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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.

Antimicrobial finishing is increasingly used in textile products to increase their functionality. Antimicrobial properties became extremely important during the COVID-19 situation. The main problems faced by antimicrobial finishing are nontoxicity and durability (wash-resistance) of antibacterial activity. Also, it is important to determine whether the antimicrobial finishing changes comfort properties, such as air permeability. This paper deals with the durability of antimicrobial treatment, that is, resistance to washing, and effect on the structural changes that may have influence on the comfort properties, such as permeability to air. Knits of various raw compositions were antimicrobial-treated with the new commercial antimicrobial product, Si Bactericidal (Smart Inovation, Portugal). After the antimicrobial treatment, knitted specimens were washed 50 times and changes in their structural parameters, air permeability, and antimicrobial activity were measured. It was found that the mentioned antimicrobial treatment gives very good antimicrobial activity to the treated fabric and is wash-resistant and long-lasting. Also, it does not change dimensions and permeability to air of the treated fabrics with some exceptions of cotton-based knitted structures.

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.

COVID-19 has forced government and health agencies to take measures to mitigate the spread of the disease and thus safeguard as many lives as possible. These measures have initially impacted the economy of many countries, and therefore they have been forced to gradually return to a new normalcy, in what they have called reopening. For reopening policies to be effective, it is necessary that the people in charge of drawing up these policies know the local behavior of the propagation of COVID-19, and beyond this they can understand that between the cases of COVID-19 and the socioeconomic conditions of their population there is a relationship. For this reason, in this article a case study is presented, which allowed to evaluate the relationship between positive cases of COVID-19 and the multidimensional poverty index (MPI) in the city of Manizales, Colombia. The results of an exploratory analysis, obtained with the use of remote sensing data, are presented, which allowed to confirm the relationship in mention, and it is hoped that this can serve the municipal administration in its decision making.

In recent years, agricultural wastes cause serious pollution to the environment. Agricultural residues can be used to produce value-added products in order to control environmental pollution, therefore these wastes are a promising feedstock as it has many advantages such as wide range of source, low cost and renewable. Rice straw and lemongrass leaves which are the renewable sources for the production of many useful products such as xylose. The aim of this study is to compare the content of xylose produced from rice straw and lemongrass leaves, in order to obtain highest yield of xylose and to optimize the acid hydrolysis time, concentration of sulphuric acid, liquid–solid ratio and the power of the microwave towards the maximum extraction of xylose. Microwave assisted acidic hydrolysis method was used to extract xylose from rice straw and lemongrass leaves. The acid hydrolysis of both rice straw and lemongrass was performed at different time (1–5 min), acid concentration (1%–5%), liquid solid ratio (1:30, 2:30, 3:30, 4:30 and 5:30) and power (160 W, 320 W, 480 W, 640 W, 800 W). The results obtained indicate that the yield of xylose from rice straw (2.98 g/L) via the microwave assisted is slightly higher than lemongrass (2.91 g/L) with optimum conditions of 3 min of hydrolysis time, 2% of sulphuric acid concentration, 1:30 of liquid solid ratio and 320 W of microwave power. Meanwhile, the optimum conditions for lemongrass leaves are 4 min of hydrolysis time, 4% of sulphuric acid concentration, 2:30 of liquid solid ratio and 480 W of microwave power. Thus, rice straw has slightly higher capability in production of xylose compared to lemongrass.

The alternative and renewable sources of energy are very much essential to reduce the dependency on fossil fuels and also to control the environmental pollution. One of the important renewable sources which replace the fossil based fuels is biodiesel. The biodiesel is highly influenced by fatty acid composition. Hence, in the present work Salvodara persica nonedible seed oil (38% oil content) which is rich in saturated fatty acids (79.16%) is mixed with Leucas aspera nonedible seed oil (22.50% oil content), which is rich in unsaturated fatty acids (90.72%) in equal proportion (1:1). The mixed seed oil fatty acid profile is analyzed by GC-FID which shows that, it is exactly half of the fatty acid profile of individual seed oils (saturation 45.26% and unsaturation 54.74%). Bio-mixed oil is transesterified and the biodiesel produced is characterized for its fuel properties. The fuel properties of biodiesel produced from mixed seed oils is compared with biodiesel from individual seed oils, already existing biodiesels and biodiesel standards. It reveals that better quality biodiesel is produced by mixing seed oil strategy. Even the emission analysis shows that the emissions are reduced except NOx. This work reveals that it is one of the important criterions to improve the biodiesel quality by mixed seed oil strategy.

