The 100 most-cited articles in Applied Sciences, 2019

Networks play important roles in modern life, and cyber security has become a vital research area. An intrusion detection system (IDS) which is an important cyber security technique, monitors the state of software and hardware running in the network. Despite decades of development, existing IDSs still face challenges in improving the detection accuracy, reducing the false alarm rate and detecting unknown attacks. To solve the above problems, many researchers have focused on developing IDSs that capitalize on machine learning methods. Machine learning methods can automatically discover the essential differences between normal data and abnormal data with high accuracy. In addition, machine learning methods have strong generalizability, so they are also able to detect unknown attacks. Deep learning is a branch of machine learning, whose performance is remarkable and has become a research hotspot. This survey proposes a taxonomy of IDS that takes data objects as the main dimension to classify and summarize machine learning-based and deep learning-based IDS literature. We believe that…

The hazardous effects of pollutants from conventional fuel vehicles have caused the scientific world to move towards environmentally friendly energy sources. Though we have various renewable energy sources, the perfect one to use as an energy source for vehicles is hydrogen. Like electricity, hydrogen is an energy carrier that has the ability to deliver incredible amounts of energy. Onboard hydrogen storage in vehicles is an important factor that should be considered when designing fuel cell vehicles. In this study, a recent development in hydrogen fuel cell engines is reviewed to scrutinize the feasibility of using hydrogen as a major fuel in transportation systems. A fuel cell is an electrochemical device that can produce electricity by allowing chemical gases and oxidants as reactants. With anodes and electrolytes, the fuel cell splits the cation and the anion in the reactant to produce electricity. Fuel cells use reactants, which are not harmful to the environment and produce water as a product of the chemical reaction. As hydrogen is one of the most efficient energy carriers,…

Non-stationary time series (TS) analysis has gained an explosive interest over the recent decades in different applied sciences. In fact, several decomposition methods were developed in order to extract various components (e.g., seasonal, trend and abrupt components) from the non-stationary TS, which allows for an improved interpretation of the temporal variability. The wavelet transform (WT) has been successfully applied over an extraordinary range of fields in order to decompose the non-stationary TS into time-frequency domain. For this reason, the WT method is briefly introduced and reviewed in this paper. In addition, this latter includes different research and applications of the WT to non-stationary TS in seven different applied sciences fields, namely the geo-sciences and geophysics, remote sensing in vegetation analysis, engineering, hydrology, finance, medicine, and other fields, such as ecology, renewable energy, chemistry and history. Finally, five challenges and future works, such as the selection of the type of wavelet, selection of the adequate mother wavelet,…

One of the most important discoveries and creative developments that is playing a vital role in the professional world today is blockchain technology. Blockchain technology moves in the direction of persistent revolution and change. It is a chain of blocks that covers information and maintains trust between individuals no matter how far they are. In the last couple of years, the upsurge in blockchain technology has obliged scholars and specialists to scrutinize new ways to apply blockchain technology with a wide range of domains. The dramatic increase in blockchain technology has provided many new application opportunities, including healthcare applications. This survey provides a comprehensive review of emerging blockchain-based healthcare technologies and related applications. In this inquiry, we call attention to the open research matters in this fast-growing field, explaining them in some details. We also show the potential of blockchain technology in revolutionizing healthcare industry.

Along with the development of industry and the improvement of people’s living standards, peoples’ demand on resources has greatly increased, causing energy crises and environmental pollution. In recent years, photocatalytic technology has shown great potential as a low-cost, environmentally-friendly, and sustainable technology, and it has become a hot research topic. However, current photocatalytic technology cannot meet industrial requirements. The biggest challenge in the industrialization of photocatalyst technology is the development of an ideal photocatalyst, which should possess four features, including a high photocatalytic efficiency, a large specific surface area, a full utilization of sunlight, and recyclability. In this review, starting from the photocatalytic reaction mechanism and the preparation of the photocatalyst, we review the classification of current photocatalysts and the methods for improving photocatalytic performance; we also further discuss the potential industrial usage of photocatalytic technology. This review also aims to provide basic and comprehensive…

In the circular economy, products, components, and materials are aimed to be kept at the utility and value all the lifetime. For this purpose, repair and remanufacturing are highly considered as proper techniques to return the value of the product during its life. Directed Energy Deposition (DED) is a very flexible type of additive manufacturing (AM), and among the AM techniques, it is most suitable for repairing and remanufacturing automotive and aerospace components. Its application allows damaged component to be repaired, and material lost in service to be replaced to restore the part to its original shape. In the past, tungsten inert gas welding was used as the main repair method. However, its heat affected zone is larger, and the quality is inferior. In comparison with the conventional welding processes, repair via DED has more advantages, including lower heat input, warpage and distortion, higher cooling rate, lower dilution rate, excellent metallurgical bonding between the deposited layers, high precision, and suitability for full automation. Hence, the proposed repairing…

Lidar imaging systems are one of the hottest topics in the optronics industry. The need to sense the surroundings of every autonomous vehicle has pushed forward a race dedicated to deciding the final solution to be implemented. However, the diversity of state-of-the-art approaches to the solution brings a large uncertainty on the decision of the dominant final solution. Furthermore, the performance data of each approach often arise from different manufacturers and developers, which usually have some interest in the dispute. Within this paper, we intend to overcome the situation by providing an introductory, neutral overview of the technology linked to lidar imaging systems for autonomous vehicles, and its current state of development. We start with the main single-point measurement principles utilized, which then are combined with different imaging strategies, also described in the paper. An overview of the features of the light sources and photodetectors specific to lidar imaging systems most frequently used in practice is also presented. Finally, a brief section on pending issues…

Nutrient deficiency in food crops is seriously affecting human health, especially those in the rural areas, and nanotechnology may become the most sustainable approach to alleviating this challenge. There are several ways of fortifying the nutrients in food such as dietary diversification, use of drugs and industrial fortification. However, the affordability and sustainability of these methods have not been completely achieved. Plants absorb nutrients from fertilizers, but most conventional fertilizers have low nutrient use and uptake efficiency. Nanofertilizers are, therefore, engineered to be target oriented and not easily lost. This review surveys the effects of the addition of macro- and nanonutrients to soil, the interaction, and the absorption capability of the plants, the environmental effect and food content of the nutrients. Most reports were obtained from recent works, and they show that plants nutrients could be enriched by applying nanoparticulate nutrients, which are easily absorbed by the plant. Although there are some toxicity issues associated with the use of…

A paradigm shift towards the utilization of carbon-neutral and low emission fuels is necessary in the internal combustion engine industry to fulfil the carbon emission goals and future legislation requirements in many countries. Hydrogen as an energy carrier and main fuel is a promising option due to its carbon-free content, wide flammability limits and fast flame speeds. For spark-ignited internal combustion engines, utilizing hydrogen direct injection has been proven to achieve high engine power output and efficiency with low emissions. This review provides an overview of the current development and understanding of hydrogen use in internal combustion engines that are usually spark ignited, under various engine operation modes and strategies. This paper then proceeds to outline the gaps in current knowledge, along with better potential strategies and technologies that could be adopted for hydrogen direct injection in the context of compression-ignition engine applications—topics that have not yet been extensively explored to date with hydrogen but have shown advantages with…

Recently, blockchain technology has gained considerable attention from researchers and practitioners. This is mainly due to its unique features including decentralization, security, reliability, and data integrity. Despite this growing interest, little is known about the current state of knowledge and practice regarding the use of blockchain technology in education. This article is a systematic review of research investigating blockchain-based educational applications. It focuses on three main themes: (1) educational applications that have been developed with blockchain technology, (2) benefits that blockchain technology could bring to education, and (3) challenges of adopting blockchain technology in education. A detailed results analysis of each theme was conducted as well as an intensive discussion based on the findings. This review also offers insight into other educational areas that could benefit from blockchain technology.

Additive manufacturing (AM) or three-dimensional (3D) printing has introduced a novel production method in design, manufacturing, and distribution to end-users. This technology has provided great freedom in design for creating complex components, highly customizable products, and efficient waste minimization. The last industrial revolution, namely industry 4.0, employs the integration of smart manufacturing systems and developed information technologies. Accordingly, AM plays a principal role in industry 4.0 thanks to numerous benefits, such as time and material saving, rapid prototyping, high efficiency, and decentralized production methods. This review paper is to organize a comprehensive study on AM technology and present the latest achievements and industrial applications. Besides that, this paper investigates the sustainability dimensions of the AM process and the added values in economic, social, and environment sections. Finally, the paper concludes by pointing out the future trend of AM in technology, applications, and materials aspects that have the potential to come up…

Healthcare information exchange is an important research topic, which can benefit both healthcare providers and patients. In healthcare data sharing, many cloud-based solutions have been proposed, but the trustworthiness of a third-party cloud service is questionable. Recently, blockchain has been introduced in healthcare record sharing, which does not rely on trusting a third party. However, existing approaches only focus on the records collected from medical examination. They are not efficient in sharing data streams continuously generated from sensors and other monitoring devices. Today, IoT devices have been widely deployed and sensors and mobile applications can monitor patients’ body conditions. The collected data are shared to laboratories and institutions for diagnosis and further study. Moreover, existing approaches are too rigid to efficiently support metadata change. In this paper, an efficient data-sharing scheme is proposed, called MedChain, which combines blockchain, digest chain, and structured P2P network techniques to overcome the above efficiency issues in the…

