bluuebunny/crossref_metadata_2025 huggingface dataset:
Caffeic Acid Phenethyl Ester Assisted by Reversible Electroporation—In Vitro Study on Human Melanoma Cells
Authors: Choromanska Anna , Saczko Jolanta , Kulbacka Julita
Published: May 2020

Melanoma is one of the most serious skin cancers. The incidence of this malignant skin lesion is continuing to increase worldwide. Melanoma is resistant to chemotherapeutic drugs and highly metastatic. Surgical resection can only be used to treat melanoma in the early stages, while chemotherapy is limited due to melanoma multi-drug resistance. The overexpression of glutathione S-transferase (GST) may have a critical role in this resistance. Caffeic acid phenethyl ester (CAPE) is a natural phenolic compound, which occurs in many plants. Previous studies demonstrated that CAPE suppresses the growth of melanoma cells and induces reactive oxygen species generation. It is also known that bioactivation of CAPE to its corresponding quinone metabolite by tyrosinase would lead to GST inhibition and selective melanoma cell death. We investigated the biochemical toxicity of CAPE in combination with microsecond electropermeabilization in two human melanoma cell lines. Our results indicate that electroporation of melanoma cells in the presence of CAPE induced high oxidative stress, which correlates with high cytotoxicity. Moreover, it can disrupt the metabolism of cancer cells by inducing apoptotic cell death. Electroporation of melanoma cells may be an efficient CAPE delivery system, enabling the application of this compound, while reducing its dose and exposure time.
Injectable SN-38-embedded Polymeric Microparticles Promote Antitumor Efficacy against Malignant Glioma in an Animal Model
Authors: Tseng Yuan-Yun , Yang Tao-Chieh , Chen Shu-Mei , Yang Shun-Tai , Tang Ya-Ling , Liu Shih-Jung
Published: May 2020

Malignant glioma (MG) is extremely aggressive and highly resistant to chemotherapeutic agents. Using electrospraying, the potent chemotherapeutic agent 7-ethyl-10-hydroxycamptothecia (SN-38) was embedded into 50:50 biodegradable poly[(d,l)-lactide-co-glycolide] (PLGA) microparticles (SMPs). The SMPs were stereotactically injected into the brain parenchyma of healthy rats and intratumorally injected into F98 glioma-bearing rats for estimating the pharmacodynamics and therapeutic efficacy. SN-38 was rapidly released after injection and its local (brain tissue) concentration remained much higher than that in the blood for more than 8 weeks. Glioma-bearing rats were divided into three groups—group A (n = 13; stereotactically injected pure PLGA microparticles), group B (n = 12; stereotactically injected Gliadel wafer and oral temozolomide), and group C (n = 13; stereotactic and intratumoral introduction of SMPs). The SMPs exhibited significant therapeutic efficacy, with prolonged survival, retarded tumor growth, and attenuated malignancy. The experimental results demonstrated that SMPs provide an effective and potential strategy for the treatment of MG.
Structural Adaptation and Physiological Mechanisms in the Leaves of Anthyllis vulneraria L. from Metallicolous and Non-Metallicolous Populations
Authors: Sujkowska-Rybkowska Marzena , Muszyńska Ewa , Labudda Mateusz
Published: May 2020

Calamine wastes highly contaminated with trace metals (TMs) are spontaneously inhabited by a legume plant Anthyllis vulneraria L. This study determined an adaptation strategy of metallicolous (M) A. vulneraria and compared it with that of the non-metallicolous (NM) ecotype. We hypothesized that TMs may lead to (i) leaf apoplast modifications and (ii) changes in the antioxidant machinery efficiency that facilitate plant growth under severe contamination. To verify our hypothesis, we implemented immunolabelling, transmission electron microscopy and biochemical measurements. NM leaves were larger and thicker compared to the M ecotype. Microscopic analysis of M leaves showed a lack of dysfunctions in mesophyll cells exposed to TMs. However, changes in apoplast composition and thickening of the mesophyll and epidermal cell walls in these plants were observed. Thick walls were abundant in xyloglucan, pectins, arabinan, arabinogalactan protein and extensin. The tested ecotypes differed also in their physiological responses. The metallicolous ecotype featured greater accumulation of photosynthetic pigments, enhanced activity of superoxide dismutase and increased content of specific phenol groups in comparison with the NM one. Despite this, radical scavenging activity at the level of 20% was similar in M and NM ecotypes, which may implicate effective reduction of oxidative stress in M plants. In summary, our results confirmed hypotheses and suggest that TMs induced cell wall modifications of leaves, which may play a role in metal stress avoidance in Anthyllis species. However, when TMs reach the protoplast, activation of antioxidant machinery may significantly strengthen the status of plants naturally growing in TM-polluted environment.
Normal Cyclic Variation in CO2 Concentration in Indoor Chambers Decreases Leaf Gas Exchange and Plant Growth
Authors: Bunce James
Published: May 2020

