Editor’s Choice Articles

The integration of building information modeling (BIM) and geographic information system (GIS) is attracting more attention than ever due to its potential benefits for both the architecture, engineering, and construction (AEC) domain and the geospatial industry. The main challenge in BIM and GIS integrated application comes from the fundamental data conversion, especially for the geometric information. BIM and GIS use different modeling paradigms to represent objects. The BIM dataset takes, for example, Industry Foundation Classes (IFC) that use solid models, such as boundary representation (B-Rep), swept solid, constructive solid geometry (CSG), and clipping, while the GIS dataset mainly uses surface models or B-Rep. The fundamental data conversion between BIM and GIS is the foundation of BIM and GIS integrated application. However, the efficiency of data conversion has been greatly impaired by the human intervention needed, especially for the conversion of the clipping geometry. The goal of this study is to automate the conversion of IFC clipping representation into the shapefile format. A process-level approach was developed with an algorithm for instantiating unbounded half spaces using B-Rep. Four IFC models were used to validate the proposed method. The results show that (1) the proposed approach can successfully automate the conversion of IFC clipping representation into the shapefile format; and (2) increasing boundary size has no effect on the file size of unbounded half spaces, but slightly increases the producing time of half spaces and processing time of building components. The efficiency of this study can be further improved by using an open-source package, instead of using the low-efficiency packages provided by ArcGIS. Full article

In the realm of the current industrial revolution, interesting innovations as well as new techniques are constantly being introduced by offering fertile ground for further investigation and improvement in the industrial engineering domain. More specifically, cutting-edge digital technologies in the field of Extended Reality (XR) have become mainstream including Augmented Reality (AR). Furthermore, Cloud Computing has enabled the provision of high-quality services, especially in the controversial field of maintenance. However, since modern machines are becoming more complex, maintenance must be carried out from experienced and well-trained personnel, while overseas support is timely and financially costly. Although AR is a back-bone technology facilitating the development of robust maintenance support tools, they are limited to the provision of predefined scenarios, covering only a limited number of scenarios. This research work aims to address this emerging challenge with the design and development of a framework, for the support of remote maintenance and repair operation based on AR, by creating suitable communication channels between the shop-floor technicians and the expert engineers who are utilizing real-time feedback from the operator’s field of view. The applicability of the developed framework is tested in vitro in a lab-based machine shop and in a real-life industrial scenario. Full article

When inspecting the lung with standard ultrasound B-mode imaging, numerous artifacts can be visualized. These artifacts are useful to recognize and evaluate several pathological conditions in Emergency and Intensive Care Medicine. More recently, the interest of the Pulmonologists has turned to the echographic study of the interstitial pathology of the lung. In fact, all lung pathologies which increase the density of the tissue, and do not consolidate the organ, are characterized by the presence of ultrasound artifacts. Many studies of the past have only assessed the number of vertical artifacts (generally known as B-Lines) as a sign of disease severity. However, recent observations suggest that the appearance of the individual artifacts, their variability, and their internal structure, may play a role for a non-invasive characterization of the surface of the lungs, directing the diagnoses and identifying groups of diseases. In this review, we discuss the meaning of lung ultrasound artifacts, and introduce hypothesis on the correlation between their presence and the structural variation of the sub-pleural tissue in light of current knowledge of the acoustic properties of the pleural plane. Full article

Silicon photonics has drawn increasing attention in the past few decades and is a promising key technology for future daily applications due to its various merits including ultra-low cost, high integration density owing to the high refractive index of silicon, and compatibility with current semiconductor fabrication process. Optical interconnects is an important issue in silicon photonic integrated circuits for transmitting light, and fiber-to-chip optical interconnects is vital in application scenarios such as data centers and optical transmission systems. There are mainly two categories of fiber-to-chip optical coupling: off-plane coupling and in-plane coupling. Grating couplers work under the former category, while edge couplers function as in-plane coupling. In this paper, we mainly focus on edge couplers in silicon photonic integrated circuits. We deliver an introduction to the research background, operation mechanisms, and design principles of silicon photonic edge couplers. The state-of-the-art of edge couplers is reviewed according to the different structural configurations of the device, while identifying the performance, fabrication feasibility, and applications. In addition, a brief comparison between edge couplers and grating couplers is conducted. Packaging issues are also discussed, and several prospective techniques for further improvements of edge couplers are proposed. Full article