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.

Nanoscale zero-value iron (nZVI) has a high specific surface area and significant abilities to reduce contaminants. In this study, the removal of color and COD from real textile wastewater was studied by nZVI particles. A series of experiments were conducted to evaluate the effect of operational parameters such as solution pH, nZVI dosage and temperature on color and COD removal. The results showed that the color and COD removals mainly depend on solution pH. The color removal efficiencies were 96.3% at 436 nm, 97.8% at 525 nm and 98.0% at 620 nm, respectively at nZVI dosage of 0.3 g/L and initial pH of 3 after 180 minutes of reaction time at 25 ○C. Moreover, the maximum COD removal efficiency obtained under these conditions was 86%.

Safety concerns require the frequently check for leaks in gas pipelines. Also, in coal mines the type gases permeating from the ground need to be monitored. Triboelectric nanogenerators (TENGs) can be applied for gas sensing without external power supply. In this paper, a two-dimensional model of a TENG was established, and a gas jet a rectangular cross section was added between two triboelectric materials. The TENG could generate distinguishable electrical signals according to the different types of gas and the different gas injection areas. This work contributes to the area of self-powered gas sensing.

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.

The possibility of actively controlling structural colors has recently attracted a lot of attention, in particular for new types of reflective displays (electronic paper). However, it has proven challenging to achieve good image quality in such devices, mainly because many subpixels are necessary and the semitransparent counter electrodes lower the total reflectance. Here we present an inorganic electrochromic nanostructure based on tungsten trioxide, gold, and a thin platinum mirror. The platinum reflector provides a wide color range and makes it possible to "reverse" the device design so that electrolyte and counter electrode can be placed behind the nanostructures with respect to the viewer. Importantly, this makes it possible to maintain high reflectance regardless of how the electrochemical cell is constructed. We show that our nanostructures clearly outperform the latest commercial color e-reader in terms of both color range and brightness.

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.

Textile industry represents a pollution problem worldwide due to the accidental discharge or dumping of polluted wastewater into the waterways, which is having a major influence on the quality of water resources. Around 17–20% of industrial water pollution arises from textile dyeing and treatment according to the World Bank report. This represents a large environmental challenge for textile manufacturers. With growing environmental awareness, there is a need for environmentally-friendly technology to remove dyes from the industrial and local wastewater. The photo decolorization of dyes is considered as a favorable technology for industrial wastewater treatment techniques owing to its environmentally friendly method, low cost, and lack of secondary pollution. The efficiency of photocatalysis system depends on the operational parameters that govern the adsorption and photodegradation of dye molecules. This comprehensive review examines the operational factors influencing the photo decolorization of dye molecules. Our study aims to review and summarize the previously published works and R&D progress in the field of photocatalysis of various water pollutants such as the toxic organic compounds (cationic and anionic dyes) using various semiconductor nanoparticles under visible, solar and UV irradiation. This paper examines the effects of operating parameters on the photocatalytic degradation of textile dyes using various photocatalysts. Our findings showed that various parameters, like initial pH of the solution to be degraded, photocatalyst concentration, reaction temperature, dye concentration and dopants content exert their individual influence on the photocatalytic degradation of any dye. By investigating previous research to elucidate the most significant active species for optimal photo decolorization reactions, this review provides guidelines that can be applied to the development of effective photodegradation systems. The results of our study will help determine the most effective and economical options for removal of dyes in industrial wastewater.

