bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessment of the Impact Resistance of a Composite Material with EN AW-7075 Matrix Reinforced with α-Al2O3 Particles Using a 7.62 × 39 mm Projectile
Authors: Kurzawa Adam , Pyka Dariusz , Jamroziak Krzysztof , Bajkowski Marcin , Bocian Miroslaw , Magier Mariusz , Koch Jan
Published: February 2020

The paper presents the results of studies on the effects of shooting composite materials produced by pressure infiltration with the EN AW-7075 alloy as a matrix and reinforcement in the form of preforms made of α-Al2O3 particles. Composite materials were made with two reinforcement contents (i.e., 30% and 40% vol. of α-Al2O3 particles). The composites produced in the form of 12 mm thick plates were subjected to impact loads from a 7.62 × 39 FMJ M43 projectile fired from a Kalashnikov. The samples of composites with different contents of strengthening particles were subjected to detailed microscopic examination to determine the mechanism of destruction. The effect of a projectile impact on the microstructure of the material within the perforation holes was identified. There were radial cracks found around the puncture holes and brittle fragmentation of the front surfaces of the specimens. The change in the volume of the reinforcement significantly affected the inlet, puncture and outlet diameters. The observations confirmed that brittle cracking dominated the destruction mechanism and the crack propagation front ran mainly in the matrix material and along the boundaries of the α-Al2O3 particles. In turn, numerical tests were conducted to describe the physical phenomena occurring due to the erosion of a projectile hitting a composite casing. They were performed with the use of the ABAQUS program. Based on constitutive models, the material constants developed from the identification of material properties were modelled and the finite element was generated from homogenization in the form of a representative volume element (RVE). The results of microscopic investigations of the destruction mechanism and numerical investigations were combined. The conducted tests and analyses shed light on the application possibilities of aluminium composites reinforced with Al2O3 particles in the construction of add-on-armour protective structures.
A Novel Dry Selective Isotropic Atomic Layer Etching of SiGe for Manufacturing Vertical Nanowire Array with Diameter Less than 20 nm
Authors: Li Junjie , Li Yongliang , Zhou Na , Wang Guilei , Zhang Qingzhu , Du Anyan , Zhang Yongkui , Gao Jianfeng , Kong Zhenzhen , Lin Hongxiao , Xiang Jinjuan , Li Chen , Yin Xiaogen , Li Yangyang , Wang Xiaolei , Yang Hong , Ma Xueli , Han Jianghao , Zhang Jing , Hu Tairan , Yang Tao , Li Junfeng , Yin Huaxiang , Zhu Huilong , Wang Wenwu , Radamson Henry
Published: February 2020

Semiconductor nanowires have great application prospects in field effect transistors and sensors. In this study, the process and challenges of manufacturing vertical SiGe/Si nanowire array by using the conventional lithography and novel dry atomic layer etching technology. The final results demonstrate that vertical nanowires with a diameter less than 20 nm can be obtained. The diameter of nanowires is adjustable with an accuracy error less than 0.3 nm. This technology provides a new way for advanced 3D transistors and sensors.
Heat Treatment and Formation of Magnetocaloric 1:13 Phase in LaFe11.4Si1.2Co0.4 Processed by Laser Beam Melting
Authors: Kagathara Jwalant , Wieland Sandra , Gärtner Eric , Uhlenwinkel Volker , Steinbacher Matthias
Published: February 2020

In recent years, magnetocaloric materials have been extensively studied as materials for use in alternative cooling systems. Shaping the magnetocaloric material to thin-walled heat exchanger structures is an important step to achieve efficient magnetocaloric cooling systems. In the present work, experimental investigations were carried out on the heat treatment of LaFe11.4Si1.2Co0.4 alloy processed by Laser Beam Melting (LBM) technology. Due to the rapid solidification after melting, LBM results in a refined micro structure, which requires much shorter heat treatment to achieve a high percentage of magnetocaloric 1:13 phase compared to conventional cast material. The influence of the heat treatment parameters (temperature, time, and cooling rate) on the resulting microstructure has been extensively studied. In addition to the conventional heat treatment process, induction technology was investigated and the results were very promising in terms of achieving good magnetocaloric properties after short-time annealing. After only 15 min holding time at 1373 K, the magnetic entropy change (∆S) of -7.9 J/kg/K (0–2 T) was achieved.
Proteolytic Activity of Bacillus subtilis upon κ-Casein Undermines Its “Caries-Safe” Effect
Authors: Duanis-Assaf Danielle , Kenan Eli , Sionov Ronit , Steinberg Doron , Shemesh Moshe
Published: February 2020