Attempts to identify crop genetic material with larger growth stimulation at projected elevated atmospheric CO2 concentrations are becoming more common. The probability of reductions in photosynthesis and yield caused by short-term variation in CO2 concentration within elevated CO2 treatments in the free-air CO2 enrichment plots raises the question of whether similar effects occur in glasshouse or indoor chamber experiments. These experiments were designed to test whether even the normal, modest, cyclic variation in CO2 concentration typical of indoor exposure systems have persistent impacts on photosynthesis and growth, and to explore mechanisms underlying the responses observed. Wheat, cotton, soybeans, and rice were grown from seed in indoor chambers at a mean CO2 concentration of 560 μmol mol−1, with “triangular” cyclic variation with standard deviations of either 4.5 or 18.0 μmol mol−1 measured with 0.1 s sampling periods with an open path analyzer. Photosynthesis, stomatal conductance, and above ground biomass at 20 to 23 days were reduced in all four species by the larger variation in CO2 concentration. Tests of rates of stomatal opening and closing with step changes in light and CO2, and tests of responses to square-wave cycling of CO2 were also conducted on individual leaves of these and three other species, using a leaf gas exchange system. Reduced stomatal conductance due to larger amplitude cycling of CO2 during growth occurred even in soybeans and rice, which had equal rates of opening and closing in response to step changes in CO2. The gas exchange results further indicated that reduced mean stomatal conductance was not the only cause of reduced photosynthesis in variable CO2 conditions.
Characterization of Atypical Protein Tyrosine Kinase (PTK) Genes and Their Role in Abiotic Stress Response in Rice
Authors: Elangovan Allimuthu , Dalal Monika , Krishna Gopinathan Kumar , Devika Sellathdurai , Kumar Ranjeet Ranjan , Sathee Lekshmy , Chinnusamy Viswanathan
Published: May 2020

Tyrosine phosphorylation constitutes up to 5% of the total phophoproteome. However, only limited studies are available on protein tyrosine kinases (PTKs) that catalyze protein tyrosine phosphorylation in plants. In this study, domain analysis of the 27 annotated PTK genes in rice genome led to the identification of 18 PTKs with tyrosine kinase domain. The kinase domain of rice PTKs shared high homology with that of dual specificity kinase BRASSINOSTEROID-INSENSITIVE 1 (BRI1) of Arabidopsis. In phylogenetic analysis, rice PTKs clustered with receptor-like cytoplasmic kinases-VII (RLCKs-VII) of Arabidopsis. mRNAseq analysis using Genevestigator revealed that rice PTKs except PTK9 and PTK16 express at moderate to high level in most tissues. PTK16 expression was highly abundant in panicle at flowering stage. mRNAseq data analysis led to the identification of drought, heat, salt, and submergence stress regulated PTK genes in rice. PTK14 was upregulated under all stresses. qRT-PCR analysis also showed that all PTKs except PTK10 were significantly upregulated in root under osmotic stress. Tissue specificity and abiotic stress mediated differential regulation of PTKs suggest their potential role in development and stress response of rice. The candidate dual specificity PTKs identified in this study paves way for molecular analysis of tyrosine phosphorylation in rice.
Development and Optimization of a Germination Assay and Long-Term Storage for Cannabis sativa Pollen
Authors: Gaudet Daniel , Yadav Narendra Singh , Sorokin Aleksei , Bilichak Andriy , Kovalchuk Igor
Published: May 2020

Pollen viability and storage is of great interest to cannabis breeders and researchers to maintain desirable germplasm for future use in breeding or for biotechnological and gene editing applications. Here, we report a simple and efficient cryopreservation method for long-term storage of Cannabis sativa pollen. Additionally, the bicellular nature of cannabis pollen was identified using DAPI (4′,6-diamidino-2-phenylindole) staining. A pollen germination assay was developed to assess cannabis pollen viability and used to demonstrate that pollen collected from different principal growth stages exhibited differential longevity. Finally, a simple and efficient method that employs pollen combined with baked whole wheat flour and subsequent desiccation under vacuum was developed for the long-term cryopreservation of C. sativa pollen. Using this method, pollen viability was maintained in liquid nitrogen after four months, suggesting long-term preservation of cannabis pollen.
Effects of Solvent Vapor Annealing on Morphology and Charge Transport of Poly(3-hexylthiophene) (P3HT) Films Incorporated with Preformed P3HT Nanowires
Authors: Jang Mingu , Huh Yang-Il , Chang Mincheol
Published: May 2020

We systematically studied the influence of solvent vapor annealing on the molecular ordering, morphologies, and charge transport properties of poly(3-hexylthiophene) (P3HT) thin films embedded with preformed crystalline P3HT nanowires (NWs). Solvent vapor annealing (SVA) with chloroform (CF) was found to profoundly impact on the structural and morphological changes, and thus on the charge transport characteristics, of the P3HT-NW-embedded P3HT films. With increased annealing time, the density of crystalline P3HT NWs was increased within the resultant films, and also intra- and intermolecular interactions of the corresponding films were significantly improved. As a result, the P3HT-NW-embedded P3HT films annealed with CF vapor for 20 min resulted in a maximized charge carrier mobility of ~0.102 cm2 V−1 s−1, which is higher than that of pristine P3HT films by 4.4-fold (μ = ~0.023 cm2 V−1 s−1).
Microstructure and Mechanical Properties of Carboxylated Nitrile Butadiene Rubber/Epoxy/XNBR-grafted Halloysite Nanotubes Nanocomposites
Authors: Paran Seyed Mohamad Reza , Naderi Ghasem , Mosallanezhad Heydar , Movahedifar Elnaz , Formela Krzysztof , Saeb Mohammad Reza
Published: May 2020

