The 100 most-cited articles in Applied Sciences, 2018

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…

Automatic metallic surface defect inspection has received increased attention in relation to the quality control of industrial products. Metallic defect detection is usually performed against complex industrial scenarios, presenting an interesting but challenging problem. Traditional methods are based on image processing or shallow machine learning techniques, but these can only detect defects under specific detection conditions, such as obvious defect contours with strong contrast and low noise, at certain scales, or under specific illumination conditions. This paper discusses the automatic detection of metallic defects with a twofold procedure that accurately localizes and classifies defects appearing in input images captured from real industrial environments. A novel cascaded autoencoder (CASAE) architecture is designed for segmenting and localizing defects. The cascading network transforms the input defect image into a pixel-wise prediction mask based on semantic segmentation. The defect regions of segmented results are classified into their specific classes via a compact…

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.

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…

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…

As a critical indictor in the Battery Management System (BMS), State of Charge (SOC) is closely related to the reliable and safe operation of lithium-ion (Li-ion) batteries. Model-based methods are an effective solution for accurate and robust SOC estimation, the performance of which heavily relies on the battery model. This paper mainly focuses on battery modeling methods, which have the potential to be used in a model-based SOC estimation structure. Battery modeling methods are classified into four categories on the basis of their theoretical foundations, and their expressions and features are detailed. Furthermore, the four battery modeling methods are compared in terms of their pros and cons. Future research directions are also presented. In addition, after optimizing the parameters of the battery models by a Genetic Algorithm (GA), four typical battery models including a combined model, two RC Equivalent Circuit Model (ECM), a Single Particle Model (SPM), and a Support Vector Machine (SVM) battery model are compared in terms of their accuracy and execution time.

Underground hydrogen storage is a potential way to balance seasonal fluctuations in energy production from renewable energies. The risks of hydrogen storage in depleted gas fields include the conversion of hydrogen to CH4(g) and H2S(g) due to microbial activity, gas–water–rock interactions in the reservoir and cap rock, which are connected with porosity changes, and the loss of aqueous hydrogen by diffusion through the cap rock brine. These risks lead to loss of hydrogen and thus to a loss of energy. A hydrogeochemical modeling approach is developed to analyze these risks and to understand the basic hydrogeochemical mechanisms of hydrogen storage over storage times at the reservoir scale. The one-dimensional diffusive mass transport model is based on equilibrium reactions for gas–water–rock interactions and kinetic reactions for sulfate reduction and methanogenesis. The modeling code is PHREEQC (pH-REdox-EQuilibrium written in the C programming language). The parameters that influence the hydrogen loss are identified. Crucial parameters are the amount of available electron…

The intent of this paper is to provide an overview of basic concepts, types, techniques, and experimental studies of the current state-of-the-art Frequency Selective Surfaces (FSSs). FSS is a periodic surface with identical two-dimensional arrays of elements arranged on a dielectric substrate. An incoming plane wave will either be transmitted (passband) or reflected back (stopband), completely or partially, depending on the nature of array element. This occurs when the frequency of electromagnetic (EM) wave matches with the resonant frequency of the FSS elements. Therefore, an FSS is capable of passing or blocking the EM waves of certain range of frequencies in the free space; consequently, identified as spatial filters. Nowadays, FSSs have been studied comprehensively and huge growth is perceived in the field of its designing and implementation for different practical applications at frequency ranges of microwave to optical. In this review article, we illustrate the recent researches on different categories of FSSs based on structure design, array element used, and applications. We…

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…

Following high profile, life changing long term mental illnesses and fatalities in sports such as skiing, cricket and American football—sports injuries feature regularly in national and international news. A mismatch between equipment certification tests, user expectations and infield falls and collisions is thought to affect risk perception, increasing the prevalence and severity of injuries. Auxetic foams, structures and textiles have been suggested for application to sporting goods, particularly protective equipment, due to their unique form-fitting deformation and curvature, high energy absorption and high indentation resistance. The purpose of this critical review is to communicate how auxetics could be useful to sports equipment (with a focus on injury prevention), and clearly lay out the steps required to realise their expected benefits. Initial overviews of auxetic materials and sporting protective equipment are followed by a description of common auxetic materials and structures, and how to produce them in foams, textiles and Additively Manufactured structures. Beneficial…

There is growing attention and rapid development on flexible electronic devices with electronic materials and sensing technology innovations. In particular, strain sensors with high elasticity and stretchability are needed for several potential applications including human entertainment technology, human–machine interface, personal healthcare, and sports performance monitoring, etc. This article presents recent advancements in the development of polydimethylsiloxane (PDMS)-based flexible resistive strain sensors for wearable applications. First of all, the article shows that PDMS-based stretchable resistive strain sensors are successfully fabricated by different methods, such as the filtration method, printing technology, micromolding method, coating techniques, and liquid phase mixing. Next, strain sensing performances including stretchability, gauge factor, linearity, and durability are comprehensively demonstrated and compared. Finally, potential applications of PDMS-based flexible resistive strain sensors are also discussed. This review indicates that the era of wearable…

This paper aims to achieve real-time and accurate detection of surface defects by using a deep learning method. For this purpose, the Single Shot MultiBox Detector (SSD) network was adopted as the meta structure and combined with the base convolution neural network (CNN) MobileNet into the MobileNet-SSD. Then, a detection method for surface defects was proposed based on the MobileNet-SSD. Specifically, the structure of the SSD was optimized without sacrificing its accuracy, and the network structure and parameters were adjusted to streamline the detection model. The proposed method was applied to the detection of typical defects like breaches, dents, burrs and abrasions on the sealing surface of a container in the filling line. The results show that our method can automatically detect surface defects more accurately and rapidly than lightweight network methods and traditional machine learning methods. The research results shed new light on defect detection in actual industrial scenarios.

The adoption of machine learning and deep learning is on the rise in the cybersecurity domain where these AI methods help strengthen traditional system monitoring and threat detection solutions. However, adversaries too are becoming more effective in concealing malicious behavior amongst large amounts of benign behavior data. To address the increasing time-to-detection of these stealthy attacks, interconnected and federated learning systems can improve the detection of malicious behavior by joining forces and pooling together monitoring data. The major challenge that we address in this work is that in a federated learning setup, an adversary has many more opportunities to poison one of the local machine learning models with malicious training samples, thereby influencing the outcome of the federated learning and evading detection. We present a solution where contributing parties in federated learning can be held accountable and have their model updates audited. We describe a permissioned blockchain-based federated learning method where incremental updates to an anomaly detection…

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…

Metal nanoparticles are extensively studied due to their unique chemical and physical properties, which differ from the properties of their respective bulk materials. Likewise, the properties of heterogeneous bimetallic structures are far more attractive than those of single-component nanoparticles. The incorporation of a second metal into the nanoparticle structure alters the surface plasmon resonance and magnetic properties of the bimetallic composite. This review focuses on the enhanced optical and magnetic properties offered by bimetallic nanoparticles and their corresponding impact on biological applications. We summarize the predominant structures of bimetallic nanoparticles, outline their synthesis methods, and highlight their use in biological applications, both diagnostic and therapeutic, which are dictated by their various optical and magnetic properties.

Research on nanotechnology application in the oil and gas industry has been growing rapidly in the past decade, as evidenced by the number of scientific articles published in the field. With oil and gas reserves harder to find, access, and produce, the pursuit of more game-changing technologies that can address the challenges of the industry has stimulated this growth. Nanotechnology has the potential to revolutionize the petroleum industry both upstream and downstream, including exploration, drilling, production, and enhanced oil recovery (EOR), as well as refinery processes. It provides a wide range of alternatives for technologies and materials to be utilized in the petroleum industry. Nanoscale materials in various forms such as solid composites, complex fluids, and functional nanoparticle-fluid combinations are key to the new technological advances. This paper aims to provide a state-of-the-art review on the application of nanoparticles and technology in the petroleum industry, and focuses on enhanced oil recovery. We briefly summarize nanotechnology application in exploration…

This paper focuses on the hardware aspects of battery management systems (BMS) for electric vehicle and stationary applications. The purpose is giving an overview on existing concepts in state-of-the-art systems and enabling the reader to estimate what has to be considered when designing a BMS for a given application. After a short analysis of general requirements, several possible topologies for battery packs and their consequences for the BMS’ complexity are examined. Four battery packs that were taken from commercially available electric vehicles are shown as examples. Later, implementation aspects regarding measurement of needed physical variables (voltage, current, temperature, etc.) are discussed, as well as balancing issues and strategies. Finally, safety considerations and reliability aspects are investigated.

