Showing papers in "Applied Sciences in 2021"

Advances in Metal Additive Manufacturing: A Review of Common Processes, Industrial Applications, and Current Challenges

Abstract: In recent years, Additive Manufacturing (AM), also called 3D printing, has been expanding into several industrial sectors due to the technology providing opportunities in terms of improved functionality, productivity, and competitiveness. While metal AM technologies have almost unlimited potential, and the range of applications has increased in recent years, industries have faced challenges in the adoption of these technologies and coping with a turbulent market. Despite the extensive work that has been completed on the properties of metal AM materials, there is still a need of a robust understanding of processes, challenges, application-specific needs, and considerations associated with these technologies. Therefore, the goal of this study is to present a comprehensive review of the most common metal AM technologies, an exploration of metal AM advancements, and industrial applications for the different AM technologies across various industry sectors. This study also outlines current limitations and challenges, which prevent industries to fully benefit from the metal AM opportunities, including production volume, standards compliance, post processing, product quality, maintenance, and materials range. Overall, this paper provides a survey as the benchmark for future industrial applications and research and development projects, in order to assist industries in selecting a suitable AM technology for their application.

Diversity of Synthetic Dyes from Textile Industries, Discharge Impacts and Treatment Methods

Abstract: Natural dyes have been used from ancient times for multiple purposes, most importantly in the field of textile dying. The increasing demand and excessive costs of natural dye extraction engendered the discovery of synthetic dyes from petrochemical compounds. Nowadays, they are dominating the textile market, with nearly 8 × 105 tons produced per year due to their wide range of color pigments and consistent coloration. Textile industries consume huge amounts of water in the dyeing processes, making it hard to treat the enormous quantities of this hazardous wastewater. Thus, they have harmful impacts when discharged in non-treated or partially treated forms in the environment (air, soil, plants and water), causing several human diseases. In the present work we focused on synthetic dyes. We started by studying their classification which depended on the nature of the manufactured fiber (cellulose, protein and synthetic fiber dyes). Then, we mentioned the characteristics of synthetic dyes, however, we focused more on their negative impacts on the ecosystem (soil, plants, water and air) and on humans. Lastly, we discussed the applied physical, chemical and biological strategies solely or in combination for textile dye wastewater treatments. Additionally, we described the newly established nanotechnology which achieves complete discharge decontamination.

A Review of the Water Desalination Technologies

Abstract: Desalination is commonly adopted nowadays to overcome the freshwater scarcity in some areas of the world if brackish water or salt water is available. Different kinds of technologies have been proposed in the last century. In this paper, the state of the mainstream solutions is reported, showing the current commercial technologies like reverse osmosis (RO), Multi-Stages Flash desalination (MSF) and Multi-Effect Distillation (MED), and the new frontiers of the research with the aim of exploiting renewable sources such as wind, solar and biomass energy. In these cases, seawater treatment plants are the same as traditional ones, with the only difference being that they use a renewable energy source. Thus, classifications are firstly introduced, considering the working principles, the main energy input required for the treatment, and the potential for coupling with renewable energy sources. Each technology is described in detail, showing how the process works and reporting some data on the state of development. Finally, a statistical analysis is given concerning the spread of the various technologies across the world and which of them are most exploited. In this section, an important energy and exergy analysis is also addressed to quantify energy losses.

Real-time hand gesture recognition based on deep learning YOLOv3 model

Abstract: Using gestures can help people with certain disabilities in communicating with other people. This paper proposes a lightweight model based on YOLO (You Only Look Once) v3 and DarkNet-53 convolutional neural networks for gesture recognition without additional preprocessing, image filtering, and enhancement of images. The proposed model achieved high accuracy even in a complex environment, and it successfully detected gestures even in low-resolution picture mode. The proposed model was evaluated on a labeled dataset of hand gestures in both Pascal VOC and YOLO format. We achieved better results by extracting features from the hand and recognized hand gestures of our proposed YOLOv3 based model with accuracy, precision, recall, and an F-1 score of 97.68, 94.88, 98.66, and 96.70%, respectively. Further, we compared our model with Single Shot Detector (SSD) and Visual Geometry Group (VGG16), which achieved an accuracy between 82 and 85%. The trained model can be used for real-time detection, both for static hand images and dynamic gestures recorded on a video.