In recent years, artificial intelligence technologies have been widely used in computer vision, natural language processing, automatic driving, and other fields. However, artificial intelligence systems are vulnerable to adversarial attacks, which limit the applications of artificial intelligence (AI) technologies in key security fields. Therefore, improving the robustness of AI systems against adversarial attacks has played an increasingly important role in the further development of AI. This paper aims to comprehensively summarize the latest research progress on adversarial attack and defense technologies in deep learning. According to the target model’s different stages where the adversarial attack occurred, this paper expounds the adversarial attack methods in the training stage and testing stage respectively. Then, we sort out the applications of adversarial attack technologies in computer vision, natural language processing, cyberspace security, and the physical world. Finally, we describe the existing adversarial defense methods respectively in three main categories, i.e.,…

This synthesis explores the state-of-the-knowledge and state-of-the-practice regarding the latest updates on polymer-modified bitumens (PmBs). The information in this study was gathered from a thorough review of the latest papers in the literatures related to modified bituminous materials, technologies, and advances. For this purpose, the paper is presented in two principle sections. In the first part, the bitumen itself is investigated in terms of chemical structure and microstructural systems. In the second part, the paper focuses on bitumen modification from different aspects for assessing the effectiveness of the introduced additives and polymers for enhancing the engineering properties of bitumen in both paving and industrial applications. In conclusion, the knowledge obtained in this study has revealed the importance of the chemical composition of base bitumen for its modification. It can be declared that while some polymers/additives can improve one or some aspects of neat bitumen properties, they can lead to compatibility problems in storage and production. In this respect,…

The 51% attack is a technique which intends to fork a blockchain in order to conduct double-spending. Adversaries controlling more than half of the total hashing power of a network can perform this attack. In a similar way, n confirmation and selfish mining are two attack techniques that comprise a similar strategy to the 51% attack. Due to the immense attacking cost to perform the 51% attack, it was considered very unlikely for a long period. However, in recent times, the attack has befallen at a frequent pace, costing millions of dollars to various cryptocurrencies. The 51% attack strategy varies based upon the adopted consensus mechanism by a particular cryptocurrency, and it enables attackers to double-spend the same crypto-coin, restrict transactions, cancel blocks, and even have full control over the price of a cryptocurrency. A crypto-coin with a low hashing power is always jeopardized by the 51% attack due to the easily attainable hashing. In this paper, we analyze the real impact of the 51% attack, revealing serious weaknesses in consensus protocols that made this attack…

Renewable energy sources have emerged as an alternative to meet the growing demand for energy, mitigate climate change, and contribute to sustainable development. The integration of these systems is carried out in a distributed manner via microgrid systems; this provides a set of technological solutions that allows information exchange between the consumers and the distributed generation centers, which implies that they need to be managed optimally. Energy management in microgrids is defined as an information and control system that provides the necessary functionality, which ensures that both the generation and distribution systems supply energy at minimal operational costs. This paper presents a literature review of energy management in microgrid systems using renewable energies, along with a comparative analysis of the different optimization objectives, constraints, solution approaches, and simulation tools applied to both the interconnected and isolated microgrids. To manage the intermittent nature of renewable energy, energy storage technology is considered to be an attractive…

Environmental management through effective utilization of biowastes has been a topic of intensive research in recent years. This study examines the effect of pyrolysis temperature on the physical and morphological characteristic of biochar (BC) derived from lignocellulosic wastes. The biochar was prepared by pyrolysing date palm biomass at various temperatures, i.e., 300, 400, 500, 600, and 700 °C. These pyrolysed biochars were then characterized for their carbon content, mineral compositions, chemical functionalities, and morphological structures, for understanding their physicochemical characteristics and microstructural evolution. It was revealed that the pyrolytic condition plays a key role in the formation of biochar microstructure. These biochar samples were then utilized without any further treatments/purifications for their practical application as reinforcement materials for polymer composites. They were blended with a polypropylene matrix by a melt mixing technique followed by injection molding process. The type of biochar was found to significantly affect the composites…

β-Caryophyllene (BCP), a natural bicyclic sesquiterpene, is a selective phytocannabinoid agonist of type 2 receptors (CB2-R). It isn’t psychogenic due to the absence of an affinity to cannabinoid receptor type 1 (CB1). Among the various biological activities, BCP exerts anti-inflammatory action via inhibiting the main inflammatory mediators, such as inducible nitric oxide synthase (iNOS), Interleukin 1 beta (IL-1β), Interleukin-6 (IL-6), tumor necrosis factor-alfa (TNF-α), nuclear factor kapp a-light-chain-enhancer of activated B cells (NF-κB), cyclooxygenase 1 (COX-1), cyclooxygenase 2 (COX-2). Peroxisome proliferator-activated receptors alpha (PPAR-α) effects are also mediated by the activation of PPAR-α and PPAR-γ receptors. In detail, many studies, in vitro and in vivo, suggest that the treatment with β-caryophyllene improves the phenotype of animals used to model various inflammatory pathologies, such as nervous system diseases (Parkinson’s disease, Alzheimer’s disease, multiple sclerosis, amyotrophic lateral sclerosis, stroke), atherosclerosis, and tumours (colon, breast,…

As a new functional material, biochar was usually prepared from biomass and solid wastes such as agricultural and forestry waste, sludge, livestock, and poultry manure. The wide application of biochar is due to its abilities to remove pollutants, remediate contaminated soil, and reduce greenhouse gas emissions. In this paper, the influence of preparation methods, process parameters, and modification methods on the physicochemical properties of biochar were discussed, as well as the mechanisms of biochar in the remediation of soil pollution. The biochar applications in soil remediation in the past years were summarized, such as the removal of heavy metals and persistent organic pollutants (POPs), and the improvement of soil quality. Finally, the potential risks of biochar application and the future research directions were analyzed.

Energy-efficiency is one of the critical issues in smart cities. It is an essential basis for optimizing smart cities planning. This study proposed four new artificial intelligence (AI) techniques for forecasting the heating load of buildings’ energy efficiency based on the potential of artificial neural network (ANN) and meta-heuristics algorithms, including artificial bee colony (ABC) optimization, particle swarm optimization (PSO), imperialist competitive algorithm (ICA), and genetic algorithm (GA). They were abbreviated as ABC-ANN, PSO-ANN, ICA-ANN, and GA-ANN models; 837 buildings were considered and analyzed based on the influential parameters, such as glazing area distribution (GLAD), glazing area (GLA), orientation (O), overall height (OH), roof area (RA), wall area (WA), surface area (SA), relative compactness (RC), for estimating heating load (HL). Three statistical criteria, such as root-mean-squared error (RMSE), coefficient determination (R2), and mean absolute error (MAE), were used to assess the potential of the aforementioned models. The results indicated that the…

Naturally occurring food-derived active ingredients have received huge attention for their chemopreventive and chemotherapy capabilities in several diseases. Rosmarinic acid (RA) is a caffeic acid ester and a naturally-occurring phenolic compound in a number of plants belonging to the Lamiaceae family, such as Rosmarinus officinalis (rosemary) from which it was formerly isolated. RA intervenes in carcinogenesis through different ways, including in tumor cell proliferation, apoptosis, metastasis, and inflammation. On the other hand, it also exerts powerful antimicrobial, anti-inflammatory, antioxidant and even antidepressant, anti-aging effects. The present review aims to provide an overview on anticancer activities of RA and to deliberate its therapeutic potential against a wide variety of diseases. Given the current evidence, RA may be considered as part of the daily diet in the treatment of several diseases, with pre-determined doses avoiding cytotoxicity.

Additive manufacturing (AM) has emerged over the past four decades as a cost-effective, on-demand modality for fabrication of geometrically complex objects. The ability to design and print virtually any object shape using a diverse array of materials, such as metals, polymers, ceramics and bioinks, has allowed for the adoption of this technology for biomedical applications in both research and clinical settings. Current advancements in tissue engineering and regeneration, therapeutic delivery, medical device fabrication and operative management planning ensure that AM will continue to play an increasingly important role in the future of healthcare. In this review, we outline current biomedical applications of common AM techniques and materials.

Lignocellulosic biomass is recalcitrant due to its heterogeneous structure, which is one of the major limitations for its use as a feedstock for methane production. Although different pretreatment methods are being used, intermediaries formed are known to show adverse effect on microorganisms involved in methane formation. This review, apart from highlighting the efficiency and limitations of the different pretreatment methods from engineering, chemical, and biochemical point of views, will discuss the strategies to increase the carbon recovery in the form of methane by way of amending pretreatments to lower inhibitory effects on microbial groups and by optimizing process conditions.