Milk is believed to be a relatively “caries-safe” food. This belief relies on the fact that caseins, which constitute around 80% of milk’s protein content, were found to inhibit the adhesion of Streptococcus mutans to enamel and, therefore, decrease biofilm formation. While S. mutans is considered a leading cause of dental disorders, Bacillus subtilis is a non-pathogenic foodborne bacterium, frequently contaminating milk and its products. This study aimed to investigate the effects of dairy-associated foodborne bacteria such as B. subtilis on biofilm formation by S. mutans in the presence of casein proteins. Our results indicate that there is a significant decrease in total biofilm formation by S. mutans exposed to a casein protein mixture in a mono-species culture, whereas, in the co-culture with B. subtilis, an inhibitory effect of the caseins mixture on S. mutans biofilm formation was observed. Proteolytic activity analysis suggested that B. subtilis is capable of breaking down milk proteins, especially κ-casein, which enables biofilm formation by S. mutans in the presence of milk caseins. Therefore, these findings may challenge the assumption that milk is “caries-safe”, especially in a complex microbial environment.
Euryops pectinatus L. Flower Extract Inhibits P-glycoprotein and Reverses Multi-Drug Resistance in Cancer Cells: A Mechanistic Study
Authors: M. Elkady Wafaa , M. Ayoub Iriny , Abdel-Mottaleb Yousra , ElShafie Mohamed F. , Wink Michael
Published: February 2020

Euryops pectinatus is a South African ornamental plant belonging to family Asteraceae. The present work evaluates the cytotoxic activity and phytochemical profile of the flower extract. Metabolite profiling was performed using HPLC-PDA-ESI-MS/MS. Total phenolics and flavonoids content were assessed. Cytotoxicity was evaluated against 6 different cancer cell lines using MTT assay. The possible underlying mechanism was proposed. We analyzed whether the extract could overcome the resistance of multidrug-resistant cancer cells for doxorubicin. The effect of combination of E. pectinatus with doxorubicin was also studied. Additionally, the potential inhibitory activity of the identified phytochemicals to PB1 protein was analyzed using in silico molecular docking. Twenty-five compounds were tentatively identified. Total phenolic and flavonoid contents represented 49.41 ± 0.66 and 23.37 ± 0.23 µg/mg dried flower extract, respectively. The extract showed selective cytotoxicity against Caco2 cells but its main effect goes beyond mere cytotoxicity. It showed strong inhibition of P-glycoprotein, which helps to overcome multidrug resistance to classical chemotherapeutic agents. In silico molecular docking showed that dicaffeoyl quinic acid, kaempferol-O-rutinoside, rutin, and isorhamnetin-O-rutinoside exhibited the most potent inhibitory activity to PB1 involved in tumor progression. Euryops pectinatus flower heads could have promising selective cytotoxicity alone or in combination with other chemotherapeutic agents to counteract multidrug resistance.
Marine Microorganism-Derived Macrolactins Inhibit Inflammatory Mediator Effects in LPS-Induced Macrophage and Microglial Cells by Regulating BACH1 and HO-1/Nrf2 Signals through Inhibition of TLR4 Activation
Authors: Kim Eun-Nam , Gao Ming , Choi Hyukjae , Jeong Gil-Saeng
Published: February 2020

Recently, many natural products with unique structure and promising pharmacological potential have been reported from marine-derived microorganisms. The macrolactin A (MA), 15-epi-dihydromacrolactin F (DMF) and macrolactin F (MF) were obtained from the culture broth extract of a marine sediment derived microorganism Bacillus sp. HC001. In this study, MA, DMF and MF inhibited the production and expression of proinflammatory mediators of inducible nitric oxide synthase (iNOS) and cyclooxygenase–2 (COX-2) in LPS-stimulated RAW264.7 and BV2 cells. Also, MA, DMF and MF exert anti-inflammatory effects through the expression of heme oxygenase (HO) -1, a stress-inducing enzyme that converts heme to carbon monoxide (CO), iron and biliberdine. Toll-like receptor 4 (TLR4) expressed by lipopolysaccharide (LPS) was inhibited by increased expression of HO-1 transcription factor Nrf2 and down regulation of BTB Domain And CNC Homolog 1 (BACH1), inhibited phosphorylation of Mitogen-activated protein kinase kinase kinase 7 (MAP3K7, TAK1) and nuclear factor kappaB (NF-κB). These results show that MA, DMF and MF effectively inhibited TLR4 by regulating BACH1 and HO-1/Nrf2 signals in LPS-stimulated RAW264.7 and BV2 cells, which suggests the possibility of use as an anti-inflammatory agent.
Antagonistic Effects of CAPE (a Component of Propolis) on the Cytotoxicity and Genotoxicity of Irinotecan and SN38 in Human Gastrointestinal Cancer Cells In Vitro
Authors: Gajek Gabriela , Marciniak Beata , Lewkowski Jarosław , Kontek Renata
Published: February 2020