A Novel K-Means Clustering Algorithm with a Noise Algorithm for Capturing Urban Hotspots

Abstract: With the development of cities, urban congestion is nearly an unavoidable problem for almost every large-scale city. Road planning is an effective means to alleviate urban congestion, which is a classical non-deterministic polynomial time (NP) hard problem, and has become an important research hotspot in recent years. A K-means clustering algorithm is an iterative clustering analysis algorithm that has been regarded as an effective means to solve urban road planning problems by scholars for the past several decades; however, it is very difficult to determine the number of clusters and sensitively initialize the center cluster. In order to solve these problems, a novel K-means clustering algorithm based on a noise algorithm is developed to capture urban hotspots in this paper. The noise algorithm is employed to randomly enhance the attribution of data points and output results of clustering by adding noise judgment in order to automatically obtain the number of clusters for the given data and initialize the center cluster. Four unsupervised evaluation indexes, namely, DB, PBM, SC, and SSE, are directly used to evaluate and analyze the clustering results, and a nonparametric Wilcoxon statistical analysis method is employed to verify the distribution states and differences between clustering results. Finally, five taxi GPS datasets from Aracaju (Brazil), San Francisco (USA), Rome (Italy), Chongqing (China), and Beijing (China) are selected to test and verify the effectiveness of the proposed noise K-means clustering algorithm by comparing the algorithm with fuzzy C-means, K-means, and K-means plus approaches. The compared experiment results show that the noise algorithm can reasonably obtain the number of clusters and initialize the center cluster, and the proposed noise K-means clustering algorithm demonstrates better clustering performance and accurately obtains clustering results, as well as effectively capturing urban hotspots.

Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review

Abstract: Machine Learning and Artificial Intelligence (AI) more broadly have great immediate and future potential for transforming almost all aspects of medicine. However, in many applications, even outside medicine, a lack of transparency in AI applications has become increasingly problematic. This is particularly pronounced where users need to interpret the output of AI systems. Explainable AI (XAI) provides a rationale that allows users to understand why a system has produced a given output. The output can then be interpreted within a given context. One area that is in great need of XAI is that of Clinical Decision Support Systems (CDSSs). These systems support medical practitioners in their clinic decision-making and in the absence of explainability may lead to issues of under or over-reliance. Providing explanations for how recommendations are arrived at will allow practitioners to make more nuanced, and in some cases, life-saving decisions. The need for XAI in CDSS, and the medical field in general, is amplified by the need for ethical and fair decision-making and the fact that AI trained with historical data can be a reinforcement agent of historical actions and biases that should be uncovered. We performed a systematic literature review of work to-date in the application of XAI in CDSS. Tabular data processing XAI-enabled systems are the most common, while XAI-enabled CDSS for text analysis are the least common in literature. There is more interest in developers for the provision of local explanations, while there was almost a balance between post-hoc and ante-hoc explanations, as well as between model-specific and model-agnostic techniques. Studies reported benefits of the use of XAI such as the fact that it could enhance decision confidence for clinicians, or generate the hypothesis about causality, which ultimately leads to increased trustworthiness and acceptability of the system and potential for its incorporation in the clinical workflow. However, we found an overall distinct lack of application of XAI in the context of CDSS and, in particular, a lack of user studies exploring the needs of clinicians. We propose some guidelines for the implementation of XAI in CDSS and explore some opportunities, challenges, and future research needs.

Sentence Representation Method Based on Multi-Layer Semantic Network

Abstract: With the development of artificial intelligence, more and more people hope that computers can understand human language through natural language technology, learn to think like human beings, and finally replace human beings to complete the highly difficult tasks with cognitive ability. As the key technology of natural language understanding, sentence representation reasoning technology mainly focuses on the sentence representation method and the reasoning model. Although the performance has been improved, there are still some problems such as incomplete sentence semantic expression, lack of depth of reasoning model, and lack of interpretability of the reasoning process. In this paper, a multi-layer semantic representation network is designed for sentence representation. The multi-attention mechanism obtains the semantic information of different levels of a sentence. The word order information of the sentence is also integrated by adding the relative position mask between words to reduce the uncertainty caused by word order. Finally, the method is verified on the task of text implication recognition and emotion classification. The experimental results show that the multi-layer semantic representation network can promote sentence representation’s accuracy and comprehensiveness.

