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Currently, most automotive industries use fossil fuels, like diesel fuel, which are harmful for the environment and are known as the main reason for global warming. To reduce the adverse effects of these fuels, scholars have investigated and suggested green fuels like biodiesel. However, further studies should be conducted to improve the functionality of biodiesel fuel in diesel engines. In the current study, three completely distinct biodiesel fuels (namely, B1 with 96 % lauric oil, B2 with 88 % oleic oil, and B3 with 89.5 % ricinoleic oil) were numerically evaluated to carefully investigate the effects of the number of carbon atoms, the O H bond, and viscosity on the performance of a CI engine. First, the predicted in-cylinder pressure, the rate of heat released, and NO emissions were compared to experimental results and an appropriate accord was obtained. For the mentioned biodiesels, the parameters of engine speed, injection angle, piston bowl center depth, and compression ratio were investigated by CFD code under different engine speeds. It was found that changing the piston bowl center depth (PBCD) value from 0.0042 to 0.009 m increased NO and the indicated power by 4% and 3%, respectively, for B1, B2, and B3 biofuels. In addition, when the engine was fueled by Corylus avellana biodiesel, the change in compression ratio from 16 to 24 increased peak pressure and torque by around 77 % and 17 %, respectively. The results showed that the cylinder fueled by high viscosity biodiesel has lower air-fuel mixing. A fuel that has more oxygen atoms in its chemical structure can produce higher NO emissions. Moreover, the injection angle of 150° led to increased fuel consumption rate and indicated power compared to the injection angle of 160°. It was determined that the compression ratio has significant effects on emission and combustion characteristics.

The present study was carried out to investigate biodiesel production via transesterification of sunflower oil employing heterogeneous catalyst derived from indigenous ginger (Zingiber Officinale) leaves. It also aims to compare the techno-economy performance of the ginger-based catalysts in 3 different forms viz. calcinated (CGL), activated by KOH (KGL) and NaOH (NGL). The plant-based catalysts were characterised by Scanning electron microscopy (SEM), Brunauer-Emmett-Teller (BET) and Fourier transform infrared spectroscopy (FTIR). The parametric effects on the biodiesel production such as reaction time, methanol to oil ratio and catalyst loading were investigated. The experimental result shows that 1.6 wt % catalyst, 6:1 M ratio of alcohol to oil, 1 h 30 min of reaction time with a speed of 200 rpm gave the best results. It was found that the KGL obtained highest biodiesel yield of 93.83% under optimum conditions. Subsequently, the specific energy and energy productivity of KGL catalyst was found to be 1.2728, 26.1544 MJ/kg and 0.0382 kg/MJ, respectively, per 1 L of biodiesel. Meanwhile, the renewable energy to non-renewable energy ratio for CGL, KGL and NGL is found to be 3.17, 4.01 and 3.67, respectively. A higher sustainable renewable energy-yield ratio and overall economical profit cost ratio are preferable for the biodiesel production process.

Background: Immunotherapy has profoundly changed the landscape of cancer management and represented the most significant breakthrough. Yet, it is a formidable challenge that the majority of cancers - the so-called "cold" tumors - poorly respond to immunotherapy. To find a general immunoregulatory modality that can be applied to a broad spectrum of cancers is an urgent need. Methods: Magnetic hyperthermia (MHT) possesses promise in cancer therapy. We develop a safe and effective therapeutic strategy by using magnetism-mediated targeting MHT-immunotherapy in "cold" colon cancer. A magnetic liposomal system modified with cell-penetrating TAT peptide was developed for targeted delivery of a CSF1R inhibitor (BLZ945), which can block the CSF1-CSF1R pathway and reduce M2 macrophages. The targeted delivery strategy is characterized by its magnetic navigation and TAT-promoting intratumoral penetration. Results: The liposomes (termed TAT-BLZmlips) can induce ICD and cause excessive CRT exposure on the cell surface, which transmits an "eat-me" signal to DCs to elicit immunity. The combination of MHT and BLZ945 can repolarize M2 macrophages in the tumor microenvironment to relieve immunosuppression, normalize the tumor blood vessels, and promote T-lymphocyte infiltration. The antitumor effector CD8+ T cells were increased after treatment. Conclusion: This work demonstrated that TAT-BLZmlips with magnetic navigation and MHT can remodel tumor microenvironment and activate immune responses and memory, thus inhibiting tumor growth and recurrence.

Fossil fuel depletion and pollution are calling for alternative, renewable energies such as biofuels. Actual challenges include the design of efficient processes and catalysts to convert various feedstocks into biofuels. Here, we review nanoferrites heterogeneous catalysts to produce biodiesel from soybean and canola oil. For that, transesterification is the main synthesis route and offers simplicity, cost-effectiveness, better process control, and high conversion yield. Catalysis with nanoferrites and composites allow to obtain yields higher than 95% conversion with less than 5.0 wt.% of catalyst loading at 80 °C in 1–2 h. More than 90% conversion yields can be achieved with a moderate alcohol/oil molar ratio, i.e., between 12:1 to 16:1. Catalyst recovery is easy due to the magnetic properties of nanoferrite, which can be effectively reused up to 4 times with less than 10% loss of catalytic efficiency.

Novel building materials were manufactured and analyzed for 226Ra, 232Th and 40K using an HPGe gamma-ray spectrometer. The results show that the highest value of 40K was 4530 Bq per kg which was measured in a sample containing fly ashes from olive stones. The highest values of 226Ra and 232Th activities were 181 and 185 Bq per kg, which were measured in a sample with fly ashes from the co-combustion of coal and coke, respectively. On the other hand, the lowest values of 40K, 226Ra and 232Th activities were obtained for samples incorporating mussel shells. The radiological health hazard parameters, such as radium equivalent activity (Raeq), activity concentration index (I), absorbed and effective dose rates, associated with these radionuclides were evaluated. These values are within the EU recommended limits in building materials, except for samples of concrete containing fly ashes from olive stones, coal and coke. This study has contributed to the inclusion of industrial wastes that have not been collected previously in the Naturally Occurring Radioactive Material (NORM) databases on radioactivity of building materials.

In view of the problems faced by a conventional moving bed coal pyrolysis process, such as the inability to deal with pulverized coal, low light tar yield, poor tar quality, etc., a moving bed pyrolysis process with baffled internals was developed to control heat and mass transfer of the gas-solid two-phase and the pyrolysis reaction process. The multi-stage gas gathering system can collect the oil and gas products released in different pyrolysis stages of coal in time. The pyrolysis behavior and product quality of Naomaohu coal at different temperatures and residence times were investigated. The results show that the baffle internals enhance heat and volatile matter transfer among particles, enabling it to process 0.4–6.0 mm pulverized coal. When pyrolysis temperature is 550°C and residence time is 3.0h, the pyrolysis tar yield reaches the highest 11.38%, which is 86.87% of the Gray-King assay yield, and the mass fraction of light components below 360°C in the tar fraction is 85.0%. With the extension of the residence time, the H2 volume fraction in the pyrolysis gas increases from 22.1% to 35.1%, and the CO volume fraction increases from 8.0% to 9.5%. Tar yield in the first and second layer reactors increases with increasing residence time, and the tar yield in the third and fourth layer reactors isobtained when the residence time is 2.0 h. As the number of beds increases, content of light components in the tar simulated distillation fraction increases; content of tar aliphatic hydrocarbon compounds decreases, and that of monocyclic aromatic hydrocarbons and bicyclic aromatic hydrocarbons gradually increases. Based on the above results, it provides technical support for large-scale industrial processing of small particle size pulverized coal, and preparation of higher yield and quality tar.