The incidence of gastrointestinal cancers is increasing every year. Irinotecan (CPT-11), a drug used in the treatment of colorectal cancer and gastric cancer, is metabolized by carboxylesterases to an active metabolite, SN-38, which is more cytotoxic. CAPE (caffeic acid phenethyl ester) is an active component of propolis, which has a high antibacterial, antiviral, and antineoplastic potential. This study analyses the impact of CAPE on the cytotoxic (MTT assay), genotoxic (comet assay) and proapoptotic (caspase-3/7 activity) potential of irinotecan and its metabolite SN-38 in cultures of gastrointestinal neoplastic cells (HCT116, HT29, AGS). Cytotoxicity and genotoxicity activities of these compounds were carried out in comparison with human peripheral blood lymphocytes (PBLs) in vitro. The antioxidant potential of CAPE was investigated in relation H2O2-induced oxidative stress in the both neoplastic cells and PBLs. CAPE expressed cytotoxic, genotoxic, and pro-apoptotic activity against AGS, HCT116, and HT29 tumor cells. CAPE, in the presence of different concentrations of irinotecan or SN38, decreased the cytotoxicity, genotoxicity, and pro-apoptotic activity in these cell lines, but it has no such action on normal human peripheral blood lymphocytes.
In Silico Strategies in Tuberculosis Drug Discovery
Authors: Macalino Stephani Joy Y. , Billones Junie B. , Organo Voltaire G. , Carrillo Maria Constancia O.
Published: February 2020

Tuberculosis (TB) remains a serious threat to global public health, responsible for an estimated 1.5 million mortalities in 2018. While there are available therapeutics for this infection, slow-acting drugs, poor patient compliance, drug toxicity, and drug resistance require the discovery of novel TB drugs. Discovering new and more potent antibiotics that target novel TB protein targets is an attractive strategy towards controlling the global TB epidemic. In silico strategies can be applied at multiple stages of the drug discovery paradigm to expedite the identification of novel anti-TB therapeutics. In this paper, we discuss the current TB treatment, emergence of drug resistance, and the effective application of computational tools to the different stages of TB drug discovery when combined with traditional biochemical methods. We will also highlight the strengths and points of improvement in in silico TB drug discovery research, as well as possible future perspectives in this field.
Organo-Inorganic Hybrid Intumescent Fire Retardant Coatings for Thermoplastics Based on Poly(Vinylphosphonic Acid)
Authors: Kandola Baljinder K. , Williams Katherine V. , Ebdon John R.
Published: February 2020

Thin coatings of crosslinked poly(vinylphosphonic acid), PVPA, display good adhesion and excellent intumescent, fire-retardant barrier properties when applied to the surfaces of a typical thermoplastic, such as poly(methyl methacrylate), but perform relatively poorly in water-soak tests. To strengthen and further improve the barrier properties of the intumescent char and to make the coating more hydrophobic, PVPA has been complexed with various inorganic and organic species. The chars formed from coatings of some of these hybrid materials are less friable than chars from coatings synthesized from crosslinked PVPA alone, and show higher levels of water tolerance with no significant reduction in dry adhesion to the substrate.
Curcumin Delivery Mediated by Bio-Based Nanoparticles: A Review
Authors: Moballegh Nasery Mahshid , Abadi Banafshe , Poormoghadam Delaram , Zarrabi Ali , Keyhanvar Peyman , Khanbabaei Hashem , Ashrafizadeh Milad , Mohammadinejad Reza , Tavakol Shima , Sethi Gautam
Published: February 2020

Todays, nano-pharmaceutics is emerging as an important field of science to develop and improve efficacy of different drugs. Although nutraceuticals are currently being utilized in the prevention and treatment of various chronic diseases such as cancers, a number of them have displayed issues associated with their solubility, bioavailability, and bio-degradability. In the present review, we focus on curcumin, an important and widely used polyphenol, with diverse pharmacological activities such as anti-inflammatory, anti-carcinogenic, anti-viral, etc. Notwithstanding, it also exhibits poor solubility and bioavailability that may compromise its clinical application to a great extent. Therefore, the manipulation and encapsulation of curcumin into a nanocarrier formulation can overcome these major drawbacks and potentially may lead to a far superior therapeutic efficacy. Among different types of nanocarriers, biological and biopolymer carriers have attracted a significant attention due to their pleiotropic features. Thus, in the present review, the potential protective and therapeutic applications of curcumin, as well as different types of bio-nanocarriers, which can be used to deliver curcumin effectively to the different target sites will be discussed.
The Antioxidant Activity of Prenylflavonoids
Authors: Santos Clementina M. M. , Silva Artur M. S.
Published: February 2020