Considering that conventional fossil fuels have limited resources and are not environmentally friendly, while energetic demand grows exponentially, the searching for alternative energetic supply is becoming more and more attractive. Biodiesel is considered an alternative energetic supply reality and is composed by fatty acids methyl esters obtained through esterification and/or transesterification reactions. Although a reality, the increase in use of biodiesel depends on lowering its production costs. In order to reach this goal, this paper describes a way to produce a low cost catalyst for biodiesel production by egg shell calcination, as well as demostrate paremeters optimum conditions by statistical analysis. Biodiesel samples were produced from macauba seed oil and methanol using an egg shell catalyst in a batch reactor. Parameters such as oil/methanol molar ratio, catalyst amount and temperature were evaluated for biodiesel conversion yield. The results were considered satisfactory as the esters conversion yield reached about 91% and parameters study revealed the stronger influence of temperature and catalyst content in ester conversion.

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.

Thermal oxidation of carbon electrodes is a common approach to improving flow battery performance. Here, we investigate how thermal pretreatment increases electrode surface area and the effect this...

Non admixed Natural gas-fuelled diesel engines are usually associated with harsh engine emissions as well as low performances at moderate to high engine loads. However, the use of Moringa biodiesel as additive in hydrogen-compressed natural gas (HCNG) is a viable novel strategy for reducing toxic emissions such as hydrocarbons and nitrogen oxides in CI engines. In this study, five hybrid HCNG–Moringa biodiesel (MB) oil samples labelled A–E were analysed for their abilities to improve the overall performance of a CI engine (Petter PH1W diesel engine). The fuels had a fixed volume by volume hydrogen : carbon ratio of 87:13% respectively, and the HCNG–MB hybrid samples consisted of 0, 5, 7, 10 and 13 %v/v MB for samples A, B, C, D and E, respectively. At higher engine loads (i.e. 58%–100%), the engine performance improved thus giving higher BTEs in the range of 19–33.9% for all the fuel blends relative to the BTE (15.1%–19%) of the unblended HCNG fuel; this confirms the suitability of MB as an additive for improving the brake thermal efficiency of CI engines; there were also reductions in HC, O2, CO2, CO and NOx emissions for all the MB–HCNG fuels relative to the case of the unblended HCNG fuel. At optimum condition, the fuel that gave the best results in terms of the aforementioned engine characteristics is the MB–HCNG fuel blend having 10% MB.

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.

Life cycle assessments of fuel bioethanol produced in Uruguay from liquid hot water pretreated switchgrass were performed for two scenarios: the production of ethanol and electricity and a biorefinery producing ethanol, electricity, furfural, acetic, and formic acid. Experimental data and material and energy balances derived from the simulations were used to analyze the global warming potential of fuel bioethanol. Greenhouse gas (GHG) emissions in both scenarios were lower than the reference emissions for fossil fuel. The biorefinery scenario had a better performance in terms of environmental impacts, and the biofuel produced met GHG reduction requirements. Switchgrass composition, enzyme dosage, fermentation and hydrolysis efficiency, and solids content had a significant effect on environmental performance, with enzyme use being the most significant factor. A multi-objective optimization was used to find the values of solid content and enzyme dose that minimize GHG emissions and the minimum ethanol selling price (MESP). An enzyme dosage of 37 mgprotein/gglucan and solids content of 21% w/w were the optimal conditions for bioethanol production with an associated GHG value of 2 ± 5 g CO2 eq./MJethanol and a MESP of $0.84/L.