As one of the most promising new energy sources, the lithium-ion battery (LIB) and its associated safety concerns have attracted great research interest. Herein, a comprehensive review on the thermal hazards of LIBs and the corresponding countermeasures is provided. In general, the thermal hazards of the LIB can be caused or aggravated by several factors including physical, electrical and thermal factors, manufacturing defect and even battery aging. Due to the activity and combustibility of traditional battery components, they usually possess a relatively high thermal hazard and a series of side reactions between electrodes and electrolytes may occur under abusive conditions, which would further lead to the thermal failure of LIBs. Besides, the thermal hazards generally manifest as the thermal runaway behaviors such as high-temperature, ejection, combustion, explosion and toxic gases for a single battery, and it can even evolve to thermal failure propagation within a battery pack. To decrease these hazards, some countermeasures are reviewed including the application of safety…

A computerized detection system for the diagnosis of Schizophrenia (SZ) using a convolutional neural system is described in this study. Schizophrenia is an anomaly in the brain characterized by behavioral symptoms such as hallucinations and disorganized speech. Electroencephalograms (EEG) indicate brain disorders and are prominently used to study brain diseases. We collected EEG signals from 14 healthy subjects and 14 SZ patients and developed an eleven-layered convolutional neural network (CNN) model to analyze the signals. Conventional machine learning techniques are often laborious and subject to intra-observer variability. Deep learning algorithms that have the ability to automatically extract significant features and classify them are thus employed in this study. Features are extracted automatically at the convolution stage, with the most significant features extracted at the max-pooling stage, and the fully connected layer is utilized to classify the signals. The proposed model generated classification accuracies of 98.07% and 81.26% for non-subject based testing and subject…

The upcoming fifth- and sixth-generation (5G and 6G, respectively) communication systems are expected to deal with enormous advances compared to the existing fourth-generation communication system. The few important and common issues related to the service quality of 5G and 6G communication systems are high capacity, massive connectivity, low latency, high security, low-energy consumption, high quality of experience, and reliable connectivity. Of course, 6G communication will provide several-fold improved performances compared to the 5G communication regarding these issues. The Internet of Things (IoT) based on the tactile internet will also be an essential part of 5G-and-beyond (5GB) (e.g., 5G and 6G) communication systems. Accordingly, 5GB wireless networks will face numerous challenges in supporting the extensive verities of heterogeneous traffic and in satisfying the mentioned service-quality-related parameters. Optical wireless communication (OWC), along with many other wireless technologies, is a promising candidate for serving the demands of 5GB communication systems. This…

Compared with conventional display technologies, liquid crystal display (LCD), and organic light emitting diode (OLED), micro-LED displays possess potential advantages such as high contrast, fast response, and relatively wide color gamut, low power consumption, and long lifetime. Therefore, micro-LED displays are deemed as a promising technology that could replace LCD and OLED at least in some applications. While the prospects are bright, there are still some technological challenges that have not yet been fully resolved in order to realize the high volume commercialization, which include efficient and reliable assembly of individual LED dies into addressable arrays, full-color schemes, defect and yield management, repair technology and cost control. In this article, we review the recent technological developments of micro-LEDs from various aspects.

Research on clouds has an enormous influence on sky sciences and related applications, and cloud classification plays an essential role in it. Much research has been conducted which includes both traditional machine learning approaches and deep learning approaches. Compared with traditional machine learning approaches, deep learning approaches achieved better results. However, most deep learning models need large data to train due to the large number of parameters. Therefore, they cannot get high accuracy in case of small datasets. In this paper, we propose a complete solution for high accuracy of classification of cloud image patches on small datasets. Firstly, we designed a suitable convolutional neural network (CNN) model for small datasets. Secondly, we applied regularization techniques to increase generalization and avoid overfitting of the model. Finally, we introduce a model average ensemble to reduce the variance of prediction and increase the classification accuracy. We experiment the proposed solution on the Singapore whole-sky imaging categories (SWIMCAT) dataset, which…

This paper summarizes the top state-of-the-art contributions reported on the MNIST dataset for handwritten digit recognition. This dataset has been extensively used to validate novel techniques in computer vision, and in recent years, many authors have explored the performance of convolutional neural networks (CNNs) and other deep learning techniques over this dataset. To the best of our knowledge, this paper is the first exhaustive and updated review of this dataset; there are some online rankings, but they are outdated, and most published papers survey only closely related works, omitting most of the literature. This paper makes a distinction between those works using some kind of data augmentation and works using the original dataset out-of-the-box. Also, works using CNNs are reported separately; as they are becoming the state-of-the-art approach for solving this problem. Nowadays, a significant amount of works have attained a test error rate smaller than 1% on this dataset; which is becoming non-challenging. By mid-2017, a new dataset was introduced: EMNIST, which involves both…

A wind power short-term forecasting method based on discrete wavelet transform and long short-term memory networks (DWT_LSTM) is proposed. The LSTM network is designed to effectively exhibit the dynamic behavior of the wind power time series. The discrete wavelet transform is introduced to decompose the non-stationary wind power time series into several components which have more stationarity and are easier to predict. Each component is dug by an independent LSTM. The forecasting results of the wind power are obtained by synthesizing the prediction values of all components. The prediction accuracy has been improved by the proposed method, which is validated by the MAE (mean absolute error), MAPE (mean absolute percentage error), and RMSE (root mean square error) of experimental results of three wind farms as the benchmarks. Wind power forecasting based on the proposed method provides an alternative way to improve the security and stability of the electric power network with the high penetration of wind power.

Channel shear connectors are known as an appropriate alternative for common shear connectors due to having a lower manufacturing cost and an easier installation process. The behavior of channel connectors is generally determined through conducting experiments. However, these experiments are not only costly but also time-consuming. Moreover, the impact of other parameters cannot be easily seen in the behavior of the connectors. This paper aims to investigate the application of a hybrid artificial neural network–particle swarm optimization (ANN-PSO) model in the behavior prediction of channel connectors embedded in normal and high-strength concrete (HSC). To generate the required data, an experimental project was conducted. Dimensions of the channel connectors and the compressive strength of concrete were adopted as the inputs of the model, and load and slip were predicted as the outputs. To evaluate the ANN-PSO model, an ANN model was also developed and tuned by a backpropagation (BP) learning algorithm. The results of the paper revealed that an ANN model could properly predict the…

News currently spreads rapidly through the internet. Because fake news stories are designed to attract readers, they tend to spread faster. For most readers, detecting fake news can be challenging and such readers usually end up believing that the fake news story is fact. Because fake news can be socially problematic, a model that automatically detects such fake news is required. In this paper, we focus on data-driven automatic fake news detection methods. We first apply the Bidirectional Encoder Representations from Transformers model (BERT) model to detect fake news by analyzing the relationship between the headline and the body text of news. To further improve performance, additional news data are gathered and used to pre-train this model. We determine that the deep-contextualizing nature of BERT is best suited for this task and improves the 0.14 F-score over older state-of-the-art models.

Traditional aquaculture provides food for humans, but produces a large amount of wastewater, threatening global sustainability. The antibiotics abuse and the water replacement or treatment causes safety problems and increases the aquaculture cost. To overcome environmental and economic problems in the aquaculture industry, a lot of efforts have been devoted into the application of microalgae for wastewater remediation, biomass production, and water quality control. In this review, the systematic description of the technologies required for microalgae-assisted aquaculture and the recent progress were discussed. It deeply reviews the problems caused by the discharge of aquaculture wastewater and introduces the principles of microalgae-assisted aquaculture. Some interesting aspects, including nutrients assimilation mechanisms, algae cultivation systems (raceway pond and revolving algal biofilm), wastewater pretreatment, algal-bacterial cooperation, harvesting technologies (fungi-assisted harvesting and flotation), selection of algal species, and exploitation of value-added microalgae…

The electric energy consumption prediction (EECP) is an essential and complex task in intelligent power management system. EECP plays a significant role in drawing up a national energy development policy. Therefore, this study proposes an Electric Energy Consumption Prediction model utilizing the combination of Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (Bi-LSTM) that is named EECP-CBL model to predict electric energy consumption. In this framework, two CNNs in the first module extract the important information from several variables in the individual household electric power consumption (IHEPC) dataset. Then, Bi-LSTM module with two Bi-LSTM layers uses the above information as well as the trends of time series in two directions including the forward and backward states to make predictions. The obtained values in the Bi-LSTM module will be passed to the last module that consists of two fully connected layers for finally predicting the electric energy consumption in the future. The experiments were conducted to compare the prediction performances of…

This paper is a critical review of metasurfaces, which are planar metamaterials. Metamaterials offer bespoke electromagnetic applications and novel properties which are not found in naturally occurring materials. However, owing to their 3D-nature and resonant characteristics, they suffer from manufacturing complexity, losses and are highly dispersive. The 2-dimensional nature of metasurfaces allows ease of fabrication and integration into devices. The phase discontinuity across the metasurface offers anomalous refraction, thereby conserving the good metamaterial properties while still offering the low-loss characteristics. The paper discusses salient features and applications of metasurfaces; wavefront shaping; phase jumps; non-linear metasurfaces; and their use as frequency selective surfaces (FSS).

Chalcogenide phase change materials based on germanium-antimony-tellurides (GST-PCMs) have shown outstanding properties in non-volatile memory (NVM) technologies due to their high write and read speeds, reversible phase transition, high degree of scalability, low power consumption, good data retention, and multi-level storage capability. However, GST-based PCMs have shown recent promise in other domains, such as in spatial light modulation, beam steering, and neuromorphic computing. This paper reviews the progress in GST-based PCMs and methods for improving the performance within the context of new applications that have come to light in recent years.

Perovskite solar cells have achieved photo-conversion efficiencies greater than 20%, making them a promising candidate as an emerging solar cell technology. While perovskite solar cells are expected to eventually compete with existing silicon-based solar cells on the market, their long-term stability has become a major bottleneck. In particular, perovskite films are found to be very sensitive to external factors such as air, UV light, light soaking, thermal stress and others. Among these stressors, light, oxygen and moisture-induced degradation can be slowed by integrating barrier or interface layers within the device architecture. However, the most representative perovskite absorber material, CH3NH3PbI3 (MAPbI3), appears to be thermally unstable even in an inert environment. This poses a substantial challenge for solar cell applications because device temperatures can be over 45 °C higher than ambient temperatures when operating under direct sunlight. Herein, recent advances in resolving thermal stability problems are highlighted through literature review. Moreover, the most recent…

The present review paper aims to document the latest developments on the applications of nanofluids as working fluid in parabolic trough collectors (PTCs). The influence of many factors such as nanoparticles and base fluid type as well as volume fraction and size of nanoparticles on the performance of PTCs has been investigated. The reviewed studies were mainly categorized into three different types of experimental, modeling (semi-analytical), and computational fluid dynamics (CFD). The main focus was to evaluate the effect of nanofluids on thermal efficiency, entropy generation, heat transfer coefficient enhancement, as well as pressure drop in PTCs. It was revealed that nanofluids not only enhance (in most of the cases) the thermal efficiency, convection heat transfer coefficient, and exergy efficiency of the system but also can decrease the entropy generation of the system. The only drawback in application of nanofluids in PTCs was found to be pressure drop increase that can be controlled by optimization in nanoparticles volume fraction and mass flow rate.