Smartness, which underpins smart cities and societies, is defined by our ability to engage with our environments, analyze them, and make decisions, all in a timely manner. Healthcare is the prime candidate needing the transformative capability of this smartness. Social media could enable a ubiquitous and continuous engagement between healthcare stakeholders, leading to better public health. Current works are limited in their scope, functionality, and scalability. This paper proposes Sehaa, a big data analytics tool for healthcare in the Kingdom of Saudi Arabia (KSA) using Twitter data in Arabic. Sehaa uses Naive Bayes, Logistic Regression, and multiple feature extraction methods to detect various diseases in the KSA. Sehaa found that the top five diseases in Saudi Arabia in terms of the actual afflicted cases are dermal diseases, heart diseases, hypertension, cancer, and diabetes. Riyadh and Jeddah need to do more in creating awareness about the top diseases. Taif is the healthiest city in the KSA in terms of the detected diseases and awareness activities. Sehaa is developed over Apache Spark allowing true scalability. The dataset used comprises 18.9 million tweets collected from November 2018 to September 2019. The results are evaluated using well-known numerical criteria (Accuracy and F1-Score) and are validated against externally available statistics. Full article

Development of Autonomous Underwater Vehicles (AUVs) has permitted the automatization of many tasks originally achieved with manned vehicles in underwater environments. Teams of AUVs designed to work within a common mission are opening the possibilities for new and more complex applications. In underwater environments, communication, localization, and navigation of AUVs are considered challenges due to the impossibility of relying on radio communications and global positioning systems. For a long time, acoustic systems have been the main approach for solving these challenges. However, they present their own shortcomings, which are more relevant for AUV teams. As a result, researchers have explored different alternatives. To summarize and analyze these alternatives, a review of the literature is presented in this paper. Finally, a summary of collaborative AUV teams and missions is also included, with the aim of analyzing their applicability, advantages, and limitations. Full article

This study proposed a developed horizontal-connection and energy-dissipation structure (HES), which could be employed for horizontal connection of prefabricated shear wall structural system. The HES consists of an external replaceable energy dissipation (ED) zone mainly for energy dissipation and an internal stiffness lifting (SL) zone for enhancing the load-bearing capacity. By the predicted displacement threshold control device, the ED zone made in bolted low-yielding steel plates could firstly dissipate the energy and can be replaced after damage, the SL zone could delay the load-bearing and the load-displacement curves of the HES would exhibit “double-step” characteristics. Detailed finite element models are established and validated in software ABAQUS. parametric analysis including aspect ratio, the shape of the steel plate in the ED zone and the displacement threshold in the SL zone, is conducted. It is found that the HES depicts high energy dissipation ability and its bearing capacity could be obtained again after the yielding of the ED zone. The optimized X-shaped steel plate in the ED zone exhibit better performance. The “double-step” design of the HES is a potential way of improving the seismic and anti-collapsing performance of prefabricated shear wall structures against large and super-large earthquakes. Full article

Pneumonia is among the top diseases which cause most of the deaths all over the world. Virus, bacteria and fungi can all cause pneumonia. However, it is difficult to judge the pneumonia just by looking at chest X-rays. The aim of this study is to simplify the pneumonia detection process for experts as well as for novices. We suggest a novel deep learning framework for the detection of pneumonia using the concept of transfer learning. In this approach, features from images are extracted using different neural network models pretrained on ImageNet, which then are fed into a classifier for prediction. We prepared five different models and analyzed their performance. Thereafter, we proposed an ensemble model that combines outputs from all pretrained models, which outperformed individual models, reaching the state-of-the-art performance in pneumonia recognition. Our ensemble model reached an accuracy of 96.4% with a recall of 99.62% on unseen data from the Guangzhou Women and Children’s Medical Center dataset. Full article

Forecasting is a crucial task for successfully integrating photovoltaic (PV) output power into the grid. The design of accurate photovoltaic output forecasters remains a challenging issue, particularly for multistep-ahead prediction. Accurate PV output power forecasting is critical in a number of applications, such as micro-grids (MGs), energy optimization and management, PV integrated in smart buildings, and electrical vehicle chartering. Over the last decade, a vast literature has been produced on this topic, investigating numerical and probabilistic methods, physical models, and artificial intelligence (AI) techniques. This paper aims at providing a complete and critical review on the recent applications of AI techniques; we will focus particularly on machine learning (ML), deep learning (DL), and hybrid methods, as these branches of AI are becoming increasingly attractive. Special attention will be paid to the recent development of the application of DL, as well as to the future trends in this topic. Full article