Many plant wastes are not currently used in production, they are disposed of in landfills or incinerated. The aim of this study is to develop a composite thermal insulation material from unused spinning waste of flax and cotton fibers and soft wood waste. Samples of thermal insulation materials from plant waste were made by drying using the technology of production of soft wood fiber boards. For composite board defined physico-mechanical characteristics and thermal conductivity. The experiment was carried out according to a second-order plan, regression models of the dependences of the material indicators on the proportion of the binder additive, drying temperature and the proportion of wood waste additives were developed. The study showed that composites from unused spinning waste of plant fibers and soft wood waste have the necessary strength under static bending, the swelling in thickness after staying in water is much lower in comparison with the performance of boards from other plant fillers. The coefficient of thermal conductivity of the boards is comparable with the indicator for mineral wool boards.

We address the problem of exposure correction of dark, blurry and noisy images captured in low-light conditions in the wild. Classical image-denoising filters work well in the frequency space but are constrained by several factors such as the correct choice of thresholds, frequency estimates etc. On the other hand, traditional deep networks are trained end-to-end in the RGB space by formulating this task as an image-translation problem. However, that is done without any explicit constraints on the inherent noise of the dark images and thus produce noisy and blurry outputs. To this end we propose a DCT/FFT based multi-scale loss function, which when combined with traditional losses, trains a network to translate the important features for visually pleasing output. Our loss function is end-to-end differentiable, scale-agnostic, and generic; i.e., it can be applied to both RAW and JPEG images in most existing frameworks without additional overhead. Using this loss function, we report significant improvements over the state-of-the-art using quantitative metrics and subjective tests., Comment: Published version 8 pages. SIViP (2021)

Biogas is a promising renewable biofuel, but hydrogen sulfide (H2S) and siloxanes are major obstacles in the conversion of bioenergy from biogas because they damage biogas-processing equipment. This study evaluated two biogas-purification systems for a simultaneous efficient removal of H2S and decamethylcyclopentasiloxane (D5), and compared their economic performance. An acidic biotrickling filter (BTF) was operated continuously for 90 days to investigate its performance under different H2S and D5 concentrations. Meanwhile, commercial iron-oxide-based adsorbents (IOBAs) and activated carbon (AC) were used in adsorption filters for H2S and D5, respectively. The results show that in terms of the ratio of D5 and H2S concentration (RD5:H2S) and pH of the recycling liquid, which are crucial BTF operating parameters, high elimination for both H2S (1.86 kg/(m3·d)) and D5 (0.282 kg/(m3·d)) was achieved at RD5:H2S of 3.7:16 and pH of 0.86. Economic analysis confirmed that BTF-AC was more profitable than IOBA-AC adsorption over a life span of 10 years because the pre-removal of D5 by a BTF significantly decreased the costs of IOBAs and AC, despite BTFs requiring a higher initial capital investment. In terms of the economic benefit of biogas production of 1200 Nm3/h, the competitive annual cost of 90.5 k$/year indicated that BTF was more profitable, yielding both cost saving and the benefit of providing heat. The advantage of BTF-AC adsorption was a more radical D5-abatament, enabling profit by selling the electricity produced from biogas to the grid.

With the development of high-throughput sequencing technology, biological sequence data reflecting life information becomes increasingly accessible. Particularly on the background of the COVID-19 pandemic, biological sequence data play an important role in detecting diseases, analyzing the mechanism and discovering specific drugs. In recent years, pretraining models that have emerged in natural language processing have attracted widespread attention in many research fields not only to decrease training cost but also to improve performance on downstream tasks. Pretraining models are used for embedding biological sequence and extracting feature from large biological sequence corpus to comprehensively understand the biological sequence data. In this survey, we provide a broad review on pretraining models for biological sequence data. Moreover, we first introduce biological sequences and corresponding datasets, including brief description and accessible link. Subsequently, we systematically summarize popular pretraining models for biological sequences based on four categories: CNN, word2vec, LSTM and Transformer. Then, we present some applications with proposed pretraining models on downstream tasks to explain the role of pretraining models. Next, we provide a novel pretraining scheme for protein sequences and a multitask benchmark for protein pretraining models. Finally, we discuss the challenges and future directions in pretraining models for biological sequences.

International audience; Interest and need for Wireless Body Area Networks (WBANs) havesignicantly increased recently. WBAN consists of miniaturized sensors de-signed to collect and transmit data through wireless network, enabling medicalspecialists to monitor patients during their normal daily life and providing realtime opinions for medical diagnosis. Many wireless technologies have provedthemselves in WBAN applications, while others are still under investigations.The choice of the technology to adopt may depend on the disease to moni-tor and the performance requirements, i.e. reliability, latency and data rate.In addition, the suitable sensor is essential when seeking to extract the datarelated to a medical measure.This paper aims at surveying the wireless technologies used in WBAN sys-tems. In addition to a detailed survey on the existing technologies, the use ofthe emerging Low Power Wide Area Network (LPWAN) technologies, and thefuture 5G, B5G and 6G is investigated, where the suitability of these technolo-gies to WBAN applications is studied from several perspectives. Furthermore,medical applications of WBAN are discussed by presenting their methodolo-gies, the adopted wireless technologies and the used sensors. Given that eachmedical application requires the appropriate sensor to extract the data, wehighlight a wide range of the sensors used in the market for medical systems.Recent and future challenges in WBAN systems are given related to the powerconsumption, the emergence of the Internet of Things (IoT) technologies inWBAN and others.

Background Telemonitoring could offer solutions to the mounting challenges for health care and could improve patient self-management. Studies have addressed the benefits and challenges of telemonitoring for certain patient groups. Objective This paper will examine the nationwide uptake of telemonitoring in chronic care in the Netherlands from 2014 to 2019 by means of an annual representative survey among patients and health care professionals. Methods Between 2014 and 2019, approximately 2900 patients with chronic diseases, 700 nurses, and 500 general practitioners (GPs) and medical specialists received a questionnaire. About 30 questions addressed topics about the use of eHealth and experiences with it, including data about telemonitoring. Results Between 2014 and 2019, the use of telemonitoring remained stable for all groups except medical specialists. In medical specialist departments, the use of telemonitoring increased from 11.2% (18/161) in 2014 to 19.6% (36/184) in 2019 (χ24=12.3; P=.02). In 2019, telemonitoring was used by 5.8% (28/485) of people with chronic disease. This was 18.2% (41/225) in GP organizations and 40.4% (44/109), 38.0% (78/205), and 8.9% (29/325) in the organizations of nurses working in primary, secondary, and elderly care, respectively. Up to 10% of the targeted patient group such as diabetics were regarded by health care professionals as suitable for using telemonitoring. The main benefits mentioned by the patients were “comfort” (421/1043, 40.4%) and “living at home for longer/more comfortably” (334/1047, 31.9%). Health care professionals added “improvement of self-management” (63/176, 35.8% to 57/71, 80.3%), “better understanding of the patient’s condition” (47/176, 26.7% to 42/71, 59.2%), “reduction of workload” (53/134, 39.6% of nurses in elderly care), “better tailoring of care plan to the patient’s situation” (95/225, 42.2% of GPs), and “saves time for patients/caregivers” (61/176, 34.7% of medical specialists). Disadvantages mentioned by professionals were that “it takes time to monitor data” (13/130, 10% to 108/225, 48.0%), “it takes time to follow up alerts” (15/130, 11.5% to 117/225, 52.0%), and “it is difficult to estimate which patients can work with telemonitoring” (22/113, 19.5% to 94/225, 41.8%). Conclusions The uptake of telemonitoring in Dutch chronic care remained stable during 2014-2019 but increased among medical specialists. According to both patients and professionals, telemonitoring improves the quality of life and quality of care. Skills for suitably including eligible patients and for allocating the tasks of data monitoring and follow-up care within the team would help to further increase the use of telemonitoring.