Concrete bridge crack detection is critical to guaranteeing transportation safety. The introduction of deep learning technology makes it possible to automatically and accurately detect cracks in bridges. We proposed an end-to-end crack detection model based on the convolutional neural network (CNN), taking the advantage of atrous convolution, Atrous Spatial Pyramid Pooling (ASPP) module and depthwise separable convolution. The atrous convolution obtains a larger receptive field without reducing the resolution. The ASPP module enables the network to extract multi-scale context information, while the depthwise separable convolution reduces computational complexity. The proposed model achieved a detection accuracy of 96.37% without pre-training. Experiments showed that, compared with traditional classification models, the proposed model has a better performance. Besides, the proposed model can be embedded in any convolutional network as an effective feature extraction structure.

Many operating parameters, individually or together, may influence the performance of anaerobic digestion towards biogas or digestate yield and quality maximization. The most preferred method of optimizing an anaerobic digestion plant often relies on how carefully the crucial parameters, such as pH, temperature, organic loading rate, hydraulic retention time, and pressure, are chosen. There is a large amount of literature available on optimization of anaerobic digestion; however, given the continued development and implementation of innovative technologies, together with the introduction of increasingly complex systems, it is necessary to update present knowledge on process parameters and their role on operational ranges and flexibilities in real-life anaerobic digestion system. Accordingly, the present review discusses the importance of the selection of operational parameters in existing technologies and their impact on biogas yield. Notably, the four broad areas of feedstock utilization (substrate, inoculum, codigestion and pretreatment), process condition (pH, temperature,…

The Polarimetric Synthetic Aperture Radar technique has provided various opportunities and challenges in agricultural activities mainly on crop management. The aim of this study is to investigate the sensitivity of 10 parameters derived from multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) data, to crop height and canopy coverage (CC) of maize, sunflower, and wheat. The correlation coefficient values indicate a high correlation for maize during the early growing stage. The coefficient determinations (R2) of 0.82 and 0.81 indicate that there is a strong relationship between the maize height and SAR parameters including VV + VH and VV, respectively. The maize CC is well correlated with VV parameter (R2 = 0.73), but it is observed that at the later growing stage the correlation became weaker. This means that the sensitivity decreases with increasing vegetation cover growth. Compared to maize, the sensitivity of SAR parameters to wheat variables is often good at the early stage. However, the highest correlation with wheat height represented by Alpha (α) decomposition parameter…

The shear strength of rockfill materials (RFM) is an important engineering parameter in the design and audit of geotechnical structures. In this paper, the predictive reliability and feasibility of random forests and Cubist models were analyzed by estimating the shear strength from the relative density, particle size, distribution (gradation), material hardness, gradation and fineness modulus, and confining (normal) stress. For this purpose, case studies of 165 rockfill samples have been applied to generate training and testing datasets to construct and validate the models. Thirteen key material properties for rockfill characterization were selected to develop the proposed models. Validation and comparison of the models have been performed using the root mean square error (RMSE), coefficient of determination (R2), and mean estimation error (MAE) between the measured and estimated values. A sensitivity analysis was also conducted to ascertain the importance of various inputs in the prediction of the output. The results demonstrated that the Cubist model has the highest prediction…

With growing consumer demand for natural products, greener extraction techniques are found to be potential alternatives especially for pharmaceutical, nutraceutical, and cosmetic manufacturing industries. Cavitation-based technology has drawn immense attention as a greener extraction method, following its rapid and effective extraction of numerous natural products compared to conventional techniques. The advantages of cavitation-based extraction (CE) are to eliminate the application of toxic solvents, reduction of extraction time and to achieve better extraction yield, as well as purity. The cavitational phenomena enhance the extraction efficiency via increased mass transfer rate between the substrate and solvent, following the cell wall rupture, due to the intense implosion of bubbles. This review includes a detailed overview of the ultrasound-assisted extraction (UAE), negative pressure cavitation (NPC) extraction, hydrodynamic cavitation extraction (HCE) and combined extractions techniques which have been implemented for the extraction of high-value-added compounds. A list of…

This study aims to analyze and compare landslide susceptibility at Woomyeon Mountain, South Korea, based on the random forest (RF) model and the boosted regression tree (BRT) model. Through the construction of a landslide inventory map, 140 landslide locations were found. Among these, 42 (30%) were reserved to validate the model after 98 (70%) had been selected at random for model training. Fourteen landslide explanatory variables related to topography, hydrology, and forestry factors were considered and selected, based on the results of information gain for the modeling. The results were evaluated and compared using the receiver operating characteristic curve and statistical indices. The analysis showed that the RF model was better than the BRT model. The RF model yielded higher specificity, overall accuracy, and kappa index than the BRT model. In addition, the RF model, with a prediction rate of 0.865, performed slightly better than the BRT model, which had a prediction rate of 0.851. These results indicate that the landslide susceptibility maps (LSMs) produced in this study had…

Predicting the penetration rate is a complex and challenging task due to the interaction between the tunnel boring machine (TBM) and the rock mass. Many studies highlight the use of empirical and theoretical techniques in predicting TBM performance. However, reliable performance prediction of TBM is of crucial importance to mining and civil projects as it can minimize the risks associated with capital costs. This study presents new applications of supervised machine learning techniques, i.e., k-nearest neighbor (KNN), chi-squared automatic interaction detection (CHAID), support vector machine (SVM), classification and regression trees (CART) and neural network (NN) in predicting the penetration rate (PR) of a TBM. To achieve this aim, an experimental database was set up, based on field observations and laboratory tests for a tunneling project in Malaysia. In the database, uniaxial compressive strength, Brazilian tensile strength, rock quality designation, weathering zone, thrust force, and revolution per minute were utilized as inputs to predict PR of TBM. Then, KNN, CHAID, SVM,…

In this study, the structure, electrical and thermal properties of ten polymer compositions based on polylactic acid (PLA), low-cost industrial graphene nanoplates (GNP) and multi-walled carbon nanotubes (MWCNT) in mono-filler PLA/MWCNT and PLA/GNP systems with 0–6 wt.% filler content were investigated. Filler dispersion was further improved by combining these two carbon nanofillers with different geometric shapes and aspect ratios in hybrid bi-filler nanocomposites. Scanning electron microscopy (SEM), transmission electron microscopy (TEM) and Raman spectroscopy exhibited uniform dispersion of nanoparticles in a polymer matrix. The obtained results have shown that for the mono-filler systems with MWCNT or GNP, the electrical conductivity increased with decades. Moreover, a small synergistic effect was observed in the GNP/MWCNT/PLA bi-filler hybrid composites when combining GNP and CNT at a ratio of 3% GNP/3% CNT and 1.5% GNP:4.5% CNT, showing higher electrical conductivity with respect to the systems incorporating individual CNTs and GNPs at the same overall filler concentration.…

Air pollution has become an important environmental issue in recent decades. Forecasts of air quality play an important role in warning people about and controlling air pollution. We used support vector regression (SVR) and random forest regression (RFR) to build regression models for predicting the Air Quality Index (AQI) in Beijing and the nitrogen oxides (NOX) concentration in an Italian city, based on two publicly available datasets. The root-mean-square error (RMSE), correlation coefficient (r), and coefficient of determination (R2) were used to evaluate the performance of the regression models. Experimental results showed that the SVR-based model performed better in the prediction of the AQI (RMSE = 7.666, R2 = 0.9776, and r = 0.9887), and the RFR-based model performed better in the prediction of the NOX concentration (RMSE = 83.6716, R2 = 0.8401, and r = 0.9180). This work also illustrates that combining machine learning with air quality prediction is an efficient and convenient way to solve some related environment problems.

Polycaprolactone (PCL) is one of the most used synthetic polymers for medical applications due to its biocompatibility and slow biodegradation character. Combining the inherent properties of the PCL matrix with the characteristic of nanofibrous particles, result into promising materials that can be suitable for different applications, including the biomedical applications. The advantages of nanofibrous structures include large surface area, a small diameter of pores and a high porosity, which make them of great interest in different applications. Electrospinning, as technique, has been heavily used for the preparation of nano- and micro-sized fibers. This review discusses the different methods for the electrospinning of PCL and its composites for advanced applications. Furthermore, the steady state conditions as well as the effect of the electrospinning parameters on the resultant morphology of the electrospun fiber are also reported.