Industry 4.0 Technologies for Manufacturing Sustainability: A Systematic Review and Future Research Directions

Abstract: Recent developments in manufacturing processes and automation have led to the new industrial revolution termed “Industry 4.0”. Industry 4.0 can be considered as a broad domain which includes: data management, manufacturing competitiveness, production processes and efficiency. The term Industry 4.0 includes a variety of key enabling technologies i.e., cyber physical systems, Internet of Things, artificial intelligence, big data analytics and digital twins which can be considered as the major contributors to automated and digital manufacturing environments. Sustainability can be considered as the core of business strategy which is highlighted in the United Nations (UN) Sustainability 2030 agenda and includes smart manufacturing, energy efficient buildings and low-impact industrialization. Industry 4.0 technologies help to achieve sustainability in business practices. However, very limited studies reported about the extensive reviews on these two research areas. This study uses a systematic literature review approach to find out the current research progress and future research potential of Industry 4.0 technologies to achieve manufacturing sustainability. The role and impact of different Industry 4.0 technologies for manufacturing sustainability is discussed in detail. The findings of this study provide new research scopes and future research directions in different research areas of Industry 4.0 which will be valuable for industry and academia in order to achieve manufacturing sustainability with Industry 4.0 technologies.

Impact of Dataset Size on Classification Performance: An Empirical Evaluation in the Medical Domain

Abstract: Dataset size is considered a major concern in the medical domain, where lack of data is a common occurrence. This study aims to investigate the impact of dataset size on the overall performance of supervised classification models. We examined the performance of six widely-used models in the medical field, including support vector machine (SVM), neural networks (NN), C4.5 decision tree (DT), random forest (RF), adaboost (AB), and naive Bayes (NB) on eighteen small medical UCI datasets. We further implemented three dataset size reduction scenarios on two large datasets and analyze the performance of the models when trained on each resulting dataset with respect to accuracy, precision, recall, f-score, specificity, and area under the ROC curve (AUC). Our results indicated that the overall performance of classifiers depend on how much a dataset represents the original distribution rather than its size. Moreover, we found that the most robust model for limited medical data is AB and NB, followed by SVM, and then RF and NN, while the least robust model is DT. Furthermore, an interesting observation is that a robust machine learning model to limited dataset does not necessary imply that it provides the best performance compared to other models.

Multi-Agent Reinforcement Learning: A Review of Challenges and Applications

Abstract: In this review, we present an analysis of the most used multi-agent reinforcement learning algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the most critical issues that must be taken into account in their extension to multi-agent scenarios. The analyzed algorithms were grouped according to their features. We present a detailed taxonomy of the main multi-agent approaches proposed in the literature, focusing on their related mathematical models. For each algorithm, we describe the possible application fields, while pointing out its pros and cons. The described multi-agent algorithms are compared in terms of the most important characteristics for multi-agent reinforcement learning applications—namely, nonstationarity, scalability, and observability. We also describe the most common benchmark environments used to evaluate the performances of the considered methods.

Phosphogypsum Recycling: A Review of Environmental Issues, Current Trends, and Prospects

Abstract: The problem of recycling and storage of phosphogypsum is topical for many countries around the world, as it is associated with environmental problems of pollution of water bodies, land, and atmosphere. Therefore, this paper analyzes the directions of phosphogypsum recycling and possible alternatives to its use. The main disadvantages of the existing methods of phosphogypsum processing were identified and trends in this waste management were also considered. Through the VOSviewer programme, a visualization of cluster interconnections was carried out in research publications of various fields of phosphogypsum utilization. Five clusters were formed: a red cluster—phosphogypsum recycling in the construction industry; green cluster—radiation pollution problem of phosphogypsum and phosphate fertilizers; yellow cluster—monitoring migration of phosphogypsum components in the ecosystem, with the mobile forms of heavy metals and their inflow into aquifers from phosphogypsum dumps; blue cluster—use of phosphogypsum in agriculture as an ameliorant and a component of fertilizer; and a purple cluster—the impact of phosphogypsum on microorganisms, particularly in bioremediation processes. Under the proposed integrated biochemical approach, the use of various bioprocesses of phosphogypsum recovery from waste dumps and implementation of new biotechnological solutions for processing phosphorus raw materials are presented.