Biomass can be torrefied to improve the fuel quality prior to its utilization. In addition, a large number of soluble alkali metals can be removed by water washing, to solve slagging and other ash-related problems. In this study, computer-controlled scanning electron microscopy (CCSEM) was used to analyze the mineral composition and elemental distribution of straw, torrefied straw char, and torrefied-water washed straw char. The mineral transformation behavior and the distribution of main elements, when these biomass species were individually co-combusted with coal, were also studied in detail. The comparison of the mineral composition of bulk ash obtained from torrefied straw combustion and straw combustion revealed that the quartz phase decreased and the aluminosilicates of K-, Na-, and Ca–Fe–K increased in the former compared to that in the latter. After the torrefied straw was washed with water, the quartz and Si-Rich phases increased. K aluminosilicate decreased and the quartz phase increased when the torrefied sample was co-combusted with coal at both mixing ratios of 1:1 and 1:4 compared to straw/coal co-combustion. When the torrefied-water washing sample and coal were co-combusted at a 1:1 ratio, the quartz phase decreased, and the Si-Rich and mullite phases increased. When the straw/torrefied char/torrefied-washed char was co-fired with coal at the 1:4 ratio, the amount of K aluminosilicate was less compared to that observed at the 1:1 ratio. It can be concluded that both torrefaction and torrefaction-washing can reduce the content of low-melting-point aluminosilicates in the mineral composition of the studied biomass after co-combustion, which can be beneficial to alleviate the ash slagging problem.

A computational method that directly translates the scattering peak information to crystalline domain shapes and orientations is presented. The method is demonstrated at a synchrotron beamline with a standard X-ray scattering setup., Characterization of thin films is of paramount importance for evaluating material processing outcomes/efficiency as well as establishing structure–property/performance relationships. This article introduces grazing-incidence diffraction tomography (GID tomography), a technique that combines grazing-incidence X-ray scattering and computed tomography to quantitatively determine the dimension and orientation of crystalline domains in thin films without restrictions on the beam coherence, substrate type or film thickness. This computational method extends the capability of synchrotron beamlines by utilizing standard X-ray scattering experiment setups.

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.

In the 21st century the academic field of Child–Computer Interaction (CCI) arose alongside burgeoning interactive technology and digital media industries that targeted children. We believe that the field of CCIU is at an important point in its development, analogous to when a child becomes a teen. Over the last few years we have each had many informal conversations with other CCI researchers in which we discuss issues such as, what is our responsibility as researchers beyond academe? What values underlie our conceptions of a ”good” childhood and the role of interactive technology in it? And, how do we ensure that our field continues to grow and evolve in ways that are consistent with our responsibilities and values? To address these and other complex questions that have been drawing our attention we came together to reflect, discuss and create a position paper for our community, in which we outline some of the issues we see facing our community at this time. To inform our deliberations with opinions beyond our own we conducted an informal consultation with 25 members of the CCI community. Our responders spanned junior to senior researchers, represented diverse geographies and included industry practitioners. These diverse responses provided further content for our reflections, and helped us see perspectives beyond our own. The result of this informal process is this speculative paper in which we propose a series of seven provocations that aim to disrupt some of the normative assumptions held in our field. Our goal in doing this is to open up dialogue in our community about these issues and promote consideration of the alternative visions we present for where we might focus our attention and efforts. We see our contribution not as truth or a definitive statement of a vision for the field, but rather as our opinion about some of the complex issues we face and that we think should be considered through dialogue as we move into the next phase of our development as an academic and scholarly community. We believe that it is urgent and critical for our field that we take up these questions, explore diverse perspectives, and critically work towards decisions and actions that will define our identity and the value of our contributions as we move forward into the next 20 years of research in CCI.

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.

This paper presents a new active electrode design for electroencephalogram (EEG) and electrocardiogram (ECG) sensors based on inertial measurement units to remove motion artefacts during signal acquisition. Rather than measuring motion data from a single source for the entire recording unit, inertial measurement units are attached to each individual EEG or ECG electrode to collect local movement data. This data is then used to remove the motion artefact by using normalised least mean square adaptive filtering. Results show that the proposed active electrode design can reduce motion contamination from EEG and ECG signals in chest movement and head swinging motion scenarios. However, it is found that the performance varies, necessitating the need for the algorithm to be paired with more sophisticated signal processing to identify scenarios where it is beneficial in terms of improving signal quality. The new instrumentation hardware allows data driven artefact removal to be performed, providing a new data driven approach compared to widely used blind-source separation methods, and helps enable in the wild EEG recordings to be performed.

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.

