In order to improve the electrochemical properties of poly(ethylene oxide), a well-known-solid polymer electrolyte, by adding zwitterion molecules, the synthesis of a new zwitterion (ZN) having imidazolium cation and sulfonyl(trifluoromethane sulfonyl)imide anion is investigated. The addition of different amounts of ZN to the mixture of lithium bis(trifluoromethane sulfonyl)imide (LiTFSI) and poly(ethylene glycol)dimethyl ether (PEGDM) of 1000 g mol⁻¹ does not significantly affect the transition temperature of PEGDM but causes a slight decrease in ionic conductivity of the electrolyte mixtures. However, even with the presence of only 0.05 mole fraction of ZN, the anodic stability of LiTFSI/PEGDM based electrolytes is improved to over 4.5 V vs. Li⁺/Li at 25 °C. This makes the new synthesized zwitterion a promising electrolyte’s additive for high voltage batteries. Full article

Generating strategies and techniques to feed the increasing world population is a significant challenge under climate change effects such as drought. Rural areas are especially sensitive to such effects as they are unable to overcome the lack of water with new agricultural production techniques. In developing countries, rural communities commonly do not have access to high-quality electricity supplies. In some cases, these communities lack electricity in their homes, which affects the opportunity to improve food production through the incorporation of new technologies. This work proposes an integrated optimizer based on model predictive control (MPC) that combines a water management system, which handles the medium-term water requirements for irrigation, with an energy management system, which handles short-term energy requirements. The proposed approach is based on predictive phenomenological models of evapotranspiration and electricity consumption considering climate conditions such as temperature, precipitation, solar radiation, and wind speed, and aims to optimize the use of energy and water and the relative yields of crops. The integrated energy–water management system (EWMS) improves water resource sustainability according to energy availability/costs and water use requirements. Simulation results using real data from a rural community in southern Chile show that the integrated EWMS based on an MPC optimizer successfully determines and satisfies the water and energy requirements under aquifer sustainability constraints. Full article

The accentuated dynamics of the real estate markets of the last 20 years, determined that a large part of the territories in the immediate vicinity of the big urban centers, to change their category of land use, in an accelerated rhythm. Most of the time, the land use changes according to the market requirements, the predominantly agricultural lands being occupied by constructions with residential or industrial functions. Identifying these changes is a difficult task due to the heterogeneity of spatial databases that come from different real estate development projects, so determining and implementing new methods to track land changes are currently highly required. This paper presents a methodologically innovative index-based approach for the rapid mapping of built-up areas, using Landsat-5, Landsat-7, and Landsat-8 satellite imagery. The approach described in this study differs from other conventional methods by the way the analysis was performed and also by the thematic indices used in the processes of built-up area delineation. The method, structured in a complex model, based on Remote Sensing and GIS techniques, can be divided into three distinct phases. The first stage is related to the pre-processing of the remote sensing data. The second stage involves the calculation of the normalized difference vegetation index (NDVI), the modified normalized difference water index (MNDWI), and the bare soil index (BI) correlated with the extraction of all areas not covered by vegetation; respectively, the elimination from the result of all areas covered by water, bare land, or uncultivated arable land. The result of this stage is represented by a distinct thematic layer that contains only built-up areas and other associated territories. The last step of the model is represented by the validation of the results, which was performed based on statistical methods and also by direct comparison with field reality, obtaining a validation coefficient which is generally above 85% for any of the methods used. The validation process shows us that by applying this method, the fast mapping of the built-up areas is significantly enhanced and the model is suitable to be implemented on a larger scale in any practical and theoretical application that aims at the rapid mapping of the built-up areas and their evolutionary modeling. Full article

