aimed to evaluate the performance of an activated biosorbent prepared from the leaves of Acalypha indica for treating textile industry effluent by simulating process control with real-time monitoring. Batch experiments were performed with synthetic and real-time dye effluents to identify the optimum conditions (pH = 3.0, dosage = 1.0 g/L; time = 1 h) for the highest adsorption capacity (6 mg g−1 and 2 mg g−1). The evaluation of physical parameters suggested best fit for Freundlich isotherm model and pseudo-second-order kinetic model. The LabVIEW-based simulation control system enabled close monitoring of pH and temperature during the process. Based on the inputs, an alteration of initial pH has resulted in substantial reduction in chemical oxygen demand (COD) (73.91%), turbidity (52.43%) and total dissolved solids (TDS) (19.43%). The average incremental increase was highest for COD (45.80 ± 0.06%) compared to TDS (10.13 ± 0.06%) and turbidity (−1.74 ± 0.03%) for varying dosage (3 g to 11 g). The proposed framework for incorporating a process-control-based monitoring system can help to achieve better performance. HIGHLIGHTS Activated carbon produced from the leaves of Acalypha indica as a biosorbent.; Adsorption isotherm provides best fit with Freundlich model.; Adsorption kinetics indicate that the textile effluent follows pseudo-second-order kinetics.; LabVIEW has been used for the measurement of pH and temperature of the effluent.; Adjustment of the initial pH led to substantial reduction in the COD, TDS and turbidity.

Effluent treatment plants act as the last line of defense against the discharge of pollutants from industrial effluents. The higher resource consumption, variety of dyes, and auxiliary chemicals used for textile manufacturing classify it as a highly polluting industry. Standards for color, organics, and dissolved solids are becoming more stringent with time, and local regulators are forced to insist on the establishment of zero liquid discharge (ZLD) units. However, the application of the ZLD concept has the major limitation of high energy consumption when compared with the conventional wastewater treatment plant. The application of carbon footprint analysis to both units would provide a comprehensive solution to the carbon footprint computation and bring out the advantages of the ZLD. The carbon footprint of a typical ZLD treatment facility in south India was found to be 10,598 tons of carbon dioxide equivalent per year (tCO2eq/year), which is only one-third more than that of a conventional treatment plant. The carbon footprint of a given ZLD treatment facility can effectively be used as a performance indicator to limit the overall energy consumption. HIGHLIGHTS Comparative evaluation of the carbon footprint between conventional and zero liquid discharge (ZLD) treatment plants for textile wastewater is the first of its kind.; Comprehensive carbon emission quantification by the inclusion of carbon footprint due to the discharge of treated effluent into water bodies/land and ground water extraction within the system boundaries.; Carbon footprint of the ZLD treatment plant was found to be nearly 35% higher than the carbon footprint of the conventional treatment plant with a capacity of 4.4 million liters per day.; The application of carbon footprint analysis is a novel performance analysis tool for comparing the different industrial wastewater treatment plants including the ZLD system.

The present laboratory diffraction contrast tomography suffers from poor spatial resolution – only grains larger than 40 µm can be reliably imaged. In this work, it is shown how this serious limitation can be lifted and the spatial resolution improved significantly., Laboratory diffraction contrast tomography (LabDCT) is a novel technique for non-destructive imaging of the grain structure within polycrystalline samples. To further broaden the use of this technique to a wider range of materials, both the spatial resolution and detection limit achieved in the commonly used Laue focusing geometry have to be improved. In this work, the possibility of improving both grain indexing and shape reconstruction was investigated by increasing the sample-to-detector distance to facilitate geometrical magnification of diffraction spots in the LabDCT projections. LabDCT grain reconstructions of a fully recrystallized iron sample, obtained in the conventional Laue focusing geometry and in a magnified geometry, are compared to one characterized by synchrotron X-ray diffraction contrast tomography, with the latter serving as the ground truth. It is shown that grain indexing can be significantly improved in the magnified geometry. It is also found that the magnified geometry improves the spatial resolution and the accuracy of the reconstructed grain shapes. The improvement is shown to be more evident for grains smaller than 40 µm than for larger grains. The underlying reasons are clarified by comparing spot features for different LabDCT datasets using a forward simulation tool.

Pervasive technologies such as Artificial Intelligence, Virtual Reality and the Internet of Things, despite their great potential for improved workability and well-being of older workers, entail wide ethical concerns. Aligned with these considerations we emphasize the need to present from the viewpoint of ethics the risks of personalized ICT solutions that aim to remedy health and support the well-being of the ageing population at workplaces. The ethical boundaries of digital technologies are opaque. The main motivation is to cope with the uncertainties of workplaces’ digitization and develop an ethics framework, termed SmartFrameWorK, for personalized health support through ICT tools at workplace environments. SmartFrameWorK is built upon a five-dimensional approach of ethics norms: autonomy, privacy, transparency, trustworthiness and accountability to incite trust in digital workplace technologies. A typology underpins these principles and guides the ethical decision-making process with regard to older worker particular needs, context, data type-related risks and digital tools’ use throughout their lifecycle. Risk analysis of pervasive technology use and multimodal data collection, highlighted the imperative for ethically aware practices for older workers' activity and behaviour monitoring. The SmartFrameWorK methodology has been applied in a case study to provide evidence that personalized digital services could elicit trust in users through a well-defined framework. Ethics compliance is a dynamic process from participants’ engagement to data management. Defining ethical determinants is pivotal towards building trust and reinforcing better workability and well-being in older workers.

A deep learning neural network has been developed to efficiently and accurately clean the background noise in experimental lab-based X-ray diffraction images. A better spot segmentation is obtained and thus grain mapping in 3D is improved compared with the conventional method., Laboratory X-ray diffraction contrast tomography (LabDCT) is a novel imaging technique for non-destructive 3D characterization of grain structures. An accurate grain reconstruction critically relies on precise segmentation of diffraction spots in the LabDCT images. The conventional method utilizing various filters generally satisfies segmentation of sharp spots in the images, thereby serving as a standard routine, but it also very often leads to over or under segmentation of spots, especially those with low signal-to-noise ratios and/or small sizes. The standard routine also requires a fine tuning of the filtering parameters. To overcome these challenges, a deep learning neural network is presented to efficiently and accurately clean the background noise, thereby easing the spot segmentation. The deep learning network is first trained with input images, synthesized using a forward simulation model for LabDCT in combination with a generic approach to extract features of experimental backgrounds. Then, the network is applied to remove the background noise from experimental images measured under different geometrical conditions for different samples. Comparisons of both processed images and grain reconstructions show that the deep learning method outperforms the standard routine, demonstrating significantly better grain mapping.