The effect of various amounts of carboxylated nitrile butadiene rubber (XNBR) functionalized halloysite nanotubes (XHNTs) on the cure characteristics, mechanical and swelling behavior of XNBR/epoxy compounds was experimentally and theoretically investigated. The morphology of the prepared XNBR/epoxy/XHNTs nanocomposites was imaged using scanning electron microscopy (SEM). The effects of various XNBR-grafted nanotubes on the damping factor of nanocomposites were evaluated by dynamic mechanical thermal analysis (DMTA). The cure behavior characterization indicated a fall in the scorch time, but a rise in the cure rate with higher loading of XHNTs into the XNBR/epoxy nanocomposites. SEM micrographs of tensile fracture surfaces were indicative of a rougher fracture surface with a uniform dispersion state of nanotubes into the polymer matrix in the XNBR/epoxy/XHNTs nanocomposites. The stress–strain behavior studies of XNBR/epoxy/XHNTs nanocomposites showed a higher tensile strength up to 40% with 7 wt % XHNTs loading. The theoretical predictions of uniaxial tensile behavior of nanocomposites using Bergström–Boyce model revealed that some of the material parameters were considerably changed with the XHNTs loading. Furthermore, the used theoretical model precisely predicted the nonlinear large strain hyperelastic behavior of nanocomposites.
A New Multiparameter Model for Multiaxial Fatigue Life Prediction of Rubber Materials
Authors: Tobajas Rafael , Elduque Daniel , Ibarz Elena , Javierre Carlos , Gracia Luis
Published: May 2020

Most of the mechanical components manufactured in rubber materials experience fluctuating loads, which cause material fatigue, significantly reducing their life. Different models have been used to approach this problem. However, most of them just provide life prediction only valid for each of the specific studied material and type of specimen used for the experimental testing. This work focuses on the development of a new generalized model of multiaxial fatigue for rubber materials, introducing a multiparameter variable to improve fatigue life prediction by considering simultaneously relevant information concerning stresses, strains, and strain energies. The model is verified through its correlation with several published fatigue tests for different rubber materials. The proposed model has been compared with more than 20 different parameters used in the specialized literature, calculating the value of the R2 coefficient by comparing the predicted values of every model, with the experimental ones. The obtained results show a significant improvement in the fatigue life prediction. The proposed model does not aim to be a universal and definitive approach for elastomer fatigue, but it provides a reliable general tool that can be used for processing data obtained from experimental tests carried out under different conditions.
The Synergistic Microbiological Effects of Industrial Produced Packaging Polyethylene Films Incorporated with Zinc Nanoparticles
Authors: Mania Szymon , Cieślik Mateusz , Konzorski Marcin , Święcikowski Paweł , Nelson Andrzej , Banach Adrianna , Tylingo Robert
Published: May 2020

Zinc compounds in polyolefin films regulate the transmission of UV-VIS radiation, affect mechanical properties and antimicrobial activity. According to hypothesis, the use of zinc- containing masterbatches in polyethylene films (PE) with different chemical nature—hydrophilic zinc oxide (ZO) and hydrophobic zinc stearate (ZS)—can cause a synergistic effect, especially due to their antimicrobial properties. PE films obtained on an industrial scale containing zinc oxide and zinc stearate masterbatches were evaluated for antimicrobial activity against E. coli and S. aureus strains. The morphology of the samples (SEM), composition (EDX), UV barrier and transparency, mechanical properties and global migration level were also determined. SEM micrographs confirmed the good dispersion of zinc additives in the PE matrix. The use of both masterbatches in one material caused a synergistic effect of antimicrobial activity against both bacterial strains. The ZO masterbatch reduced the transparency of films, increased their UV-barrier ability and improved tensile strength, while the ZS masterbatch did not significantly change the tested parameters. The global migration limit was not exceeded for any of the samples. The use of ZO and ZS masterbatch mixtures enables the design of packaging with high microbiological protection with a controlled transmission for UV and VIS radiation.
Doxycycline and Zinc Loaded Silica-Nanofibrous Polymers as Biomaterials for Bone Regeneration
Authors: Toledano Manuel , Toledano-Osorio Manuel , Osorio Raquel , Carrasco-Carmona Álvaro , Gutiérrez-Pérez José-Luis , Gutiérrez-Corrales Aida , Serrera-Figallo María-Angeles , Lynch Christopher D. , Torres-Lagares Daniel
Published: May 2020