Background Due to the COVID-19 pandemic, the demand for remote electrocardiogram (ECG) monitoring has increased drastically in an attempt to prevent the spread of the virus and keep vulnerable individuals with less severe cases out of hospitals. Enabling clinicians to set up remote patient ECG monitoring easily and determining how to classify the ECG signals accurately so relevant alerts are sent in a timely fashion is an urgent problem to be addressed for remote patient monitoring (RPM) to be adopted widely. Hence, a new technique is required to enable routine and widespread use of RPM, as is needed due to COVID-19. Objective The primary aim of this research is to create a robust and easy-to-use solution for personalized ECG monitoring in real-world settings that is precise, easily configurable, and understandable by clinicians. Methods In this paper, we propose a Personalized Monitoring Model (PMM) for ECG data based on motif discovery. Motif discovery finds meaningful or frequently recurring patterns in patient ECG readings. The main strategy is to use motif discovery to extract a small sample of personalized motifs for each individual patient and then use these motifs to predict abnormalities in real-time readings of that patient using an artificial logical network configured by a physician. Results Our approach was tested on 30 minutes of ECG readings from 32 patients. The average diagnostic accuracy of the PMM was always above 90% and reached 100% for some parameters, compared to 80% accuracy for the Generalized Monitoring Models (GMM). Regardless of parameter settings, PMM training models were generated within 3-4 minutes, compared to 1 hour (or longer, with increasing amounts of training data) for the GMM. Conclusions Our proposed PMM almost eliminates many of the training and small sample issues associated with GMMs. It also addresses accuracy and computational cost issues of the GMM, caused by the uniqueness of heartbeats and training issues. In addition, it addresses the fact that doctors and nurses typically do not have data science training and the skills needed to configure, understand, and even trust existing black box machine learning models.

Edge computing promises to bring computation close to the end-users to support emergent applications such as virtual reality. However, the computational capacity at the edge of the network is currently limited. To become a pervasive paradigm, edge computing needs highly dispersed decentralized deployments, that, contrary to cloud, cannot benefit from economies of scale. In this situation, crowdsourcing appears attractive - there are plenty of computing devices at the disposal of the general public, and these devices are located exactly where computing power is needed the most - at the edge of the network. Crowdsourcing has been a success maker for scientific computing projects, e.g., SETI@home, or distributed ledger systems empowering decentralized finance. However, as of now, there is no crowdsourced system that addresses the needs of edge computing. In this position paper, we aim to identify the causes of this shortcoming, analyze the potential ways to overcome it, and outline future directions.

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.