A Novel Advancing Signal Processing Method Based on Coupled Multi-Stable Stochastic Resonance for Fault Detection

Abstract: In recent years, methods for detecting motor bearing faults have attracted increasing attention. However, it is very difficult to detect the faults from weak motor bearing signals under the strong noise. Stochastic resonance (SR) is a popular signal processing method, which can process weak signals with the noise, but the traditional SR is burdensome in determining its parameters. Therefore, in this paper, a new advancing coupled multi-stable stochastic resonance method, with two first-order multi-stable stochastic resonance systems, namely CMSR, is proposed to detect motor bearing faults. Firstly, the effects of the output signal-to-noise ratio (SNR) for system parameters and coupling coefficients are analyzed in-depth by numerical simulation technology. Then, the SNR is considered as the fitness function for the seeker optimization algorithm (SOA), which can adaptively optimize and determine the system parameters of the SR by using the subsampling technique. An advancing coupled multi-stable stochastic resonance method is realized, and the pre-processed signal is input into the CMSR to detect the faults of motor bearings by using Fourier transform. The faults of motor bearings are determined according to the output signal. Finally, the actual vibration data of induction motor bearings are used to prove the effectiveness of the proposed CMSR. The comparison results with the MSR show that the CMSR can obtain a higher output SNR, which is more beneficial to extract weak signal features and realize fault detection. At the same time, this method also has practical application value for engineering rotating machinery.

Can We Use Functional Annotation of Prokaryotic Taxa (FAPROTAX) to Assign the Ecological Functions of Soil Bacteria

Abstract: FAPROTAX is a promising tool for predicting ecological relevant functions of bacterial and archaeal taxa derived from 16S rRNA amplicon sequencing. The database was initially developed to predict the function of marine species using standard microbiological references. This study, however, has attempted to access the application of FAPROTAX in soil environments. We hypothesized that FAPROTAX was compatible with terrestrial ecosystems. The potential use of FAPROTAX to assign ecological functions of soil bacteria was investigated using meta-analysis and our newly designed experiments. Soil samples from two major terrestrial ecosystems, including agricultural land and forest, were collected. Bacterial taxonomy was analyzed using Illumina sequencing of the 16S rRNA gene and ecological functions of the soil bacteria were assigned by FAPROTAX. The presence of all functionally assigned OTUs (Operation Taxonomic Units) in soil were manually checked using peer-reviewed articles as well as standard microbiology books. Overall, we showed that sample source was not a predominant factor that limited the application of FAPROTAX, but poor taxonomic identification was. The proportion of assigned taxa between aquatic and non-aquatic ecosystems was not significantly different (p > 0.05). There were strong and significant correlations (σ = 0.90–0.95, p < 0.01) between the number of OTUs assigned to genus or order level and the number of functionally assigned OTUs. After manual verification, we found that more than 97% of the FAPROTAX assigned OTUs have previously been detected and potentially performed functions in agricultural and forest soils. We further provided information regarding taxa capable of N-fixation, P and K solubilization, which are three main important elements in soil systems and can be integrated with FAPROTAX to increase the proportion of functionally assigned OTUs. Consequently, we concluded that FAPROTAX can be used for a fast-functional screening or grouping of 16S derived bacterial data from terrestrial ecosystems and its performance could be enhanced through improving the taxonomic and functional reference databases.

Review on Silver Nanoparticles as a Novel Class of Antibacterial Solutions

Abstract: Nanomaterials represent a promising novel class of materials to be used as antibacterial solutions. Inhomogeneity of synthesis and characterization methods, as well as resulting variate physical and chemical properties make selection of proper nanostructure difficult when designing antimicrobial experiments. Present study focuses on the already existing evidence regarding silver nanoparticles and their antibacterial applications, with focus on various modulatory factors of reported antimicrobial efficiency. Present paper focuses on synthesis and characterization methods, factors modulating antibacterial efficiency, laboratory quantification procedures, as well as up–to-date knowledge on mechanisms of antibacterial action for silver nanoparticles. Moreover, challenges and future prospects for antimicrobial applications of silver nanoparticles are reviewed and discussed.

Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring

Abstract: Structural health monitoring (SHM) is an important aspect of the assessment of various structures and infrastructure, which involves inspection, monitoring, and maintenance to support economics, quality of life and sustainability in civil engineering. Currently, research has been conducted in order to develop non-destructive techniques for SHM to extend the lifespan of monitored structures. This paper will review and summarize the recent advancements in non-destructive testing techniques, namely, sweep frequency approach, ground penetrating radar, infrared technique, fiber optics sensors, camera-based methods, laser scanner techniques, acoustic emission and ultrasonic techniques. Although some of the techniques are widely and successfully utilized in civil engineering, there are still challenges that researchers are addressing. One of the common challenges within the techniques is interpretation, analysis and automation of obtained data, which requires highly skilled and specialized experts. Therefore, researchers are investigating and applying artificial intelligence, namely machine learning algorithms to address the challenges. In addition, researchers have combined multiple techniques in order to improve accuracy and acquire additional parameters to enhance the measurement processes. This study mainly focuses on the scope and recent advancements of the Non-destructive Testing (NDT) application for SHM of concrete, masonry, timber and steel structures.