The main target of bone tissue engineering is to design biomaterials that support bone regeneration and vascularization. Nanostructured membranes of (MMA)1-co-(HEMA)1/(MA)3-co-(HEA)2 loaded with 5% wt of SiO2-nanoparticles (HOOC-Si-Membrane) were doped with zinc (Zn-HOOC-Si-Membrane) or doxycycline (Dox-HOOC-Si-Membrane). Critical bone defects were effectuated on six New Zealand-bred rabbit skulls and covered with the membranes. After six weeks, the bone architecture was evaluated with micro computed tomography. Three histological analyses were utilized to analyse bone regeneration, including von Kossa silver nitrate, toluidine blue and fluorescence. All membrane-treated defects exhibited higher number of osteocytes and bone perimeter than the control group without the membrane. Zn-HOOC-Si-Membranes induced higher new bone and osteoid area than those treated with HOOC-Si-Membranes, and control group, respectively. Zn-HOOC-Si-Membranes and Dox-HOOC-Si-Membranes attained the lowest ratio M1 macrophages/M2 macrophages. Dox-HOOC-Si-Membranes caused the lowest number of osteoclasts, and bone density. At the trabecular new bone, Zn-HOOC-Si-Membranes produced the highest angiogenesis, bone thickness, connectivity, junctions and branches. Zn-HOOC-Si-Membranes enhanced biological activity, attained a balanced remodeling, and achieved the greatest regenerative efficiency after osteogenesis and angiogenesis assessments. The bone-integrated Zn-HOOC-Si-Membranes can be considered as bioactive modulators provoking a M2 macrophages (pro-healing cells) increase, being a potential biomaterial for promoting bone repair.
The Basis for Coexistence Found from within: The Mystic Universality and Ethicality of Donghak (東學, Eastern Learning)
Authors: Seong Haeyoung
Published: May 2020

The rush of Western civilization, headed by Christianity, caused a considerable identity crisis in the 19th century Joseon dynasty. The founder of Donghak 東學, Suun Choe Je-u 水雲 崔濟愚, sought a way out of the crisis through religion. Suun contended that the religions of both east and west are grounded in the same Way of Heaven, and that it can be ascertained through an experience of mystical union induced by chanting a 21-character incantation. He also emphasized the importance of practicing this Way of Heaven in real life. According to him, the Western invasion is an act of selfishness, and goes against the Heavenly Way. The Heavenly Way is considered a foundation that enables communication and coexistence in a religiously diverse society. Despite the fact that his belief in the universality of the Heavenly Way is based on a personal experience—which is problematic to all mysticisms—Donghak provided a powerful discourse to deal with a variety of challenges of his time. In this age of religious pluralism, Suun’s universalism is significant in exploring the intellectual and spiritual foundation of the modern pluralistic thoughts of Korea.
The Role of Personality Factors and Empathy in the Acceptance and Performance of a Social Robot for Psychometric Evaluations
Authors: Rossi Silvia , Conti Daniela , Garramone Federica , Santangelo Gabriella , Staffa Mariacarla , Varrasi Simone , Di Nuovo Alessandro
Published: May 2020

Research and development in socially assistive robotics have produced several novel applications in the care of senior people. However, some are still unexplored such as their use as psychometric tools allowing for a quick and dependable evaluation of human users’ intellectual capacity. To fully exploit the application of a social robot as a psychometric tool, it is necessary to account for the users’ factors that might influence the interaction with a robot and the evaluation of user cognitive performance. To this end, we invited senior participants to use a prototype of a robot-led cognitive test and analyzed the influence of personality traits and user’s empathy on the cognitive performance and technology acceptance. Results show a positive influence of a personality trait, the “openness to experience”, on the human-robot interaction, and that other factors, such as anxiety, trust, and intention to use, are influencing technology acceptance and correlate the evaluation by psychometric tests.
Two-Path Network with Feedback Connections for Pan-Sharpening in Remote Sensing
Authors: Fu Shipeng , Meng Weihua , Jeon Gwanggil , Chehri Abdellah , Zhang Rongzhu , Yang Xiaomin
Published: May 2020

High-resolution multi-spectral images are desired for applications in remote sensing. However, multi-spectral images can only be provided in low resolutions by optical remote sensing satellites. The technique of pan-sharpening wants to generate high-resolution multi-spectral (MS) images based on a panchromatic (PAN) image and the low-resolution counterpart. The conventional deep learning based pan-sharpening methods process the panchromatic and the low-resolution image in a feedforward manner where shallow layers fail to access useful information from deep layers. To make full use of the powerful deep features that have strong representation ability, we propose a two-path network with feedback connections, through which the deep features can be rerouted for refining the shallow features in a feedback manner. Specifically, we leverage the structure of a recurrent neural network to pass the feedback information. Besides, a power feature extraction block with multiple projection pairs is designed to handle the feedback information and to produce power deep features. Extensive experimental results show the effectiveness of our proposed method.
Simulating Multi-Directional Narrowband Reflectance of the Earth’s Surface Using ADAM (A Surface Reflectance Database for ESA’s Earth Observation Missions)
Authors: Bacour Cédric , Bréon François-Marie , Gonzalez Louis , Price Ivan , Muller Jan-Peter , Straume Anne
Published: May 2020