Background In the health care environment, teamwork is paramount, especially when referring to patient safety. We are interested in recent and innovative solutions such as escape games, which is a type of adventure game that may be highly useful as an educational tool, potentially combining good communication skills with successful gamification. They involve teams of 5 to 10 individuals who are “locked” in the same room and must collaborate to solve puzzles while under pressure from a timer. Objective The purpose of this paper was to describe the steps involved in creating and implementing an educational escape game. This tool can then be put into service or further developed by trainers who wish to use it for learning interprofessional collaboration. Therefore, we started with an experience of creating an educational escape game for emergency medicine teams. Methods We chose to develop an educational escape game by using 6 successive steps. First, we built a team. Second, we chose the pedagogical objectives. Third, we gamified (switched from objectives to scenario). Next, we found the human and material resources needed. Thereafter, we designed briefing and debriefing. Lastly, we tested the game. Results By following these 6 steps, we created the first ambulant educational escape game that teaches people, or nurses, doctors, and paramedics, working in emergency medicine to work as a team. Conclusions From a pedagogic point of view, this game may be a good tool for helping people in multidisciplinary fields (medical and paramedical teams) to learn how to work collaboratively and to communicate as a group. Above all, it seems to be an innovative tool that complements medical simulation–based learning and thus consolidates traditional education.

Accurate detection of hate speech against politicians, policy making and political ideas is crucial to maintain democracy and free speech. Unfortunately, the amount of labelled data necessary for training models to detect hate speech are limited and domain-dependent. In this paper, we address the issue of classification of hate speech against policy makers from Twitter in Italian, producing the first resource of this type in this language. We collected and annotated 1264 tweets, examined the cases of disagreements between annotators, and performed in-domain and cross-domain hate speech classifications with different features and algorithms. We achieved a performance of ROC AUC 0.83 and analyzed the most predictive attributes, also finding the different language features in the anti-policymakers and anti-immigration domains. Finally, we visualized networks of hashtags to capture the topics used in hateful and normal tweets., to appear in SN social sciences - special issue on hate speech

Biodiesel is an eco-friendly, renewable, and potential liquid biofuel mitigating greenhouse gas emissions. Biodiesel has been produced initially from vegetable oils, non-edible oils, and waste oils. However, these feedstocks have several disadvantages such as requirement of land and labor and remain expensive. Similarly, in reference to waste oils, the feedstock content is succinct in supply and unable to meet the demand. Recent studies demonstrated utilization of lignocellulosic substrates for biodiesel production using oleaginous microorganisms. These microbes accumulate higher lipid content under stress conditions, whose lipid composition is similar to vegetable oils. In this paper, feedstocks used for biodiesel production such as vegetable oils, non-edible oils, oleaginous microalgae, fungi, yeast, and bacteria have been illustrated. Thereafter, steps enumerated in biodiesel production from lignocellulosic substrates through pretreatment, saccharification and oleaginous microbe-mediated fermentation, lipid extraction, transesterification, and purification of biodiesel are discussed. Besides, the importance of metabolic engineering in ensuring biofuels and biorefinery and a brief note on integration of liquid biofuels have been included that have significant importance in terms of circular economy aspects. Fil: Chintagunta, Anjani Devi. Vignan’s Foundation for Science, Technology and Research. Department of Biotechnology; India Fil: Zuccaro, Gaetano. Institut National de la Recherche Agronomique; Francia. Università degli Studi di Napoli Federico II; Italia Fil: Kumar, Mahesh. Central Agricultural University; India Fil: Kumar, S. P. Jeevan. Indian Institute of Seed Science; India. Directorate of Floricultural Research; India Fil: Garlapati, Vijay Kumar. Jaypee University of Information Technology; India Fil: Postemsky, Pablo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida. Universidad Nacional del Sur. Centro de Recursos Naturales Renovables de la Zona Semiárida; Argentina Fil: Kumar, N. S. Sampath. Vignan’s Foundation for Science, Technology and Research. Department of Biotechnology; India Fil: Chandel, Anuj K.. Universidade de Sao Paulo; Brasil Fil: Simal Gandara, Jesus. Universidad de Vigo; España

Personal protective equipment (PPE) such as face masks is vital in battling the COVID-19 crisis, but the dominant polypropylene-based PPE are lack of antiviral/antibacterial activities and environmental friendliness, and have hazardous impact on the soil and aquatic ecosystems. The work presented herein focused on developing biodegradable, antiviral, and antibacterial cellulose nonwovens (AVAB-CNWs) as a multi-functional bioprotective layer for better protection against coronavirus SARS-CoV-2 and addressing environmental concerns raised by the piling of COVID-19 related wastes. Both guanidine-based polymer and neomycin sulfate (NEO) were reactive-modified and covalently grafted onto the surface of cellulose nonwovens, thereby conferring outstanding antiviral and antibacterial activities to the nonwovens without deteriorating the microstructure and biodegradability. Through adjusting the grafting amount of active components and selecting appropriate reagents for pretreatment, the antimicrobial activity and hydrophobicity for self-cleaning of the nonwovens can be tuned. More importantly, we demonstrated for the first time that such multi-functional nonwovens are capable of inactivating SARS-CoV-2 instantly, leading to high virucidal activity (> 99.35%), which is unachievable by conventional masks used nowadays. Meanwhile, the robust breathability and biodegradability of AVAB-CNWs were well maintained. The applications of the as-prepared nonwovens as high-performance textile can be readily extended to other areas in the fight against COVID-19., Graphical Abstract ga1

Public transport has been identified as high risk as the corona-virus carrying droplets generated by the infected passengers could be distributed to other passengers. Therefore, predicting the patterns of droplet spreading in public transport environment is of primary importance. This paper puts forward a novel computational and artificial intelligence (AI) framework for fast prediction of the spread of droplets produced by a sneezing passenger in a bus. The formation of droplets of salvia is numerically modelled using a volume of fluid methodology applied to the mouth and lips of an infected person during the sneezing process. This is followed by a large eddy simulation of the resultant two phase flow in the vicinity of the person while the effects of droplet evaporation and ventilation in the bus are considered. The results are subsequently fed to an AI tool that employs deep learning to predict the distribution of droplets in the entire volume of the bus. This combined framework is two orders of magnitude faster than the pure computational approach. It is shown that the droplets with diameters less than 250 micrometers are most responsible for the transmission of the virus, as they can travel the entire length of the bus., Graphical Abstract ga1

New structural type of 2D AA′ n −1M n X3 n +1 type halide perovskites stabilized by symmetric diammonium cations has attracted research attention recently due to the short interlayer distance and better charge‐transport for high‐performance solar cells (PSCs). However, the distribution control of quantum wells (QWs) and its influence on optoelectronic properties are largely underexplored. Here effective phase‐alignment is reported through dynamical control of film formation to improve charge transfer between quantum wells (QWs) for 2D perovskite (BDA)(MA) n ‐1Pb n I3 n +1 (BDA = 1,4‐butanediamine, 〈n〉 = 4) film. The in situ optical spectra reveal a significantly prolonged crystallization window during the perovskite deposition via additive strategy. It is found that finer thickness gradient by n values in the direction orthogonal to the substrate leads to more efficient charge transport between QWs and suppressed charge recombination in the additive‐treated film. As a result, a power conversion efficiency of 14.4% is achieved, which is not only 21% higher than the control one without additive treatment, but also one of the high efficiencies of the low‐n (n ≤ 4) AA′ n −1M n X3 n +1 PSCs. Furthermore, the bare device retains 92% of its initial PCE without any encapsulation after ambient exposure for 1200 h., The in situ optical spectra reveal a significantly prolonged crystallization window during the perovskite deposition via additive strategy. Finer thickness gradient by n values in the direction orthogonal to the substrate leads to more efficient charge transport between quantum wells and suppressed charge recombination in the additive‐treated film. Finally, the power conversion efficiency of 14.4% is obtained.

Learning how to model a problem described in natural language as a linear program requires students to practice using various and numerous exercises. Moreover, immediate feedback on the validity of their solutions helps a better and faster understanding. In this paper, we present an idea on how students and teachers can automatically evaluate linear programming models. We also describe how this idea was implemented on the learning platform caseine.org and is now used by hundreds of students from various universities.