The feasibility of using hyperspectral imaging with convolutional neural network (CNN) to identify rice seed varieties was studied. Hyperspectral images of 4 rice seed varieties at two different spectral ranges (380–1030 nm and 874–1734 nm) were acquired. The spectral data at the ranges of 441–948 nm (Spectral range 1) and 975–1646 nm (Spectral range 2) were extracted. K nearest neighbors (KNN), support vector machine (SVM) and CNN models were built using different number of training samples (100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1500, 2000, 2500 and 3000). KNN, SVM and CNN models in the Spectral range 2 performed slightly better than those in the Spectral range 1. The model performances improved with the increase in the number of training samples. The improvements were not significant when the number of training samples was large. CNN model performed better than the corresponding KNN and SVM models in most cases, which indicated the effectiveness of using CNN to analyze spectral data. The results of this study showed that CNN could be adopted in spectral data analysis…

Titanium dioxide (TiO2) has been used extensively because of its unique thermal and electric properties. Different techniques have been used for the preparation of TiO2 nanofluids which include single-step and two-step methods. In the natural world, TiO2 exists in three different crystalline forms as anatase, brookite, and rutile. Nanoparticles are not used directly in many heat transfer applications, and this provides a major challenge to researchers to advance towards stable nanofluid preparation methods. The primary step involved in the preparation of nanofluid is the production of nano-sized solid particles by using a suitable technique, and then these particles are dispersed into base fluids like oil, water, paraffin oil or ethylene glycol. However, nanofluid can also be prepared directly by using a liquid chemical method or vapor deposition technique (VDT). Nanofluids are mostly used in heat transfer applications and the size and cost of the heat transfer device depend upon the working fluid properties, thus, in the past decade scientists have made great efforts to formulate…

The PASCAL VOC Challenge performance has been significantly boosted by the prevalently CNN-based pipelines like Faster R-CNN. However, directly applying the Faster R-CNN to the small remote sensing objects usually renders poor performance. To address this issue, this paper investigates on how to modify Faster R-CNN for the task of small object detection in optical remote sensing images. First of all, we not only modify the RPN stage of Faster R-CNN by setting appropriate anchors but also leverage a single high-level feature map of a fine resolution by designing a similar architecture adopting top-down and skip connections. In addition, we incorporate context information to further boost small remote sensing object detection performance while we apply a simple sampling strategy to solve the issue about the imbalanced numbers of images between different classes. At last, we introduce a simple yet effective data augmentation method named ‘random rotation’ during training. Experimental results show that our modified Faster R-CNN algorithm improves the mean average precision by a large…

An energy management strategy (EMS) is important for hybrid electric vehicles (HEVs) since it plays a decisive role on the performance of the vehicle. However, the variation of future driving conditions deeply influences the effectiveness of the EMS. Most existing EMS methods simply follow predefined rules that are not adaptive to different driving conditions online. Therefore, it is useful that the EMS can learn from the environment or driving cycle. In this paper, a deep reinforcement learning (DRL)-based EMS is designed such that it can learn to select actions directly from the states without any prediction or predefined rules. Furthermore, a DRL-based online learning architecture is presented. It is significant for applying the DRL algorithm in HEV energy management under different driving conditions. Simulation experiments have been conducted using MATLAB and Advanced Vehicle Simulator (ADVISOR) co-simulation. Experimental results validate the effectiveness of the DRL-based EMS compared with the rule-based EMS in terms of fuel economy. The online learning architecture is also…

The use of microbes to change the concentration of heavy metals in soil and improve the ability of plants to deal with elevated metals concentrations has significant economic and ecological benefits. This paper reviews the origins and toxic effects of heavy metal pollution in soil, and describes the heavy metal accumulation mechanisms of microbes, and compares their different bioconcentration abilities. Biosorption, which depends on the special structure of the cell wall, is found to be the primary mechanism. Furthermore, Escherichia coli are found to adsorb more heavy metals than other species. Factors influencing microbial treatment of wastewater and soil containing heavy metals include temperature, pH, and different substrates. Finally, problems in the application of microbial treatment of heavy metal contamination are considered, and possible directions for future research are discussed.

Current studies show that traditional deterministic models tend to struggle to capture the non-linear relationship between the concentration of air pollutants and their sources of emission and dispersion. To tackle such a limitation, the most promising approach is to use statistical models based on machine learning techniques. Nevertheless, it is puzzling why a certain algorithm is chosen over another for a given task. This systematic review intends to clarify this question by providing the reader with a comprehensive description of the principles underlying these algorithms and how they are applied to enhance prediction accuracy. A rigorous search that conforms to the PRISMA guideline is performed and results in the selection of the 46 most relevant journal papers in the area. Through a factorial analysis method these studies are synthetized and linked to each other. The main findings of this literature review show that: (i) machine learning is mainly applied in Eurasian and North American continents and (ii) estimation problems tend to implement Ensemble Learning and Regressions,…

Nanotechnology is a potential and emerging field with multiple applications in different areas of study. The beneficial effects of the use of nanoparticles in agriculture have already been proven. The objective of this research was to determine if the foliar application of Cu nanoparticles (NPs) could increase the content of the bioactive compounds in tomato fruits. Our study considered four treatments with different concentrations of Cu nanoparticles (50, 125, 250, 500 mg L−1, diameter 50 nm) applied twice during the development of the culture. The effects on the fruit quality and the contents of the antioxidant compounds were determined. The application of the Cu nanoparticles induced the production of fruits with greater firmness. Vitamin C, lycopene, and the ABTS antioxidant capacity increased compared to the Control. In addition, a decrease in the ascorbate peroxidase (APX) and glutathione peroxidase (GPX) enzymatic activity was observed, while the superoxide dismutase (SOD) and catalase (CAT) enzymes showed a significant increase. The application of Cu NPs induced a greater…

Indoor surface contamination by microbes is a major public health concern. A damp environment is one potential sources for microbe proliferation. Smart photocatalytic coatings on building surfaces using semiconductors like titania (TiO 2 ) can effectively curb this growing threat. Metal-doped titania in anatase phase has been proved as a promising candidate for energy and environmental applications. In this present work, the antimicrobial efficacy of copper (Cu) doped TiO 2 (Cu-TiO 2 ) 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 TiO 2 was carried out via sol-gel technique. Cu-TiO 2 further calcined at various temperatures (in the range of 500 °C – 700 °C) to evaluate the thermal stability of TiO 2 anatase phase. The physico-chemical properties of the samples were characterised 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…

Discharge of waste in general, and food waste, in particular, is considered one of the major environmental problems today, as waste generation increases continuously, reaching values of 32% of all food produced worldwide. There are many different options that can be applied to the management and evaluation of waste treatment, and Anaerobic Digestion seems to be one of the most suitable solutions because of its benefits, including renewable energy generation in form of biogas. Moreover, if FW (food waste) is digested in anaerobic digesters from Waste Water Treatment Plants, a common solution is provided for both residues. Furthermore, co-digestion of food waste and sewage sludge provides benefits in terms of anaerobic process stability enhancing the buffer capacity of ammonia (for example) and biogas formation, which can be increased up to 80% when compared with monodigestion. The present paper reviews food waste anaerobic digestion from its generation, characteristics and different options for its management, and it does focus specifically on the anaerobic digestion and co-digestion…

Due to the development of photovoltaic (PV) technology and the support from governments across the world, the conversion efficiency of solar energy has been improved. However, the PV power output is influenced by environment factors, resulting in features of randomness and intermittency. These features may have a negative influence on power systems. As a result, accurate and timely power prediction data is necessary for power grids to absorb solar energy. In this paper, we propose a new PV power prediction model based on the Gradient Boost Decision Tree (GBDT), which ensembles several binary trees by the gradient boosting ensemble method. The Gradient Boost method builds a strong learner by combining weak learners through iterative methods and the Decision Tree is a basic classification and regression method. As an ensemble machine learning algorithm, the Gradient Boost Decision Tree algorithm can offer higher forecast accuracy than one single learning algorithm. So GBDT is of value in both theoretical research and actual practice in the field of photovoltaic power prediction. The…

Gas sensors are devices that convert a gas volume fraction into electrical signals, and they are widely used in many fields such as environmental monitoring. Graphene is a new type of two-dimensional crystal material that has many excellent properties including large specific surface area, high conductivity, and high Young’s modulus. These features make it ideally suitable for application for gas sensors. In this paper, the main characteristics of gas sensor are firstly introduced, followed by the preparation methods and properties of graphene. In addition, the development process and the state of graphene gas sensors are introduced emphatically in terms of structure and performance of the sensor. The emergence of new candidates including graphene, polymer and metal/metal oxide composite enhances the performance of gas detection significantly. Finally, the clear direction of graphene gas sensors for the future is provided according to the latest research results and trends. It provides direction and ideas for future research.

Cracking is a common problem in concrete structures in real-life service conditions. In fact, crack-free concrete structures are very rare to find in real world. Concrete can undergo early-age cracking depending on the mix composition, exposure environment, hydration rate, and curing conditions. Understanding the causes and consequences of cracking thoroughly is essential for selecting proper measures to resolve the early-age cracking problem in concrete. This paper will help to identify the major causes and consequences of the early-age cracking in concrete. Also, this paper will be useful to adopt effective remedial measures for reducing or eliminating the early-age cracking problem in concrete. Different types of early-age crack, the factors affecting the initiation and growth of early-age cracks, the causes of early-age cracking, and the modeling of early-age cracking are discussed in this paper. A number of examples for various early-age cracking problems of concrete found in different structural elements are also shown. Above all, some recommendations are given for minimizing…

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.