This study applies a methodology to evaluate the ecotoxicological potential of raw materials and cement-based construction materials. In this study, natural aggregates and Portland cement were replaced with non-conventional recycled concrete aggregates (RA) and fly ash (FA), respectively, in the production of two concrete products alternative to conventional concrete (used as reference). The experimental program involved assessing both the chemical properties (non-metallic and metallic parameters) and ecotoxicity data (battery of tests with the luminescent bacterium Vibrio fischeri, the freshwater crustacean Daphnia magna, and the yeast Saccharomyces cerevisiae) of eluates obtained from leaching tests of RA, FA, and the three concrete mixes. Even though the results indicated that RA and FA have the ability to release some chemicals into the water and induce its alkalinisation, the respective eluate samples presented no or low levels of potential ecotoxicity. However, eluates from concrete mixes produced with a replacement ratio of Portland cement with 60% of FA and 100% of natural aggregates and produced with 60% of FA and 100% of RA were classified as clearly ecotoxic mainly towards Daphnia magna mobility. Therefore, raw materials with weak evidences of ecotoxicity could lead to the production of concrete products with high ecotoxicological potential. Overall, the results obtained highlight the importance of integrating data from the chemical and ecotoxicological characterization of materials’ eluate samples aiming to assess the possible environmental risk of the construction materials, namely of incorporating non-conventional raw materials in concrete, and contributing to achieve construction sustainability. Full article

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. Full article

In this article, we probe the multiple-slip effects on magnetohydrodynamic unsteady Casson nano-fluid flow over a penetrable stretching sheet, sheet entrenched in a porous medium with thermo-diffusion effect, and injection/suction in the presence of heat source. The flow is engendered due to the unsteady time-dependent stretching sheet retained inside the porous medium. The leading non-linear partial differential equations are transmuted in the system of coupled nonlinear ordinary differential equations by using appropriate transformations, then the transformed equations are solved by using the variational finite element method numerically. The velocity, temperature, solutal concentration, and nano-particles concentration, as well as the rate of heat transfer, the skin friction coefficient, and Sherwood number for solutal concentration, are presented for several physical parameters. Next, the effects of these various physical parameters are conferred with graphs and tables. The exact values of flow velocity, skin friction, and Nusselt number are compared with a numerical solution acquired with the finite element method (FEM), and also with numerical results accessible in literature. In the end, we rationalize the convergence of the finite element numerical solution, and the calculations are carried out by reducing the mesh size. Full article

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. Full article

AutoDock and Vina are two of the most widely used protein–ligand docking programs. The fact that these programs are free and available under an open source license, also makes them a very popular first choice for many users and a common starting point for many virtual screening campaigns, particularly in academia. Here, we evaluated the performance of AutoDock and Vina against an unbiased dataset containing 102 protein targets, 22,432 active compounds and 1,380,513 decoy molecules. In general, the results showed that the overall performance of Vina and AutoDock was comparable in discriminating between actives and decoys. However, the results varied significantly with the type of target. AutoDock was better in discriminating ligands and decoys in more hydrophobic, poorly polar and poorly charged pockets, while Vina tended to give better results for polar and charged binding pockets. For the type of ligand, the tendency was the same for both Vina and AutoDock. Bigger and more flexible ligands still presented a bigger challenge for these docking programs. A set of guidelines was formulated, based on the strengths and weaknesses of both docking program and their limits of validation. Full article

Ethanol production from cellulosic material is considered one of the most promising options for future biofuel production contributing to both the energy diversification and decarbonization of the transport sector, especially where electricity is not a viable option (e.g., aviation). Compared to conventional (or first generation) ethanol production from food and feed crops (mainly sugar and starch based crops), cellulosic (or second generation) ethanol provides better performance in terms of greenhouse gas (GHG) emissions savings and low risk of direct and indirect land-use change. However, despite the policy support (in terms of targets) and significant R&D funding in the last decade (both in EU and outside the EU), cellulosic ethanol production appears to be still limited. The paper provides a comprehensive overview of the status of cellulosic ethanol production in EU and outside EU, reviewing available literature and highlighting technical and non-technical barriers that still limit its production at commercial scale. The review shows that the cellulosic ethanol sector appears to be still stagnating, characterized by technical difficulties as well as high production costs. Competitiveness issues, against standard starch based ethanol, are evident considering many commercial scale cellulosic ethanol plants appear to be currently in idle or on-hold states. Full article

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. Full article

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 the proposed model and the state-of-the-art models for the IHEPC dataset with several variants. The experimental results indicate that EECP-CBL framework outperforms the state-of-the-art approaches in terms of several performance metrics for electric energy consumption prediction on several variations of IHEPC dataset in real-time, short-term, medium-term and long-term timespans. Full article

The Active Rotary Inertia Driver (ARID) system is a novel vibration control system that can effectively mitigate the swing vibration of suspended structures. Parametric analysis is carried out using Simulink based on the mathematical model and the effectiveness is further validated by a series of experiments. Firstly, the active controller is designed based on the system mathematical model and the LQR (linear quadratic regulator) algorithm. Next, the parametric analysis is carried out using Simulink to study the key parameters such as the coefficient of the control algorithm, the rotary inertia ratio. Lastly, the ARID system control effectiveness and the parametric analysis results are further validated by the shaking table experiments. The effectiveness and robustness of the ARID system are well verified. The dynamic characteristics of this system are further studied, and the conclusions of this paper provide a theoretical basis for further development of such unique control system. Full article