COVID‐19 is a major, urgent, and ongoing threat to global health. Globally more than 24 million have been infected and the disease has claimed more than a million lives as of November 2020. Predicting which patients will need respiratory support is important to guiding individual patient treatment and also to ensuring sufficient resources are available. The ability of six common Early Warning Scores (EWS) to identify respiratory deterioration defined as the need for advanced respiratory support (high‐flow nasal oxygen, continuous positive airways pressure, non‐invasive ventilation, intubation) within a prediction window of 24 h is evaluated. It is shown that these scores perform sub‐optimally at this specific task. Therefore, an alternative EWS based on the Gradient Boosting Trees (GBT) algorithm is developed that is able to predict deterioration within the next 24 h with high AUROC 94% and an accuracy, sensitivity, and specificity of 70%, 96%, 70%, respectively. The GBT model outperformed the best EWS (LDTEWS:NEWS), increasing the AUROC by 14%. Our GBT model makes the prediction based on the current and baseline measures of routinely available vital signs and blood tests.

Natural Language Processing (NLP) is one of the most captivating applications of Deep Learning. In this survey, we consider how the Data Augmentation training strategy can aid in its development. We begin with the major motifs of Data Augmentation summarized into strengthening local decision boundaries, brute force training, causality and counterfactual examples, and the distinction between meaning and form. We follow these motifs with a concrete list of augmentation frameworks that have been developed for text data. Deep Learning generally struggles with the measurement of generalization and characterization of overfitting. We highlight studies that cover how augmentations can construct test sets for generalization. NLP is at an early stage in applying Data Augmentation compared to Computer Vision. We highlight the key differences and promising ideas that have yet to be tested in NLP. For the sake of practical implementation, we describe tools that facilitate Data Augmentation such as the use of consistency regularization, controllers, and offline and online augmentation pipelines, to preview a few. Finally, we discuss interesting topics around Data Augmentation in NLP such as task-specific augmentations, the use of prior knowledge in self-supervised learning versus Data Augmentation, intersections with transfer and multi-task learning, and ideas for AI-GAs (AI-Generating Algorithms). We hope this paper inspires further research interest in Text Data Augmentation.

The aim of the SPICE project is to build social cohesion, both between and within citizen communities, by developing tools and methods to support citizen curation. We define citizen curation as a process in which cultural objects are used as a resource by citizens to develop their own personal interpretations. Within communities, citizens can use their interpretations to build a representation of themselves and their shared perspective on culture. Interpretations can also be used to support social cohesion across groups. In this short position paper we outline the methodologies and technologies needed to be built in order to build a recommender system of cultural objects that will implement these goals of social cohesion and inclusion.

Graphic abstract Fossil fuels are the primary energy source of almost all societies and economies, but it is finite and scarce. The use of non-renewable fossil fuels threatens earth’s environment. At the same time, waste from agricultural and industrial activities is increasing. Most of this waste is discarded or poorly managed, causing many other environmental issues. Converting waste to energy is a promising route to address these challenges. We investigated the hydrothermal liquefaction (HTL) of high moisture content, tobacco-processing waste in a multiple batch thermal reactor to produce biocrude oil. The effects of operating conditions were studied and optimized for maximum liquid biocrude oil yield. HTL operating conditions considered were temperatures from 280 to 340 °C and residence times from 15 to 45 min for a fixed ratio of biomass to deionized water of 1:3. The reaction temperature was found to affect the yields and distribution of products significantly. The maximum yield of the liquid biocrude oil obtained was more than 52% w/w at 310 °C and 15 min. Under these conditions, almost 90% of the energy was recovered in biocrude oil and solid products. The liquid fraction was mainly composed of phenols, ketones, and nitrogenous compounds. This study provides a potential framework for eco-technologies for biomass waste-to-energy conversion with respect to converting tobacco processing residues to liquid biofuels and biochemicals.

Hybrid organic–inorganic perovskites are one of the promising candidates for the next‐generation semiconductors due to their superlative optoelectronic properties. However, one of the limiting factors for potential applications is their chemical and structural instability in different environments. Herein, the stability of (FAPbI3)0.85(MAPbBr3)0.15 perovskite solar cell is explored in different atmospheres using impedance spectroscopy. An equivalent circuit model and distribution of relaxation times (DRTs) are used to effectively analyze impedance spectra. DRT is further analyzed via machine learning workflow based on the non‐negative matrix factorization of reconstructed relaxation time spectra. This exploration provides the interplay of charge transport dynamics and recombination processes under environment stimuli and illumination. The results reveal that in the dark, oxygen atmosphere induces an increased hole concentration with less ionic character while ionic motion is dominant under ambient air. Under 1 Sun illumination, the environment‐dependent impedance responses show a more striking effect compared with dark conditions. In this case, the increased transport resistance observed under oxygen atmosphere in equivalent circuit analysis arises due to interruption of photogenerated hole carriers. The results not only shed light on elucidating transport mechanisms of perovskite solar cells in different environments but also offer an effective interpretation of impedance responses., The stability of (FAPbI3)0.85(MAPbBr3)0.15 perovskite solar cell is explored in different atmospheres using impedance spectroscopy. An equivalent circuit model and distribution of relaxation times are used to analyze the impedance spectra supported by an unsupervised machine learning method. The transport behavior at the interface and bulk is strongly dependent on the environmental conditions particularly under 1 Sun illumination.

BackgroundWith the development of biomedicine, the number of biomedical documents has increased rapidly bringing a great challenge for researchers trying to retrieve the information they need. Information retrieval aims to meet this challenge by searching relevant documents from abundant documents based on the given query. However, sometimes the relevance of search results needs to be evaluated from multiple aspects in specific retrieval tasks, thereby increasing the difficulty of biomedical information retrieval.ObjectiveThis study aimed to find a more systematic method for retrieving relevant scientific literature for a given patient.MethodsIn the initial retrieval stage, we supplemented query terms through query expansion strategies and applied query boosting to obtain an initial ranking list of relevant documents. In the re-ranking phase, we employed a text classification model and relevance matching model to evaluate documents from different dimensions and then combined the outputs through logistic regression to re-rank all the documents from the initial ranking list.ResultsThe proposed ensemble method contributed to the improvement of biomedical retrieval performance. Compared with the existing deep learning–based methods, experimental results showed that our method achieved state-of-the-art performance on the data collection provided by the Text Retrieval Conference 2019 Precision Medicine Track.ConclusionsIn this paper, we proposed a novel ensemble method based on deep learning. As shown in the experiments, the strategies we used in the initial retrieval phase such as query expansion and query boosting are effective. The application of the text classification model and relevance matching model better captured semantic context information and improved retrieval performance.

The aim of the study was to assess leaching of Cl−, SO42−, and PO43− from ashes formed in household furnaces. The ashes were obtained following the combustion of conventional fuels, namely wood and hard coal, and alternative fuels with various fractions of municipal waste in a household boiler. Aqueous leachates of the ashes were used to determine concentrations of chlorides by titration (Mohr’s method) (21.3–3049.6 mg/dm3), sulphates by the gravimetric method (12.2–244.1 mg/dm3), and phosphates by spectrophotometry (0.01–67.2 mg/dm3). It was found that co-combustion of municipal waste with plastic-coated paper cartons, diapers, or a mixed waste fraction leaves the greatest amount of ashes on the furnace grate. The highest amounts of Cl−, SO42−, and PO43− were leached from ashes generated from burning a mix of wood and coals, or wood alone (different species). The addition of municipal waste to the process of burning the conventional and alternative fuels studied did not significantly increase Cl−, SO42−, and PO43− content in aqueous extracts of ashes, the exception being diapers and plywood. In light of the study results, it was concluded that all the ashes could be reused (as an additive to concrete) except for the ash generated from the combustion of a mixed municipal waste fraction and coal (due to the content of Cl−) and diapers (due to the content of PO43−). It was demonstrated that Cl−, SO42−, and PO43− content in the entire set of samples and in individual ash groups is highly heterogeneous and variable.

