Showing papers in "Applied Sciences in 2018"

Mini-LED and Micro-LED: Promising Candidates for the Next Generation Display Technology

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

Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks

Abstract: 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 convolutional neural network (CNN). Metallic defects under various conditions can be successfully detected using an industrial dataset. The experimental results demonstrate that this method meets the robustness and accuracy requirements for metallic defect detection. Meanwhile, it can also be extended to other detection applications.

Swarm Intelligence Algorithms for Feature Selection: A Review

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

Small Object Detection in Optical Remote Sensing Images via Modified Faster R-CNN

Abstract: 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 margin on detecting small remote sensing objects.

A Review of the Synthesis and Applications of Polymer–Nanoclay Composites

Abstract: 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.

Visualization and Interpretation of Convolutional Neural Network Predictions in Detecting Pneumonia in Pediatric Chest Radiographs.

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

A State-of-the-Art Review of Nanoparticles Application in Petroleum with a Focus on Enhanced Oil Recovery

Abstract: 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 and reservoir characterization, drilling and completion, production and stimulation, and refinery. Thereafter, this paper focuses on the application of nanoparticles in EOR. The different types of nanomaterials, e.g., silica, aluminum oxides, iron oxide, nickel oxide, titanium oxide, zinc oxide, zirconium oxide, polymers, and carbon nanotubes that have been studied in EOR are discussed with respect to their properties, their performance, advantages, and disadvantages. We then elaborate upon the parameters that will affect the performance of nanoparticles in EOR, and guidelines for promising recovery factors are emphasized. The mechanisms of the nanoparticles in the EOR processes are then underlined, such as wettability alteration, interfacial tension reduction, disjoining pressure, and viscosity control. The objective of this review is to present a wide range of knowledge and expertise related to the nanotechnology application in the petroleum industry in general, and the EOR process in particular. The challenges and future research directions for nano-EOR are pinpointed.

Research on a Surface Defect Detection Algorithm Based on MobileNet-SSD

Abstract: 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.

Overview of Lithium-Ion battery modeling methods for state-of-charge estimation in electrical vehicles

Abstract: 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.

Energy Management Strategy for a Hybrid Electric Vehicle Based on Deep Reinforcement Learning

Abstract: 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 proved to be effective. The proposed method ensures the optimality, as well as real-time applicability, in HEVs.

Preparation Techniques of TiO2 Nanofluids and Challenges: A Review

Abstract: 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 stable and cost-effective nanofluids with enhanced thermophysical properties. This review focuses on the different synthesis techniques and important physical properties (thermal conductivity and viscosity) that need to be considered very carefully during the preparation of TiO2 nanofluids for desired applications.

Classification of Heart Sound Signal Using Multiple Features

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

Graphene Nanoplatelets-Based Advanced Materials and Recent Progress in Sustainable Applications

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

Review of Auxetic Materials for Sports Applications: Expanding Options in Comfort and Protection

Abstract: 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 characteristics, limitations and commercial prospects are discussed, leading to a consideration of possible further work required to realise potential uses (such as in personal protective equipment and highly conformable garments).

Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study

Abstract: 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 machine learning model are chained together on the distributed ledger. By integrating federated learning with blockchain technology, our solution supports the auditing of machine learning models without the necessity to centralize the training data. Experiments with a realistic intrusion detection use case and an autoencoder for anomaly detection illustrate that the increased complexity caused by blockchain technology has a limited performance impact on the federated learning, varying between 5 and 15%, while providing full transparency over the distributed training process of the neural network. Furthermore, our blockchain-based federated learning solution can be generalized and applied to more sophisticated neural network architectures and other use cases.

Frequency Selective Surfaces: A Review

Abstract: 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 also focus on theoretical breakthroughs with fabrication techniques, experimental verifications of design examples as well as prospects and challenges, especially in the microwave regime. We emphasize their significant performance parameters, particularly focusing on how advancement in this field could facilitate innovation in advanced electromagnetics.

Variety Identification of Single Rice Seed Using Hyperspectral Imaging Combined with Convolutional Neural Network

Abstract: 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 with promising results. More varieties of rice need to be studied in future research to extend the use of CNNs in spectral data analysis.

A Review of AlGaN-Based Deep-Ultraviolet Light-Emitting Diodes on Sapphire

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

Cu-Doped TiO2: Visible Light Assisted Photocatalytic Antimicrobial Activity

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

Bimetallic Nanoparticles: Enhanced Magnetic and Optical Properties for Emerging Biological Applications

Abstract: 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. For example, the incorporation of a second metal into a nanoparticle structure influences and can potentially enhance the optical/plasmonic and magnetic properties of the material. This review focuses on the enhanced optical/plasmonic and magnetic properties offered by bimetallic nanoparticles and their corresponding impact on biological applications. In this review, 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/plasmonic and magnetic properties.

Polydimethylsiloxane (PDMS)-Based Flexible Resistive Strain Sensors for Wearable Applications

Abstract: 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 intelligent electronic systems has arrived.

Enabling Technologies for Operator 4.0: A Survey

Abstract: 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 40 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

Battery Management System Hardware Concepts: An Overview

Abstract: 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.

Foliar application of copper nanoparticles increases the fruit quality and the content of bioactive compounds in tomatoes

Abstract: 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 accumulation of bioactive compounds in tomato fruits.

Research Progress of Gas Sensor Based on Graphene and Its Derivatives: A Review

Abstract: 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.

Light Scattering by a Dielectric Sphere: Perspectives on the Mie Resonances

Abstract: 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 size-induced aspects, such as the dynamic depolarization and the radiation reaction on a small sphere are exposed through the newly introduced Mie–Pade approximative perspective. The applicability of this approximation is further expanded including the second type of resonances, i.e., the dielectric resonances. For this type of resonances, the Mie–Pade approximation reveals the main character of the two different cases of resonances of either magnetic or electric origin. A unified picture is therefore described encompassing both plasmonic and dielectric resonances, and the resonant conditions of all three different types are extracted as functions of the permittivity and the size of the sphere. Lastly, the directional scattering behavior of the first two dielectric resonances is exposed in a simple manner, namely the Kerker conditions for maximum forward and backscattering between the first magnetic and electric dipole contributions of a dielectric sphere. The presented results address several prominent functional features, aiming at readers with either theoretical or applied interest for the scattering aspects of a resonant sphere.

Landslide Susceptibility Modeling Based on GIS and Novel Bagging-Based Kernel Logistic Regression

Abstract: 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 overall confirmed that a combination of the meta model with a decision tree classifier based on a functional algorithm can decrease the over-fitting and variance problems of data, which could enhance the prediction power of the landslide model. The resultant susceptibility maps could be useful for hazard mitigation in the study area and other similar landslide-prone areas.

Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest

Abstract: 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 Health-care Monitoring System, so that the input risk-factors from end-user android application can be stored and analyzed in a secure remote server. The prediction result from the proposed HPM can be accessed by users through an Android application; thus, it is expected to provide an effective way to find the risk of diabetes and hypertension at the initial stage.

Core@shell Nanoparticles: Greener Synthesis Using Natural Plant Products

Abstract: 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 review summarizes the known strategies for the greener production of core-shell nanoparticles using plants extract or their derivatives and highlights their salient attributes, such as low costs, the lack of dependence on the use of any toxic materials, and the environmental friendliness for the sustainable assembly of stabile nanostructures.

Opportunities for Industry 4.0 to Support Remanufacturing

Abstract: 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.