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 their advantages and disadvantages. The challenges and prospects for the development of eco-friendly and economical algae harvesting processes have also been outlined here. Full article

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. Full article

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. Full article

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. Full article

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 internal combination of different blockchain types and the combination of blockchain with other technologies improve the blockchain-based energy trading system to better satisfy the practical requirements of modern power systems. However, there are still some problems to be solved, for example, the lack of regulatory system, environmental challenges and so on. In the future, we will strive for a better optimized structure and establish a comprehensive security assessment model for blockchain-based energy trading system. Full article

Additive manufacturing provides a number of benefits in terms of infinite freedom to design complex parts and reduced lead-times while globally reducing the size of supply chains as it brings all production processes under one roof. However, additive manufacturing (AM) lags far behind conventional manufacturing in terms of surface quality. This proves a hindrance for many companies considering investment in AM. The aim of this work is to investigate the effect of varying process parameters on the resultant roughness of the down-facing surfaces in selective laser melting (SLM). A systematic experimental study was carried out and the effects of the interaction of the different parameters and their effect on the surface roughness (Sa) were analyzed. It was found that the interaction and interdependency between parameters were of greatest significance to the obtainable surface roughness, though their effects vary greatly depending on the applied levels. This behavior was mainly attributed to the difference in energy absorbed by the powder. Predictive process models for optimization of process parameters for minimizing the obtained Sa in 45° and 35° down-facing surface, individually, were achieved with average error percentages of 5% and 6.3%, respectively, however further investigation is still warranted. Full article

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. Full article

The growing demand for electricity, caused by dynamic economic growth, leads to a decrease in the available non-renewable energy resources constituting the foundation of global power generation. A search for alternative sources of energy that can support conventional energy technologies utilizing fossil fuels is not only of key significance for the power industry but is also important from the point of view of environmental conservation and sustainable development. Plant biomass, with its specific chemical structure and high calorific value, is a promising renewable source of energy which can be utilized in numerous conversion processes, enabling the production of solid, liquid, and gaseous fuels. Methods of thermal biomass conversion include pyrolysis, i.e., a process allowing one to obtain a multifunctional product known as biochar. The article presents a review of information related to the broad uses of carbonization products. It also discusses the legal aspects and quality standards applicable to these materials. The paper draws attention to the lack of uniform legal and quality conditions, which would allow for a much better use of biochar. The review also aims to highlight the high potential for a use of biochar in different environments. The presented text attempts to emphasize the importance of biochar as an alternative to classic products used for energy, environmental and agricultural purposes. Full article

The precise estimation and forecast of the safety factor (SF) in civil engineering applications is considered as an important issue to reduce engineering risk. The present research investigates new artificial intelligence (AI) techniques for the prediction of SF values of retaining walls, as important and resistant structures for ground forces. These structures have complicated performances in dynamic conditions. Consequently, more than 8000 designs of these structures were dynamically evaluated. Two AI models, namely the imperialist competitive algorithm (ICA)-artificial neural network (ANN), and the genetic algorithm (GA)-ANN were used for the forecasting of SF values. In order to design intelligent models, parameters i.e., the wall thickness, stone density, wall height, soil density, and internal soil friction angle were examined under different dynamic conditions and assigned as inputs to predict SF of retaining walls. Various models of these systems were constructed and compared with each other to obtain the best one. Results of models indicated that although both hybrid models are able to predict SF values with a high accuracy and they can be introduced as new models in the field, the retaining wall performance could be properly predicted in dynamic conditions using the ICA-ANN model. Under these conditions, a combination of engineering design and artificial intelligence techniques can be used to control and secure retaining walls in dynamic conditions. Full article

The survey focuses on the most significant contributions in the field of fiber optic plasmonic sensors (FOPS) in recent years. FOPSs are plasmonic sensor-based fiber optic probes that use an optical field to measure the biological agents. Owing to their high sensitivity, high resolution, and low cost, FOPS turn out to be potential alternatives to conventional biological fiber optic sensors. FOPS use optical transduction mechanisms to enhance sensitivity and resolution. The optical transduction mechanisms of FOPS with different geometrical structures and the photonic properties of the geometries are discussed in detail. The studies of optical properties with a combination of suitable materials for testing the biosamples allow for diagnosing diseases in the medical field. Full article