This Perspective Paper explores the challenges of implementing local initiatives guided by the tenets of the Collective Impact (CI) approach. As such, it draws implications of CI for integrated health and social care efforts to improve and sustain health and social outcomes within a community-wide context, based on our efforts to deploy a CI intervention in the regional town of Muswellbrook, New South Wales (NSW) Australia. A program of health and wellbeing activities providing mental health and wellness messages and activities was implemented in the township over 2 years by the Family Action Centre (FAC), University of Newcastle, Australia. A key takeaway was the importance of authentic community engagement and active involvement as opposed to mere consultation.

Early measurements of tissue viability after myocardial infarction (MI) are essential for accurate diagnosis and treatment planning but are challenging to obtain. Here, manganese, a calcium analogue and clinically approved magnetic resonance imaging (MRI) contrast agent, is used as an imaging biomarker of myocardial viability in the first hours after experimental MI. Safe Mn2+ dosing is confirmed by measuring in vitro beating rates, calcium transients, and action potentials in cardiomyocytes, and in vivo heart rates and cardiac contractility in mice. Quantitative T1 mapping‐manganese‐enhanced MRI (MEMRI) reveals elevated and increasing Mn2+ uptake in viable myocardium remote from the infarct, suggesting MEMRI offers a quantitative biomarker of cardiac inotropy. MEMRI evaluation of infarct size at 1 h, 1 and 14 days after MI quantifies myocardial viability earlier than the current gold‐standard technique, late‐gadolinium‐enhanced MRI. These data, coupled with the re‐emergence of clinical Mn2+‐based contrast agents open the possibility of using MEMRI for direct evaluation of myocardial viability early after ischemic onset in patients., Early measurements of tissue viability after myocardial infarction (MI) are essential for accurate diagnosis and treatment planning. Here, the safety and efficacy of manganese‐enhanced magnetic resonance imaging (MEMRI) in vitro in cardiomyocytes and in vivo in mouse models of MI are evaluated. It is demonstrated that MEMRI offers a novel biomarker of myocardial viability in the first hours after MI.

Incidents of hydraulic or oil spills in the oceans/seas or ports occur with some regularity during the exploitation, production and transportation of petroleum products. Immediate, safe, effective and environmentally friendly measures must be adopted to reduce the impact of the oil spill on marine life. Due to the difficulty to detect and clean these areas, semi-autonomous vehicles can make a significant contribution by implementing a cooperative and coordinated response. The paper proposes a concept study of Hybrid Monitoring Detection and Cleaning System (HMDCS-UV) for a maritime region using semi-autonomous unmanned vehicles. This system is based on a cooperative decision architecture for an unmanned aerial vehicle to monitor and detect dirty zones (i.e., hydraulic spills), and clean them up using a swarm of unmanned surface vehicles. The proposed solutions were implemented in a real cloud and were evaluated using different simulation scenarios. Experimental results show that the proposed HMDCS-UV can detect and reduce the level of hydraulic pollution in maritime regions with a significant gain in terms of energy consumption.

Background The COVID-19 pandemic has stunted medical education activities, resulting in most conferences being cancelled or postponed. To continue professional education during this crisis, web-based conferences can be conducted via livestream and an audience interaction platform as an alternative. Objective The unprecedented COVID-19 pandemic has affected human connections worldwide. Conventional conferences have been replaced by web-based conferences. However, web-based conferencing has its challenges and limitations. This paper reports the logistics and preparations required for converting an international, on-site, multidisciplinary conference into a completely web-based conference within 3 weeks during the pandemic. Methods The program was revised, and a teleconference system, live recording system, director system setup, and broadcasting platform were arranged to conduct the web-based conference. Results We used YouTube (Alphabet Inc) and WeChat (Tencent Holdings Limited) for the web-based conference. Of the 24 hours of the conventional conference, 21.5 hours (90%) were retained in the web-based conference via live broadcasting. The conference was attended by 71% (37/52) of the original international faculties and 71% (27/38) of the overall faculties. In total, 61 out of 66 presentations (92%) were delivered. A special session—“Dialysis access management under the impact of viral epidemics”—was added to replace precongress workshops and competitions. The conference received 1810, 1452, and 1008 visits on YouTube and 6777, 4623, and 3100 visits on WeChat on conference days 1, 2, and 3, respectively. Conclusions Switching from a conventional on-site conference to a completely web-based format within a short period is a feasible method for maintaining professional education in a socially responsible manner during a pandemic.

Mesenchymal stromal cells (MSCs) function as a formidable regulator of inflammation and tissue homeostasis and expanded MSCs are shown to be effective in treating various inflammatory diseases. Their therapeutic effects require the existence of certain inflammatory cytokines. However, in the absence of sufficient proinflammatory stimuli or in the presence of anti‐inflammatory medications, MSCs are animated to promote immune responses and unable to alleviate inflammatory disorders. In this study, it is demonstrated that steroid co‐administration interferes the efficacy of MSCs in treating acute graft‐versus‐host disease (aGvHD). Molecular analysis reveals that vascular endothelial growth factor C (VEGF‐C) is highly induced in MSCs by steroids and TNFα and VEGF‐C in turn promotes CD8+ T cell response. This immune promoting effect is abolished by blockade or specific genetic ablation of VEGFR3 in CD8+ T cells. Additionally, administration of VEGF‐C alone exacerbates aGvHD progression through eliciting more vigorous CD8+ T cell activation and proliferation. Further studies demonstrate that VEGF‐C augments the PI3K/AKT signaling process and the expression of downstream genes, such as Cyclin D1. Thus, the data demonstrate that steroids can reverse the immunosuppressive effect of MSCs via promoting VEGF‐C‐augmented CD8+ T cell response and provide novel information for designing efficacious MSC‐based therapies., Steroids are first‐line drugs for treating acute graft‐versus‐host disease (aGvHD). Recently, mesenchymal stromal cells (MSCs) have emerged as a superb choice. However, concomitant use of both worsens the disease course. This manuscript reveals that steroids, together with TNFα, synergistically induce MSCs to produce abundant vascular endothelial growth factor C, which enhances CD8+ T cell response and exacerbates aGvHD progression. ​

In this study, the performance of the ozonation process was investigated in the removal of antibiotics (Cephalexin, Ciprofloxacin, and Clarithromycin) from medical laboratory wastewater. Central composite design, one of the response surface methodologies, was applied to optimize process parameters and to develop a mathematical model. Analysis of variance (ANOVA) was performed to analyze the data and obtain the interactions between the responses and process variables. Independent variables were initial pH, reaction time, and ozone dosage, while the responses were the removal of Cephalexin, Ciprofloxacin, and Clarithromycin. Statgraphics Centurion XVI.I software was used for developing the quadratic regression models in the estimation of removals. The determined correlation coefficients were very high for the removal of all three antibiotics and the model was sufficiently in good agreement with experimental results. The experimental removal fractions of Cephalexin, Ciprofloxacin, and Clarithromycin under optimum process conditions were 99.5%, 98.3%, and 99.6%, respectively. As a result of the study, it was determined that ozonation is an effective treatment method in antibiotic removal from medical laboratory wastewater and the central composite design is a convenient tool for process optimization.

During use, textile items can develop unpleasant odors that arise from many different sources, both internal and external to the human body. Laundering is not always effective at removing odors, with odor potentially building up over time due to incomplete removal of soils and odorous compounds and/or malodors transferred during the laundering process. Textile odor can lead to consumer dissatisfaction, particularly as there are high expectations that clothing and textile products meet multiple aesthetic and functional needs. The problem of odor in textiles is complex and multi-faceted, with odorous volatile compounds, microorganisms, and precursors to odor, such as sweat, being transferred to, and retained by, fabrics. This article reviews the literature that specifically relates to odor within textiles. Methods for evaluating odor in textiles, including methods for collecting odor on textile substrates, as well as sensory and instrumental methods of odor detection, were reviewed. Literature that examined differences among fabrics that varied by fabric properties were reviewed. As well, the effectiveness of specific odor controlling finishing technologies to control malodor within textiles was also examined.