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…

As the risk of diseases diabetes and hypertension increases, machine learning algorithms are being utilized to improve early stage diagnosis. This study proposes a Hybrid Prediction Model (HPM), which can provide early prediction of type 2 diabetes (T2D) and hypertension based on input risk-factors from individuals. The proposed HPM consists of Density-based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection to remove the outlier data, Synthetic Minority Over-Sampling Technique (SMOTE) to balance the distribution of class, and Random Forest (RF) to classify the diseases. Three benchmark datasets were utilized to predict the risk of diabetes and hypertension at the initial stage. The result showed that by integrating DBSCAN-based outlier detection, SMOTE, and RF, diabetes and hypertension could be successfully predicted. The proposed HPM provided the best performance result as compared to other models for predicting diabetes as well as hypertension. Furthermore, our study has demonstrated that the proposed HPM can be applied in real cases in the IoT-based…

In this study, the natural convection of a magnetohydrodynamic nanofluid in an enclosure under the effects of thermal radiation and the shape factor of nanoparticles was analyzed numerically using the control-volume-based finite element method (CVFEM). Columns, spheres, and lamina are examples of the nanoparticle shapes used in the investigation. The study of nanofluid flow and heat transfer was accomplished with an extensive range of nanofluid volume fractions, radiation parameters, Hartmann numbers, Rayleigh numbers, and nanoparticle shape factors. Also, the correlation between the average Nusselt number and the influencing parameters of the current study was determined. The findings demonstrate that laminar nanoparticles have a more notable impact on the average and local Nusselt numbers than the other nanoparticle shapes.

Industrial production plays an important role for achieving a green economy and the sustainable development goals. Therefore, the nascent transformation of industrial production due to digitalization into a so-called Industrial Internet of Things (IIoT) is of great interest from a sustainable development point of view. This paper discusses how the environmental dimension of a sustainable development can potentially benefit from the IIoT—focusing especially on three topics: resource efficiency, sustainable energy and transparency. It presents a state of the art literature analysis of IIoT-enabled approaches addressing the three environmental topics. This analysis is compared with the findings of a survey among Chinese industrial companies, investigating the sustainability-related expectations of participants coming along with the implementation of IIoT solutions. China has been chosen as a case study because it brings together a strong industrial sector, ambitious plans regarding industrial digitalization and a high relevance and need for more sustainability. The survey was conducted…

Defects are considered as one of the wastes in manufacturing systems that negatively affect the delivery times, cost and quality of products leading to manufacturing companies facing a critical situation with the customers and to not comply with the IPC-A-610E standard for the acceptability of electronic components. This is the case is a manufacturing company located in Tijuana, Mexico. Due to an increasing demand on the products manufactured by this company, several defects have been detected in the welding process of electronic boards, as well as in the components named Thru-Holes. It is for this reason that this paper presents a lean manufacturing application case study. The objective of this research is to reduce at least 20% the defects that are generated during the welding process. In addition, it is intended to increase 20% the capacity of three double production lines where electronic boards are processed. As method, the Plan-Do-Check-Act (PDCA) cycle, is applied. The Pareto charts and the flowchart are used as support tools. As results, defects decreased 65%, 79%, and 77%…

Light scattering by a small spherical particle, a central topic for electromagnetic scattering theory, is here considered. In this short review, some of the basic features of its resonant scattering behavior are covered. First, a general physical picture is described by a full electrodynamic perspective, the Lorenz–Mie theory. The resonant spectrum of a dielectric sphere reveals the existence of two distinctive types of polarization enhancement: the plasmonic and the dielectric resonances. The corresponding electrostatic (Rayleigh) picture is analyzed and the polarizability of a homogeneous spherical inclusion is extracted. This description facilitates the identification of the first type of resonance, i.e., the localized surface plasmon (plasmonic) resonance, as a function of the permittivity. Moreover, the electrostatic picture is linked with the plasmon hybridization model through the case of a step-inhomogeneous structure, i.e., a core–shell sphere. The connections between the electrostatic and electrodynamic models are reviewed in the small size limit and details on…

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…

Remanufacturing is the process of bringing end-of-life products back to good-as-new. It plays a critical role in decoupling economic growth from growth in resource use, and in accelerating the circular economy. However, the uptake of remanufacturing activities faces obstacles. This paper reviews the challenges encountered by the remanufacturing sector and discusses how the Industry 4.0 revolution could help to effectively address these issues and unlock the potential of remanufacturing. Two case studies are included in this paper to exemplify how technology enablers from Industry 4.0 can increase efficiency, reliability, and digitization of the remanufacturing process.

Differential evolution (DE) has been extensively used in optimization studies since its development in 1995 because of its reputation as an effective global optimizer. DE is a population-based metaheuristic technique that develops numerical vectors to solve optimization problems. DE strategies have a significant impact on DE performance and play a vital role in achieving stochastic global optimization. However, DE is highly dependent on the control parameters involved. In practice, the fine-tuning of these parameters is not always easy. Here, we discuss the improvements and developments that have been made to DE algorithms. In particular, we present a state-of-the-art survey of the literature on DE and its recent advances, such as the development of adaptive, self-adaptive and hybrid techniques.

Field emission electron sources in vacuum electronics are largely considered to achieve faster response, higher efficiency and lower energy consumption in comparison with conventional thermionic emitters. Carbon nanotubes had a leading role in renewing attention to field emission technologies in the early 1990s, due to their exceptional electron emitting properties enabled by their large aspect ratio, high electrical conductivity, and thermal and chemical stability. In the last decade, the search for improved emitters has been extended to several carbon nanostructures, comprising carbon nanotubes, either individual or films, diamond structures, graphitic materials, graphene, etc. Here, we review the main results in the development of carbon-based field emitters.

Among an array of hybrid nanoparticles, core-shell nanoparticles comprise of two or more materials, such as metals and biomolecules, wherein one of them forms the core at the center, while the other material/materials that were located around the central core develops a shell. Core-shell nanostructures are useful entities with high thermal and chemical stability, lower toxicity, greater solubility, and higher permeability to specific target cells. Plant or natural products-mediated synthesis of nanostructures refers to the use of plants or its extracts for the synthesis of nanostructures, an emerging field of sustainable nanotechnology. Various physiochemical and greener methods have been advanced for the synthesis of nanostructures, in contrast to conventional approaches that require the use of synthetic compounds for the assembly of nanostructures. Although several biological resources have been exploited for the synthesis of core-shell nanoparticles, but plant-based materials appear to be the ideal candidates for large-scale green synthesis of core-shell nanoparticles. This…

Prognostics, such as remaining useful life (RUL) prediction, is a crucial task in condition-based maintenance. A major challenge in data-driven prognostics is the difficulty of obtaining a sufficient number of samples of failure progression. However, for traditional machine learning methods and deep neural networks, enough training data is a prerequisite to train good prediction models. In this work, we proposed a transfer learning algorithm based on Bi-directional Long Short-Term Memory (BLSTM) recurrent neural networks for RUL estimation, in which the models can be first trained on different but related datasets and then fine-tuned by the target dataset. Extensive experimental results show that transfer learning can in general improve the prediction models on the dataset with a small number of samples. There is one exception that when transferring from multi-type operating conditions to single operating conditions, transfer learning led to a worse result.

In data-driven methods for prognostics, the remaining useful lifetime (RUL) is predicted based on the health indicator (HI). The HI detects the condition of equipment or components by monitoring sensor data such as vibration signals. To construct the HI, multiple features are extracted from signals using time domain, frequency domain, and time–frequency domain analyses, and which are then fused. However, the process of selecting and fusing features for the HI is very complex and labor-intensive. We propose a novel time–frequency image feature to construct HI and predict the RUL. To convert the one-dimensional vibration signals to a two-dimensional (2-D) image, the continuous wavelet transform (CWT) extracts the time–frequency image features, i.e., the wavelet power spectrum. Then, the obtained image features are fed into a 2-D convolutional neural network (CNN) to construct the HI. The estimated HI from the proposed model is used for the RUL prediction. The accuracy of the RUL prediction is improved by using the image features. The proposed method compresses the complex process…

Falling, as one of the main harm threats to the elderly, has drawn researchers’ attentions and has always been one of the most valuable research topics in the daily health-care for the elderly in last two decades. Before 2014, several researchers reviewed the development of fall detection, presented issues and challenges, and navigated the direction for the study in the future. With smart sensors and Internet of Things (IoT) developing rapidly, this field has made great progress. However, there is a lack of a review and discussion on novel sensors, technologies and algorithms introduced and employed from 2014, as well as the emerging challenges and new issues. To bridge this gap, we present an overview of fall detection research and discuss the core research questions on this topic. A total of 6830 related documents were collected and analyzed based on the key words. Among these documents, the twenty most influential and highly cited articles are selected and discussed profoundly from three perspectives: sensors, algorithms and performance. The findings would assist researchers in…