Prenylated flavonoids combine the flavonoid moiety and the lipophilic prenyl side-chain. A great number of derivatives belonging to the class of chalcones, flavones, flavanones, isoflavones and other complex structures possessing different prenylation patterns have been studied in the past two decades for their potential as antioxidant agents. In this review, current knowledge on the natural occurrence and structural characteristics of both natural and synthetic derivatives was compiled. An exhaustive survey on the methods used to evaluate the antioxidant potential of these prenylflavonoids and the main results obtained were also presented and discussed. Whenever possible, structure-activity relationships were explored.
Z-Ajoene Inhibits Growth of Colon Cancer by Promotion of CK1α Dependent β-Catenin Phosphorylation
Authors: Li Hua , Jeong Ji Hye , Kwon Sung Won , Lee Sang Kook , Lee Hwa Jin , Ryu Jae-Ha
Published: February 2020

Aberrant activation of a Wnt/β-catenin pathway results in nuclear accumulation of β-catenin in colon cancer. Inhibiting β-catenin is one strategy for treating colon cancer. Here, we identified Z-ajoene, a sulfur containing compound isolated from crushed garlic, as an inhibitor of colon cancer cell growth. Z-Ajoene repressed β-catenin response transcriptional activity, intracellular β-catenin levels, and its representative target protein levels (c-Myc and cyclin D1) in SW480 colon cancer cells. To clarify the regulatory mechanism of decreased β-catenin levels, we examined the effect of Z-ajoene on β-catenin phosphorylation, which is involved in β-catenin degradation. Z-Ajoene promoted the phosphorylation of β-catenin at Ser45 in a casein kinase 1α (CK1α)-dependent manner, which is an essential step in β-catenin degradation in the cytosol. These findings indicate that Z-ajoene from garlic may be a potential chemotherapeutic agent by modulating CK1α activity and the Wnt/β-catenin signaling pathway.
Use of Alginate Extracted from Moroccan Brown Algae to Stimulate Natural Defense in Date Palm Roots
Authors: Bouissil Soukaina , El Alaoui-Talibi Zainab , Pierre Guillaume , Michaud Philippe , El Modafar Cherkaoui , Delattre Cedric
Published: February 2020

Our study aimed to search for seaweed polysaccharides able to stimulate date palm defense mechanisms. Extraction, purification, characterization, and elicitor activity of sodium alginate (FSSA and BBSA) from Moroccan brown seaweeds Fucus spiralis and Bifurcaria bifurcata were investigated. FSSA and BBSA were characterized by proton nuclear magnetic resonance spectroscopy (1H-NMR) and size exclusion chromatography (HPLC-SEC). The mannuronic acid/guluronic acid (M/G) ratio of FSSA was M/G = 0.92 indicating that FSSA contained 48% and 52% of mannuronic and guluronic acids respectively, and the M/G ratio of BBSA was 0.47 indicating that BBSA contained 32% and 68% of mannuronic and guluronic acids respectively. Elicitor activity of FSSA and BBSA was carried out by developing an innovative study model on the date palm. The elicitor capacities were evaluated by investigating phenolic metabolism including phenylalanine ammonia-lyase (PAL) activity and total polyphenol content in seedling roots of date palm maintained in alginates solution (FSSA and BBSA) at different concentrations. The results obtained show that the PAL activity and the phenolic compound content were significantly stimulated with 1 mg·mL−1 of FSSA and BBSA; after 1 day of treatment with FSSA, and after 12 h of treatment with BBSA. These results show clearly those alginates extracted from Moroccan brown algae induced in date palm roots the stimulation of natural defense mechanisms.
Harnessing Ionic Interactions and Hydrogen Bonding for Nucleophilic Fluorination
Authors: Oh Young-Ho , Choi Hyoju , Park Chanho , Kim Dong Wook , Lee Sungyul
Published: February 2020