Supplemental Digital Content is Available in the Text., Objectives: To evaluate self-reported sensory pain scores of women with generalized vulvodynia (GV) and provoked vestibulodynia (PVD), characterize pain phenotypes, and assess feasibility of using the Internet for recruitment and data collection among women with vulvodynia. Methods: Descriptive online survey. Data collected using an online survey accessed via a link on the National Vulvodynia Association web site. Convenience sample, 60 women aged 18 to 45 years (mean = 32.7 ± 5.5); 50 white, 2 black/African American, 4 Hispanic/Latino, and 4 Native American/Alaskan Native, diagnosed with vulvodynia, not in menopause. Pain assessment and medication modules from PAINReportlt. Results: Women with GV (n = 35) compared to PVD (n = 25). Estimated mean pain sites (2.5 ± 1.4 vs 2.2 ± 1.0, P = 0.31), mean current pain (8.7 ± 1.4 vs 5.5 ± 4.0, P = 0.0008), worst pain (8.1 ± 1.8 vs 6.1 ± 3.6, P = 0.02), and least pain in the past 24 hours (4.4 ± 1.8 vs 2.0 ± 2.0, P < 0.0001). Average pain intensity (7.1 ± 1.2 vs 4.6 ± 2.9, P = 0.0003) on a scale of 0 to 10, mean number of neuropathic words (8.3 ± 3.6 vs 7.7 ± 5.0), and mean number of nociceptive words (6.9 ± 4 vs 7.5 ± 4.4). Nineteen (54%) women with GV compared to 9 (38%) with PVD were not satisfied with pain levels. Conclusion: Women with GV reported severe pain, whereas those with PVD reported moderate to severe pain. Pain quality descriptors may aid a clinician's decisions about whether to prescribe adjuvant drugs vs opioids to women with vulvodynia.

Combining natural materials and pretreatment processes with nanomaterials is an alternative to be evaluated for the increase in efficiency and cost reduction of water treatment processes. This work aimed to study the combination of pretreatment processes and primary treatments with bentonite impregnated with magnetic nanoparticles (Bentonite@MNPs) to reuse wastewater from the food indust|ry. Bentonite@MNPs were obtained by green synthesis using glucose as a reducing/stabilizing agent, and the nanocomposite was characterized by SEM, EDS, FTIR, VSM, and XRD analysis. The synthesized nanocomposite was used to remove nitrates and COD from wastewater from the production of guava bars that had been pretreated with coagulation-flocculation-sedimentation processes. Batch adsorption experiments were conducted, where the influence of several parameters such as contact time, adsorbent amount, and the temperature was studied. According to the results, the wastewater treated with the coagulation-flocculation-sedimentation processes was still outside the standards for agricultural reuse. After applying the treatment with Bentonite@MNPs, a reduction of COD, nitrates, chlorides, and sulfates of 82%, 75.4%, 98.18%, and 70%, respectively, was achieved. The adsorption isotherm data were best explained by the Langmuir model for COD, and Frendluich model for Nitrates and the adsorption capacity of Bentonite@MNPs for COD and Nitrates was 34.84 and 28.11 mg/g when adsorption temperature was 318 K. The results of thermodynamic studies (ΔG°, ΔH°, and ΔS°) showed the spontaneous, endothermic and randomness in the adsorption process. Desorption studies showed excellent regenerative efficiency (up to the 10th cycle) of nanocomposites using 0.01 M NaOH, so this wastewater treatment system for the food industry is promising to implement for the sake of using wastewater as a circular economy component.

This work addresses the joint management of residual microalgae and pine wood waste through pyrolysis to obtain a solid product for its use as soil amendment and two other by-products (liquid and gaseous) that can be used for energy purposes. Two management routes have been followed. The first route is through the co-pyrolysis of mixtures of both residual materials in several proportions and the later use of their solid fraction for soil amendment. The second route is the pyrolysis of pine wood waste and its direct combination with dried residual microalgae, also using it as soil amendment. The solid fraction assessment shows that from seven solid products (biochar) three stand out for their positive applicability in agriculture as soil amendment. In addition, they also present the benefit of serving as carbon sink, giving a negative balance of CO2 emissions. However, caution is suggested due to biochar applicability being subject to soil characteristics. To ensure the sustainability of the overall process, the energy available in liquid and gaseous fractions has been assessed for covering the drying needs of the residual microalgae in both cases. These results suggest that the pyrolysis process is a sustainable way to manage specific evaluated residues and their products.

Environmental concern for our planet has changed significantly over time due to climate change, caused by an increasing population and the subsequent demand for electricity, and thus increased power generation. Considering that natural gas is regarded as a promising fuel for such a purpose, the need to integrate carbon capture technologies in such plants is becoming a necessity, if gas power plants are to be aligned with the reduction of CO2 in the atmosphere, through understanding the capturing efficacy of different absorbents under different operating conditions. Therefore, this study provided for the first time the comparison of available absorbents in relation to amine solvents (MEA, DEA, and DEA) CO2 removal efficiency, cost, and recirculation rate to achieve Climate change action through caron capture without causing absorbent disintegration. The study analyzed Flue under different amine-based solvent solutions (monoethanolamine (MEA), diethanolamine (DEA), and methyldiethanolamine (MDEA)), in order to compare their potential for CO2 reduction under different operating conditions and costs. This was simulated using ProMax 5.0 software modeled as a simple absorber tower to absorb CO2 from flue gas. Furthermore, MEA, DEA, and MDEA adsorbents were used with a temperature of 38 &deg, C and their concentration varied from 10 to 15%. Circulation rates of 200&ndash, 300 m3/h were used for each concentration and solvent. The findings deduced that MEA is a promising solvent compared to DEA and MDEA in terms of the highest CO2 captured, however, it is limited at the top outlet for clean flue gas, which contained 3.6295% of CO2 and less than half a percent of DEA and MDEA, but this can be addressed either by increasing the concentration to 15% or increasing the MEA circulation rate to 300 m3/h.

Background Coronavirus disease 2019 (COVID-19) has become a pandemic since its first appearance in late 2019. Deaths caused by COVID-19 are still increasing day by day and early diagnosis has become crucial. Since current diagnostic methods have many disadvantages, new investigations are needed to improve the performance of diagnosis. Methods A novel method is proposed to automatically diagnose COVID-19 by using Electrocardiogram (ECG) data with deep learning for the first time. Moreover, a new and effective method called hexaxial feature mapping is proposed to represent 12-lead ECG to 2D colorful images. Gray-Level Co-Occurrence Matrix (GLCM) method is used to extract features and generate hexaxial mapping images. These generated images are then fed into a new Convolutional Neural Network (CNN) architecture to diagnose COVID-19. Results Two different classification scenarios are conducted on a publicly available paper-based ECG image dataset to reveal the diagnostic capability and performance of the proposed approach. In the first scenario, ECG data labeled as COVID-19 and No-Findings (normal) are classified to evaluate COVID-19 classification ability. According to results, the proposed approach provides encouraging COVID-19 detection performance with an accuracy of 96.20% and F1-Score of 96.30%. In the second scenario, ECG data labeled as Negative (normal, abnormal, and myocardial infarction) and Positive (COVID-19) are classified to evaluate COVID-19 diagnostic ability. The experimental results demonstrated that the proposed approach provides satisfactory COVID-19 prediction performance with an accuracy of 93.00% and F1-Score of 93.20%. Furthermore, different experimental studies are conducted to evaluate the robustness of the proposed approach. Conclusion Automatic detection of cardiovascular changes caused by COVID-19 can be possible with a deep learning framework through ECG data. This not only proves the presence of cardiovascular changes caused by COVID-19 but also reveals that ECG can potentially be used in the diagnosis of COVID-19. We believe the proposed study may provide a crucial decision-making system for healthcare professionals. Source code All source codes are made publicly available at: https://github.com/mkfzdmr/COVID-19-ECG-Classification

Medical emergencies involving children are rare events. The experience of emergency physicians is therefore low and the results are correspondingly poor. Assistance services to help in emergencies are regularly requested. The use case is thus very complicated, a complex system consisting of multiple devices is necessary to provide the most efficient and effective service. This short paper presents prototypically tested ideas on how such a ubiquitous approach can be designed and how communication between devices can be simplified and ensured.