Landslides cause a considerable amount of damage around the world every year. Landslide susceptibility assessments are useful for the mitigation of the associated potential risks to local economic development, land use planning, and decision makers. The main aim of this study was to present a novel hybrid approach of bagging (B)-based kernel logistic regression (KLR), named the BKLR model, for spatial prediction of landslides in the Shangnan County, China. We first selected 15 conditioning factors for landslide susceptibility modeling. Then, the prediction capability of all conditioning factors was evaluated using the least square support vector machine method. Model validation and comparison were performed based on the area under the receiver operating characteristic curve and several statistical-based indexes, including positive predictive rate, negative predictive rate, sensitivity, specificity, kappa index, and root mean square error. Results indicated that the BKLR ensemble model outperformed and outclassed the KLR and the benchmark support vector machine model. Our findings…

Acoustic metamaterial science is an emerging field at the frontier of modern acoustics. It provides a prominent platform for acoustic wave control in subwavelength-sized metadevices or metasystems. However, most of the metamaterials can only work in a narrow frequency band once fabricated, which limits the practical application of acoustic metamaterials. This paper highlights some recent progress in tunable acoustic metamaterials based on various modulation techniques. Acoustic metamaterials have been designed to control the attenuation of acoustic waves, invisibility cloaking, and acoustic wavefront engineering, such as focusing via manipulating the acoustic impedance of metamaterials. The reviewed techniques are promising in extending the novel acoustics response into wider frequency bands, in that tunable acoustic metamaterials may be exploited for unusual applications compared to conventional acoustic devices.

The impact of ageing when using various State of Charge (SOC) levels for an electrified vehicle is investigated in this article. An extensive test series is conducted on Li-ion cells, based on graphite and NMC/LMO electrode materials. Lifetime cycling tests are conducted during a period of three years in various 10% SOC intervals, during which the degradation as function of number of cycles is established. An empirical battery model is designed from the degradation trajectories of the test result. An electric vehicle model is used to derive the load profiles for the ageing model. The result showed that, when only considering ageing from different types of driving in small Depth of Discharges (DODs), using a reduced charge level of 50% SOC increased the lifetime expectancy of the vehicle battery by 44–130%. When accounting for the calendar ageing as well, this proved to be a large part of the total ageing. By keeping the battery at 15% SOC during parking and limiting the time at high SOC, the contribution from the calendar ageing could be substantially reduced.

Typical active safety systems that control the dynamics of passenger cars rely on the real-time monitoring of the vehicle sideslip angle (VSA), together with other signals such as the wheel angular velocities, steering angle, lateral acceleration, and the rate of rotation about the vertical axis, which is known as the yaw rate. The VSA (also known as the attitude or “drifting” angle) is defined as the angle between the vehicle’s longitudinal axis and the direction of travel, taking the centre of gravity as a reference. It is basically a measure of the misalignment between vehicle orientation and trajectory; therefore, it is a vital piece of information enabling directional stability assessment, such as in transience following emergency manoeuvres, for instance. As explained in the introduction, the VSA is not measured directly for impracticality, and it is estimated on the basis of available measurements such as wheel velocities, linear and angular accelerations, etc. This work is intended to provide a comprehensive literature review on the VSA estimation problem. Two main…

This paper addresses trajectory tracking of an omni-directional mobile robot (OMR) with three mecanum wheels and a fully symmetrical configuration. The omni-directional wheeled robot outperforms the non-holonomic wheeled robot due to its ability to rotate and translate independently and simultaneously. A kinematics model of the OMR is established and a model predictive control (MPC) algorithm with control and system constraints is designed to achieve point stabilization and trajectory tracking. Simulation results validate the accuracy of the established kinematics model and the effectiveness of the proposed MPC controller.

Multi-walled carbon nanotubes (MWCNT) have provided unprecedented advances in the design of electrochemical sensors. They are composed by sp2 carbon units oriented as multiple concentric tubes of rolled-up graphene, and present remarkable active surface area, chemical inertness, high strength, and low charge-transfer resistance in both aqueous and non-aqueous solutions. MWCNT are very versatile and have been boosting the development of a new generation of electrochemical sensors with application in medicine, pharmacology, food industry, forensic chemistry, and environmental fields. This work highlights the most important synthesis methods and relevant electrochemical properties of MWCNT for the construction of electrochemical sensors, and the numerous configurations and successful applications of these devices. Thousands of studies have been attesting to the exceptional electroanalytical performance of these devices, but there are still questions in MWCNT electrochemistry that deserve more investigation, aiming to provide new outlooks and advances in this field. Additionally,…

Due to the more powerful and miniaturized nature of modern devices, conventional heat-transfer working fluids are not capable of meeting the cooling needs of these systems. Therefore, it is necessary to improve the heat-transfer abilities of commonly used cooling fluids. Recently, nanoparticles with different characteristics have been introduced to base liquids to enhance the overall thermal conductivity. This paper studies the influence of various parameters, including base liquid, temperature, nanoparticle concentration, nanoparticle size, nanoparticle shape, nanoparticle material, and the addition of surfactant, on nanofluid thermal conductivity. The mechanisms of thermal conductivity enhancement by different parameters are discussed. The impact of nanoparticles on the enhanced thermal conductivity of nanofluids is clearly shown through plotting the thermal conductivities of nanofluids as a function of temperature and/or nanoparticle concentration on the same graphs as their respective base liquids. Additionally, the thermal conductivity of hybrid nanofluids, and the effects of…

According to the EU-Directives 2002/95/EC, 2002/96/EC, lead-based piezoceramics must be substituted in the future with more environmentally friendly alternatives, only when a reliable alternative is found. This is why an increasing interest has grown in the research community to find lead free piezoelectric materials that fulfil the requirements for this substitution. Different families of compounds have been shown to be possible candidates for this use, such as bismuth and niobates based perovskites, pyrochlores, etc. However, a material with piezoelectric coefficients similar to those of PZT (lead zirconate titanate, Pb[ZrxTi1-x]O3) has not been yet found. Besides, each of these families has its specific characteristics in terms of remnant polarization, coercive field or application temperature. Thus, the choice of each material should be made according to the specific needs of the application. In this sense, Aurivillius-type structure materials (also known as Bismuth Layered Structure Ferroelectrics, BLSF) can take advantage of their specific properties for uses as Lead Free…

ZnO nanoparticles (NPs) are synthesized by deoxidizing ZnO powder in a vacuum drying process. This process reduces the size of the NPs and increases the concentration of oxygen vacancies on their surfaces. ZnO NPs with sufficient oxygen vacancies are highly effective for the photodecomposition of methylene blue (MB) dye in water under ultraviolet irradiation. The MB degradation efficiency exceeds 99 percent after 50 min of light irradiation, and the catalytic property of the NPs remains stable over several complete degradation cycles. It is revealed that the concentration of oxygen vacancies on the surface, and the photocatalytic activity, are both higher for smaller NPs. Oxygen vacancies reduce the recombination rate of photo-generated charge carriers by capturing the electrons and hence, improve the efficiency of redox reactions. In addition, a smaller particle size leads to a larger specific surface area and a higher photonic efficiency for the ZnO NPs.

Wire and arc-based additive manufacturing (WAAM) is a rapidly developing technology which employs a welding arc to melt metal wire for additive manufacturing purposes. During WAAM of thin-walled structures, as the wall height increases, the heat dissipation to the substrate is slowed down gradually and so is the solidification of the molten pool, leading to variation of the bead geometry. Though gradually reducing the heat input via adjusting the process parameters can alleviate this issue, as suggested by previous studies, it relies on experience to a large extent and inevitably sacrifices the deposition rate because the wire feed rate is directly coupled with the heat input. This study introduces for the first time an in-process active cooling system based on thermoelectric cooling technology into WAAM, which aims to eliminate the difference in heat dissipation between upper and lower layers. The case study shows that, with the aid of thermoelectric cooling, the bead width error is reduced by 56.8%, the total fabrication time is reduced by 60.9%, and the average grain size is…

The real estate market is exposed to many fluctuations in prices because of existing correlations with many variables, some of which cannot be controlled or might even be unknown. Housing prices can increase rapidly (or in some cases, also drop very fast), yet the numerous listings available online where houses are sold or rented are not likely to be updated that often. In some cases, individuals interested in selling a house (or apartment) might include it in some online listing, and forget about updating the price. In other cases, some individuals might be interested in deliberately setting a price below the market price in order to sell the home faster, for various reasons. In this paper, we aim at developing a machine learning application that identifies opportunities in the real estate market in real time, i.e., houses that are listed with a price substantially below the market price. This program can be useful for investors interested in the housing market. We have focused in a use case considering real estate assets located in the Salamanca district in Madrid (Spain) and…