A Survey on Bias in Deep NLP

Abstract: Deep neural networks are hegemonic approaches to many machine learning areas, including natural language processing (NLP). Thanks to the availability of large corpora collections and the capability of deep architectures to shape internal language mechanisms in self-supervised learning processes (also known as “pre-training”), versatile and performing models are released continuously for every new network design. These networks, somehow, learn a probability distribution of words and relations across the training collection used, inheriting the potential flaws, inconsistencies and biases contained in such a collection. As pre-trained models have been found to be very useful approaches to transfer learning, dealing with bias has become a relevant issue in this new scenario. We introduce bias in a formal way and explore how it has been treated in several networks, in terms of detection and correction. In addition, available resources are identified and a strategy to deal with bias in deep NLP is proposed.

How to Promote User Purchase in Metaverse? A Systematic Literature Review on Consumer Behavior Research and Virtual Commerce Application Design

Abstract: Virtual commerce applies immersive technology such as augmented reality and virtual reality into e-commerce to shift consumer perception from 2D product catalogs to 3D immersive virtual spaces. In virtual commerce, the alignment of application design paradigms and the factors influencing consumer behavior is paramount to promote purchase of products and services. The question of their relation needs to be answered, together with the possible improvement of application design. This paper used a systematic literature review approach to synthesize research on virtual commerce from both application design and consumer behavior research, considering the promotion of purchase in virtual commerce settings. Throughout the review, influential factors to purchase and preeminent design artifacts were identified. Then, the research gaps were discovered by mapping the design artifacts to the influential factors, which can inspire future research opportunities on the synergy of these two research directions. Moreover, the evolution of virtual commerce research along with multiple directions were discussed, including the suggestion of meta-commerce as a future trend.

Photodynamic Therapy—An Up-to-Date Review

Abstract: The healing power of light has attracted interest for thousands of years. Scientific discoveries and technological advancements in the field have eventually led to the emergence of photodynamic therapy, which soon became a promising approach in treating a broad range of diseases. Based on the interaction between light, molecular oxygen, and various photosensitizers, photodynamic therapy represents a non-invasive, non-toxic, repeatable procedure for tumor treatment, wound healing, and pathogens inactivation. However, classic photosensitizing compounds impose limitations on their clinical applications. Aiming to overcome these drawbacks, nanotechnology came as a solution for improving targeting efficiency, release control, and solubility of traditional photosensitizers. This paper proposes a comprehensive path, starting with the photodynamic therapy mechanism, evolution over the years, integration of nanotechnology, and ending with a detailed review of the most important applications of this therapeutic approach.

The Use of Microalgae and Cyanobacteria in the Improvement of Agricultural Practices: A Review on Their Biofertilising, Biostimulating and Biopesticide Roles

Abstract: The increase in worldwide population observed in the last decades has contributed to an increased demand for food supplies, which can only be attained through an improvement in agricultural productivities. Moreover, agricultural practices should become more sustainable, as the use of chemically-based fertilisers, pesticides and growth stimulants can pose serious environmental problems and lead to the scarcity of finite resources, such as phosphorus and potassium, thus increasing the fertilisers’ costs. One possible alternative for the development of a more sustainable and highly effective agriculture is the use of biologically-based compounds with known activity in crops’ nutrition, protection and growth stimulation. Among these products, microalgal and cyanobacterial biomass (or their extracts) are gaining particular attention, due to their undeniable potential as a source of essential nutrients and metabolites with different bioactivities, which can significantly improve crops’ yields. This manuscript highlights the potential of microalgae and cyanobacteria in the improvement of agricultural practices, presenting: (i) how these photosynthetic microorganisms interact with higher plants; (ii) the main bioactive compounds that can be isolated from microalgae and cyanobacteria; and (iii) how microalgae and cyanobacteria can influence plants’ growth at different levels (nutrition, protection and growth stimulation).

Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study

Abstract: In the last decade, sentiment analysis has been widely applied in many domains, including business, social networks and education. Particularly in the education domain, where dealing with and processing students’ opinions is a complicated task due to the nature of the language used by students and the large volume of information, the application of sentiment analysis is growing yet remains challenging. Several literature reviews reveal the state of the application of sentiment analysis in this domain from different perspectives and contexts. However, the body of literature is lacking a review that systematically classifies the research and results of the application of natural language processing (NLP), deep learning (DL), and machine learning (ML) solutions for sentiment analysis in the education domain. In this article, we present the results of a systematic mapping study to structure the published information available. We used a stepwise PRISMA framework to guide the search process and searched for studies conducted between 2015 and 2020 in the electronic research databases of the scientific literature. We identified 92 relevant studies out of 612 that were initially found on the sentiment analysis of students’ feedback in learning platform environments. The mapping results showed that, despite the identified challenges, the field is rapidly growing, especially regarding the application of DL, which is the most recent trend. We identified various aspects that need to be considered in order to contribute to the maturity of research and development in the field. Among these aspects, we highlighted the need of having structured datasets, standardized solutions and increased focus on emotional expression and detection.

Applications of Cold Atmospheric Pressure Plasma Technology in Medicine, Agriculture and Food Industry

Abstract: In recent years, cold atmospheric pressure plasma (CAPP) technology has received substantial attention due to its valuable properties including operational simplicity, low running cost, and environmental friendliness. Several different gases (air, nitrogen, helium, argon) and techniques (corona discharge, dielectric barrier discharge, plasma jet) can be used to generate plasma at atmospheric pressure and low temperature. Plasma treatment is routinely used in materials science to modify the surface properties (e.g., wettability, chemical composition, adhesion) of a wide range of materials (e.g., polymers, textiles, metals, glasses). Moreover, CAPP seems to be a powerful tool for the inactivation of various pathogens (e.g., bacteria, fungi, viruses) in the food industry (e.g., food and packing material decontamination, shelf life extension), agriculture (e.g., disinfection of seeds, fertilizer, water, soil) and medicine (e.g., sterilization of medical equipment, implants). Plasma medicine also holds great promise for direct therapeutic treatments in dentistry (tooth bleaching), dermatology (atopic eczema, wound healing) and oncology (melanoma, glioblastoma). Overall, CAPP technology is an innovative, powerful and effective tool offering a broad application potential. However, its limitations and negative impacts need to be determined in order to receive regulatory approval and consumer acceptance.

Extensive Benchmarking of DFT+U Calculations for Predicting Band Gaps

Abstract: Accurate computational predictions of band gaps are of practical importance to the modeling and development of semiconductor technologies, such as (opto)electronic devices and photoelectrochemical cells. Among available electronic-structure methods, density-functional theory (DFT) with the Hubbard U correction (DFT+U) applied to band edge states is a computationally tractable approach to improve the accuracy of band gap predictions beyond that of DFT calculations based on (semi)local functionals. At variance with DFT approximations, which are not intended to describe optical band gaps and other excited-state properties, DFT+U can be interpreted as an approximate spectral-potential method when U is determined by imposing the piecewise linearity of the total energy with respect to electronic occupations in the Hubbard manifold (thus removing self-interaction errors in this subspace), thereby providing a (heuristic) justification for using DFT+U to predict band gaps. However, it is still frequent in the literature to determine the Hubbard U parameters semiempirically by tuning their values to reproduce experimental band gaps, which ultimately alters the description of other total-energy characteristics. Here, we present an extensive assessment of DFT+U band gaps computed using self-consistent ab initio U parameters obtained from density-functional perturbation theory to impose the aforementioned piecewise linearity of the total energy. The study is carried out on 20 compounds containing transition-metal or p-block (group III-IV) elements, including oxides, nitrides, sulfides, oxynitrides, and oxysulfides. By comparing DFT+U results obtained using nonorthogonalized and orthogonalized atomic orbitals as Hubbard projectors, we find that the predicted band gaps are extremely sensitive to the type of projector functions and that the orthogonalized projectors give the most accurate band gaps, in satisfactory agreement with experimental data. This work demonstrates that DFT+U may serve as a useful method for high-throughput workflows that require reliable band gap predictions at moderate computational cost.