The ADAM (A Surface Reflectance Database for ESA’s Earth Observation Missions) product (a climatological database coupled to its companion calculation toolkit) enables users to simulate realistic hyperspectral and directional global Earth surface reflectances (i.e., top-of-canopy/bottom-of-atmosphere) over the 240–4000 nm spectral range (at 1-nm resolution) and in any illumination/observation geometry, at 0.1° × 0.1° spatial resolution for a typical year. ADAM aims to support the preparation of optical Earth observation missions as well as the design of operational processing chains for the retrieval of atmospheric parameters by characterizing the expected surface reflectance, accounting for its anisotropy. Firstly, we describe (1) the methods used in the development of the gridded monthly ADAM climatologies (over land surfaces: monthly means of normalized reflectances derived from MODIS observations in seven spectral bands for the year 2005; over oceans: monthly means over the 1999–2009 period of chlorophyll content from SeaWiFS and of wind speed from SeaWinds), and (2) the underlying modeling approaches of ADAM toolkit to simulate the spectro-directional variations of the reflectance depending on the assigned surface type. Secondly, we evaluate ADAM simulation performances over land surfaces. A comparison against POLDER multi-spectral/multi-directional measurements for year 2008 shows reliable simulation results with root mean square differences below 0.027 and R2 values above 0.9 for most of the 14 land cover IGBP classes investigated, with no significant bias identified. Only for the “Snow and ice” class is the performance lower pointing to a limitation of climatological data to represent actual snow properties. An evaluation of the modeled reflectance in the specific backscatter direction against CALIPSO data reveals that ADAM tends to overestimate (underestimate) the so-called “hot-spot” by a factor of about 1.5 (1.5 to 2) for barren (vegetated) surfaces.
The Local Median Filtering Method for Correcting the Laser Return Intensity Information from Discrete Airborne Laser Scanning Data
Authors: Wu Bingxiao , Zheng Guang , Ju Weimin
Published: May 2020

Laser return intensity (LRI) information obtained from airborne laser scanning (ALS) data has been used to classify land cover types and to reveal canopy physiological features. However, the sensor-related and environmental parameters may introduce noise. In this study, we developed a local median filtering (LMF) method to point-by-point correct the LRI information. For each point, we deduced the reference variation range for its LRI. Then, we replaced the outliers of LRI with their local median values. To evaluate the LMF method, we assessed the discrepancy of LRI information from the same and diverse land cover types. Moreover, we used the corrected LRI to distinguish points from grass, road, and bare land, which were classified as ground type in ALS data. The results show that using the LMF method could increase the similarity of pointwise LRI from the same land cover type and the discrepancy of those from different kinds of targets. Using the LMF-corrected LRI could improve the overall classification accuracy of three land cover types by about 3% (all over 81%, κ ≥ 0.73, p < 0.05), compared to those using the original and range-normalized LRI. The sensor-related metrics brought more noise to the original LRI information than the environmental factors. Using the LMF method could effectively correct LRI information from historical ALS datasets.
Random Forest Spatial Interpolation
Authors: Sekulić Aleksandar , Kilibarda Milan , Heuvelink Gerard B.M. , Nikolić Mladen , Bajat Branislav
Published: May 2020

For many decades, kriging and deterministic interpolation techniques, such as inverse distance weighting and nearest neighbour interpolation, have been the most popular spatial interpolation techniques. Kriging with external drift and regression kriging have become basic techniques that benefit both from spatial autocorrelation and covariate information. More recently, machine learning techniques, such as random forest and gradient boosting, have become increasingly popular and are now often used for spatial interpolation. Some attempts have been made to explicitly take the spatial component into account in machine learning, but so far, none of these approaches have taken the natural route of incorporating the nearest observations and their distances to the prediction location as covariates. In this research, we explored the value of including observations at the nearest locations and their distances from the prediction location by introducing Random Forest Spatial Interpolation (RFSI). We compared RFSI with deterministic interpolation methods, ordinary kriging, regression kriging, Random Forest and Random Forest for spatial prediction (RFsp) in three case studies. The first case study made use of synthetic data, i.e., simulations from normally distributed stationary random fields with a known semivariogram, for which ordinary kriging is known to be optimal. The second and third case studies evaluated the performance of the various interpolation methods using daily precipitation data for the 2016–2018 period in Catalonia, Spain, and mean daily temperature for the year 2008 in Croatia. Results of the synthetic case study showed that RFSI outperformed most simple deterministic interpolation techniques and had similar performance as inverse distance weighting and RFsp. As expected, kriging was the most accurate technique in the synthetic case study. In the precipitation and temperature case studies, RFSI mostly outperformed regression kriging, inverse distance weighting, random forest, and RFsp. Moreover, RFSI was substantially faster than RFsp, particularly when the training dataset was large and high-resolution prediction maps were made.
Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges
Authors: Shi Wenzhong , Zhang Min , Zhang Rui , Chen Shanxiong , Zhan Zhao
Published: May 2020

Change detection based on remote sensing (RS) data is an important method of detecting changes on the Earth’s surface and has a wide range of applications in urban planning, environmental monitoring, agriculture investigation, disaster assessment, and map revision. In recent years, integrated artificial intelligence (AI) technology has become a research focus in developing new change detection methods. Although some researchers claim that AI-based change detection approaches outperform traditional change detection approaches, it is not immediately obvious how and to what extent AI can improve the performance of change detection. This review focuses on the state-of-the-art methods, applications, and challenges of AI for change detection. Specifically, the implementation process of AI-based change detection is first introduced. Then, the data from different sensors used for change detection, including optical RS data, synthetic aperture radar (SAR) data, street view images, and combined heterogeneous data, are presented, and the available open datasets are also listed. The general frameworks of AI-based change detection methods are reviewed and analyzed systematically, and the unsupervised schemes used in AI-based change detection are further analyzed. Subsequently, the commonly used networks in AI for change detection are described. From a practical point of view, the application domains of AI-based change detection methods are classified based on their applicability. Finally, the major challenges and prospects of AI for change detection are discussed and delineated, including (a) heterogeneous big data processing, (b) unsupervised AI, and (c) the reliability of AI. This review will be beneficial for researchers in understanding this field.
The Generation of Soil Spectral Dynamic Feedback Using Landsat 8 Data for Digital Soil Mapping
Authors: Zeng Canying , Yang Lin , Zhu A-Xing
Published: May 2020