A cyber-physical system (CPS) is a combination of physical system components with cyber capabilities that have a very tight interconnectivity. CPS is a widely used technology in many applications, including electric power systems, communications, and transportation, and healthcare systems. These are critical national infrastructures. Cybersecurity attack is one of the major threats for a CPS because of many reasons, including complexity and interdependencies among various system components, integration of communication, computing, and control technology. Cybersecurity attacks may lead to various risks affecting the critical infrastructure business continuity, including degradation of production and performance, unavailability of critical services, and violation of the regulation. Managing cybersecurity risks is very important to protect CPS. However, risk management is challenging due to the inherent complex and evolving nature of the CPS system and recent attack trends. This paper presents an integrated cybersecurity risk management framework to assess and manage the risks in a…

In this paper, we present a conceptual study on a Virtual Power Plant (VPP) architecture for the optimal management of Distributed Energy Resources (DERs) owned by prosumers participating in Demand-Side Management (DSM) programs. Compared to classical VPP architectures, which aim to aggregate several DERs dispersed throughout the electrical grid, in the proposed VPP architecture the supervised physical domain is limited to single users, i.e., to single Points of Delivery (PODs) of the distribution network. The VPP architecture is based on a service-oriented approach, where multiple agents cooperate to implement the optimal management of the prosumer’s assets, by also considering different forms of Demand Response (DR) requests. The considered DR schemes range from Price-Based DRs to Event-Based DRs, covering both the normal operating functions and the emergency control requests applied in modern distribution networks. With respect to centralized approaches, in this study the control perspective is moved from the system level to the single prosumer’s level, who is allowed to…

The cumulative global photovoltaic (PV) waste reached 250,000 metric tonnes by the end of 2016 and is expected to increase considerably in the future. Hence, adequate end-of-life (EoL) management for PV modules must be developed. Today, most of the EoL modules go to landfill, mainly because recycling processes for PV modules are not yet economically feasible and regulation in most countries is not yet well established. Nevertheless, several methods for recycling PV modules are under development. Life cycle assessment (LCA) is a methodology that quantifies the environmental impacts of a process or a product. An attributional LCA was undertaken to compare landfill, incineration, reuse and recycling (mechanical, thermal and chemical routes) of EoL crystalline silicon (c-Si) solar modules, based on a combination of real process data and assumptions. The results show that recovery of materials from solar modules results in lower environmental impacts compared to other EoL scenarios, considering our assumptions. The impacts could be even lower with the adoption of more complex processes…

In this study, the amount of calcite precipitate resulting from microbially induced calcite precipitation (MICP) was estimated in order to determine the optimal conditions for precipitation. Two microbial species (Staphylococcus saprophyticus and Sporosarcina pasteurii) were tested by varying certain parameters such as (1) initial potential of hydrogen (pH) of urea-CaCl2 medium, (2) temperature during precipitation, and (3) the reaction duration. The pH values used for testing were 6, 7, 8, 9, and 10, the temperatures were 20, 30, 40, and 50 °C, and the reaction durations were 2, 3, and 4 days. Maximum calcite precipitation was observed at a pH of 7 and temperature of 30 °C. Most of the precipitation occurred within a reaction duration of 3 days. Under similar conditions, the amount of calcite precipitated by S. saprophyticus was estimated to be five times more than that by S. pasteurii. Both the species were sensitive to temperature; however, S. saprophyticus was less sensitive to pH and required a shorter reaction duration than S. pasteurii.

Chalcogenide glasses have the advantages of a wide transparency window (over 20 μm) and high optical nonlinearity (up to a thousand times greater than that of silica glasses), making them good candidates for mid-infrared supercontinuum generation. In this review, we describe both the history and recent developments in mid-infrared supercontinuum generation from chalcogenide fibers according to three kinds of fiber structures: step-index, microstructured and tapered fibers. We also review the coherence properties of mid-infrared supercontinuum generation and all-fiber supercontinuum sources based on chalcogenide fibers.

The proportion of instances belonging to each class in a data-set plays an important role in machine learning. However, the real world data often suffer from class imbalance. Dealing with multi-class tasks with different misclassification costs of classes is harder than dealing with two-class ones. Undersampling and oversampling are two of the most popular data preprocessing techniques dealing with imbalanced data-sets. Ensemble classifiers have been shown to be more effective than data sampling techniques to enhance the classification performance of imbalanced data. Moreover, the combination of ensemble learning with sampling methods to tackle the class imbalance problem has led to several proposals in the literature, with positive results. The ensemble margin is a fundamental concept in ensemble learning. Several studies have shown that the generalization performance of an ensemble classifier is related to the distribution of its margins on the training examples. In this paper, we propose a novel ensemble margin based algorithm, which handles imbalanced classification by employing…

It is becoming increasingly more important to provide a low-cost point-of-care diagnostic device with the ability to detect and monitor various biological and chemical compounds. Traditional laboratories can be time-consuming and very costly. Through the combination of well-established materials and fabrication methods, it is possible to produce devices that meet the needs of many patients, healthcare and medical professionals, and environmental specialists. Existing research has demonstrated that inkjet-printed and paper-based electrochemical sensors are suitable for this application due to advantages provided by the carefully selected materials and fabrication method. Inkjet printing provides a low cost fabrication method with incredible control over the material deposition process, while paper-based substrates enable pump-free microfluidic devices due to their natural wicking ability. Furthermore, electrochemical sensing is incredibly selective and provides accurate and repeatable quantitative results without expensive measurement equipment. By merging each of these favorable…

Gully erosion triggers land degradation and restricts the use of land. This study assesses the spatial relationship between gully erosion (GE) and geo-environmental variables (GEVs) using Weights-of-Evidence (WoE) Bayes theory, and then applies three data mining methods—Random Forest (RF), boosted regression tree (BRT), and multivariate adaptive regression spline (MARS)—for gully erosion susceptibility mapping (GESM) in the Shahroud watershed, Iran. Gully locations were identified by extensive field surveys, and a total of 172 GE locations were mapped. Twelve gully-related GEVs: Elevation, slope degree, slope aspect, plan curvature, convergence index, topographic wetness index (TWI), lithology, land use/land cover (LU/LC), distance from rivers, distance from roads, drainage density, and NDVI were selected to model GE. The results of variables importance by RF and BRT models indicated that distance from road, elevation, and lithology had the highest effect on GE occurrence. The area under the curve (AUC) and seed cell area index (SCAI) methods were used to validate the three GE maps.…

Digital holography (DH) is a promising technique for modern three-dimensional (3D) imaging. Coherent holography records the complex amplitude of a 3D object holographically, giving speckle noise upon reconstruction and presenting a serious drawback inherent in coherent optical systems. On the other hand, incoherent holography records the intensity distribution of the object, allowing a higher signal-to-noise ratio as compared to its coherent counterpart. Currently there are two incoherent digital holographic techniques: optical scanning holography (OSH) and Fresnel incoherent correlation holography (FINCH). In this review, we first explain the principles of OSH and FINCH. We then compare, to some extent, the differences between OSH and FINCH. Finally, some of the recent applications of the two incoherent holographic techniques are reviewed.

Carbon dioxide (CO2) utilization alternatives for manufacturing formic acid (FA) such as electrochemical reduction (ER) or homogeneous catalysis of CO2 and H2 could be efficient options for developing more environmentally-friendly production alternatives to FA fossil-dependant production. However, these alternatives are currently found at different technological readiness levels (TRLs), and some remaining technical challenges need to be overcome to achieve at least carbon-even FA compared to the commercial process, especially ER of CO2, which is still farther from its industrial application. The main technical limitations inherited by FA production by ER are the low FA concentration achieved and the high overpotentials required, which involve high consumptions of energy (ER cell) and steam (distillation). In this study, a comparison in terms of carbon footprints (CF) using the Life Cycle Assessment (LCA) tool was done to evaluate the potential technological challenges assuring the environmental competitiveness of the FA production by ER of CO2. The CF of the FA conventional…

This investigation explored the viability of using open cell polyurethane auxetic foams to augment the conformable layer in a sports helmet and improve its linear impact acceleration attenuation. Foam types were compared by examining the impact severity on an instrumented anthropomorphic headform within a helmet consisting of three layers: a rigid shell, a stiff closed cell foam, and an open cell foam as a conformable layer. Auxetic and conventional foams were interchanged to act as the helmet’s conformable component. Attenuation of linear acceleration was examined by dropping the combined helmet and headform on the front and the side. The helmet with auxetic foam reduced peak linear accelerations (p < 0.05) relative to its conventional counterpart at the highest impact energy in both orientations. Gadd Severity Index reduced by 11% for frontal impacts (38.9 J) and 44% for side impacts (24.3 J). The conformable layer within a helmet can influence the overall impact attenuating properties. The helmet fitted with auxetic foam can attenuate impact severity more than when fitted with…

In this paper, a novel state of health (SOH) estimation method based on partial charge voltage and current data is proposed. The extraction of feature variables, which are energy signal, the Ah-throughput, and the charge duration, is discussed and analyzed. The support vector machine (SVM) with radial basis function (RBF) as kernel function is applied for the SOH estimation. The predictive performance of the SOH by the SVM are performed with full and partial charging data. Experiment results show that the addressed approach enables estimating the SOH accurately for practical application.