Applications of Magnesium and its Alloys: A Review

Abstract: Magnesium is a promising material. It has a remarkable mix of mechanical and biomedical properties that has made it suitable for a vast range of applications. Moreover, with alloying, many of these inherent properties can be further improved. Today, it is primarily used in the automotive, aerospace, and medical industries. However, magnesium has its own set of drawbacks that the industry and research communities are actively addressing. Magnesium’s rapid corrosion is its most significant drawback, and it dramatically impeded magnesium’s growth and expansion into other applications. This article reviews both the engineering and biomedical aspects and applications for magnesium and its alloys. It will also elaborate on the challenges that the material faces and how they can be overcome and discuss its outlook.

How to Correctly Detect Face-Masks for COVID-19 from Visual Information?

Abstract: The new Coronavirus disease (COVID-19) has seriously affected the world. By the end of November 2020, the global number of new coronavirus cases had already exceeded 60 million and the number of deaths 1,410,378 according to information from the World Health Organization (WHO). To limit the spread of the disease, mandatory face-mask rules are now becoming common in public settings around the world. Additionally, many public service providers require customers to wear face-masks in accordance with predefined rules (e.g., covering both mouth and nose) when using public services. These developments inspired research into automatic (computer-vision-based) techniques for face-mask detection that can help monitor public behavior and contribute towards constraining the COVID-19 pandemic. Although existing research in this area resulted in efficient techniques for face-mask detection, these usually operate under the assumption that modern face detectors provide perfect detection performance (even for masked faces) and that the main goal of the techniques is to detect the presence of face-masks only. In this study, we revisit these common assumptions and explore the following research questions: (i) How well do existing face detectors perform with masked-face images? (ii) Is it possible to detect a proper (regulation-compliant) placement of facial masks? and iii) How useful are existing face-mask detection techniques for monitoring applications during the COVID-19 pandemic? To answer these and related questions we conduct a comprehensive experimental evaluation of several recent face detectors for their performance with masked-face images. Furthermore, we investigate the usefulness of multiple off-the-shelf deep-learning models for recognizing correct face-mask placement. Finally, we design a complete pipeline for recognizing whether face-masks are worn correctly or not and compare the performance of the pipeline with standard face-mask detection models from the literature. To facilitate the study, we compile a large dataset of facial images from the publicly available MAFA and Wider Face datasets and annotate it with compliant and non-compliant labels. The annotation dataset, called Face-Mask-Label Dataset (FMLD), is made publicly available to the research community.

What Disease Does This Patient Have? A Large-Scale Open Domain Question Answering Dataset from Medical Exams

Abstract: Open domain question answering (OpenQA) tasks have been recently attracting more and more attention from the natural language processing (NLP) community. In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA, collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. We implement both rule-based and popular neural methods by sequentially combining a document retriever and a machine comprehension model. Through experiments, we find that even the current best method can only achieve 36.7%, 42.0%, and 70.1% of test accuracy on the English, traditional Chinese, and simplified Chinese questions, respectively. We expect MedQA to present great challenges to existing OpenQA systems and hope that it can serve as a platform to promote much stronger OpenQA models from the NLP community in the future.

Mobile Learning Technologies for Education: Benefits and Pending Issues

Abstract: Today’s world demands more efficient learning models that allow students to play a more active role in their education. Technology is having an impact on how instruction is delivered and how information is found and share. Until very recently, the educational models encouraged memorization as an essential learning skill. These days, technologies have changed the educational model and access to information. Knowledge is available online, mostly free, and easily accessible. Reading, sharing, listening and, doing are currently necessary skills for education. Mobile devices have become a complete set of applications, support, and help for educational organizations. By conducting an analysis of the behavior and use of mobile devices on current students, efficient educational applications can be developed. Although there are several initiatives for the use of mobile learning in education, there are also issues linked to this technology that must be addressed. In this work, we present the results of a literature review of mobile learning; the findings described are the result of the analysis of several articles obtained in three scientific repositories. This work also lists certain issues that, if properly addressed, can avoid possible complications to the implementation of this technology in education.