In this article, sugarcane molasses and agave juice were compared as potential feedstocks for producing bioethanol in Mexico in terms of their environmental impact and economic factors. Life cycle assessment (LCA) using SimaPro was carried out to calculate environmental impacts by using a cradle-to-gate approach. A preliminary economic analysis was performed to determine the economic feasibility of the studied options. Also, capital goods costs were obtained using the Aspen Plus economy package. Moreover, a sensitivity analysis was involved to compare the environmental and economic viability of producing bioethanol from sugarcane molasses and agave juice. LCA results revealed that cultivation and fermentation were the most harmful stages when producing bioethanol from sugarcane molasses and agave juice, respectively. Furthermore, when it was derived from agave juice rather than sugarcane molasses, it had more environmental benefits. This was ascribed to the lower consumption rate of fertilizers, pesticides, and emissions given off from the former. Regarding financial aspects, the preliminary analysis showed that producing bioethanol was not economically viable when grid energy alone was used. However, if power from the grid is partially replaced with renewable energy, producing bioethanol becomes economically feasible, and sugarcane molasses is the most suitable feedstock., En este artículo, se compararon la melaza de caña de azúcar y el jugo de agave como materias primas potenciales para producir bioetanol en México en términos de su impacto ambiental y factores económicos. Se llevó a cabo una evaluación del ciclo de vida (LCA) utilizando SimaPro para calcular los impactos ambientales utilizando un enfoque de la cuna a la puerta. Se realizó un análisis económico preliminar para determinar la factibilidad económica de las opciones estudiadas. Asimismo, los costos de bienes de capital se obtuvieron utilizando el paquete económico Aspen Plus. Además, se realizó un análisis de sensibilidad para comparar la viabilidad ambiental y económica de producir bioetanol a partir de melaza de caña de azúcar y jugo de agave. Los resultados de LCA revelaron que el cultivo y la fermentación fueron las etapas más dañinas al producir bioetanol a partir de melaza de caña de azúcar y jugo de agave, respectivamente. Es más, cuando se derivaba del jugo de agave en lugar de la melaza de caña de azúcar, tenía más beneficios ambientales. Esto se atribuyó al menor consumo de fertilizantes, plaguicidas y emisiones de los primeros. En cuanto a los aspectos financieros, el análisis preliminar mostró que la producción de bioetanol no era económicamente viable cuando se utilizaba únicamente la energía de la red. Sin embargo, si la energía de la red se reemplaza parcialmente con energía renovable, la producción de bioetanol se vuelve económicamente factible y la melaza de caña de azúcar es la materia prima más adecuada. el análisis preliminar mostró que la producción de bioetanol no era económicamente viable cuando se utilizaba únicamente la energía de la red. Sin embargo, si la energía de la red se reemplaza parcialmente con energía renovable, la producción de bioetanol se vuelve económicamente factible y la melaza de caña de azúcar es la materia prima más adecuada. el análisis preliminar mostró que la producción de bioetanol no era económicamente viable cuando se utilizaba únicamente la energía de la red. Sin embargo, si la energía de la red se reemplaza parcialmente con energía renovable, la producción de bioetanol se vuelve económicamente factible y la melaza de caña de azúcar es la materia prima más adecuada.

Today, silicon solar cells (amorphous films and wafer-based) are a main source of green energy. These cells and their components are produced by employing various technologies. Unfortunately, during the production process, chemicals that are harmful for the environment and for human life are used. For example, hydrofluoric acid is used to texture the top electrode to improve light harvesting. In this work, and also in recent ones, we report a way to obtain 3D textures on the top electrode by using zinc oxide nanorods. The efficiency of a textured solar cell structure is compared with the one obtained for a planar zinc oxide/silicon structure. The present results show the possibility to produce efficient solar cells on a relatively thin 50 μm thick silicon substrate. Solar cells with structured top electrodes were examined by numerous measuring techniques. Scanning electron microscopy revealed a grain-like morphology of the magnesium-doped zinc oxide film. The size of the grains is closely related to the structure of the nanorods. The external quantum efficiency of the cells was measured. The obtained solar cell shows response in a wide spectral range from ultraviolet to infrared. Current–voltage and current–voltage–temperature measurements were performed to evaluate basic photovoltaic parameters. At room temperature, the cells efficiency equals to 9.1% for textured structures and 5.4% for planar structures, respectively. The work, therefore, describes an environmentally friendly technology for PV architecture with surface textures increasing the efficiency of PV cells.

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.

The increasing rate of food waste (FW) generation globally, makes it an attractive resource for renewable energy through anaerobic digestion (AD). The biogas recovered from AD can be upgraded by the methanation of internally produced carbon dioxide, CO2 with externally sourced hydrogen gas, H2 (biomethanation). In this work, H2 was added to AD reactors processing FW in three successive phases, with digestate from preceding phases recycled in succession with the addition of fresh inoculum to enhance acclimation. The concentration of H2 was increased for succeeding phases: 5%, 10% and 15% of the reactor headspace in Phase 1 (EH1), Phase 2 (EH2) and Phase 3 (EH3), respectively. The H2 utilisation rate and biomethane yields increased as acclimation progressed from EH1 through EH3. Biomethane yield from the controls: EH1_Control, EH2_Control and EH3_Control were 417.6, 435.4 and 453.3 NmL-CH4/gVSadded accounting for 64.8, 73.9 and 77.8% of the biogas respectively. And the biomethane yield from the test reactors EH1_Test, EH2_Test and EH3_Test were 468.3, 483.6, and 499.0 NmL-CH4/gVSadded, accounting for 77.2, 78.1 and 81.0% of the biogas respectively. A progressive in-situ biomethanation could lead to biomethane production that meets higher fuel standards for gas-to-grid (GtG) injections and vehicle fuel – i.e. >95% CH4. This would increase the energy yield and carbon savings compared to conventional biogas upgrade methods. For example, biogas upgrade for GtG by in-situ biomethanation could yield 7.3 MWh/tFW energy and 1343 kg-CO2e carbon savings, which is better than physicochemical upgrade options (i.e., 4.6–4.8 MWh/tFW energy yield and 846–883 kg-CO2e carbon savings).