Rice husk and cotton straw were used to produce biochar under 300, 400, 500, 600, and 700 °C pyrolysis conditions, and the physicochemical properties of the obtained biochar samples were characterised. The effects of various adsorbent amounts, initial pH, and adsorption time on the Cd2+ adsorption performance were studied. The results showed that, at increasing pyrolysis temperatures, the biochar yield decreased, the ash content increased, the pH transitioned from acidic/neutral to basic/strongly basic, the biochar aromaticity gradually increased, and the biochar structure became more stable. In contrast, the hydrophilicity and polarity decreased, the specific surface area increased, and the number of oxygen-containing functional groups decreased. All these factors resulted in differences in the Cd2+ adsorption by the biochar samples. With increasing adsorbent content, the rate of Cd2+ adsorbed on the biochar gradually increased. The adsorption performance was optimal when the initial solution pH of the rice-husk and cotton-straw biochar samples was 5 and 6, respectively. The…

Convolutional Neural Networks (CNN) have been applied to diverse machine learning tasks for different modalities of raw data in an end-to-end fashion. In the audio domain, a raw waveform-based approach has been explored to directly learn hierarchical characteristics of audio. However, the majority of previous studies have limited their model capacity by taking a frame-level structure similar to short-time Fourier transforms. We previously proposed a CNN architecture which learns representations using sample-level filters beyond typical frame-level input representations. The architecture showed comparable performance to the spectrogram-based CNN model in music auto-tagging. In this paper, we extend the previous work in three ways. First, considering the sample-level model requires much longer training time, we progressively downsample the input signals and examine how it affects the performance. Second, we extend the model using multi-level and multi-scale feature aggregation technique and subsequently conduct transfer learning for several music classification tasks. Finally, we…

Retinal vessels identification and localization aim to separate the different retinal vasculature structure tissues, either wide or narrow ones, from the fundus image background and other retinal anatomical structures such as optic disc, macula, and abnormal lesions. Retinal vessels identification studies are attracting more and more attention in recent years due to non-invasive fundus imaging and the crucial information contained in vasculature structure which is helpful for the detection and diagnosis of a variety of retinal pathologies included but not limited to: Diabetic Retinopathy (DR), glaucoma, hypertension, and Age-related Macular Degeneration (AMD). With the development of almost two decades, the innovative approaches applying computer-aided techniques for segmenting retinal vessels are becoming more and more crucial and coming closer to routine clinical applications. The purpose of this paper is to provide a comprehensive overview for retinal vessels segmentation techniques. Firstly, a brief introduction to retinal fundus photography and imaging modalities of retinal…

Integrated ultra-low-loss waveguides are highly desired for integrated photonics to enable applications that require long delay lines, high-Q resonators, narrow filters, etc. Here we present an ultra-low-loss silicon waveguide on 500 nm thick SOI platform. Meter-scale delay lines, million-Q resonators and tens of picometer bandwidth grating filters are experimentally demonstrated. We design a low-loss low-reflection taper to seamlessly integrate the ultra-low-loss waveguide with standard heterogeneous Si/III-V integrated photonics platform to allow realization of high-performance photonic devices such as ultra-low-noise lasers and optical gyroscopes.

In this study, 29 cold-pressed camelina (Camelina sativa L.) oils, pressed from seeds grown in Poland and purchased directly from local producers, were analyzed. The degree of change in the tested oils’ characteristic hydrolytic and oxidative lipid values was determined. Oxidative stability was determined using the Rancimat and PDSC methods. Fatty acid and phytosterol contents were determined by GC-FID, and tocopherols by HPLC. The analyzed oils were characterized by good, but variable, quality, and met the requirements specified for cold-pressed edible oils. Highly desirable fatty acid composition, low SFA content (about 6%), high α-linolenic acid content (34.7–37.1%), and optimal PUFA n-3 to PUFA n-6 ratio (1.79–2.17) were shown. The high nutritional value of camelina oils was confirmed on the basis of high contents of tocopherols (55.8–76.1 mg/100 g), phytosterols (331–442 mg/100 g), and carotenoids (103–198 mg of β-carotene/kg). The optimal nutritional quality indices were as follows: 0.05–0.07 for the atherogenicity index (AI), and 0.1–0.2 for the thrombogenicity index (TI).…

Air pollutants such as volatile organic compounds (VOCs), nitrogen oxides (NOx), and sulfur dioxide (SO2), as well as water pollutants (e.g., heavy metals phosphorous, fluoride, boron, phenolic compounds, and dyes), are harmful to humans and the environment. Effective control and reduction of their pollution is therefore an important topic for today’s scientists. Fly ash (FA) is a type of industrial waste that can cause multiple environmental problems if discharged into the air. On the other hand, because of its high porosity, large specific surface area, and other unique characteristics, FA can also be used as a low-cost and high efficient adsorbent for treatment of environment pollutants. This paper reviews the effects of FA on treatment of the air and water pollution, including to the current status of global FA utilization, physicochemical properties, principle of adsorption, and the application direction of FA in the future. Since most researchers only studied the adsorption capacity of pure FA or zeolite (synthesized from FA), the research on the fabrication of nanofiber…

Bone char was prepared from bovine bone for the removal of methylene blue from aqueous solution. The effects of particle size, contact time, and adsorption temperature on the removal rate of methylene blue were investigated. It was found that bone char particle size had an insignificant effect. The equilibration time was found at approximately 80 min. The removal rate decreased with an increase in temperature. The intraparticle diffusion was the main rate-limiting step. The experimental data was analyzed by kinetic, isotherm, and thermodynamic equations. The results show that the pseudo-second-order kinetic model and Freundlich, Temkin, and Dubinin–Kaganer–Radushkevich isotherm models are true of the adsorption process. The spontaneous and exothermic ion-exchange adsorption process was certified by the negative values of free energy change and enthalpy change, and 13.29 kJ mol−1 of adsorption energy.

Life Cycle assessments (LCAs) on electric mobility are providing a plethora of diverging results. 44 articles, published from 2008 to 2018 have been investigated in this review, in order to find the extent and the reason behind this deviation. The first hurdle can be found in the goal definition, followed by the modelling choice, as both are generally incomplete and inconsistent. These gaps influence the choices made in the Life Cycle Inventory (LCI) stage, particularly in regards to the selection of the electricity mix. A statistical regression is made with results available in the literature. It emerges that, despite the wide-ranging scopes and the numerous variables present in the assessments, the electricity mix’s carbon intensity can explain 70% of the variability of the results. This encourages a shared framework to drive practitioners in the execution of the assessment and policy makers in the interpretation of the results.

Nitrate pollution in surface water has become an environmental problem of global concern. The effective way for controlling the nitrate pollution of surface water is to identify the pollution source and reduce the input of nitrate. In recent decades, nitrogen (δ15N) and oxygen (δ18O) isotopes of nitrate has been used as an effective approach for identifying the source and fate of nitrate pollution in surface water. However, owing to the complexity of nitrate pollution source and the influence of isotopic fractionation, the application of this method has some limitations. In this work, we systematically discussed the fundamental principle of using nitrogen and oxygen isotopes to trace the nitrate source, the fate identification of nitrate, and the major testing techniques. Subsequently, the applications of nitrogen and oxygen isotopes for source identification of surface water were illustrated. However, there are still significant gaps in the application of the source identification and transformation mechanisms to nitrate and many research questions on these topics need to be…

Recently, fire accidents in buildings have become bigger around the world, and it has become necessary to build an efficient building disaster management system suitable for fires in a Smart City. As building fires increase the number of casualties and property damage, it is necessary to take appropriate action accordingly. There has been an increasing effort to develop such disaster management systems worldwide by applying information communication technology (ICT), and many studies have been conducted in practice. In this paper, an augmented reality (AR)-based Smart Building and Town Disaster Management System is suggested in order to acquire visibility and to grasp occupants in case of fire disasters in buildings. This system provides visualization information and optimal guide for quick initial response by utilizing smart element AR-based disaster management service through linkage of physical virtual domain in the building. Additionally, we show a scenario flow chart of the fire extinguishment process according to the time from the ignition stage to the extinguishment stage in…

Currently gear fault diagnosis is mainly based on vibration signals with a few studies on acoustic signal analysis. However, vibration signal acquisition is limited by its contact measuring while traditional acoustic-based gear fault diagnosis relies heavily on prior knowledge of signal processing techniques and diagnostic expertise. In this paper, a novel deep learning-based gear fault diagnosis method is proposed based on sound signal analysis. By establishing an end-to-end convolutional neural network (CNN), the time and frequency domain signals can be fed into the model as raw signals without feature engineering. Moreover, multi-channel information from different microphones can also be fused by CNN channels without using an extra fusion algorithm. Our experiment results show that our method achieved much better performance on gear fault diagnosis compared with other traditional gear fault diagnosis methods involving feature engineering. A publicly available sound signal dataset for gear fault diagnosis is also released and can be downloaded as instructed in the conclusion…