The generation of energy from renewable sources is subjected to very dynamic changes in environmental parameters and asset operating conditions. This is a very relevant issue to be considered when developing reliability studies, modeling asset degradation and projecting renewable energy production. To that end, Artificial Neural Network (ANN) models have proven to be a very interesting tool, and there are many relevant and interesting contributions using ANN models, with different purposes, but somehow related to real-time estimation of asset reliability and energy generation. This document provides a precise review of the literature related to the use of ANN when predicting behaviors in energy production for the referred renewable energy sources. Special attention is paid to describe the scope of the different case studies, the specific approaches that were used over time, and the main variables that were considered. Among all contributions, this paper highlights those incorporating intelligence to anticipate reliability problems and to develop ad-hoc advanced maintenance policies.…

Technology and innovations have fueled the evolution of Industry 4.0, the fourth industrial revolution. Industry 4.0 encourages growth and development through its efficiency capacity, as documented in the literature. The growth of the construction industry is a subset of the universal set of the gross domestic product value; thus, Industry 4.0 has a spillover effect on the engineering and construction industry. In this study, we aimed to map the state of Industry 4.0 in the construction industry, to identify its key areas, and evaluate and interpret the available evidence. We focused our literature search on Web of Science and Scopus between January 2015 and May 2019. The search was dependent on the following keywords: “Industry 4.0” OR “Industrial revolution 4.0” AND TOPIC: “construction” OR “building”. From the 82 papers found, 20 full-length papers were included in this review. Results from the targeted papers were split into three clusters: technology, security, and management. With building information modelling (BIM) as the core in the cyber-physical system, the…

The circular economy (CE) is an emergent concept to rethink and redesign how our economy works. The concept recognizes effective and efficient economic functioning at multiple scales—governments and individuals, globally and locally; for businesses, large and small. CE represents a systemic shift that builds long-term resilience at multiple levels (macro, meso and micro); generating new business and economic opportunities while providing environmental and societal benefits. Blockchain, an emergent and critical technology, is introduced to the circular economy environment as a potential enabler for many circular economic principles. Blockchain technology supported information systems can improve circular economy performance at multiple levels. Product deletion, a neglected but critical effort in product management and product portfolio management, is utilized as an illustrative business scenario as to blockchain’s application in a circular economy research context. Product deletion, unlike product proliferation, has received minimal attention from both academics and practitioners.…

In recent years, constant developments in Internet of Things (IoT) generate large amounts of data, which put pressure on Cloud computing’s infrastructure. The proposed Fog computing architecture is considered the next generation of Cloud Computing for meeting the requirements posed by the device network of IoT. One of the obstacles of Fog Computing is distribution of computing resources to minimize completion time and operating cost. The following study introduces a new approach to optimize task scheduling problem for Bag-of-Tasks applications in Cloud–Fog environment in terms of execution time and operating costs. The proposed algorithm named TCaS was tested on 11 datasets varying in size. The experimental results show an improvement of 15.11% compared to the Bee Life Algorithm (BLA) and 11.04% compared to Modified Particle Swarm Optimization (MPSO), while achieving balance between completing time and operating cost.

Microalgae have been considered as one of the most promising biomass feedstocks for various industrial applications such as biofuels, animal/aquaculture feeds, food supplements, nutraceuticals, and pharmaceuticals. Several biotechnological challenges associated with algae cultivation, including the small size and negative surface charge of algal cells as well as the dilution of its cultures, need to be circumvented, which increases the cost and labor. Therefore, efficient biomass recovery or harvesting of diverse algal species represents a critical bottleneck for large-scale algal biorefinery process. Among different algae harvesting techniques (e.g., centrifugation, gravity sedimentation, screening, filtration, and air flotation), the flocculation-based processes have acquired much attention due to their promising efficiency and scalability. This review covers the basics and recent research trends of various flocculation techniques, such as auto-flocculation, bio-flocculation, chemical flocculation, particle-based flocculation, and electrochemical flocculation, and also discusses…

Organic crystals with second-order optical nonlinearity feature very high and ultra-fast optical nonlinearities and are therefore attractive for various photonics applications. During the last decade, they have been found particularly attractive for terahertz (THz) photonics. This is mainly due to the very intense and ultra-broadband THz-wave generation possible with these crystals. We review recent progress and challenges in the development of organic crystalline materials for THz-wave generation and detection applications. We discuss their structure, intrinsic properties, and advantages compared to inorganic alternatives. The characteristic properties of the most widely employed organic crystals at present, such as DAST, DSTMS, OH1, HMQ-TMS, and BNA are analyzed and compared. We summarize the most important principles for THz-wave generation and detection, as well as organic THz-system configurations based on either difference-frequency generation or optical rectification. In addition, we give state-of-the-art examples of very intense and ultra-broadband THz systems that rely on…

With the rapid growth of renewable energy resources, energy trading has been shifting from the centralized manner to distributed manner. Blockchain, as a distributed public ledger technology, has been widely adopted in the design of new energy trading schemes. However, there are many challenging issues in blockchain-based energy trading, e.g., low efficiency, high transaction cost, and security and privacy issues. To tackle these challenges, many solutions have been proposed. In this survey, the blockchain-based energy trading in the electrical power system is thoroughly investigated. Firstly, the challenges in blockchain-based energy trading are identified and summarized. Then, the existing energy trading schemes are studied and classified into three categories based on their main focuses: energy transaction, consensus mechanism, and system optimization. Blockchain-based energy trading has been a popular research topic, new blockchain architectures, models and products are continually emerging to overcome the limitations of existing solutions, forming a virtuous circle. The…

Aspect-based sentiment classification (ABSC) aims to predict sentiment polarities of different aspects within sentences or documents. Many previous studies have been conducted to solve this problem, but previous works fail to notice the correlation between the aspect’s sentiment polarity and the local context. In this paper, a Local Context Focus (LCF) mechanism is proposed for aspect-based sentiment classification based on Multi-head Self-Attention (MHSA). This mechanism is called LCF design, and utilizes the Context features Dynamic Mask (CDM) and Context Features Dynamic Weighted (CDW) layers to pay more attention to the local context words. Moreover, a BERT-shared layer is adopted to LCF design to capture internal long-term dependencies of local context and global context. Experiments are conducted on three common ABSC datasets: the laptop and restaurant datasets of SemEval-2014 and the ACL twitter dataset. Experimental results demonstrate that the LCF baseline model achieves considerable performance. In addition, we conduct ablation experiments to prove the significance and…

With the proliferation of smart devices capable of communicating over a network using different protocols, each year more and more successful attacks are recorded against these, underlining the necessity of developing and implementing mechanisms to protect against such attacks. This paper will review some existing solutions used to secure a communication channel, such as Transport Layer Security or symmetric encryption, as well as provide a novel approach to achieving confidentiality and integrity of messages. The method, called Value-to-Keyed-Hash Message Authentication Code (Value-to-HMAC) mapping, uses signatures to send messages, instead of encryption, by implementing a Keyed-Hash Message Authentication Code generation algorithm. Although robust solutions exist that can be used to secure the communication between devices, this paper considers that not every Internet of Things (IoT) device or network design is able to afford the overhead and drop in performance, or even support such protocols. Therefore, the Value-to-HMAC method was designed to maximize performance while ensuring…

We propose a sign language translation system based on human keypoint estimation. It is well-known that many problems in the field of computer vision require a massive dataset to train deep neural network models. The situation is even worse when it comes to the sign language translation problem as it is far more difficult to collect high-quality training data. In this paper, we introduce the KETI (Korea Electronics Technology Institute) sign language dataset, which consists of 14,672 videos of high resolution and quality. Considering the fact that each country has a different and unique sign language, the KETI sign language dataset can be the starting point for further research on the Korean sign language translation. Using the KETI sign language dataset, we develop a neural network model for translating sign videos into natural language sentences by utilizing the human keypoints extracted from the face, hands, and body parts. The obtained human keypoint vector is normalized by the mean and standard deviation of the keypoints and used as input to our translation model based on the…

Older adults face significant loss and limitations in terms of mobility, cognitive ability, and socialization. By using augmented reality and virtual reality technologies they have the potential to overcome such loss and limitations, and to eventually improve their quality of life. However, this group is often excluded in augmented reality and virtual reality deployment. Further, limited studies address their challenges when using augmented reality and virtual reality. Therefore, for a critical review of augmented reality and virtual reality for older adults, we developed a framework to evaluate related factors, including physical, social, and psychological wellbeing. Through the critical review, we identified that most augmented reality and virtual reality studies focus on physical wellbeing of older adults but also make substantial efforts to increase their psychological wellbeing. Fun factors that would motivate them are also extensively considered. Further, social isolation continues to be a significant issue for older adults, but the appropriate content to increase their social…

Diabetes is one of the most common diseases worldwide. Many Machine Learning (ML) techniques have been utilized in predicting diabetes in the last couple of years. The increasing complexity of this problem has inspired researchers to explore the robust set of Deep Learning (DL) algorithms. The highest accuracy achieved so far was 95.1% by a combined model CNN-LSTM. Even though numerous ML algorithms were used in solving this problem, there are a set of classifiers that are rarely used or even not used at all in this problem, so it is of interest to determine the performance of these classifiers in predicting diabetes. Moreover, there is no recent survey that has reviewed and compared the performance of all the proposed ML and DL techniques in addition to combined models. This article surveyed all the ML and DL techniques-based diabetes predictions published in the last six years. In addition, one study was developed that aimed to implement those rarely and not used ML classifiers on the Pima Indian Dataset to analyze their performance. The classifiers obtained an accuracy of…

Fire is an abnormal event which can cause significant damage to lives and property. In this paper, we propose a deep learning-based fire detection method using a video sequence, which imitates the human fire detection process. The proposed method uses Faster Region-based Convolutional Neural Network (R-CNN) to detect the suspected regions of fire (SRoFs) and of non-fire based on their spatial features. Then, the summarized features within the bounding boxes in successive frames are accumulated by Long Short-Term Memory (LSTM) to classify whether there is a fire or not in a short-term period. The decisions for successive short-term periods are then combined in the majority voting for the final decision in a long-term period. In addition, the areas of both flame and smoke are calculated and their temporal changes are reported to interpret the dynamic fire behavior with the final fire decision. Experiments show that the proposed long-term video-based method can successfully improve the fire detection accuracy compared with the still image-based or short-term video-based method by…