Canola (Brassica napus L.) is cultivated worldwide and utilized as a vegetable oil, biodiesel, and livestock feed. It is also a major living modified (LM) crop alongside corn, soybean, and cotton. Many canola events have been authorized for food, feed, and processing use in South Korea. Concerns about the unintentional release of LM canola into the natural environment have increased environmental monitoring and post-management of living modified organisms (LMOs) is on the rise. The Ministry of Environment (MOE) and the National Institute of Ecology (NIE) conducted an environmental LMO monitoring and post-management project for LM canola from 2014 to 2017. The number of suspicious LM samples gradually increased each year. In this study, a multiplex PCR method was established to detect seven single LM canola events (Topas 19/2, Rf3, Dp-73496-4, Ms8, GT73, Mon88032, and T45) to cover 14 approved LM canola events. This method was utilized to detect 22 LMs out of 260 suspicious canola samples. Thus, this new method is more efficient in terms of time and cost than conventional PCR methods for the identification and monitoring of LMOs. Full article

In order to further promote the development of functional pavement technology, a Special Issue of “Achievements and Prospects of Functional Pavement” has been proposed by a group of guest editors. To reach this objective, articles included in this Special Issue are related to different aspects of functional pavement, including green roads to decrease carbon emission, noise, and pollution, safety pavement to increase skid resistance by water drainage and snow removal, intelligent roads for monitoring, power generation, temperature control and management, and durable roads to increase service life with new theory, new design methods, and prediction models, as highlighted in this editorial. Full article

A modular multilevel converter (MMC) is an advanced voltage source converter applicable to a wide range of medium and high-voltage applications. It has competitive advantages such as quality output performance, high modularity, simple scalability, and low voltage and current rating demand for the power switches. Remarkable studies have been carried out regarding its topology, control, and operation. The main purpose of this review is to present the current state of the art of the MMC technology and to offer a better understanding of its operation and control for stationary applications. In this study, the MMC configuration is presented regarding its conventional and advanced submodule (SM) and overall topologies. The mathematical modeling, output voltage, and current control under different grid conditions, submodule balancing control, circulating current control, and modulation methods are discussed to provide the state of the MMC technology. The challenges linked to the MMC are associated with submodule balancing control, circulating current control, control complexity, and transient performance. Advanced nonlinear and predictable control strategies are expected to improve the MMC control and performance in comparison with conventional control methods. Finally, the power losses associated with the advanced wide bandgap (WBG) power devices (such as SiC, GaN) are explored by using different modulation schemes and switching frequencies. The results indicate that although the phase-shifted carrier-based pulse width modulation (PSC-PWM) has higher power losses, it outputs a better quality voltage with lower total harmonic distortion (THD) in comparison with phase-disposition pulse width modulation (PD-PWM) and sampled average modulation pulse width modulation (SAM-PWM). In addition, WBG switches such as silicon carbide (SiC) and gallium nitride (GaN) devices have lower power losses and higher efficiency, especially at high switching frequency in the MMC applications. Full article

Diabetic Retinopathy and Diabetic Macular Edema (DME) represent one of the main causes of blindness in developed countries. They are characterized by fluid deposits in the retinal layers, causing a progressive vision loss over the time. The clinical literature defines three DME types according to the texture and disposition of the fluid accumulations: Cystoid Macular Edema (CME), Diffuse Retinal Thickening (DRT) and Serous Retinal Detachment (SRD). Detecting each one is essential as, depending on their presence, the expert will decide on the adequate treatment of the pathology. In this work, we propose a robust detection and visualization methodology based on the analysis of independent image regions. We study a complete and heterogeneous library of 375 texture and intensity features in a dataset of 356 labeled images from two of the most used capture devices in the clinical domain: a CIRRUS^TM HD-OCT 500 Carl Zeiss Meditec and 179 OCT images from a modular HRA + OCT SPECTRALIS^® from Heidelberg Engineering, Inc. We extracted 33,810 samples for each type of DME for the feature analysis and incremental training of four different classifier paradigms. This way, we achieved an 84.04% average accuracy for CME, 78.44% average accuracy for DRT and 95.40% average accuracy for SRD. These models are used to generate an intuitive visualization of the fluid regions. We use an image sampling and voting strategy, resulting in a system capable of detecting and characterizing the three types of DME presenting them in an intuitive and repeatable way. Full article