Rail surface defects seriously affect the safety of railway systems. At present, human inspection and rail vehicle inspection are the main approaches for the detection of rail surface defects. However, there are many shortcomings to these approaches, such as low efficiency, high cost, and so on. This paper presents a novel visual inspection approach based on unmanned aerial vehicle (UAV) images, and focuses on two key issues of UAV-based rail images: image enhancement and defects segmentation. With regards to the first aspect, a novel image enhancement algorithm named Local Weber-like Contrast (LWLC) is proposed to enhance rail images. The rail surface defects and backgrounds can be highlighted and homogenized under various sunlight intensity by LWLC, due to its illuminance independent, local nonlinear and other advantages. With regards to the second, a new threshold segmentation method named gray stretch maximum entropy (GSME) is presented in this paper. The proposed GSME method emphasizes gray stretch and de-noising on UAV-based rail images, and selects an optimal segmentation…

This review introduces several areas of importance in acoustic emission (AE) technology, starting from signal attenuation. Signal loss is a critical issue in any large-scale AE monitoring, but few systematic studies have appeared. Information on damping and attenuation has been gathered from metal, polymer, and composite fields to provide a useful method for AE monitoring. This is followed by discussion on source location, bridge monitoring, sensing and signal processing, and pressure vessels and tanks, then special applications are briefly covered. Here, useful information and valuable sources are identified with short comments indicating their significance. It is hoped that readers note developments in areas outside of their own specialty for possible cross-fertilization.

In this paper, we review liquid-crystal-on-silicon (LCoS) technology and focus on its new application in emerging augmented reality (AR) displays. In the first part, the LCoS working principles of three commonly adopted LC modes—vertical alignment and twist nematic for amplitude modulation, and homogeneous alignment for phase modulation—are introduced and their pros and cons evaluated. In the second part, the fringing field effect is analyzed, and a novel pretilt angle patterning method for suppressing the effect is presented. Moreover, we illustrate how to integrate the LCoS panel in an AR display system. Both currently available intensity modulators and under-developing holographic displays are covered, with special emphases on achieving high image quality, such as a fast response time and high-resolution. The rapidly increasing application of LCoS in AR head-mounted displays and head-up displays is foreseeable.

Convolutional neural networks (CNNs) with log-mel audio representation and CNN-based end-to-end learning have both been used for environmental event sound recognition (ESC). However, log-mel features can be complemented by features learned from the raw audio waveform with an effective fusion method. In this paper, we first propose a novel stacked CNN model with multiple convolutional layers of decreasing filter sizes to improve the performance of CNN models with either log-mel feature input or raw waveform input. These two models are then combined using the Dempster–Shafer (DS) evidence theory to build the ensemble DS-CNN model for ESC. Our experiments over three public datasets showed that our method could achieve much higher performance in environmental sound recognition than other CNN models with the same types of input features. This is achieved by exploiting the complementarity of the model based on log-mel feature input and the model based on learning features directly from raw waveforms.

Novelty detection is a classification problem to identify abnormal patterns; therefore, it is an important task for applications such as fraud detection, fault diagnosis and disease detection. However, when there is no label that indicates normal and abnormal data, it will need expensive domain and professional knowledge, so an unsupervised novelty detection approach will be used. On the other hand, nowadays, using novelty detection on high dimensional data is a big challenge and previous research suggests approaches based on principal component analysis (PCA) and an autoencoder in order to reduce dimensionality. In this paper, we propose deep autoencoders with density based clustering (DAE-DBC); this approach calculates compressed data and error threshold from deep autoencoder model, sending the results to a density based cluster. Points that are not involved in any groups are not considered a novelty; the grouping points will be defined as a novelty group depending on the ratio of the points exceeding the error threshold. We have conducted the experiment by substituting components…

The concentration of dissolved oxygen (DO) in the aeration tank(s) of an activated sludge system is one of the most important process control parameters. The DO concentration in the aeration tank(s) is maintained at a desired level by using a Proportional-Integral-Derivative (PID) controller. Since the traditional PID parameter adjustment is not adaptive, the unknown disturbances make it difficult to adjust the DO concentration rapidly and precisely to maintain at a desired level. A Radial Basis Function (RBF) neural network (NN)-based adaptive PID (RBFNNPID) algorithm is proposed and simulated in this paper for better control of DO in an activated sludge process-based wastewater treatment. The powerful learning and adaptive ability of the RBF neural network makes the adaptive adjustment of the PID parameters to be realized. Hence, when the wastewater quality and quantity fluctuate, adjustments to some parameters online can be made by RBFNNPID algorithm to improve the performance of the controller. The RBFNNPID algorithm is based on the gradient descent method. Simulation results…

Ultraviolet plasmonics (UV) has become an active topic of research due to the new challenges arising in fields such as biosensing, chemistry or spectroscopy. Recent studies have pointed out aluminum, gallium, magnesium and rhodium as promising candidates for plasmonics in the UV range. Aluminum and magnesium present a high oxidation tendency that has a critical effect in their plasmonic performance. Nevertheless, gallium and rhodium have drawn a lot of attention because of their low tendency of oxidation and, at the same time, good plasmonic response in the UV and excellent photocatalytic properties. Here, we present a short overview of the current state of UV plasmonics with the latest findings in the plasmonic response and applications of aluminum, gallium, magnesium and rhodium nanoparticles.

A microbial fuel cell (MFC) is a type of bio-electrochemical system with novel features, such as electricity generation, wastewater treatment, and biosensor applications. In recent years, progressive trends in MFC research on its chemical, electrochemical, and microbiological aspects has resulted in its noticeable applications in the field of sensing. This review was consequently aimed to provide an overview of the most interesting new applications of MFCs in sensors, such as providing the required electrical current and power for remote sensors (energy supply device for sensors) and detection of pollutants, biochemical oxygen demand (BOD), and specific DNA strands by MFCs without an external analytical device (self-powered biosensors). Moreover, in this review, procedures of MFC operation as a power supply for pH, temperature, and organic loading rate (OLR) sensors, and also self-powered biosensors of toxicity, pollutants, and BOD have been discussed.

As a result of industrialization and environmental pollution, increasing importance is being given to eco-friendly materials and technology. In particular, eco-friendly biocomposites using polylactic acid (PLA) have attracted great interest. In this work, fiber-reinforced composites were investigated in order to enhance the mechanical properties and improve the economic efficiency of PLA. Specifically, composite materials using natural fibers, such as kenaf were actively studied. In the utilization of natural fibers, such as kenaf, the treatment method for increasing the bonding force between the fiber and the matrix is very important. In this study, the surface of kenaf was treated using an acetylation technique, and the PLA composite material was prepared using surface-treated kenaf. Changes in fiber properties were observed with acetylation treatment time. The mechanical properties, thermal stability, and water absorption resistance of the acetylated kenaf and PLA composites prepared for each condition were evaluated. Finally, was concluded that acetylation treatment is effective…

In this study, natural clay minerals with green appearance were treated with sulfuric acid. Mass percentage of acid (wt%), temperature (T), contact time (t) and liquid-to-solid mass ratio (R) are used as the prevailing factors that determine the extent of acid-activation. The values of these factors range from 15–50%, 60–90 °C, 1.5–6 h and 4–7, respectively. The study has focused on the structural changes as well as textural characteristics of the clay. Three activated clay samples were prepared under different treatment conditions. The samples were characterized using X-ray powder diffraction (XRD), fourier transform infrared (FTIR), scanning electron microscope (SEM), chemical analysis and N2 adsorption techniques. Characterization of the treated clay minerals exhibited significant structural changes to a greater extent of acid-activation, from being partially crystalline to being amorphous silica. The surface area and total pore volume of clay increased proportionally with the level of acid treatment. The average pore diameter behaved differently. During the strong acid…

The growing demand for electricity is a challenge for the electricity sector as it not only involves the search for new sources of energy, but also the increase of generation capacity of the existing electrical infrastructure and the need to upgrade the existing grid. Therefore, new ways to reduce the consumption of energy are necessary to be implemented. When comparing an average house with an energy efficient house, it is possible to reduce annual energy bills up to 40%. Homeowners and tenants should consider developing an energy conservation plan in their homes. This is both an ecological and economically rational action. With this goal in mind, the need for the energy optimization arises. However, this has to be made by ensuring a fair level of comfort in the household, which in turn spawns a few control challenges. In this paper, the ON/OFF, proportional-integral-derivative (PID) and Model Predictive Control (MPC) control methods of an air conditioning (AC) of a room are compared. The model of the house of this study has a PV domestic generation. The recorded climacteric data…