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. Full article

The beneficial role of combining fiber reinforcement with lime stabilization in altering soil behavior has been established in the literature. However, the coupling effect of their combination still remains unclear in terms of its magnitude and microscopic mechanism, especially for natural fibers with special microstructures. The objective of this study was to investigate the coupling effect of wheat straw fiber reinforcement and lime stabilization on the mechanical behavior of Hefei clayey soil. To achieve this, an experimental program including unconsolidated–undrained (UU) triaxial tests and SEM analysis was implemented. Static compaction test samples were prepared on untreated soil, fiber-reinforced soil, lime-stabilized soil, and lime-stabilized/fiber-reinforced soil at optimum moisture content with determining of the maximum dry density of the untreated soil. The lime was added in three different contents of 2%, 4%, and 6%, and 13 mm long wheat straw fiber slices with a cross section one-quarter that of the intact ones were mixed in at 0.2%, 0.4%, and 0.6% by dry weight of soil. Analysis of the derived results indicated that the addition of a small amount of wheat straw fibers into lime-stabilized soil improved the intensity of the strain-softening behavior associated with mere lime stabilization. The observed evidence that the shear strength increase brought by a combination of 0.4% fiber reinforcement and 4% lime stabilization was smaller than the summation of the shear strength increases brought by their presence alone in a sample demonstrated a coupling effect between fiber reinforcement and lime stabilization. This coupling effect was also detected in the comparisons of the secant modulus and failure pattern between the combined treatment and the individual treatments. These manifestations of the coupling effect were explained by a microscopic mechanism wherein the fiber reinforcing effect was made more effective by the ways in which lime chemically stabilized the soil and lime stabilization development was quickened by the water channels passing through the surfaces and honeycomb pores of the wheat straw fibers. Full article

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 organic crystals. Finally, we present some recent breakthrough demonstrations in nonlinear THz photonics enabled by very intense organic crystalline THz sources, as well as examples of THz spectroscopy and THz imaging using organic crystals as THz sources for various scientific and technological applications. Full article

The prediction of earth pressure balance (EPB) shield performance is an essential part of project scheduling and cost estimation of tunneling projects. This paper establishes an efficient multi-objective optimization model to predict the shield performance during the tunneling process. This model integrates the adaptive neuro-fuzzy inference system (ANFIS) with the genetic algorithm (GA). The hybrid model uses shield operational parameters as inputs and computes the advance rate as output. GA enhances the accuracy of ANFIS for runtime parameters tuning by multi-objective fitness function. Prior to modeling, datasets were established, and critical operating parameters were identified through principal component analysis. Then, the tunneling case for Guangzhou metro line number 9 was adopted to verify the applicability of the proposed model. Results were then compared with those of the ANFIS model. The comparison showed that the multi-objective ANFIS-GA model is more successful than the ANFIS model in predicting the advance rate with a high accuracy, which can be used to guide the tunnel performance in the field. Full article

Chaotic systems without equilibrium are of interest because they are the systems with hidden attractors. A nonequilibrium system with chaos is introduced in this work. Chaotic behavior of the system is verified by phase portraits, Lyapunov exponents, and entropy. We have implemented a real electronic circuit of the system and reported experimental results. By using this new chaotic system, we have constructed S-boxes which are applied to propose a novel image encryption algorithm. In the designed encryption algorithm, three S-boxes with strong cryptographic properties are used for the sub-byte operation. Particularly, the S-box for the sub-byte process is selected randomly. In addition, performance analyses of S-boxes and security analyses of the encryption processes have been presented. Full article

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 (organic matter, nitrogen, nitrogen and phosphorous compounds). The removal efficiency was similar between flowering ornamental and natural wetland plants. However, pollutant removal was better when using ornamental plants than in unplanted CWs. The use of ornamental flowering plants in CWs is an excellent option, and efforts should be made to increase the adoption of these system types and use them in domiciliary, rural and urban areas. Full article

This review summarizes all studies which used dielectric-based materials as a passivation layer at the rear surface of copper indium gallium (di)selenide, Cu(In,Ga)Se₂, (CIGS)-based thin film solar cells, up to 2019. The results regarding the kind of dielectric materials, the deposition techniques, contacting approaches, the existence of additional treatments, and current–voltage characteristics (J–V) of passivated devices are emphasized by a detailed table. The techniques used to implement the passivation layer, the contacting approach for the realization of the current flow between rear contact and absorber layer, additional light management techniques if applicable, the solar simulator results, and further characterization techniques, i.e., external quantum efficiency (EQE) and photoluminescence (PL), are shared and discussed. Three graphs show the difference between the reference and passivated devices in terms of open-circuit voltage (V_oc), short-circuit current (J_sc), and efficiency (η), with respect to the thicknesses of the absorber layer. The effects of the passivation layer at the rear surface are discussed based on these three graphs. Furthermore, an additional section is dedicated to the theoretical aspects of the passivation mechanism. Full article