The soil spectral dynamic feedback captured from high temporal resolution remote sensing data, such as MODIS, during the soil drying process after a rainfall could assist with digital soil mapping. However, this method is ineffective in utilizing the images with a relatively high spatial resolution. There are an insufficient number of images in the soil drying process since those high spatial resolution images tend to have a low temporal resolution. This study is aimed at generating soil spectral dynamic feedback by integrating the feedback captured from the images with a high spatial resolution during the process of multiple drying after different rainfall events. The Landsat 8 data with a temporal resolution of 16 day was exemplified. Each single spectral feedback obtained from Landsat 8 was first adjusted to eliminate the impact of different rainfall magnitudes. Then, the soil spectral dynamic feedback was reorganized and generated based on the adjusted feedback. Finally, the soil spectral dynamic feedback generated based on Landsat 8 was used for mapping topsoil texture and compared with the mapping results based on the MODIS data and the fusion data of MODIS and Landsat 8. As revealed by the results, not only could the generated soil spectral dynamic feedback based on Landsat 8 data improve the details of the spatial distribution of soil texture, but it also enhances the accuracy of mapping. The mapping accuracy based on Landsat 8 data is higher than that based on the MODIS data and fusion data. The improvements of accuracy are more obvious in the areas with more complex surface conditions. This study widens the scope of application for soil spectral dynamic feedback and provides support for large-scale and high-precision digital soil mapping.
Adaptive Echolocation and Flight Behaviors in Bats Can Inspire Technology Innovations for Sonar Tracking and Interception
Authors: Diebold Clarice Anna , Salles Angeles , Moss Cynthia F.
Published: May 2020

Target tracking and interception in a dynamic world proves to be a fundamental challenge faced by both animals and artificial systems. To track moving objects under natural conditions, agents must employ strategies to mitigate interference and conditions of uncertainty. Animal studies of prey tracking and capture reveal biological solutions, which can inspire new technologies, particularly for operations in complex and noisy environments. By reviewing research on target tracking and interception by echolocating bats, we aim to highlight biological solutions that could inform new approaches to artificial sonar tracking and navigation systems. Most bat species use wideband echolocation signals to navigate dense forests and hunt for evasive insects in the dark. Importantly, bats exhibit rapid adaptations in flight trajectory, sonar beam aim, and echolocation signal design, which appear to be key to the success of these animals in a variety of tasks. The rich suite of adaptive behaviors of echolocating bats could be leveraged in new sonar tracking technologies by implementing dynamic sensorimotor feedback control of wideband sonar signal design, head, and ear movements.
RESPOnSE—A Framework for Enforcing Risk-Aware Security Policies in Constrained Dynamic Environments
Authors: Michailidou Christina , Gkioulos Vasileios , Shalaginov Andrii , Rizos Athanasios , Saracino Andrea
Published: May 2020

The enforcement of fine-grained access control policies in constrained dynamic networks can become a challenging task. The inherit constraints present in those networks, which result from the limitations of the edge devices in terms of power, computational capacity and storage, require an effective and efficient access control mechanism to be in place to provide suitable monitoring and control of actions and regulate the access over the resources. In this article, we present RESPOnSE, a framework for the specification and enforcement of security policies within such environments, where the computational burden is transferred to high-tier nodes, while low-tier nodes apply risk-aware policy enforcement. RESPOnSE builds on a combination of two widely used access control models, Attribute-Based Access Control and Role-Based Access Control, exploiting the benefits each one provides. Moreover, the proposed mechanism is founded on a compensatory multicriteria decision-making algorithm, based on the calculation of the Euclidean distance between the run-time values of the attributes present in the security policy and their ideal values, as those are specified within the established policy rules.
BLESeis: Low-Cost IoT Sensor for Smart Earthquake Detection and Notification
Authors: Won Jongbin , Park Junyoung , Park Jong-Woong , Kim In-Ho
Published: May 2020

The Internet of Things (IoT) has been implemented to provide solutions for certain event detection because of ease of installation, computing and communication capability, and cost-effectiveness. Seismic event detection, however, is still a challenge due to a lack of high-fidelity sensing and classification efficiency. This study proposes BLESeis, an IoT sensor for smart earthquake detection. BLESeis comprises three main parts: (1) high-fidelity vibration sensing using a MEMS accelerometer and digital filtering; (2) an embedded earthquake detection algorithm; (3) BLE (Bluetooth low energy) beacon for earthquake notification. For high-fidelity vibration sensing, a triggering algorithm and embedded finite impulse response (FIR) low-pass filter are developed. The acquired vibration is then classified by the earthquake detection algorithm developed to identify the earthquake signal from other vibration sources using time and frequency domain analysis. Upon detection of an earthquake, the BLE beacon broadcasts using the proposed data packet for efficient notification and visualization. The performance of the proposed system is evaluated through numerical simulations and a set of experiments using shaking table tests. The experiments show the feasibility of the low-cost earthquake detection and notification system.
Intact Detection of Highly Occluded Immature Tomatoes on Plants Using Deep Learning Techniques
Authors: Mu Yue , Chen Tai-Shen , Ninomiya Seishi , Guo Wei
Published: May 2020