Path loss prediction is of great significance for the performance optimization of wireless networks. With the development and deployment of the fifth-generation (5G) mobile communication systems, new path loss prediction methods with high accuracy and low complexity should be proposed. In this paper, the principle and procedure of machine-learning-based path loss prediction are presented. Measured data are used to evaluate the performance of different models such as artificial neural network, support vector regression, and random forest. It is shown that these machine-learning-based models outperform the log-distance model. In view of the fact that the volume of measured data sometimes cannot meet the requirements of machine learning algorithms, we propose two mechanisms to expand the training dataset. On one hand, old measured data can be reused in new scenarios or at different frequencies. On the other hand, the classical model can also be utilized to generate a number of training samples based on the prior information obtained from measured results. Measured data are employed to…

Currently, enterprises have to make quick and resilient responses to changing market requirements. In light of this, low-code development platforms provide the technology mechanisms to facilitate and automate the development of software applications to support current enterprise needs and promote digital transformation. Based on a theory-building research methodology through the literature and other information sources review, the main contribution of this paper is the current characterisation of the emerging low-code domain following the foundations of the computer-aided software engineering field. A context analysis, focused on the current status of research related to the low-code development platforms, is performed. Moreover, benchmarking among the existing low-code development platforms addressed to manufacturing industry is analysed to identify the current lacking features. As an illustrative example of the emerging low-code paradigm and respond to the identified uncovered features, the virtual factory open operating system (vf-OS) platform is described as an open multi-sided…

Carbon fiber reinforced polymer (CFRP) plays an important role in many fields, especially in aviation and civil industries. The electrical conductivity of CFRP is critical for its electrical behavior, such as its lightning strike vulnerability, electromagnetic shielding ability, and potential uses for self-sensing. In addition, the electrical conductivity is related to the mechanical integrity. Therefore, electrical properties can be measured as an indication when detecting delamination and other defects in CFRP. This review provides a comprehensive basis for readers to grasp recent research progresses on electrical behaviors of CFRP.

Biopolymers are materials synthesised or derived from natural sources, such as plants, animals, microorganisms or any other living organism. The use of these polymers has grown significantly in recent years as industry shifts away from unsustainable fossil fuel resources and looks towards a softer and more sustainable environmental approach. This review article covers the main classes of biopolymers: Polysaccharides, proteins, microbial-derived and lignin. In addition, an overview of the leading biomedical applications of biopolymers is also provided, which includes tissue engineering, medical implants, wound dressings, and the delivery of bioactive molecules. The future clinical applications of biopolymers are vast, due to their inherent biocompatibility, biodegradability and low immunogenicity. All properties which their synthetic counterparts do not share.

As the number of textual data is exponentially increasing, it becomes more important to develop models to analyze the text data automatically. The texts may contain various labels such as gender, age, country, sentiment, and so forth. Using such labels may bring benefits to some industrial fields, so many studies of text classification have appeared. Recently, the Convolutional Neural Network (CNN) has been adopted for the task of text classification and has shown quite successful results. In this paper, we propose convolutional neural networks for the task of sentiment classification. Through experiments with three well-known datasets, we show that employing consecutive convolutional layers is effective for relatively longer texts, and our networks are better than other state-of-the-art deep learning models.

The analysis of financial data represents a challenge that researchers had to deal with. The rethinking of the basis of financial markets has led to an urgent demand for developing innovative models to understand financial assets. In the past few decades, researchers have proposed several systems based on traditional approaches, such as autoregressive integrated moving average (ARIMA) and the exponential smoothing model, in order to devise an accurate data representation. Despite their efficacy, the existing works face some drawbacks due to poor performance when managing a large amount of data with intrinsic complexity, high dimensionality and casual dynamicity. Furthermore, these approaches are not suitable for understanding hidden relationships (dependencies) between data. This paper proposes a review of some of the most significant works providing an exhaustive overview of recent machine learning (ML) techniques in the field of quantitative finance showing that these methods outperform traditional approaches. Finally, the paper also presents comparative studies about the…

The notion of digital transformation encompasses the adoption and integration of a variety of new information and communication technologies for the development of more efficient, flexible, agile, and sustainable solutions for industrial systems. Besides technology, this process also involves new organizational forms and leads to new business models. As such, this work addresses the contribution of collaborative networks to such a transformation. An analysis of the collaborative aspects required in the various dimensions of the 4th industrial revolution is conducted based on a literature survey and experiences gained from several research projects. A mapping between the identified collaboration needs and research results that can be adopted from the collaborative networks area is presented. Furthermore, several new research challenges are identified and briefly characterized.

The Inconel 625 (IN625) superalloy has a high strength, excellent fatigue, and creep resistance under high-temperature and high-pressure conditions, and is one of the critical materials used for manufacturing high-temperature bearing parts of aeroengines. However, the poor workability of IN625 alloy prevents IN625 superalloy to be used in wider applications, especially in applications requiring high geometrical complexity. Laser powder bed fusion (LPBF) is a powerful additive manufacturing process which can produce metal parts with high geometrical complexity and freedom. This paper reviews the studies that have been done on LPBF of IN625 focusing on the microstructure, mechanical properties, the development of residual stresses, and the mechanism of defect formation. Mechanical properties such as microhardness, tensile properties, and fatigue properties reported by different researchers are systematically summarized and analyzed. Finally, the remaining issues and suggestions on future research on LPBF of IN625 alloy parts are put forward.

The vegetation in constructed wetlands (CWs) plays an important role in wastewater treatment. Popularly, the common emergent plants in CWs have been vegetation of natural wetlands. However, there are ornamental flowering plants that have some physiological characteristics similar to the plants of natural wetlands that can stimulate the removal of pollutants in wastewater treatments; such importance in CWs is described here. A literature survey of 87 CWs from 21 countries showed that the four most commonly used flowering ornamental vegetation genera were Canna, Iris, Heliconia and Zantedeschia. In terms of geographical location, Canna spp. is commonly found in Asia, Zantedeschia spp. is frequent in Mexico (a country in North America), Iris is most commonly used in Asia, Europe and North America, and species of the Heliconia genus are commonly used in Asia and parts of the Americas (Mexico, Central and South America). This review also compares the use of ornamental plants versus natural wetland plants and systems without plants for removing pollutants (COD, BOD, nitrogen and…

This paper gives an overview of the different research works related to electromyographic signals (EMG) classification based on Support Vector Machines (SVM). The article summarizes the techniques used to make the classification in each reference. Furthermore, it includes the obtained accuracy, the number of signals or channels used, the way the authors made the feature vector, and the type of kernels used. Hence, this article also includes a compilation about the bands used to filter signals, the number of signals recommended, the most commonly used sampling frequencies, and certain features that can create the characteristics of the vector. This research gathers articles related to different kinds of SVM-based classification and other tools for signal processing in the field.

With the ever-increasing demand for power sources of high energy density and stability for emergent electrical vehicles and portable electronic devices, rechargeable batteries (such as lithium-ion batteries, fuel batteries, and metal–air batteries) have attracted extensive interests. Among the emerging battery technologies, metal–air batteries (MABs) are under intense research and development focus due to their high theoretical energy density and high level of safety. Although significant progress has been achieved in improving battery performance in the past decade, there are still numerous technical challenges to overcome for commercialization. Herein, this mini-review summarizes major issues vital to MABs, including progress on packaging and crucial manufacturing technologies for cathode, anode, and electrolyte. Future trends and prospects of advanced MABs by additive manufacturing and nanoengineering are also discussed.

Permanent magnet synchronous motors (PMSM) have been used in a lot of industrial fields. In this paper, a review of faults and diagnosis methods of PMSM is presented. Firstly, the electrical, mechanical and magnetic faults of the permanent magnet synchronous motor are introduced. Next, common fault diagnosis methods, such as model-based fault diagnosis, different signal processing methods, and data-driven diagnostic algorithms are enumerated. The research summarized in this paper mainly includes fault performance, harmonic characteristics, different time-frequency analysis techniques, intelligent diagnosis algorithms proposed recently and so on.

Deep neural networks represent, nowadays, the most effective machine learning technology in biomedical domain. In this domain, the different areas of interest concern the Omics (study of the genome—genomics—and proteins—transcriptomics, proteomics, and metabolomics), bioimaging (study of biological cell and tissue), medical imaging (study of the human organs by creating visual representations), BBMI (study of the brain and body machine interface) and public and medical health management (PmHM). This paper reviews the major deep learning concepts pertinent to such biomedical applications. Concise overviews are provided for the Omics and the BBMI. We end our analysis with a critical discussion, interpretation and relevant open challenges.