Poly(lactic acid)/graphene and poly(lactic acid)/carbon nanotube nanocomposites were prepared by an easy and low-cost method of melt blending of preliminary grinded poly(lactic acid) (PLA) with nanosized carbon fillers used as powder. Morphological, structural and mechanical properties were investigated to reveal the influence of carbon nanofiller on the PLA–based composite. The dependence of tensile strength on nanocomposite loading was defined by a series of experiments over extruded filaments using a universal mechanical testing instrument. The applying the XRD technique disclosed that compounds crystallinity significantly changed upon addition of multi walled carbon nanotubes. We demonstrated that Raman spectroscopy can be used as a quick and unambiguous method to determine the homogeneity of the nanocomposites in terms of carbon filler dispersion in a polymer matrix. Full article

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. Full article

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. Full article

Most faults can stop a motor, and time is lost in fixing the damaged motor. This is a reason why it is essential to develop fault-detection methods. This paper describes the acoustic-based fault detection of two commutator motors: the commutator motor of an electric impact drill and the commutator motor of a blender. Acoustic signals were recorded by a smartphone. Five states of the electric impact drill and three states of the blender were analysed: for the electric impact drill, these states were healthy, damaged gear train, faulty fan with five broken rotor blades, faulty fan with 10 broken rotor blades, and shifted brush (motor off); for the blender, these states were healthy, faulty fan with two broken rotor blades, and faulty fan with five broken rotor blades. A feature extraction method, MSAF-RATIO-27-MULTIEXPANDED-4-GROUPS (Method of Selection of Amplitudes of Frequency Ratio of 27% Multiexpanded 4 Groups), was developed and used for the computation of feature vectors. The nearest mean (NM) and support vector machine (SVM) classifiers were used for data classification. Analysis of the recognition of acoustic signals was carried out. The analysed value of TE_EID (the total efficiency of recognition of the electric impact drill) was equal to 96% for the NM classifier and 88.8% for SVM. The analysed value of TE_B (the total efficiency of recognition of the blender) was equal to 100% for the NM classifier and 94.11% for SVM. Full article

Cardiac disorders are critical and must be diagnosed in the early stage using routine auscultation examination with high precision. Cardiac auscultation is a technique to analyze and listen to heart sound using electronic stethoscope, an electronic stethoscope is a device which provides the digital recording of the heart sound called phonocardiogram (PCG). This PCG signal carries useful information about the functionality and status of the heart and hence several signal processing and machine learning technique can be applied to study and diagnose heart disorders. Based on PCG signal, the heart sound signal can be classified to two main categories i.e., normal and abnormal categories. We have created database of 5 categories of heart sound signal (PCG signals) from various sources which contains one normal and 4 are abnormal categories. This study proposes an improved, automatic classification algorithm for cardiac disorder by heart sound signal. We extract features from phonocardiogram signal and then process those features using machine learning techniques for classification. In features extraction, we have used Mel Frequency Cepstral Coefficient (MFCCs) and Discrete Wavelets Transform (DWT) features from the heart sound signal, and for learning and classification we have used support vector machine (SVM), deep neural network (DNN) and centroid displacement based k nearest neighbor. To improve the results and classification accuracy, we have combined MFCCs and DWT features for training and classification using SVM and DWT. From our experiments it has been clear that results can be greatly improved when Mel Frequency Cepstral Coefficient and Discrete Wavelets Transform features are fused together and used for classification via support vector machine, deep neural network and k-neareast neighbor(KNN). The methodology discussed in this paper can be used to diagnose heart disorders in patients up to 97% accuracy. The code and dataset can be accessed at “https://github.com/yaseen21khan/Classification-of-Heart-Sound-Signal-Using-Multiple-Features-/blob/master/README.md”. Full article

Steel bars, which are commonly used as reinforcements in concrete structures, are slender rods and are good conduits for stress wave propagation. In this paper, a lead zirconate titanate (PZT)-based steel bar corrosion monitoring approach was proposed. Two PZT transducers are surface-bonded on the two ends of a steel rod, respectively. One works as actuator to generate stress waves, and the other functions as a sensor to detect the propagated stress waves. Time reverse technology was applied in this research to monitor the corrosion of the steel bars with a high signal to noise ratio (SNR). Accelerated corrosion experiments of steel bars were conducted. The anti-corrosion performance of the protected piezoceramic transducers was tested first, and then they were used to monitor the corrosion of the steel bar using the time reversal method. The degree of corrosion in the steel bar was determined by the ratio of mass loss during the experiment. The experimental results show that the peak values of the signal that were obtained by time reversal operation are linearly related to the degree of corrosion of the steel bar, which demonstrates the feasibility of the proposed approach for monitoring the corrosion of steel bars using the time reversal method enabled by piezoceramic transducers. Full article