How to simultaneously achieve both high open‐circuit voltage (V oc) and high short‐circuit current density (J sc) is a big challenge for realising high power conversion efficiency (PCE) in all‐small‐molecule organic solar cells (all‐SM OSCs). Herein, a novel small molecule (SM)‐donor, namely FYSM−SiCl, with trialkylsilyl and chlorine substitutions was designed and synthesized. Compared to the original SM‐donor FYSM−H, FYSM−Si with trialkylsilyl substitution showed a decreased crystallinity and lower highest occupied molecular orbital (HOMO) level, while FYSM−SiCl had an improved crystallinity, more ordered packing arrangement, significantly lower HOMO level, and predominant “face‐on” orientation. Matched with a SM‐acceptor Y6, the FYSM−SiCl‐based all‐SM OSCs exhibited both high V oc of 0.85 V and high J sc of 23.7 mA cm−2, which is rare for all‐SM OSCs and could be attributed to the low HOMO level of FYSM−SiCl donor and the delicate balance between high crystallinity and suitable blend morphology. As a result, FYSM−SiCl achieved a high PCE of 13.4 % in all‐SM OSCs, which was much higher than those of the FYSM−H‐ (10.9 %) and FYSM−Si‐based devices (12.2 %). This work demonstrated a promising method for the design of efficient SM‐donors by a side‐chain engineering strategy via the introduction of trialkylsilyl and chlorine substitutions., Superb substitution: A small molecule donor FYSM−SiCl with trialkylsilyl and chlorine substitutions is developed. Compared to its unsubstituted analogue FYSM−H, FYSM−SiCl has a deep‐shifted highest occupied molecular orbital level, improved crystallinity and hole‐mobility, and a predominantly “face‐on” orientation. FYSM−SiCl‐based all‐small‐molecule solar cells achieve a much higher power conversion efficiency of 13.4 % compared to the FYSM−H‐based ones (10.9 %).

Background Less attractive specialties in medicine are struggling to recruit and retain physicians. When properly organized and delivered, continuing medical education (CME) activities that include short courses, coaching in the workplace, and communities of practice might offer a solution to this problem. This position paper discusses how educationalists can create CME activities based on the self-determination theory that increase physicians’ intrinsic motivation to work in these specialties. Main content The authors propose a set of guidelines for the design of CME activities that offer physicians meaningful training experiences within the limits of the available resources and support. First, to increase physicians’ sense of professional relatedness, educationalists must conduct a learner needs assessment, evaluate CME’s long-term outcomes in work-based settings, create social learning networks, and involve stakeholders in every step of the CME design and implementation process. Moreover, providing accessible, practical training formats and giving informative performance feedback that authentically connects to learners' working life situation increases physicians’ competence and autonomy, so that they can confidently and independently manage the situations in their practice contexts. For each guideline, application methods and instruments are proposed, making use of relevant literature and connecting to the self-determination theory. Conclusions By reducing feelings of professional isolation and reinforcing feelings of competence and autonomy in physicians, CME activities show promise as a strategy to recruit and retain physicians in less attractive specialties.

We present an automated method to determine oil release from lipid-based materials. Oil-release tests can provide information regarding the ability to retain oil within the structuring network of lipid-based materials. This test provides a first insight into the stability of these materials and their possible applications in food, cosmetic, and pharmaceutical products. The method presented features a simple setup comprising a camera that automatically captures images of the evolution of the oil stain released from lipid-based materials placed on a filter paper. Image postprocessing is performed with two custom-made scripts developed for the freeware application ImageJ. The scripts allow direct calculation of the oil-stain area from all images stored in a folder returning as output numerical values in a table. This method was shown to be:•inexpensive, as the employed tools and equipment are available in most laboratories both in academia and industry,•self-running, as the method automatically captures images at predefined time intervals for a certain time span,•practical, as manual-image analysis is unnecessary (200 images can be automatically analyzed in 3 min)., Graphical abstract Image, graphical abstract

Parkinson's disease (PD) is characterized by the progressive deterioration of dopamine (DA) neurons, and therapeutic endeavors are aimed at preventing DA loss. However, lack of effective brain delivery approaches limits this strategy. In this study, a “Trojan horse” system is used for substantia nigra‐targeted delivery of a blood brain barrier‐penetrating peptide (RVG29) conjugated to the surface of nanoparticles loaded with the natural autophagy inducer 4,4′‐dimethoxychalcone (DMC) (designated as RVG‐nDMC). Here, the neuroprotective effects of DMC are demonstrated in PD. Specifically, RVG‐nDMC penetrates the blood brain barrier with enhanced brain‐targeted delivery efficiency and is internalized by DA neurons and microglia. In vivo studies demonstrate that RVG‐nDMC ameliorates motor deficits and nigral DA neuron death in PD mice without causing overt adverse effects in the brain or other major organs. Moreover, RVG‐nDMC reverses tyrosine hydroxylase ubiquitination and degradation, alleviates oxidative stress in DA neurons, and exerts antiinflammatory effects in microglia. The “Trojan horse” strategy for targeted delivery of DMC thus provides a potentially powerful and clinically feasible approach for PD intervention., A Trojan horse system (RVG‐nDMC) is constructed for blood brain barrier‐penetrated delivery of 4,4′‐dimethoxychalcone (DMC) to ameliorate motor deficits and nigral dopamine (DA) neuronal death in Parkinson's disease. Mechanistically, RVG‐nDMC reduces tyrosine hydroxylase ubiquitination and degradation, alleviates the oxidative stress in DA neurons, and exerts antiinflammation effects in microglia for synergistic Parkinsonian intervention.

Milk is a necessity for human life. However, it is susceptible to contamination and adulteration. Microfluidic analysis devices have attracted significant attention for the high-throughput quality inspection and contaminant analysis of milk samples in recent years. This review describes the major proposals presented in the literature for the pretreatment, contaminant detection, and quality inspection of milk samples using microfluidic lab-on-a-chip and lab-on-paper platforms in the past five years. The review focuses on the sample separation, sample extraction, and sample preconcentration/amplification steps of the pretreatment process and the determination of aflatoxins, antibiotics, drugs, melamine, and foodborne pathogens in the detection process. Recent proposals for the general quality inspection of milk samples, including the viscosity and presence of adulteration, are also discussed. The review concludes with a brief perspective on the challenges facing the future development of microfluidic devices for the analysis of milk samples in the coming years.

Technologies for the isolation of lignin from pulping process streams are reviewed in this article. Based on published data, the WestVaco process, the LignoBoost process, the LigoForce SystemTM and the SLRP process are reviewed and discussed for the isolation of lignin from Kraft black liquor. The three new processes that have now joined the WestVaco process are compared from the perspective of product quality. Further, isolation processes of lignosulfonates from spent sulfite liquor are reviewed. The limitation for this review is that data are only available from lab scale and pilot scale experiments and not from industrial processes. Key output of this paper is a technology summary of the state of the art processes for technical lignins, showing the pros and cons of each process.