Automatic detection of intact tomatoes on plants is highly expected for low-cost and optimal management in tomato farming. Mature tomato detection has been wildly studied, while immature tomato detection, especially when occluded with leaves, is difficult to perform using traditional image analysis, which is more important for long-term yield prediction. Therefore, tomato detection that can generalize well in real tomato cultivation scenes and is robust to issues such as fruit occlusion and variable lighting conditions is highly desired. In this study, we build a tomato detection model to automatically detect intact green tomatoes regardless of occlusions or fruit growth stage using deep learning approaches. The tomato detection model used faster region-based convolutional neural network (R-CNN) with Resnet-101 and transfer learned from the Common Objects in Context (COCO) dataset. The detection on test dataset achieved high average precision of 87.83% (intersection over union ≥ 0.5) and showed a high accuracy of tomato counting (R2 = 0.87). In addition, all the detected boxes were merged into one image to compile the tomato location map and estimate their size along one row in the greenhouse. By tomato detection, counting, location and size estimation, this method shows great potential for ripeness and yield prediction.
Bragg Peak Localization with Piezoelectric Sensors for Proton Therapy Treatment
Authors: Otero Jorge , Felis Ivan , Herrero Alicia , Merchán José A. , Ardid Miguel
Published: May 2020

A full chain simulation of the acoustic hadrontherapy monitoring for brain tumours is presented in this work. For the study, a proton beam of 100 MeV is considered. In the first stage, Geant4 is used to simulate the energy deposition and to study the behaviour of the Bragg peak. The energy deposition in the medium produces local heating that can be considered instantaneous with respect to the hydrodynamic time scale producing a sound pressure wave. The resulting thermoacoustic signal has been subsequently obtained by solving the thermoacoustic equation. The acoustic propagation has been simulated by FEM methods in the brain and the skull, where a set of piezoelectric sensors are placed. Last, the final received signals in the sensors have been processed in order to reconstruct the position of the thermal source and, thus, to determine the feasibility and accuracy of acoustic beam monitoring in hadrontherapy.
Routing Based Multi-Agent System for Network Reliability in the Smart Microgrid
Authors: Singh Niharika , Elamvazuthi Irraivan , Nallagownden Perumal , Ramasamy Gobbi , Jangra Ajay
Published: May 2020

Microgrids help to achieve power balance and energy allocation optimality for the defined load networks. One of the major challenges associated with microgrids is the design and implementation of a suitable communication-control architecture that can coordinate actions with system operating conditions. In this paper, the focus is to enhance the intelligence of microgrid networks using a multi-agent system while validation is carried out using network performance metrics i.e., delay, throughput, jitter, and queuing. Network performance is analyzed for the small, medium and large scale microgrid using Institute of Electrical and Electronics Engineers (IEEE) test systems. In this paper, multi-agent-based Bellman routing (MABR) is proposed where the Bellman–Ford algorithm serves the system operating conditions to command the actions of multiple agents installed over the overlay microgrid network. The proposed agent-based routing focuses on calculating the shortest path to a given destination to improve network quality and communication reliability. The algorithm is defined for the distributed nature of the microgrid for an ideal communication network and for two cases of fault injected to the network. From this model, up to 35%–43.3% improvement was achieved in the network delay performance based on the Constant Bit Rate (CBR) traffic model for microgrids.
Reply to Comments: Hurdle Clearance Detection and Spatiotemporal Analysis in 400 Meters Hurdles Races Using Shoe-Mounted Magnetic and Inertial Sensor
Authors: Falbriard Mathieu , Mohr Maurice , Aminian Kamiar
Published: May 2020

The current document answers the comment addressed by Schmidt, M [...]
Does Social Relation or Economic Interest Affect the Choice Behavior of Land Lease Agreement in China? Evidence from the Largest Wheat−Producing Henan Province
Authors: Liu Ruifeng , Gao Zhifeng , Nian Yefan , Ma Hengyun
Published: May 2020

As China is transferring from a centrally planned economy to a market economy, the land market becomes more active. With the rapid transition from an agricultural society to an industrialized society, both economic and social factors could influence the choice of land lease agreement. This paper investigates the choice behavior of land lease agreement based on the household data collected from the largest wheat-producing province in China, Henan province. The results provide strong evidence that economic interest plays a key role in the choice of land lease agreement. Meanwhile, the paper has also found that social relations still affect the choice of land lease agreement. To speed up the transfer of land use rights and scale of operation, this study proposes: reducing the land dependence of rural households; improving agricultural machinery and achieving farming mechanization; creating more non-farm work opportunities for rural laborers; promoting the citizenization of rural households; respecting farmers’ choice of contracts; and attaching importance to the role of social relations in farmland transfer.
Compositional Data Analysis Approach in the Measurement of Social-Spatial Segregation: Towards a Sustainable and Inclusive City
Authors: Cruz-Sandoval Marco , Roca Elisabet , Ortego María Isabel
Published: May 2020