In the engine room and stern adjacent to the main excitation force of the ship, there are many fuel and fresh water tank structures required for ship operation which are always exposed to vibrations. Therefore, it is necessary to review the anti-vibration design to prevent such vibration problems at the design stage, and for this reason, although commercial finite element analysis (FEA) programs are widely used, approximate analysis methods are still developed and used because of the limited time until modeling and analysis results are obtained. Until now, only known elastic boundary conditions have been used in many studies using approximate analysis methods used to calculate natural vibrations for beams or plates. However, many local structures, such as tank edges and equipment foundations, consist of connected structures and it is very difficult to find suitable elastic boundary conditions. Vibration analysis of many local structures in ships, such as tanks and supports for equipment, can be simplified by breaking them up into smaller subsystems which are related through geometrical conditions and natural conditions at junctions. In this study, polynomials for simple support and fixed support were proposed to represent each subsystem and a polynomial to be applied to the plate constituting the tank was proposed by combining them. Until now, there have been many studies on single beams or single plates for approximate analysis. However, there was no research on this to the extent that no reference material could be found for the connected structure. The proposed method has been applied to tanks which are bounded by bulkhead and a deck. The results of this study shows good agreements with those obtained by the FEA Software (Patran/Nastran). Full article

In the development of autonomous vehicles, the design of real-time motion-planning is a crucial problem. The computation of the vehicle trajectory requires the consideration of safety, dynamic and comfort aspects. Moreover, the prediction of the vehicle motion in the surroundings and the real-time planning of the autonomous vehicle trajectory can be complex tasks. The goal of this paper is to present low-complexity motion-planning for overtaking scenarios in parallel traffic. The developed method is based on the generation of a graph, which contains feasible vehicle trajectories. The reduction of the complexity in the real-time computation is achieved through the reduction of the graph with clustering. In the motion-planning algorithm, the predicted motion of the surrounding vehicles is taken into consideration. The prediction algorithm is based on density functions of the surrounding vehicle motion, which are developed through real measurements. The resulted motion-planning algorithm is able to guarantee a safe and comfortable trajectory for the autonomous vehicle. The effectiveness of the method is illustrated through simulation examples using a high-fidelity vehicle dynamic simulator. Full article

An integrated method for fault detection of bearing using wavelet packet energy (WPE) and fast kurtogram (FK) is proposed. The method consists of three stages. Firstly, several commonly used wavelet functions were compared to select the appropriate wavelet function for the application of WPE. Then the analyzed signal is decomposed using WPE and the energy of each decomposed signal is calculated and selected for signal reconstruction. Secondly, the reconstructed signal is analyzed by FK to select the best central frequency and bandwidth for the band-pass filter. Finally, the filtered signal is processed using the squared envelope frequency spectrum and compared with the theoretical fault characteristic frequency for fault feature extraction. The procedure and performance of the proposed approach are illustrated and estimated by the simulation analysis, proving that the proposed method can effectively extract the weak transients. Moreover, the analysis results of gearbox bearing and rolling bearing cases show that the proposed method can provide more accurate fault features compared with the individual FK method. Full article

The earlier activation of the catalytic converters in internal combustion engines is becoming highly challenging due to the reduction in exhaust gas temperature caused by the application of ${\mathrm{CO}}_2$ reduction technologies. In this context, the use of pre-turbine catalysts arises as a potential way to increase the conversion efficiency of the exhaust aftertreatment system. In this work, a small-sized oxidation catalyst consisting of a honeycomb thin-wall metallic substrate was placed upstream of the turbine to benefit from the higher temperature and pressure prior to the turbine expansion. The change in engine performance and emissions in comparison to the baseline configuration are analyzed under driving conditions. As an individual element, the pre-turbine catalyst contributed positively with a relevant increase in the overall CO and HC conversion efficiency. However, its placement produced secondary effects on the engine and baseline aftertreatment response. Although small-sized monoliths are advantageous to minimize the thermal inertia impact on the turbocharger lag, the catalyst cross-section is in trade-off with the additional pressure drop that the monolith causes. As a result, the higher exhaust manifold pressure in pre-turbine pre-catalyst configuration caused a fuel consumption increase higher than 3% while the engine-out CO and HC emissions did around 50%. These increments were not completely offset despite the high pre-turbine pre-catalyst conversion efficiency (>40%) because the partial abatement of the emissions in this device conditioned the performance of the close-coupled oxidation catalyst. Full article