This paper proposes a noble multi-robot path planning algorithm using Deep q learning combined with CNN (Convolution Neural Network) algorithm. In conventional path planning algorithms, robots need to search a comparatively wide area for navigation and move in a predesigned formation under a given environment. Each robot in the multi-robot system is inherently required to navigate independently with collaborating with other robots for efficient performance. In addition, the robot collaboration scheme is highly depends on the conditions of each robot, such as its position and velocity. However, the conventional method does not actively cope with variable situations since each robot has difficulty to recognize the moving robot around it as an obstacle or a cooperative robot. To compensate for these shortcomings, we apply Deep q learning to strengthen the learning algorithm combined with CNN algorithm, which is needed to analyze the situation efficiently. CNN analyzes the exact situation using image information on its environment and the robot navigates based on the situation analyzed…

Short-term load forecasting is a key task to maintain the stable and effective operation of power systems, providing reasonable future load curve feeding to the unit commitment and economic load dispatch. In recent years, the boost of internal combustion engine (ICE) based vehicles leads to the fossil fuel shortage and environmental pollution, bringing significant contributions to the greenhouse gas emissions. One of the effective ways to solve problems is to use electric vehicles (EVs) to replace the ICE based vehicles. However, the mass rollout of EVs may cause severe problems to the power system due to the huge charging power and stochastic charging behaviors of the EVs drivers. The accurate model of EV charging load forecasting is, therefore, an emerging topic. In this paper, four featured deep learning approaches are employed and compared in forecasting the EVs charging load from the charging station perspective. Numerical results show that the gated recurrent units (GRU) model obtains the best performance on the hourly based historical data charging scenarios, and it,…

Face recognition (FR) is defined as the process through which people are identified using facial images. This technology is applied broadly in biometrics, security information, accessing controlled areas, keeping of the law by different enforcement bodies, smart cards, and surveillance technology. The facial recognition system is built using two steps. The first step is a process through which the facial features are picked up or extracted, and the second step is pattern classification. Deep learning, specifically the convolutional neural network (CNN), has recently made commendable progress in FR technology. This paper investigates the performance of the pre-trained CNN with multi-class support vector machine (SVM) classifier and the performance of transfer learning using the AlexNet model to perform classification. The study considers CNN architecture, which has so far recorded the best outcome in the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) in the past years, more specifically, AlexNet and ResNet-50. In order to determine performance optimization of the CNN…

Emotion recognition has attracted major attention in numerous fields because of its relevant applications in the contemporary world: marketing, psychology, surveillance, and entertainment are some examples. It is possible to recognize an emotion through several ways; however, this paper focuses on facial expressions, presenting a systematic review on the matter. In addition, 112 papers published in ACM, IEEE, BASE and Springer between January 2006 and April 2019 regarding this topic were extensively reviewed. Their most used methods and algorithms will be firstly introduced and summarized for a better understanding, such as face detection, smoothing, Principal Component Analysis (PCA), Local Binary Patterns (LBP), Optical Flow (OF), Gabor filters, among others. This review identified a clear difficulty in translating the high facial expression recognition (FER) accuracy in controlled environments to uncontrolled and pose-variant environments. The future efforts in the FER field should be put into multimodal systems that are robust enough to face the adversities of real world…

Worldwide, millions of tons of crustaceans are produced every year and consumed as protein-rich seafood. However, the shells of the crustaceans and other non-edible parts constituting about half of the body mass are usually discarded as waste. These discarded crustacean shells are a prominent source of polysaccharide (chitin) and protein. Chitosan is a de-acetylated form of chitin obtained from the crustacean waste that has attracted attention for applications in food, biomedical, and paint industries due to its characteristic properties, like solubility in weak acids, film-forming ability, pH-sensitivity, biodegradability, and biocompatibility. We present an overview of the application of chitosan in composite coatings for applications in food, paint, and water treatment. In the context of food industries, the main focus is on fabrication and application of chitosan-based composite films and coatings for prolonging the post-harvest life of fruits and vegetables, whereas anti-corrosion and self-healing properties are the main properties considered for antifouling applications in…

Food and beverage industries operate their production units under stringent hygiene standards to verify high-quality products. However, the presence of biofilms can cause hygienic problems in the industries in the case of pathogenic organisms. Microorganisms can form biofilms, which are resistant to cleaning and disinfection. Microorganisms in biofilms are closely packed in a matrix that acts as a barrier to cleaning and disinfection. Biofilms are observed in processing equipment and open surfaces, resulting in food safety problems or weakening of production efficiency. This review provides a recap of the biofouling process, including the production mechanisms and control techniques of microbial adhesion. Microbial adhesion and colonization are the sine qua non of the establishment of bacterial pathogenesis and this report focuses on their prevention.

Today, chitosan (CS) is probably considered as a biofunctional polysaccharide with the most notable growth and potential for applications in various fields. The progress in chitin chemistry and the need to replace additives and non-natural polymers with functional natural-based polymers have opened many new opportunities for CS and its derivatives. Thanks to the specific reactive groups of CS and easy chemical modifications, a wide range of physico-chemical and biological properties can be obtained from this ubiquitous polysaccharide that is composed of β-(1,4)-2-acetamido-2-deoxy-d-glucose repeating units. This review is presented to share insights into multiple native/modified CSs and chitooligosaccharides (COS) associated with their functional properties. An overview will be given on bioadhesive applications, antimicrobial activities, adsorption, and chelation in the wine industry, as well as developments in medical fields or biodegradability.

The presence of water strongly affects rock properties and would be related to a series of geological disasters. To understand water saturation effects on the mechanical behavior of different rock types and interpret the underlying mechanisms of differences in water sensitivity, three kinds of rocks, namely sandstone, granite and marble, were selected for tests. Uniaxial compression experiments were conducted on specimens under oven-dried and water-saturated conditions. Acoustic emission (AE) techniques were also applied to monitor and record AE signals during tests. Experimental results reveal that water weakens the mechanical parameters of the three tested rocks, such as uniaxial compressive strength (UCS), elastic modulus and critical strain. The sandstone undergoes the greatest weakening with the addition of pore water, the mechanical properties of the granite exhibit relatively minor reductions, while the marble is the least affected by water saturation. The water-weakening degree of rock properties depends on the porosity as well as the mineralogy, especially the proportion of…

Different manufacturing processes can be utilized to fabricate light-weight high-strength materials for their applications in a wide spectrum of industries such as aerospace, automotive and biomedical sectors among which accumulative roll bonding (ARB) is a promising severe plastic deformation (SPD) method capable of creating ultrafine grains (UFG) in the final microstructure. The present review discusses recent advancements in the ARB process starting with the ARB basics, intricacies of the underlying mechanisms and physics, different materials, surface and rolling parameters, and finally its key effects on different properties such as strength, ductility, fatigue, toughness, superplasticity, tribology and thermal characteristics. Moreover, results of recent computational investigations have also been briefed towards the end. It is believed that ARB processing is an emerging area with tremendous opportunities in the industrial sector and ample potential in tailoring microstructures for high-performance materials.

The most dangerous landslide disasters always cause serious economic losses and human deaths. The contribution of this work is to present an integrated landslide modelling framework, in which an adaptive neuro-fuzzy inference system (ANFIS) is combined with the two optimization algorithms of whale optimization algorithm (WOA) and grey wolf optimizer (GWO) at Anyuan County, China. It means that WOA and GWO are used as two meta-heuristic algorithms to improve the prediction performance of the ANFIS-based methods. In addition, the step-wise weight assessment ratio analysis (SWARA) method is used to obtain the initial weight of each class of landslide influencing factors. To validate the effectiveness of the proposed framework, 315 landslide events in history were selected for our experiments and were randomly divided into the training and verification sets. To perform landslide susceptibility mapping, fifteen geological, hydrological, geomorphological, land cover, and other factors are considered for the modelling construction. The landslide susceptibility maps by SWARA, SWARA-ANFIS,…

The main aim of this study was to compare the performances of the hybrid approaches of traditional bivariate weights of evidence (WoE) with multivariate logistic regression (WoE-LR) and machine learning-based random forest (WoE-RF) for landslide susceptibility mapping. The performance of the three landslide models was validated with receiver operating characteristic (ROC) curves and area under the curve (AUC). The results showed that the areas under the curve obtained using the WoE, WoE-LR, and WoE-RF methods were 0.720, 0.773, and 0.802 for the training dataset, and were 0.695, 0.763, and 0.782 for the validation dataset, respectively. The results demonstrate the superiority of hybrid models and that the resultant maps would be useful for land use planning in landslide-prone areas.

The vast majority of gaseous chemical substances exhibit fundamental rovibrational absorption bands in the mid-infrared spectral region (2.5–25 μm), and the absorption of light by these fundamental bands provides a nearly universal means for their detection. A main feature of optical techniques is the non-intrusive in situ detection of trace gases. We reviewed primarily mid-infrared tunable laser-based broadband absorption spectroscopy for trace gas detection, focusing on 2008–2018. The scope of this paper is to discuss recent developments of system configuration, tunable lasers, detectors, broadband spectroscopic techniques, and their applications for sensitive, selective, and quantitative trace gas detection.

Piperine is the main compound present in black pepper, and is the carrier of its specific pungent taste, which is responsible for centuries of human dietary utilization and worldwide popularity as a food ingredient. Along with the application as a food ingredient and food preservative, it is used in traditional medicine for many purposes, which has in most cases been justified by modern scientific studies on its biological effects. It has been confirmed that piperine has many bioactive effects, such as antimicrobial action, as well as many physiological effects that can contribute to general human health, including immunomodulatory, hepatoprotective, antioxidant, antimetastatic, antitumor, and many other activities. Clinical studies demonstrated remarkable antioxidant, antitumor, and drug availability-enhancing characteristics of this compound, together with immunomodulatory potential. All these facts point to the therapeutic potential of piperine and the need to incorporate this compound into general health-enhancing medical formulations, as well as into those that would be used as…

Reliable prediction of remaining useful life (RUL) plays an indispensable role in prognostics and health management (PHM) by reason of the increasing safety requirements of industrial equipment. Meanwhile, data-driven methods in RUL prognostics have attracted widespread interest. Deep learning as a promising data-driven method has been developed to predict RUL due to its ability to deal with abundant complex data. In this paper, a novel scheme based on a health indicator (HI) and a hybrid deep neural network (DNN) model is proposed to predict RUL by analyzing equipment degradation. Explicitly, HI obtained by polynomial regression is combined with a convolutional neural network (CNN) and long short-term memory (LSTM) neural network to extract spatial and temporal features for efficacious prognostics. More specifically, valid data selected from the raw sensor data are transformed into a one-dimensional HI at first. Next, both the preselected data and HI are sequentially fed into the CNN layer and LSTM layer in order to extract high-level spatial features and long-term temporal…

The present review article attempts to summarize the use of deep eutectic solvents in the extraction of flavonoids, one of the most important classes of plant secondary metabolites. All of the applications reviewed have reported success in isolation and extraction of the target compounds; competitive, if not superior, extraction rates compared with conventional solvents; and satisfactory behavior of the extract in the latter applications (such as direct analysis, synthesis, or catalysis), wherever attempted.