We review recent works for nucleophilic fluorination of organic compounds in which the Coulombic interactions between ionic species and/or hydrogen bonding affect the outcome of the reaction. SN2 fluorination of aliphatic compounds promoted by ionic liquids is first discussed, focusing on the mechanistic features for reaction using alkali metal fluorides. The influence of the interplay of ionic liquid cation, anion, nucleophile and counter-cation is treated in detail. The role of ionic liquid as bifunctional (both electrophilic and nucleophilic) activator is envisaged. We also review the SNAr fluorination of diaryliodonium salts from the same perspective. Nucleophilic fluorination of guanidine-containing of diaryliodonium salts, which are capable of forming hydrogen bonds with the nucleophile, is exemplified as an excellent case where ionic interactions and hydrogen bonding significantly affect the efficiency of reaction. The origin of experimental observation for the strong dependence of fluorination yields on the positions of -Boc protection is understood in terms of the location of the nucleophile with respect to the reaction center, being either close to far from it. Recent advances in the synthesis of [18F]F-dopa are also cited in relation to SNAr fluorination of diaryliodonium salts. Discussions are made with a focus on tailor-making promoters and solvent engineering based on ionic interactions and hydrogen bonding.
Brownian Motion and Thermophoresis Effects on MHD Three Dimensional Nanofluid Flow with Slip Conditions and Joule Dissipation Due to Porous Rotating Disk
Authors: Alreshidi Nasser Aedh , Shah Zahir , Dawar Abdullah , Kumam Poom , Shutaywi Meshal , Watthayu Wiboonsak
Published: February 2020

This paper examines the time independent and incompressible flow of magnetohydrodynamic (MHD) nanofluid through a porous rotating disc with velocity slip conditions. The mass and heat transmission with viscous dissipation is scrutinized. The proposed partial differential equations (PDEs) are converted to ordinary differential equation (ODEs) by mean of similarity variables. Analytical and numerical approaches are applied to examine the modeled problem and compared each other, which verify the validation of both approaches. The variation in the nanofluid flow due to physical parameters is revealed through graphs. It is witnessed that the fluid velocities decrease with the escalation in magnetic, velocity slip, and porosity parameters. The fluid temperature escalates with heightening in the Prandtl number, while other parameters have opposite impacts. The fluid concentration augments with the intensification in the thermophoresis parameter. The validity of the proposed model is presented through Tables.
Synthesis of Bisphenol Neolignans Inspired by Honokiol as Antiproliferative Agents
Authors: Cardullo Nunzio , Barresi Vincenza , Muccilli Vera , Spampinato Giorgia , D’Amico Morgana , Condorelli Daniele Filippo , Tringali Corrado
Published: February 2020

Honokiol (2) is a natural bisphenol neolignan showing a variety of biological properties, including antitumor activity. Some studies pointed out 2 as a potential anticancer agent in view of its antiproliferative and pro-apoptotic activity towards tumor cells. As a further contribution to these studies, we report here the synthesis of a small library of bisphenol neolignans inspired by honokiol and the evaluation of their antiproliferative activity. The natural lead was hence subjected to simple chemical modifications to obtain the derivatives 3–9; further neolignans (12a-c, 13a-c, 14a-c, and 15a) were synthesized employing the Suzuki–Miyaura reaction, thus obtaining bisphenols with a substitution pattern different from honokiol. These compounds and the natural lead were subjected to antiproliferative assay towards HCT-116, HT-29, and PC3 tumor cell lines. Six of the neolignans show GI50 values lower than those of 2 towards all cell lines. Compounds 14a, 14c, and 15a are the most effective antiproliferative agents, with GI50 in the range of 3.6–19.1 µM, in some cases it is lower than those of the anticancer drug 5-fluorouracil. Flow cytometry experiments performed on these neolignans showed that the inhibition of proliferation is mainly due to an apoptotic process. These results indicate that the structural modification of honokiol may open the way to obtaining antitumor neolignans more potent than the natural lead.
Droughts Amplify Differences Between the Energy Balance Components of Amazon Forests and Croplands
Authors: Caioni Charles , Silvério Divino Vicente , Macedo Marcia N. , Coe Michael T. , Brando Paulo M.
Published: February 2020

Droughts can exert a strong influence on the regional energy balance of the Amazon and Cerrado, as can the replacement of native vegetation by croplands. What remains unclear is how these two forcing factors interact and whether land cover changes fundamentally alter the sensitivity of the energy balance components to drought events. To fill this gap, we used remote sensing data to evaluate the impacts of drought on evapotranspiration (ET), land surface temperature (LST), and albedo on cultivated areas, savannas, and forests. Our results (for seasonal drought) indicate that increases in monthly dryness across Mato Grosso state (southern Amazonia and northern Cerrado) drive greater increases in LST and albedo in croplands than in forests. Furthermore, during the 2007 and 2010 droughts, croplands became hotter (0.1–0.8 °C) than savannas (0.3–0.6 °C) and forests (0.2–0.3 °C). However, forest ET was consistently higher than ET in all other land uses. This finding likely indicates that forests can access deeper soil water during droughts. Overall, our findings suggest that forest remnants can play a fundamental role in the mitigation of the negative impacts of extreme drought events, contributing to a higher ET and lower LST.
UNVI-Based Time Series for Vegetation Discrimination Using Separability Analysis and Random Forest Classification
Authors: Liu Hualiang , Zhang Feizhou , Zhang Lifu , Lin Yukun , Wang Siheng , Xie Yefeng
Published: February 2020