In post-natal life, stem cells contribute to the preservation of many tissues. In adults, stem cells remain localized, in particular, specialized microanatomical areas named as niches, which are crucial in the control of stem cell quiescence and activity through the production of many regulatory signals. Bone physiologically includes both the endosteal niche and the vascular niche, which are involved in the attraction, retention and release of the residing stem cells during the formation of new vessels as well as in the control of the differentiation of bone-forming osteoblasts and bone-resorbing osteoclasts. In tumors, cancer cells are able to take control of the niches to support all the stages of the tumorigenesis, transforming them in the so-called pre-metastatic and metastatic niches. Hence, there is emerging importance of the interactions between cancer cells, bone cells and niches in driving metastatic progression. This review article summarizes the literature data concerning the role of bone vascular and endosteal niches in the regulation of bone metastasis, focusing on their cellular and molecular interactions and the potential therapeutic approaches. Full article

Data-driven machine learning techniques play an important role in fault diagnosis, safety, and maintenance of the industrial robotic manipulator. However, these methods require data that, more often that not, are hard to obtain, especially data collected from fault condition states and, without enough and appropriated (balanced) data, no acceptable performance should be expected. Generative adversarial networks (GAN) are receiving a significant interest, especially in the image analysis field due to their outstanding generative capabilities. This paper investigates whether or not GAN can be used as an oversampling tool to compensate for an unbalanced data set in an industrial manipulator fault diagnosis task. A comprehensive empirical analysis is performed taking into account six different scenarios for mitigating the unbalanced data, including classical under and oversampling (SMOTE) methods. In all of these, a wavelet packet transform is used for feature generation while a random forest is used for fault classification. Aspects such as loss functions, learning curves, random input distributions, data shuffling, and initial conditions were also considered. A non-parametric statistical test of hypotheses reveals that all GAN based fault-diagnosis outperforms both under and oversampling classical methods while, within GAN based methods, an average accuracy difference as high as 1.68% can be achieved. Full article

The increasing portability of physical manuscripts to the digital environment makes it common for systems to offer automatic mechanisms for offline Handwritten Text Recognition (HTR). However, several scenarios and writing variations bring challenges in recognition accuracy, and, to minimize this problem, optical models can be used with language models to assist in decoding text. Thus, with the aim of improving results, dictionaries of characters and words are generated from the dataset and linguistic restrictions are created in the recognition process. In this way, this work proposes the use of spelling correction techniques for text post-processing to achieve better results and eliminate the linguistic dependence between the optical model and the decoding stage. In addition, an encoder–decoder neural network architecture in conjunction with a training methodology are developed and presented to achieve the goal of spelling correction. To demonstrate the effectiveness of this new approach, we conducted an experiment on five datasets of text lines, widely known in the field of HTR, three state-of-the-art Optical Models for text recognition and eight spelling correction techniques, among traditional statistics and current approaches of neural networks in the field of Natural Language Processing (NLP). Finally, our proposed spelling correction model is analyzed statistically through HTR system metrics, reaching an average sentence correction of 54% higher than the state-of-the-art method of decoding in the tested datasets. Full article

The objective of optimal sensor placement in a dynamic system is to obtain a sensor layout that provides as much information as possible for structural health monitoring (SHM). Whereas most studies use only one modal assurance criterion for SHM, this work considers two additional metrics, signal redundancy and noise ratio, combining into three optimization objectives: Linear independence of mode shapes, dynamic information redundancy, and vibration response signal strength. A modified multiobjective evolutionary algorithm was combined with particle swarm optimization to explore the optimal solution sets. In the final determination, a multiobjective decision-making (MODM) strategy based on distance measurement was used to optimize the aforementioned objectives. We applied it to a reduced finite-element beam model of a reference building and compared it with other selection methods. The results indicated that MODM suitably balanced the objective functions and outperformed the compared methods. We further constructed a three-story frame structure for experimentally validating the effectiveness of the proposed algorithm. The results indicated that complete structural modal information can be effectively obtained by applying the MODM approach to identify sensor locations. Full article