Background The COVID-19 pandemic has impacted people’s lives at unprecedented speed and scale, including how they eat and work, what they are concerned about, how much they move, and how much they can earn. Traditional surveys in the area of public health can be expensive and time-consuming, and they can rapidly become outdated. The analysis of big data sets (such as electronic patient records and surveillance systems) is very complex. Google Trends is an alternative approach that has been used in the past to analyze health behaviors; however, most existing studies on COVID-19 using these data examine a single issue or a limited geographic area. This paper explores Google Trends as a proxy for what people are thinking, needing, and planning in real time across the United States. Objective We aimed to use Google Trends to provide both insights into and potential indicators of important changes in information-seeking patterns during pandemics such as COVID-19. We asked four questions: (1) How has information seeking changed over time? (2) How does information seeking vary between regions and states? (3) Do states have particular and distinct patterns in information seeking? (4) Do search data correlate with—or precede—real-life events? Methods We analyzed searches on 38 terms related to COVID-19, falling into six themes: social and travel; care seeking; government programs; health programs; news and influence; and outlook and concerns. We generated data sets at the national level (covering January 1, 2016, to April 15, 2020) and state level (covering January 1 to April 15, 2020). Methods used include trend analysis of US search data; geographic analyses of the differences in search popularity across US states from March 1 to April 15, 2020; and principal component analysis to extract search patterns across states. Results The data showed high demand for information, corresponding with increasing searches for coronavirus linked to news sources regardless of the ideological leaning of the news source. Changes in information seeking often occurred well in advance of action by the federal government. The popularity of searches for unemployment claims predicted the actual spike in weekly claims. The increase in searches for information on COVID-19 care was paralleled by a decrease in searches related to other health behaviors, such as urgent care, doctor’s appointments, health insurance, Medicare, and Medicaid. Finally, concerns varied across the country; some search terms were more popular in some regions than in others. Conclusions COVID-19 is unlikely to be the last pandemic faced by the United States. Our research holds important lessons for both state and federal governments in a fast-evolving situation that requires a finger on the pulse of public sentiment. We suggest strategic shifts for policy makers to improve the precision and effectiveness of non-pharmaceutical interventions and recommend the development of a real-time dashboard as a decision-making tool.

The boom of plant phenotype highlights the need to measure the physiological characteristics of an individual plant. However, continuous real‐time monitoring of a plant's internal physiological status remains challenging using traditional silicon‐based sensor technology, due to the fundamental mismatch between rigid sensors and soft and curved plant surfaces. Here, the first flexible electronic sensing device is reported that can harmlessly cohabitate with the plant and continuously monitor its stem sap flow, a critical plant physiological characteristic for analyzing plant health, water consumption, and nutrient distribution. Due to a special design and the materials chosen, the realized plant‐wearable sensor is thin, soft, lightweight, air/water/light‐permeable, and shows excellent biocompatibility, therefore enabling the sap flow detection in a continuous and non‐destructive manner. The sensor can serve as a noninvasive, high‐throughput, low‐cost toolbox, and holds excellent potentials in phenotyping. Furthermore, the real‐time investigation on stem flow insides watermelon reveals a previously unknown day/night shift pattern of water allocation between fruit and its adjacent branch, which has not been reported before., A plant‐wearable sensor in situ reveals water transport inside a plant. Due to a special design and the materials chosen, the realized plant‐wearable sensor is thin, soft, lightweight, air/water/light‐permeable, and shows excellent biocompatibility, therefore enabling detection in a continuous and non‐destructive manner.

Background Accurate, objective pain assessment is required in the health care domain and clinical settings for appropriate pain management. Automated, objective pain detection from physiological data in patients provides valuable information to hospital staff and caregivers to better manage pain, particularly for patients who are unable to self-report. Galvanic skin response (GSR) is one of the physiologic signals that refers to the changes in sweat gland activity, which can identify features of emotional states and anxiety induced by varying pain levels. This study used different statistical features extracted from GSR data collected from postoperative patients to detect their pain intensity. To the best of our knowledge, this is the first work building pain models using postoperative adult patients instead of healthy subjects. Objective The goal of this study was to present an automatic pain assessment tool using GSR signals to predict different pain intensities in noncommunicative, postoperative patients. Methods The study was designed to collect biomedical data from postoperative patients reporting moderate to high pain levels. We recruited 25 participants aged 23-89 years. First, a transcutaneous electrical nerve stimulation (TENS) unit was employed to obtain patients' baseline data. In the second part, the Empatica E4 wristband was worn by patients while they were performing low-intensity activities. Patient self-report based on the numeric rating scale (NRS) was used to record pain intensities that were correlated with objectively measured data. The labels were down-sampled from 11 pain levels to 5 different pain intensities, including the baseline. We used 2 different machine learning algorithms to construct the models. The mean decrease impurity method was used to find the top important features for pain prediction and improve the accuracy. We compared our results with a previously published research study to estimate the true performance of our models. Results Four different binary classification models were constructed using each machine learning algorithm to classify the baseline and other pain intensities (Baseline [BL] vs Pain Level [PL] 1, BL vs PL2, BL vs PL3, and BL vs PL4). Our models achieved higher accuracy for the first 3 pain models than the BioVid paper approach despite the challenges in analyzing real patient data. For BL vs PL1, BL vs PL2, and BL vs PL4, the highest prediction accuracies were achieved when using a random forest classifier (86.0, 70.0, and 61.5, respectively). For BL vs PL3, we achieved an accuracy of 72.1 using a k-nearest-neighbor classifier. Conclusions We are the first to propose and validate a pain assessment tool to predict different pain levels in real postoperative adult patients using GSR signals. We also exploited feature selection algorithms to find the top important features related to different pain intensities. International Registered Report Identifier (IRRID) RR2-10.2196/17783

Supramolecular peptide hydrogel has shown promising potential in vaccine development largely because of its ability to function both as antigen depot and immune adjuvant. Nap‐GdFdFdY, a tetrapeptide hydrogel that has been previously reported to exhibit adjuvant effect, is inadvertently found to contain conserved peptide sequence for insulin, proinsulin, and glutamic acid decarboxylase, 3 major autoantigens for the autoimmune type 1 diabetes (T1D). At present, despite being managed clinically with insulin replacement therapy, T1D remains a major health threat with rapidly increasing incidences, especially in children and young adults, and antigen‐specific immune tolerance induction has been proposed as a feasible approach to prevent or delay T1D progression at an early stage. Here, it is reported that innoculation of Nap‐GdFdFdY leads to complete protection of nonobese diabetic (NOD) mice from T1D development till the age of 36 weeks. Better maintenance of pancreatic islet morphology with minimal immune cell infiltration is also observed from mice exposed to Nap‐GdFdFdY. This beneficial impact is mainly due to its facilitative role on enhancing peripheral T regulatory cell (Treg) population, shown as increased splenic Treg percentage, and function, demonstrated by maintenance of circulating TGF‐β1 level. Serum cytokine microarray data further implicate a “buffering” role of Nap‐GdFdFdY on systemic inflammatory tone in NOD mice. Thus, with its versatility, applicability, and excellent potency, Nap‐GdFdFdY is posited as a novel therapeutic intervention for T1D., A hydrogel of Nap‐GdFdFdY containing conserved sequence of autoantigens including insulin, proinsulin, and glutamic acid decarboxylase as a novel therapeutic intervention for the autoimmune type 1 diabetes. Better pancreatic islet morphology with minimal immune cell infiltration is observed from mice with hydrogel because of enhancing peripheral T regulatory cell population and maintenance of circulating TGF‐β1 level.

Identifying a forged printed document with scanned evidence can be a challenge. Microscopic printing is showing random shape which depends on the printing source as well as printing material. This paper presents a statistical analysis of the printing patterns under a microscopic scale, analyses the effect of printing direction, printing substrate (uncoated and coated paper), and printing technology (conventional offset, waterless offset, and electrophotography). The analysis shows a negligible effect of printing direction, yet, using the shape descriptor indexes, the printing materials and technologies are distinguishable under a microscopic scale. As a result, the algorithms based on Support Vectors Machine and Random Forest are developed, with shape descriptor indexes as features, for printing source identification. Both proposed algorithms, equally, achieve a high classification accuracy rate, over 92% accuracy with complex geometric-shape patterns. Thanks to the lightweight and efficiency of the Support Vectors Machine, the study shows promising applications for real-world and potential implementation in the Internet of Things devices.