Today, air pollution is the biggest environmental health problem in the world. Air pollution leads to adverse effects on human health, climate and ecosystems. Air is contaminated by toxic gases released by industry, vehicle emissions and the increased concentration of harmful gases and particulate matter in the atmosphere. Air pollution can cause many serious health problems such as respiratory, cardiovascular and skin diseases in humans. Nowadays, where air pollution has become the largest environmental health risk, the interest in monitoring air quality is increasing. Recently, mobile technologies, especially the Internet of Things, data and machine learning technologies have a positive impact on the way we manage our health. With the production of IoT-based portable air quality measuring devices and their widespread use, people can monitor the air quality in their living areas instantly. In this study, e-nose, a real-time mobile air quality monitoring system with various air parameters such as CO2, CO, PM10, NO2 temperature and humidity, is proposed. The proposed e-nose is…

The boundary element method (BEM) in the context of acoustics or Helmholtz problems is reviewed in this paper. The basis of the BEM is initially developed for Laplace’s equation. The boundary integral equation formulations for the standard interior and exterior acoustic problems are stated and the boundary element methods are derived through collocation. It is shown how interior modal analysis can be carried out via the boundary element method. Further extensions in the BEM in acoustics are also reviewed, including half-space problems and modelling the acoustic field surrounding thin screens. Current research in linking the boundary element method to other methods in order to solve coupled vibro-acoustic and aero-acoustic problems and methods for solving inverse problems via the BEM are surveyed. Applications of the BEM in each area of acoustics are referenced. The computational complexity of the problem is considered and methods for improving its general efficiency are reviewed. The significant maintenance issues of the standard exterior acoustic solution are considered, in…

For an agricultural country such as Thailand, converting agricultural waste into biochar offers a potential solution to manage massive quantities of crop residues following harvest. This research studied the structure and chemical composition of biochar obtained from cassava rhizomes, cassava stems and corncobs, produced using a patented locally-manufactured biochar kiln using low-cost appropriate technology designed to be fabricated locally by farmers. The research found that cassava stems yielded the highest number of Brunauer-Emmett-Teller (BET) surface area in the biochar product, while chemical analysis indicated that corncobs yielded the highest amount of C (81.35%). The amount of H in the corncob biochar was also the highest (2.42%). The study also showed biochar produced by slow pyrolysis was of a high quality, with stable C and low H/C ratio. Biochar’s high BET surface area and total pore volume makes it suitable for soil amendment, contributing to reduced soil density, higher soil moisture and aeration and reduced leaching of plant nutrients from the rhizosphere. Biochar…

Machine learning plays an important role in building intrusion detection systems. However, with the increase of data capacity and data dimension, the ability of shallow machine learning is becoming more limited. In this paper, we propose a fuzzy aggregation approach using the modified density peak clustering algorithm (MDPCA) and deep belief networks (DBNs). To reduce the size of the training set and the imbalance of the samples, MDPCA is used to divide the training set into several subsets with similar sets of attributes. Each subset is used to train its own sub-DBNs classifier. These sub-DBN classifiers can learn and explore high-level abstract features, automatically reduce data dimensions, and perform classification well. According to the nearest neighbor criterion, the fuzzy membership weights of each test sample in each sub-DBNs classifier are calculated. The output of all sub-DBNs classifiers is aggregated based on fuzzy membership weights. Experimental results on the NSL-KDD and UNSW-NB15 datasets show that our proposed model has higher overall accuracy, recall, precision…

Speech synthesis, also known as text-to-speech (TTS), has attracted increasingly more attention. Recent advances on speech synthesis are overwhelmingly contributed by deep learning or even end-to-end techniques which have been utilized to enhance a wide range of application scenarios such as intelligent speech interaction, chatbot or conversational artificial intelligence (AI). For speech synthesis, deep learning based techniques can leverage a large scale of pairs to learn effective feature representations to bridge the gap between text and speech, thus better characterizing the properties of events. To better understand the research dynamics in the speech synthesis field, this paper firstly introduces the traditional speech synthesis methods and highlights the importance of the acoustic modeling from the composition of the statistical parametric speech synthesis (SPSS) system. It then gives an overview of the advances on deep learning based speech synthesis, including the end-to-end approaches which have achieved start-of-the-art performance in recent years. Finally, it discusses…

A dropout early warning system enables schools to preemptively identify students who are at risk of dropping out of school, to promptly react to them, and eventually to help potential dropout students to continue their learning for a better future. However, the inherent class imbalance between dropout and non-dropout students could pose difficulty in building accurate predictive modeling for a dropout early warning system. The present study aimed to improve the performance of a dropout early warning system: (a) by addressing the class imbalance issue using the synthetic minority oversampling techniques (SMOTE) and the ensemble methods in machine learning; and (b) by evaluating the trained classifiers with both receiver operating characteristic (ROC) and precision–recall (PR) curves. To that end, we trained random forest, boosted decision tree, random forest with SMOTE, and boosted decision tree with SMOTE using the big data samples of the 165,715 high school students from the National Education Information System (NEIS) in South Korea. According to our ROC and PR curve analysis,…

In this study, a novel technique to support smart city planning in estimating and controlling the heating load (HL) of buildings, was proposed, namely PSO-XGBoost. Accordingly, the extreme gradient boosting machine (XGBoost) was developed to estimate HL first; then, the particle swarm optimization (PSO) algorithm was applied to optimize the performance of the XGBoost model. The classical XGBoost model, support vector machine (SVM), random forest (RF), Gaussian process (GP), and classification and regression trees (CART) models were also investigated and developed to predict the HL of building systems, and compared with the proposed PSO-XGBoost model; 837 investigations of buildings were considered and analyzed with many influential factors, such as glazing area distribution (GAD), glazing area (GA), orientation (O), overall height (OH), roof area (RA), wall area (WA), surface area (SA), and relative compactness (RC). Mean absolute percentage error (MAPE), root-mean-squared error (RMSE), variance account for (VAF), mean absolute error (MAE), and determination coefficient (R2), were…

Target detection of electronic components on PCB (Printed circuit board) based on vision is the core technology for 3C (Computer, Communication and Consumer Electronics) manufacturing companies to achieve quality control and intelligent assembly of robots. However, the number of electronic components on PCB is large, and the shape is different. At present, the accuracy of the algorithm for detecting all electronic components is not high. This paper proposes an improved algorithm based on YOLO (you only look once) V3 (Version 3), which uses a real PCB picture and a virtual PCB picture with synthesized data as a joint training dataset, which greatly increases the recognizability of training electronic components and provides the greatest possibility for data enhancement. After analyzing the feature distribution of the five dimensionality-reduced output layers of Darknet-53 and the size distribution of the detection target, it is proposed to adjust the original three YOLO output layers to four YOLO output layers and generate 12 anchor boxes for electronic component detection. The…

Recently, deep learning has been successfully applied to network security assessments and intrusion detection systems (IDSs) with various breakthroughs such as using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) to classify malicious traffic. However, these state-of-the-art systems also face tremendous challenges to satisfy real-time analysis requirements due to the major delay of the flow-based data preprocessing, i.e., requiring time for accumulating the packets into particular flows and then extracting features. If detecting malicious traffic can be done at the packet level, detecting time will be significantly reduced, which makes the online real-time malicious traffic detection based on deep learning technologies become very promising. With the goal of accelerating the whole detection process by considering a packet level classification, which has not been studied in the literature, in this research, we propose a novel approach in building the malicious classification system with the primary support of word embedding and the LSTM model. Specifically, we…

With the rapid development of nanotechnology and widespread use of nanoproducts, the ecotoxicity of nanoparticles (NPs) and their potential hazards to the environment have aroused great concern. Nanoparticles have increasingly been released into aquatic environments through various means, accumulating in aquatic organisms through food chains and leading to toxic effects on aquatic organisms. Nanoparticles are mainly classified into nano-metal, nano-oxide, carbon nanomaterials and quantum dots according to their components. Different NPs may have different levels of toxicity and effects on various aquatic organisms. In this paper, algae are used as model organisms to review the adsorption and distribution of NPs to algal cells, as well as the ecotoxicity of NPs on algae and fate in a water environment, systematically. Meanwhile, the toxic effects of NPs on algae are discussed with emphasis on three aspect effects on the cell membrane, cell metabolism and the photosynthesis system. Furthermore, suggestions and prospects are provided for future studies in this area.

A traditional A-Star (A*) algorithm generates an optimal path by minimizing the path cost. For a vessel, factors of path length, obstacle collision risk, traffic separation rule and manoeuvrability restriction should be all taken into account for path planning. Meanwhile, the water current also plays an important role in voyaging and berthing for vessels. In consideration of these defects of the traditional A-Star algorithm when it is used for vessel path planning, an improved A-Star algorithm has been proposed. To be specific, the risk models of obstacles (bridge pier, moored or anchored ship, port, shore, etc.) considering currents, traffic separation, berthing, manoeuvrability restriction have been built firstly. Then, the normal path generation and the berthing path generation with the proposed improved A-Star algorithm have been represented, respectively. Moreover, the problem of combining the normal path and the berthing path has been also solved. To verify the effectiveness of the proposed A-Star path planning methods, four cases have been studied in simulation and real…