Sentiment Analysis and Topic Modeling on Tweets about Online Education during COVID-19

Abstract: Amid the worldwide COVID-19 pandemic lockdowns, the closure of educational institutes leads to an unprecedented rise in online learning. For limiting the impact of COVID-19 and obstructing its widespread, educational institutions closed their campuses immediately and academic activities are moved to e-learning platforms. The effectiveness of e-learning is a critical concern for both students and parents, specifically in terms of its suitability to students and teachers and its technical feasibility with respect to different social scenarios. Such concerns must be reviewed from several aspects before e-learning can be adopted at such a larger scale. This study endeavors to investigate the effectiveness of e-learning by analyzing the sentiments of people about e-learning. Due to the rise of social media as an important mode of communication recently, people’s views can be found on platforms such as Twitter, Instagram, Facebook, etc. This study uses a Twitter dataset containing 17,155 tweets about e-learning. Machine learning and deep learning approaches have shown their suitability, capability, and potential for image processing, object detection, and natural language processing tasks and text analysis is no exception. Machine learning approaches have been largely used both for annotation and text and sentiment analysis. Keeping in view the adequacy and efficacy of machine learning models, this study adopts TextBlob, VADER (Valence Aware Dictionary for Sentiment Reasoning), and SentiWordNet to analyze the polarity and subjectivity score of tweets’ text. Furthermore, bearing in mind the fact that machine learning models display high classification accuracy, various machine learning models have been used for sentiment classification. Two feature extraction techniques, TF-IDF (Term Frequency-Inverse Document Frequency) and BoW (Bag of Words) have been used to effectively build and evaluate the models. All the models have been evaluated in terms of various important performance metrics such as accuracy, precision, recall, and F1 score. The results reveal that the random forest and support vector machine classifier achieve the highest accuracy of 0.95 when used with Bow features. Performance comparison is carried out for results of TextBlob, VADER, and SentiWordNet, as well as classification results of machine learning models and deep learning models such as CNN (Convolutional Neural Network), LSTM (Long Short Term Memory), CNN-LSTM, and Bi-LSTM (Bidirectional-LSTM). Additionally, topic modeling is performed to find the problems associated with e-learning which indicates that uncertainty of campus opening date, children’s disabilities to grasp online education, and lagging efficient networks for online education are the top three problems.

Augmented Reality Maintenance Assistant Using YOLOv5

Abstract: Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

Digital Twin for Supply Chain Coordination in Modular Construction

Abstract: Over the past decades, the construction industry has been attracted to modular construction because of its benefits for reduced project scheduling and costs. However, schedule deviation risks in the logistics process of modular construction can derail its benefits and thus interfere with its widespread application. To address this issue, we aim to develop a digital twin framework for real-time logistics simulation, which can predict potential logistics risks and accurate module arrival time. The digital twin, a virtual replica of the physical module, updates its virtual asset based on building information modeling (BIM) in near real-time using internet of thing (IoT) sensors. Then, the virtual asset is transferred and exploited for logistics simulation in a geographic information system (GIS)-based routing application. We tested this framework in a case project where modules are manufactured at a factory, delivered to the site via a truck, and assembled onsite. The results show that potential logistical risks and accurate module arrival time can be detected via the suggested digital twin framework. This paper’s primary contribution is the development of a framework that mediates IoT, BIM, and GIS for reliable simulation which predicts potential logistics risks and accurate module delivery time. Such reliable risk prediction enables effective supply chain coordination, which can improve project performance and the widespread application of modular construction.

Recent Developments in Lignin- and Tannin-Based Non-Isocyanate Polyurethane Resins for Wood Adhesives—A Review

Abstract: This review article aims to summarize the potential of using renewable natural resources, such as lignin and tannin, in the preparation of NIPUs for wood adhesives. Polyurethanes (PUs) are extremely versatile polymeric materials, which have been widely used in numerous applications, e.g., packaging, footwear, construction, the automotive industry, the lighting industry, insulation panels, bedding, furniture, metallurgy, sealants, coatings, foams, and wood adhesives. The isocyanate-based PUs exhibit strong adhesion properties, excellent flexibility, and durability, but they lack renewability. Therefore, this study focused on the development of non-isocyanate polyurethane lignin and tannin resins for wood adhesives. PUs are commercially synthesized using polyols and polyisocyanates. Isocyanates are toxic, costly, and not renewable; thus, a search of suitable alternatives in the synthesis of polyurethane resins is needed. The reaction with diamine compounds could result in NIPUs based on lignin and tannin. The research on bio-based components for PU synthesis confirmed that they have good characteristics as an alternative for the petroleum-based adhesives. The advantages of improved strength, low curing temperatures, shorter pressing times, and isocyanate-free properties were demonstrated by lignin- and tannin-based NIPUs. The elimination of isocyanate, associated with environmental and human health hazards, NIPU synthesis, and its properties and applications, including wood adhesives, are reported comprehensively in this paper. The future perspectives of NIPUs’ production and application were also outlined.