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.

The potential emergence of a second-generation ethanol industry in Canada will depend on future production technologies and prices. Financial viability might be improved by producing cellulosic ethanol with co-products such as lignin pellets or electricity. Financial returns to ethanol production will depend on price variability and possible price spillovers among ethanol and its co-products. We use a multivariate BEKK–GARCH approach to investigate past mean price interactions and volatility spillovers between ethanol and electricity prices. Wood pellets are investigated in a univariate framework because of data constraints. Results show substantial price interactions and volatility spillovers among these products. If a second-generation ethanol industry emerges, co-production of products from common feedstocks may strengthen already established relationships between the prices of these energy products. These conditions could create increased risk and the clustering of high–low price fluctuations among co-products. For investors, results suggest that risk reduction strategies should protect against correlated volatility. For policy makers, results suggest that policies that target one commodity may lead to unintended impacts on co-product(s). In sum, understanding links between markets is important for designing future policies and for insights into how a second-generation ethanol industry may emerge.

An increase in the consumption of petroleum products in the transport and energy systems and intensive development of some branches of industry (especially petrochemical industry and oil refining) results in the accumulation of numerous waste in the form of waste oils, oil sludge and other oily liquids. The problem of waste disposal can be solved by using oily liquids as fuel for combustion at thermal power plants in order to produce thermal and electrical energy. To study the rheological characteristics of oil-water-coal emulsions based on anthracite with the solid phase concentration of 40%, an oil phase with the concentration of 49% (motor oils Motul 8100 X-cess 5W-40 and Comma Xtech 5W-30), water (10%) and nonionic surfactant (1%) as an emulsifier-stabilizer. It was found that the rheological characteristics of oil-water-coal emulsions depend not only on the granulometric composition of coal, but also on the viscosity of the dispersed medium (motor oils). Physicochemical and service properties of oil-water-coal systems under consideration allow using them as fuel. Given the availability and relative cheapness of feedstock, this type of fuel can successfully compete with traditional types of liquid fuels.

Biorefining of Chinese food waste (FW) into transport fuels was assessed in terms of amount of fuel produced and over all Global Warming Potential (GWP) for six different scenarios including biogas, biomethane, bioethanol and biodiesel in different combinations. The life-cycle perspective used included GWP aspects of material and energy use, emissions during biorefining and management of residues, as well as substitution of fossil fuels according to the energy content of biofuels. All of the six FW biorefineries revealed savings in GWP ranging from −19 to −138 kg CO2 eqv. per ton of wet FW. Compared to the reference scenario with only anaerobic digestion (S0), introducing biogas upgrading to biomethane (S1) improved the GWP by 37%; while producing bioethanol prior to anaerobic digestion (S2) decreased the savings in GWP. Introducing biodiesel prior to anaerobic digestion (S3) revealed around 60% improvement in GWP, while combining biodiesel and biomethane gave the largest improvement in GWP, 84% compared to the reference scenario, and the most fuels (around 2400 MJ in terms of 30 kg biodiesel and 35 kg biomethane per ton of wet FW). A sensitivity analysis revealed that the electricity production based on the biogas was an important parameter and appears in all refineries, while the results was less sensitive to the production of biodiesel and biomethane. The residue management contributed also to the GWP, but did not vary much among the biorefinery scenarios.