Showing papers in "Applied Sciences in 2019"

Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey

Abstract: 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 this type of taxonomy framework is fit for cyber security researchers. The survey first clarifies the concept and taxonomy of IDSs. Then, the machine learning algorithms frequently used in IDSs, metrics, and benchmark datasets are introduced. Next, combined with the representative literature, we take the proposed taxonomic system as a baseline and explain how to solve key IDS issues with machine learning and deep learning techniques. Finally, challenges and future developments are discussed by reviewing recent representative studies.

Blockchain Technology in Healthcare: A Comprehensive Review and Directions for Future Research

Abstract: 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.

Hydrogen Fuel Cell Vehicles; Current Status and Future Prospect

Abstract: 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, the fuel cell can produce direct current (DC) power to run the electric car. By integrating a hydrogen fuel cell with batteries and the control system with strategies, one can produce a sustainable hybrid car.

Recent Advances and Applications of Semiconductor Photocatalytic Technology

Abstract: 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 information on the industrialization of photocatalysis technology.

Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review

Abstract: 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, selection of the scale, the combination between wavelet transform and machine learning algorithm and the interpretation of the obtained components, are also discussed.

Application of Directed Energy Deposition-Based Additive Manufacturing in Repair

Abstract: 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 method based on DED appears to be a capable method of repairing. Therefore, the focus of this study was to present an overview of the DED process and its role in the repairing of metallic components. The outcomes of this study confirm the significant capability of DED process as a repair and remanufacturing technology.

Assessing Blockchain Consensus and Security Mechanisms against the 51% Attack

Abstract: 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 possible. We discuss the five most advanced protection techniques to prevent this attack and their main limitations. We conclude that in most cases, security techniques fail to provide real protection against the 51% attack because the weaknesses are inherited from the consensus protocols.

Review of Artificial Intelligence Adversarial Attack and Defense Technologies

Abstract: 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., modifying data, modifying models and using auxiliary tools.

The Potential of Additive Manufacturing in the Smart Factory Industrial 4.0: A Review

Abstract: 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 with new ideas for the future of AM explorations.

The Role of Nanotechnology in the Fortification of Plant Nutrients and Improvement of Crop Production

Abstract: 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 nanoparticles in crop, biologically synthesized nanoparticles may be preferred for agricultural purposes. This would circumvent the concerns associated with toxicity, in addition to being pollution free. This report, therefore, offers more understanding on the application of nanotechnology in biofortification of plant nutrients and the future possibilities offered by this practice. It also highlights some of the ills associated with the introduction of nanomaterials into the soil for crop’s improvement.

A Comparative Study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in Estimating the Heating Load of Buildings’ Energy Efficiency for Smart City Planning

Abstract: 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 GA-ANN model provided the highest performance in estimating the heating load of buildings’ energy efficiency, with an RMSE of 1.625, R2 of 0.980, and MAE of 0.798. The remaining models (i.e., PSO-ANN, ICA-ANN, ABC-ANN) yielded lower performance with RMSE of 1.932, 1.982, 1.878; R2 of 0.972, 0.970, 0.973; MAE of 1.027, 0.980, 0.957, respectively.

Application of Nanofluids in Thermal Performance Enhancement of Parabolic Trough Solar Collector: State-of-the-Art

Abstract: 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.

Blockchain-Based Applications in Education: A Systematic Review

Abstract: 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.

Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals

Abstract: 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 based testing, respectively. The developed model can likely aid clinicians as a diagnostic tool to detect early stages of SZ.

Bitumen and Bitumen Modification: A Review on Latest Advances

Abstract: 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, several studies showed the effectiveness of waxes for improving the compatibility of polymers with bitumen in addition to some benefits regarding warm mix asphalt (WMA) production.

MedChain: Efficient Healthcare Data Sharing via Blockchain

Abstract: 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 existing approaches for sharing both types of healthcare data. Based on MedChain, a session-based healthcare data-sharing scheme is devised, which brings flexibility in data sharing. The evaluation results show that MedChain can achieve higher efficiency and satisfy the security requirements in data sharing.

Current Biomedical Applications of 3D Printing and Additive Manufacturing

Abstract: 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.

Energy Management in Microgrids with Renewable Energy Sources: A Literature Review

Abstract: 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 option due to increased technological maturity, energy density, and capability of providing grid services such as frequency response. Finally, future directions on predictive modeling mainly for energy storage systems are also proposed.

Application of a Hybrid Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) Model in Behavior Prediction of Channel Shear Connectors Embedded in Normal and High-Strength Concrete

Abstract: 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 behavior of channel connectors and eliminate the need for conducting costly experiments to some extent. In addition, in this case, the ANN-PSO model showed better performance than the ANN-BP model by resulting in superior performance indices.

A Review: Thermal Stability of Methylammonium Lead Halide Based Perovskite Solar Cells

Abstract: 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 and promising strategies for overcoming thermal degradation are also summarized.

The Role of Optical Wireless Communication Technologies in 5G/6G and IoT Solutions: Prospects, Directions, and Challenges

Abstract: 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 review paper clearly presents how OWC technologies, such as visible light communication, light fidelity, optical camera communication, and free space optics communication, will be an effective solution for successful deployment of 5G/6G and IoT systems.

A Review of Hydrogen Direct Injection for Internal Combustion Engines: Towards Carbon-Free Combustion

Abstract: 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 compressed natural gas.

An Overview of Lidar Imaging Systems for Autonomous Vehicles

Abstract: 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 for lidar development in autonomous vehicles has been included, in order to present some of the problems which still need to be solved before implementation may be considered as final. The reader is provided with a detailed bibliography containing both relevant books and state-of-the-art papers for further progress in the subject.

Preparation and Modification of Biochar Materials and their Application in Soil Remediation

Abstract: 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.

Supervised machine learning techniques to the prediction of tunnel boring machine penetration rate

Abstract: 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, CART, and NN predictive models were developed to select the best one. A simple ranking technique, as well as some performance indices, were calculated for each developed model. According to the obtained results, KNN received the highest-ranking value among all five predictive models and was selected as the best predictive model of this study. It can be concluded that KNN is able to provide high-performance capacity in predicting TBM PR. KNN model identified uniaxial compressive strength (0.2) as the most important and revolution per minutes (0.14) as the least important factor for predicting the TBM penetration rate.

Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform

Abstract: 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.

Random Forests and Cubist Algorithms for Predicting Shear Strengths of Rockfill Materials

Abstract: 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 performance with (RMSE = 0.0959, R2 = 0.9697 and MAE = 0.0671), followed by the random forests model with (RMSE = 0.1133, R2 = 0.9548 and MAE= 0.0665), the artificial neural network (ANN) model with (RMSE = 0.1320, R2 = 0.9386 and MAE = 0.0841), and the conventional multiple linear regression technique with (RMSE = 0.1361, R2 = 0.9345 and MAE = 0.0888). The results indicated that the Cubist and random forests models are able to generate better predictive results of the shear strength of RFM than ANN and conventional regression models. The Cubist model was considered to be more promising for interpreting the complex relationships between the influential properties of RFM and the shear strengths of RFM to some extent, which can be extremely helpful in estimating the shear strength of rockfill materials.

exBAKE: Automatic Fake News Detection Model Based on Bidirectional Encoder Representations from Transformers (BERT)

Abstract: 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.

A Review on the Thermal Hazards of the Lithium-Ion Battery and the Corresponding Countermeasures

Abstract: 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 devices, fire-retardant additives, battery management systems, hazard warnings and firefighting should a hazard occur.

Micro-LEDs, a Manufacturability Perspective

Abstract: 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.