Surface contamination by microbes is a major public health concern. A damp environment is one of potential sources for microbe proliferation. Smart photocatalytic coatings on building surfaces using semiconductors like titania (TiO₂) can effectively curb this growing threat. Metal-doped titania in anatase phase has been proven as a promising candidate for energy and environmental applications. In this present work, the antimicrobial efficacy of copper (Cu)-doped TiO₂ (Cu-TiO₂) was evaluated against Escherichia coli (Gram-negative) and Staphylococcus aureus (Gram-positive) under visible light irradiation. Doping of a minute fraction of Cu (0.5 mol %) in TiO₂ was carried out via sol-gel technique. Cu-TiO₂ further calcined at various temperatures (in the range of 500–700 °C) to evaluate the thermal stability of TiO₂ anatase phase. The physico-chemical properties of the samples were characterized through X-ray diffraction (XRD), Raman spectroscopy, X-ray photo-electron spectroscopy (XPS) and UV–visible spectroscopy techniques. XRD results revealed that the anatase phase of TiO₂ was maintained well, up to 650 °C, by the Cu dopant. UV–vis results suggested that the visible light absorption property of Cu-TiO₂ was enhanced and the band gap is reduced to 2.8 eV. Density functional theory (DFT) studies emphasize the introduction of Cu⁺ and Cu²⁺ ions by replacing Ti⁴⁺ ions in the TiO₂ lattice, creating oxygen vacancies. These further promoted the photocatalytic efficiency. A significantly high bacterial inactivation (99.9999%) was attained in 30 min of visible light irradiation by Cu-TiO₂. Full article

Pneumonia affects 7% of the global population, resulting in 2 million pediatric deaths every year. Chest X-ray (CXR) analysis is routinely performed to diagnose the disease. Computer-aided diagnostic (CADx) tools aim to supplement decision-making. These tools process the handcrafted and/or convolutional neural network (CNN) extracted image features for visual recognition. However, CNNs are perceived as black boxes since their performance lack explanations. This is a serious bottleneck in applications involving medical screening/diagnosis since poorly interpreted model behavior could adversely affect the clinical decision. In this study, we evaluate, visualize, and explain the performance of customized CNNs to detect pneumonia and further differentiate between bacterial and viral types in pediatric CXRs. We present a novel visualization strategy to localize the region of interest (ROI) that is considered relevant for model predictions across all the inputs that belong to an expected class. We statistically validate the models’ performance toward the underlying tasks. We observe that the customized VGG16 model achieves 96.2% and 93.6% accuracy in detecting the disease and distinguishing between bacterial and viral pneumonia respectively. The model outperforms the state-of-the-art in all performance metrics and demonstrates reduced bias and improved generalization. Full article

Recent advancements in material technologies have promoted the development of various preparation strategies and applications of novel polymer–nanoclay composites. Innovative synthesis pathways have resulted in novel polymer–nanoclay composites with improved properties, which have been successfully incorporated in diverse fields such as aerospace, automobile, construction, petroleum, biomedical and wastewater treatment. These composites are recognized as promising advanced materials due to their superior properties, such as enhanced density, strength, relatively large surface areas, high elastic modulus, flame retardancy, and thermomechanical/optoelectronic/magnetic properties. The primary focus of this review is to deliver an up-to-date overview of polymer–nanoclay composites along with their synthesis routes and applications. The discussion highlights potential future directions for this emerging field of research. Full article

The fast development of smart sensors and wearable devices has provided the opportunity to develop intelligent operator workspaces. The resultant Human-Cyber-Physical Systems (H-CPS) integrate the operators into flexible and multi-purpose manufacturing processes. The primary enabling factor of the resultant Operator 4.0 paradigm is the integration of advanced sensor and actuator technologies and communications solutions. This work provides an extensive overview of these technologies and highlights that the design of future workplaces should be based on the concept of intelligent space. Full article

Displays based on inorganic light-emitting diodes (LED) are considered as the most promising one among the display technologies for the next-generation. The chip for LED display bears similar features to those currently in use for general lighting, but it size is shrunk to below 200 microns. Thus, the advantages of high efficiency and long life span of conventional LED chips are inherited by miniaturized ones. As the size gets smaller, the resolution enhances, but at the expense of elevating the complexity of fabrication. In this review, we introduce two sorts of inorganic LED displays, namely relatively large and small varieties. The mini-LEDs with chip sizes ranging from 100 to 200 μm have already been commercialized for backlight sources in consumer electronics applications. The realized local diming can greatly improve the contrast ratio at relatively low energy consumptions. The micro-LEDs with chip size less than 100 μm, still remain in the laboratory. The full-color solution, one of the key technologies along with its three main components, red, green, and blue chips, as well color conversion, and optical lens synthesis, are introduced in detail. Moreover, this review provides an account for contemporary technologies as well as a clear view of inorganic and miniaturized LED displays for the display community. Full article