This paper presents a comprehensive overview of the frequency-domain filtered-x least mean-square (FxLMS) algorithms for active noise control (ANC). The direct use of frequency-domain adaptive filters for ANC results in two kinds of delays, i.e., delay in the signal path and delay in the weight adaptation. The effects of the two kinds of delays on the convergence behavior and stability of the adaptive algorithms are analyzed in this paper. The first delay can violate the so-called causality constraint, which is a major concern for broadband ANC, and the second delay can reduce the upper bound of the step size. The modified filter-x scheme has been employed to remove the delay in the weight adaptation, and several delayless filtering approaches have been presented to remove the delay in the signal path. However, state-of-the-art frequency-domain FxLMS algorithms only remove one kind of delay, and some of these algorithms have a very high peak complexity and hence are impractical for real-time systems. This paper thus proposes a new delayless frequency-domain ANC algorithm that…

The automation and intellectualization of the manufacturing processes in the iron and steel industry needs the strong support of inspection technologies, which play an important role in the field of quality control. At present, visual inspection technology based on image processing has an absolute advantage because of its intuitive nature, convenience, and efficiency. A major breakthrough in this field can be achieved if sufficient research regarding visual inspection technologies is undertaken. Therefore, the purpose of this article is to study the latest developments in steel inspection relating to the detected object, system hardware, and system software, existing problems of current inspection technologies, and future research directions. The paper mainly focuses on the research status and trends of inspection technology. The network framework based on deep learning provides space for the development of end-to-end mode inspection technology, which would greatly promote the implementation of intelligent manufacturing.

Iron nanoparticles (FeNP) were synthesized using Acacia nilotica seedless pods extract. The synthesized FeNP were characterized by Fourier transform infrared (FTIR), UV/Vis spectroscopy, dynamic light scattering (DLS), electron microscopy (TEM), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and X-ray diffraction (XRD). The XRD pattern confirmed the synthesis of crystalline phase of α-Fe2O3. EDS spectroscopy showed the presence of elemental iron and oxygen, indicating that the nanoparticles are essentially present in oxide form. UV absorption in the range of 450–550 nm confirmed the formation of FeNP. DLS indicated an average FeNP particle size of 229 nm. The synthesized FeNP was tested for adsorption and oxidation degradation of methyl orange (MO) under different conditions and found to be effective in both degradation and adsorption processes. Furthermore, the synthesized FeNP has the potential to terminate the pathogenicity of several human opportunistic pathogens; belongs to gram-negative and gram-positive bacteria and one species of Candida as well.

In this paper, a Firefly algorithm is proposed for identification and comparative study of five, seven and eight parameters of a single and double diode solar cell and photovoltaic module under different solar irradiation and temperature. Further, a metaheuristic algorithm is proposed in order to predict the electrical parameters of three different solar cell technologies. The first is a commercial RTC mono-crystalline silicon solar cell with single and double diodes at 33 °C and 1000 W/m2. The second, is a flexible hydrogenated amorphous silicon a-Si:H solar cell single diode. The third is a commercial photovoltaic module (Photowatt-PWP 201) in which 36 polycrystalline silicon cells are connected in series, single diode, at 25 °C and 1000 W/m2 from experimental current-voltage. The proposed constrained objective function is adapted to minimize the absolute errors between experimental and predicted values of voltage and current in two zones. Finally, for performance validation, the parameters obtained through the Firefly algorithm are compared with recent research papers reporting…

Inspired by the process of migration and reproduction of flora, this paper proposes a novel artificial flora (AF) algorithm. This algorithm can be used to solve some complex, non-linear, discrete optimization problems. Although a plant cannot move, it can spread seeds within a certain range to let offspring to find the most suitable environment. The stochastic process is easy to copy, and the spreading space is vast; therefore, it is suitable for applying in intelligent optimization algorithm. First, the algorithm randomly generates the original plant, including its position and the propagation distance. Then, the position and the propagation distance of the original plant as parameters are substituted in the propagation function to generate offspring plants. Finally, the optimal offspring is selected as a new original plant through the selection function. The previous original plant becomes the former plant. The iteration continues until we find out optimal solution. In this paper, six classical evaluation functions are used as the benchmark functions. The simulation results show…

Quartz-enhanced photoacoustic spectroscopy (QEPAS) is an improvement of the conventional microphone-based photoacoustic spectroscopy. In the QEPAS technique, a commercially available millimeter-sized piezoelectric element quartz tuning fork (QTF) is used as an acoustic wave transducer. With the merits of high sensitivity and selectivity, low cost, compactness, and a large dynamic range, QEPAS sensors have been applied widely in gas detection. In this review, recent developments in state-of-the-art QEPAS-based trace gas sensing technique over the past five years are summarized and discussed. The prospect of QEPAS-based gas sensing is also presented.

The Johnson–Holmquist-II(JH-2) model is introduced as the constitutive model for rock materials in tunnel smooth blasting. However, complicated and/or high-cost experiments need to be carried out to obtain the parameters of the JH-2 constitutive model. This study chooses Barre granite as an example to propose a quick and convenient determination method for the parameters of the JH-2 model using a series of computational and extrapolated methods. The validity of the parameters is verified via comparing the results of 3D numerical simulations with laboratory blast-loading experiments. Subsequently, the verified parameter determination method, together with the JH-2 damage constitutive model, is applied in the numerical simulation of smooth blasting in Zigaojian tunnel, Hangzhou–Huangshan high-speed railway. The overbreak/underbreak induced by rock blasting and joints/discontinuities is well estimated through comparing the damage contours resulting from the numerical study with the tunnel profiles measured from the tunnel site. The peak particle velocities (PPVs) of the near field are…

Microextrusion-based bioprinting within a support bath material is an emerging additive manufacturing paradigm for complex three-dimensional (3D) tissue construct fabrication. Although a support bath medium enables arbitrary in-process geometries to be printed, a significant challenge lies in preserving the shape fidelity upon the extraction of the support bath material. Based on the bioprinting in a support bath paradigm, this paper advances quantitative analyses to systematically determine the printability of cell-laden liquid hydrogel precursors towards filament-based tissue constructs. First, a yield stress nanoclay material is judiciously selected as the support bath medium owing to its insensitivity to temperature and ionic variations that are considered in the context of the current gelatin-alginate bio-ink material formulation. Furthermore, phenomenological observations for the rheology-mediated print outcomes enable the compositions for the bio-ink material (10% gelatin, 3% alginate), in tandem with the support bath medium (4% nanoclay, 0.5% CaCl2), to be identified. To…

Human-robot collaboration (HRC) is a complex procedure in manufacturing due to the problems posed by compatibility and operational safety among humans and robots, and by task definitions among them in a collaborative order. In this paper, the research results of the human-robot collaboration study for the case of an automotive brake disc assembly is presented. The analytic hierarchy process (AHP) is proposed as a decision-making method for the human-robot collaboration system, and detailed hierarchical task analysis (HTA) is applied to allocate operational tasks to humans and robots, thus reducing the chance of duty interference. Additionally, a virtual environment software (Tecnomatix Process Simulate, version 11.1,80, Siemens, Munich, BY, Germany, 2012) is used to model the assembly workstation, providing an opportunity to evaluate the feasibility of the process through different scenarios. Finally, an experimental test is conducted to evaluate the performance of the assembly procedure. This research proves that, although human-robot collaboration increases the total process time…

In this paper, discrete orthonormal Stockwell transform (DOST)-based vibration imaging is proposed as a preprocessing step for supporting load and rotational speed invariant scenarios for signals of various health conditions. For any health condition, features can easily be extracted from its generated health pattern. To automate the feature selection process, a convolutional neural network (CNN)-based transfer learning (TL) approach for diagnosis has also been introduced. Transfer learning allows an established model to use feature knowledge obtained under one set of working conditions through hidden layers to diagnose faults that occur under other working conditions. The network learns from the massive source dataset, and that knowledge is applied to the target data to identify faults. Using the bearing dataset of Case Western Reserve University, the proposed approach yields an average 99.8% classification accuracy and, specifically, 99.99% for healthy condition (HC), 99.95% for inner race fault (IRF), 99.96% for ball fault (BF), 99.68% for outer race fault for 12 o’clock sensor…

The objective of this research is introduce a new machine learning ensemble approach that is a hybridization of Bagging ensemble (BE) and Logistic Model Trees (LMTree), named as BE-LMtree, for improving the performance of the landslide susceptibility model. The LMTree is a relatively new machine learning algorithm that was rarely explored for landslide study, whereas BE is an ensemble framework that has proven highly efficient for landslide modeling. Upper Reaches Area of Red River Basin (URRB) in Northwest region of Viet Nam was employed as a case study. For this work, a GIS database for the URRB area has been established, which contains a total of 255 landslide polygons and eight predisposing factors i.e., slope, aspect, elevation, land cover, soil type, lithology, distance to fault, and distance to river. The database was then used to construct and validate the proposed BE-LMTree model. Quality of the final BE-LMTree model was checked using confusion matrix and a set of statistical measures. The result showed that the performance of the proposed BE-LMTree model is high with the…