The location and context in which people live influences and conditions their opportunities in life. This becomes relevant in a world subject to rapid urban and demographic growth, in which different economic, social, and political forces generate and accentuate disparities in cities. The foregoing generates an unequal distribution of the different social groups in the territory known as socio-spatial segregation. The study of this phenomenon incorporates a large number of variables belonging to different dimensions. Nonetheless, few studies have addressed socio-spatial segregation with a multivariate analysis approach. In addition, the existing studies may have obtained misleading outcomes by not acknowledging the inherent compositional nature of their variables. The objective of the present study is twofold: (i) To assess whether the phenomenon of socio-spatial segregation in Guadalajara, Mexico exists; and (ii) to introduce and stress the use of compositional techniques for the study of socio-spatial segregation. The study applied principal component analysis and cluster analysis considering the compositional nature of census variables, particularly from economic and educative indicators. In addition, the study used geographical information tools to depict and interpret the results. The results are intended to serve in the fulfillment of the Sustainable Development Goals towards inclusive and sustainable cities.
An Integrated Planning Framework for Sustainable Water and Energy Supply
Authors: Ahmadi Esmaeil , McLellan Benjamin , Ogata Seiichi , Mohammadi-Ivatloo Behnam , Tezuka Tetsuo
Published: May 2020

This study aims to reveal the economic, technical, and environmental impacts of different system configurations (centralized or decentralized, components, and technologies) on transition plans to achieve a higher share of renewable energy and desalination supplies for regions facing water scarcity. The main contribution of this research is the comparative evaluation of on-grid decentralized or distributed renewable-powered desalination systems for sustainable water and energy supply planning. Applying a novel nexus approach, an interactive multi-period planning model is developed to highlight synergies and to identify conflicts of planning both energy and water sectors at the same time as endogenous subsystems of one overall system. For studying these synergies in this study, the pace of technology deployment and the path of decline in overall costs are assumed to be a function of experience and knowledge as two-factor learning curves. Using data from 81 projects, the levelized cost and capacity factor of utility-scale photovoltaic and wind supplies in the Middle East were calculated. The results indicate that a scenario with a decentralized water sector and renewable-powered multiple-effect distillation technology has the best overall performance among the proposed scenarios.
Suitability of Remediated PFAS-Affected Soil in Cement Pastes and Mortars
Authors: Fehervari Andras , Gates Will P. , Gallage Chathuranga , Collins Frank
Published: May 2020

Australia and many other parts of the world face issues of contamination in groundwater and soils by per- and poly-fluoroalkyl substances (PFAS). While the pyrolytic treatment of contaminated soils can destroy PFAS, the resulting heat-treated soils currently have limited applications. The purpose of this study was to demonstrate the usefulness of remediated soils in concrete applications. Using heat-treated soil as a fine aggregate, with a composition and particle size distribution similar to that of traditional concrete sands, proved to be a straightforward process. In such situations, complete fine aggregate replacement could be achieved with minimal loss of compressive strength. At high fine aggregate replacement (≥ 60%), a wetting agent was required for maintaining adequate workability. When using the heat-treated soil as a supplementary cementitious material, the initial mineralogy, the temperature of the heat-treatment and the post-treatment storage (i.e., keeping the soil dry) were found to be key factors. For cement mortars where minimal strength loss is desired, up to 15% of cement can be replaced, but up to 45% replacement can be achieved if moderate strengths are acceptable. This study successfully demonstrates that commercially heat-treated remediated soils can serve as supplementary cementitious materials or to replace fine aggregates in concrete applications.
Yield Spread and Economic Policy Uncertainty: Evidence from Japan
Authors: Wang Mei-Chih , Kuo Pao-Lan , Chen Chan-Sheng , Chiu Chien-Liang , Chang Tsangyao
Published: May 2020

In this paper, we adopt the nonlinear autoregressive distributed lags (NARDL) model extended by Shin et al. (2014) to investigate the relationship between the treasury yield spread and economic policy uncertainty (EPU) in Japan. This model helps us to explore the short- and long-run asymmetric reactions of explained variables through positive and negative partial sum decompositions of changes in the explanatory variable(s). In our research, the testing of the NARDL specification reveals the existence of a significant long-run asymmetric equilibrium between the yield spread and EPU in Japan. On the other hand, we find a significant positive nexus between the treasury yield spread and EPU reduction in the long run. We speculate that because of low inflation, a poor economic outlook and the low interest rate environment since 1990, financial agents are markedly sensitive to negative shocks resulting from EPU. This means that when facing a good economy, bond agents are quick to sell, especially with higher-risk long-term interest rate bonds. Meanwhile, because the Bank of Japan announced the Stock Purchasing Plan in October 2002 and from the point view of portfolio management, while the influence of a positive economic outlook dominates the negative outlook, flight from quality has no role in asset portfolio adjustment. The empirical implications are that the long history of unconventional monetary policy supports the demand for both bonds and stock markets. When taking the stock market into consideration, the correlations between the yield spread, EPU and stock market capture the full wealth effects of the low interest rate environment in Japan.