Land cover data is crucial for earth system modelling, natural resources management, and conservation planning. Remotely sensed time-series data capture dynamic behavior of vegetation, and have been widely used for land cover mapping. Temporal profiles of vegetation index (VI), especially normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), are the most used features derived from time-series spectral data. Whether NDVI or EVI is optimal to generate temporal profiles has not been evaluated. The universal normalized vegetation index (UNVI), a relatively new index with all spectral bands incorporated, has been proved to be more effective than several commonly used satellite-derived VIs in some application scenarios. In this study, we explored the ability of UNVI time series for discriminating different vegetation types in Chaoyang prefecture, northeast China, in comparison with normalized NDVI, EVI, triangle vegetation index (TVI), and tasseled cap transformation greenness (TCG). These five indices were calculated using Landsat 8 surface reflectance data, and two comparative experiments were conducted. The first experiment analyzed class separabilities using pairwise JM (Jeffries–Matusita) distance as indicator, and the results showed that UNVI was superior to EVI, TVI, and TCG, and almost equivalent to NDVI, especially during the peak of vegetation growing season and for the most indistinguishable vegetation pair broadleaf and shrubs. The second experiment compared the vegetation classification accuracies using the features of these VI temporal profiles and the corresponding phenological parameters, and the results showed that UNVI can better classify the five major vegetation in Chaoyang prefecture than other four indices. Therefore, we conclude that UNVI time series has considerable potential for regional land cover mapping, and we recommend that the use of the UNVI is considered in the future time series related studies.
On the Capabilities of the Italian Airborne FMCW AXIS InSAR System
Authors: Esposito Carmen , Natale Antonio , Palmese Gianfranco , Berardino Paolo , Lanari Riccardo , Perna Stefano
Published: February 2020

Airborne Synthetic Aperture Radar (SAR) systems are gaining increasing interest within the remote sensing community due to their operational flexibility and observation capabilities. Among these systems, those exploiting the Frequency-Modulated Continuous-Wave (FMCW) technology are compact, lightweight, and comparatively low cost. For these reasons, they are becoming very attractive, since they can be easily mounted onboard ever-smaller and highly flexible aerial platforms, like helicopters or unmanned aerial vehicles (UAVs). In this work, we present the imaging and topographic capabilities of a novel Italian airborne SAR system developed in the frame of cooperation between a public research institute (IREA-CNR) and a private company (Elettra Microwave S.r.l.). The system, which is named AXIS (standing for Airborne X-band Interferometric SAR), is based on FMCW technology and is equipped with a single-pass interferometric layout. In the work we first provide a description of the AXIS system. Then, we describe the acquisition campaign carried out in April 2018, just after the system completion. Finally, we perform an analysis of the radar data acquired during the campaign, by presenting a quantitative assessment of the quality of the SLC (Single Look Complex) SAR images and the interferometric products achievable through the system. The overall analysis aims at providing first reference values for future research and operational activities that will be conducted with this sensor.
Assessing the Impact of Tides and Atmospheric Fronts on Submesoscale Physical and Bio-Optical Distributions near a Coastal Convergence Zone
Authors: Gould Richard W. , Anderson Stephanie , Lewis M. David , Miller W. David , Shulman Igor , Smith Geoffrey B. , Smith Travis A. , Wang David W. , Wijesekera Hemantha W.
Published: February 2020

Optically-active constituents vary over short time and space scales in coastal waters, and they are impacted by a variety of complex, inter-related forcing processes. As part of the Integrated Coastal Bio-Optical Dynamics (ICoBOD) project, we conducted a field campaign in Mississippi Sound in the northern Gulf of Mexico during spring 2018 to examine the impact of the passage of atmospheric and tidal fronts on fine-scale physical and bio-optical property distributions in a shallow, dynamic, coastal environment. During a 25-day experiment, we deployed eight moorings over a roughly 7 × 7 km box encompassing a frontal zone, to collect a time series of physical and bio-optical measurements. We describe changes in diver visibility related to the passage of a short-duration, high-turbidity surface plume and nepheloid layer development/decay during a tidal cycle. Maximum nepheloid layer development was observed during low tide and lasted about 9–12 h. The strongest turbidity signal extended about 4–5 m above the bottom (approximately half of the water column), although anomalously elevated values were observed all the way to the surface. In addition, high-resolution (50 m) hydrodynamic model simulations provide insight into the frontal dynamics and aid interpretation of the observed patterns. Mooring observations confirmed model-predicted heat flux changes associated with the passage of an atmospheric cold front.
Comparison of True-Color and Multispectral Unmanned Aerial Systems Imagery for Marine Habitat Mapping Using Object-Based Image Analysis
Authors: Papakonstantinou Apostolos , Stamati Chrysa , Topouzelis Konstantinos
Published: February 2020