The increasingly rapid creation, sharing and exchange of information nowadays put researchers and data scientists ahead of a challenging task of data analysis and extracting relevant information out of data. To be able to learn from data, the dimensionality of the data should be reduced first. Feature selection (FS) can help to reduce the amount of data, but it is a very complex and computationally demanding task, especially in the case of high-dimensional datasets. Swarm intelligence (SI) has been proved as a technique which can solve NP-hard (Non-deterministic Polynomial time) computational problems. It is gaining popularity in solving different optimization problems and has been used successfully for FS in some applications. With the lack of comprehensive surveys in this field, it was our objective to fill the gap in coverage of SI algorithms for FS. We performed a comprehensive literature review of SI algorithms and provide a detailed overview of 64 different SI algorithms for FS, organized into eight major taxonomic categories. We propose a unified SI framework and use it to explain different approaches to FS. Different methods, techniques, and their settings are explained, which have been used for various FS aspects. The datasets used most frequently for the evaluation of SI algorithms for FS are presented, as well as the most common application areas. The guidelines on how to develop SI approaches for FS are provided to support researchers and analysts in their data mining tasks and endeavors while existing issues and open questions are being discussed. In this manner, using the proposed framework and the provided explanations, one should be able to design an SI approach to be used for a specific FS problem. Full article

Graphene is the first 2D crystal ever isolated by mankind. It consists of a single graphite layer, and its exceptional properties are revolutionizing material science. However, there is still a lack of convenient mass-production methods to obtain defect-free monolayer graphene. In contrast, graphene nanoplatelets, hybrids between graphene and graphite, are already industrially available. Such nanomaterials are attractive, considering their planar structure, light weight, high aspect ratio, electrical conductivity, low cost, and mechanical toughness. These diverse features enable applications ranging from energy harvesting and electronic skin to reinforced plastic materials. This review presents progress in composite materials with graphene nanoplatelets applied, among others, in the field of flexible electronics and motion and structural sensing. Particular emphasis is given to applications such as antennas, flexible electrodes for energy devices, and strain sensors. A separate discussion is included on advanced biodegradable materials reinforced with graphene nanoplatelets. A discussion of the necessary steps for the further spread of graphene nanoplatelets is provided for each revised field. Full article

Solar photovoltaic (PV) power forecasting has become an important issue with regard to the power grid in terms of the effective integration of large-scale PV plants. As the main influence factor of PV power generation, solar irradiance and its accurate forecasting are the prerequisite for solar PV power forecasting. However, previous forecasting approaches using manual feature extraction (MFE), traditional modeling and single deep learning (DL) models could not satisfy the performance requirements in partial scenarios with complex fluctuations. Therefore, an improved DL model based on wavelet decomposition (WD), the Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) is proposed for day-ahead solar irradiance forecasting. Given the high dependency of solar irradiance on weather status, the proposed model is individually established under four general weather type (i.e., sunny, cloudy, rainy and heavy rainy). For certain weather types, the raw solar irradiance sequence is decomposed into several subsequences via discrete wavelet transformation. Then each subsequence is fed into the CNN based local feature extractor to automatically learn the abstract feature representation from the raw subsequence data. Since the extracted features of each subsequence are also time series data, they are individually transported to LSTM to construct the subsequence forecasting model. In the end, the final solar irradiance forecasting results under certain weather types are obtained via the wavelet reconstruction of these forecasted subsequences. This case study further verifies the enhanced forecasting accuracy of our proposed method via a comparison with traditional and single DL models. Full article

This paper reviews the progress of AlGaN-based deep-ultraviolet (DUV) light emitting diodes (LEDs), mainly focusing in the work of the authors’ group. The background to the development of the current device structure on sapphire is described and the reason for using a (0001) sapphire with a miscut angle of 1.0° relative to the m-axis is clarified. Our LEDs incorporate uneven quantum wells (QWs) grown on an AlN template with dense macrosteps. Due to the low threading dislocation density of AlGaN and AlN templates of about 5 × 10⁸/cm², the number of nonradiative recombination centers is decreased. In addition, the uneven QW show high external quantum efficiency (EQE) and wall-plug efficiency, which are considered to be boosted by the increased internal quantum efficiency (IQE) by enhancing carrier localization adjacent to macrosteps. The achieved LED performance is considered to be sufficient for practical applications. The advantage of the uneven QW is discussed in terms of the EQE and IQE. A DUV-LED die with an output of over 100 mW at 280–300 nm is considered feasible by applying techniques including the encapsulation. In addition, the fundamental achievements of various groups are reviewed for the future improvements of AlGaN-based DUV-LEDs. Finally, the applications of DUV-LEDs are described from an industrial viewpoint. The demonstrations of W/cm²-class irradiation modules are shown for UV curing. Full article