The use of unmanned aerial systems (UAS) over the past years has exploded due to their agility and ability to image an area with high-end products. UAS are a low-cost method for close remote sensing, giving scientists high-resolution data with limited deployment time, accessing even the most inaccessible areas. This study aims to produce marine habitat mapping by comparing the results produced from true-color RGB (tc-RGB) and multispectral high-resolution orthomosaics derived from UAS geodata using object-based image analysis (OBIA). The aerial data was acquired using two different types of sensors—one true-color RGB and one multispectral—both attached to a UAS, capturing images simultaneously. Additionally, divers’ underwater images and echo sounder measurements were collected as in situ data. The produced orthomosaics were processed using three scenarios by applying different classifiers for the marine habitat classification. In the first and second scenario, the k-nearest neighbor (k-NN) and fuzzy rules were applied as classifiers, respectively. In the third scenario, fuzzy rules were applied in the echo sounder data to create samples for the classification process, and then the k-NN algorithm was used as the classifier. The in situ data collected were used as reference and training data. Additionally, these data were used for the calculation of the overall accuracy of the OBIA process in all scenarios. The classification results of the three scenarios were compared. Using tc-RGB instead of multispectral data provides better accuracy in detecting and classifying marine habitats when applying the k-NN as the classifier. In this case, the overall accuracy was 79%, and the Kappa index of agreement (KIA) was equal to 0.71, which illustrates the effectiveness of the proposed approach. The results showed that sub-decimeter resolution UAS data revealed the sub-bottom complexity to a large extent in relatively shallow areas as they provide accurate information that permits the habitat mapping in extreme detail. The produced habitat datasets are ideal as reference data for studying complex coastal environments using satellite imagery.
DEM Generation With a Scale Factor Using Multi-Aspect SAR Imagery Applying Radargrammetry
Authors: Feng Shanshan , Lin Yun , Wang Yanping , Yang Yanhui , Shen Wenjie , Teng Fei , Hong Wen
Published: February 2020

Digital elevation model (DEM) generation using multi-aspect synthetic aperture radar (SAR) imagery applying radargrammetry has become a hotspot. The traditional radargrammetric method is to solve the rigorous radar projection equations to obtain the three dimensional coordinates of targets. In this paper, we propose a new DEM generation method based on the offset between multi-aspect images formed on ground plane. The ground object will be projected to different positions from different viewing aspect angles if the height of object is not equal to the height of imaging plane. The linear relationship between the offset of imaging positions and height of the object is derived and scale factor is obtained finally. Height information can be retrieved from offset of imaging positions directly through the DEM extraction model presented in this paper. Thus the solution to nonlinear equations point by point can be avoided. Real C band airborne circular SAR images is used to verify the proposed approach. When extracted DEM applied in multi-aspect imaging process, superimposition of multi-aspect images will no longer be defocusing and can achieve finer observation of the scanned scene.
Measurement System of a Magnetic Suspension System for a Jet Engine Rotor
Authors: Kurnyta-Mazurek Paulina , Kurnyta Artur , Henzel Maciej
Published: February 2020

This paper presents laboratory results on the measurement system of a magnetic suspension bearing system for a jet engine rotor of an unmanned aerial vehicle (UAV). Magnetic suspension technology enables continuous diagnostics of a rotary machine and eliminates of the negative properties of classical bearings. This rotor-bearing system consists of two radial magnetic bearings and one axial (thrust) magnetic bearing. The concept of the bearing system with a magnetically suspended rotor for UAV is presented in this paper. Rotor geometric and inertial characteristics were assumed according to the parameters of a TS-21 jet engine. Preliminary studies of the measurement system of rotor engines were made on a laboratory stand with homopolar active magnetic bearings. The measurement system consisted of strain gauges, accelerometers, and contactless proximity sensors. During the research, strains were registered with the use of a wireless data acquisition (DAQ) system. Measurements were performed for different operational parameters of rotational rotor speed, control system parameters, and with the presence of disturbance signals from the control system. In this paper, obtained operational characteristics are presented and discussed.
A Sound Source Identification Algorithm Based on Bayesian Compressive Sensing and Equivalent Source Method
Authors: Zan Ming , Xu Zhongming , Huang Linsen , Zhang Zhifei
Published: February 2020

Near-field acoustic holography (NAH) based on equivalent source method (ESM) is an effective method for identifying sound sources. Conventional ESM focuses on relatively low frequencies and cannot provide a satisfactory solution at high frequencies. So its improved method called wideband acoustic holography (WBH) has been proposed, which has high reconstruction accuracy at medium-to-high frequencies. However, it is less accurate for coherent sound sources at low frequencies. To improve the reconstruction accuracy of conventional ESM and WBH, a sound source identification algorithm based on Bayesian compressive sensing (BCS) and ESM is proposed. This method uses a hierarchical Laplace sparse prior probability distribution, and adaptively adjusts the regularization parameter, so that the energy is concentrated near the correct equivalent source. Referring to the function beamforming idea, the original algorithm with order v can improve its dynamic range, and then more accurate position information is obtained. Based on the simulation of irregular microphone array, comparisons with conventional ESM and WBH show that the proposed method is more accurate, suitable for a wider range of frequencies, and has better reconstruction performance for coherent sources. By increasing the order v, the coherent sources can be located accurately. Finally, the stability and reliability of the proposed method are verified by experiments.
The Design of CNN Architectures for Optimal Six Basic Emotion Classification Using Multiple Physiological Signals
Authors: Oh SeungJun , Lee Jun-Young , Kim Dong Keun
Published: February 2020

This study aimed to design an optimal emotion recognition method using multiple physiological signal parameters acquired by bio-signal sensors for improving the accuracy of classifying individual emotional responses. Multiple physiological signals such as respiration (RSP) and heart rate variability (HRV) were acquired in an experiment from 53 participants when six basic emotion states were induced. Two RSP parameters were acquired from a chest-band respiration sensor, and five HRV parameters were acquired from a finger-clip blood volume pulse (BVP) sensor. A newly designed deep-learning model based on a convolutional neural network (CNN) was adopted for detecting the identification accuracy of individual emotions. Additionally, the signal combination of the acquired parameters was proposed to obtain high classification accuracy. Furthermore, a dominant factor influencing the accuracy was found by comparing the relativeness of the parameters, providing a basis for supporting the results of emotion classification. The users of this proposed model will soon be able to improve the emotion recognition model further based on CNN using multimodal physiological signals and their sensors.
Predicting Forage Quality of Warm-Season Legumes by Near Infrared Spectroscopy Coupled with Machine Learning Techniques
Authors: Baath Gurjinder S. , Baath Harpinder K. , Gowda Prasanna H. , Thomas Johnson P. , Northup Brian K. , Rao Srinivas C. , Singh Hardeep
Published: February 2020

Warm-season legumes have been receiving increased attention as forage resources in the southern United States and other countries. However, the near infrared spectroscopy (NIRS) technique has not been widely explored for predicting the forage quality of many of these legumes. The objective of this research was to assess the performance of NIRS in predicting the forage quality parameters of five warm-season legumes—guar (Cyamopsis tetragonoloba), tepary bean (Phaseolus acutifolius), pigeon pea (Cajanus cajan), soybean (Glycine max), and mothbean (Vigna aconitifolia)—using three machine learning techniques: partial least square (PLS), support vector machine (SVM), and Gaussian processes (GP). Additionally, the efficacy of global models in predicting forage quality was investigated. A set of 70 forage samples was used to develop species-based models for concentrations of crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), and in vitro true digestibility (IVTD) of guar and tepary bean forages, and CP and IVTD in pigeon pea and soybean. All species-based models were tested through 10-fold cross-validations, followed by external validations using 20 samples of each species. The global models for CP and IVTD of warm-season legumes were developed using a set of 150 random samples, including 30 samples for each of the five species. The global models were tested through 10-fold cross-validation, and external validation using five individual sets of 20 samples each for different legume species. Among techniques, PLS consistently performed best at calibrating (R2c = 0.94–0.98) all forage quality parameters in both species-based and global models. The SVM provided the most accurate predictions for guar and soybean crops, and global models, and both SVM and PLS performed better for tepary bean and pigeon pea forages. The global modeling approach that developed a single model for all five crops yielded sufficient accuracy (R2cv/R2v = 0.92–0.99) in predicting CP of the different legumes. However, the accuracy of predictions of in vitro true digestibility (IVTD) for the different legumes was variable (R2cv/R2v = 0.42–0.98). Machine learning algorithms like SVM could help develop robust NIRS-based models for predicting forage quality with a relatively small number of samples, and thus needs further attention in different NIRS based applications.
Data-Driven Bias Correction and Defect Diagnosis Model for In-Service Vehicle Acceleration Measurements
Authors: Bai Lei , Liu Rengkui , Li Qing
Published: February 2020

Track quality instruments use low-cost accelerometers placed on or attached to the floors of operating trains, and these instruments collect substantial amounts of data over short inspection periods. The measurements collected by the instruments are the main data source for track irregularity evaluation. However, considerable measurement bias exists in the vertical and lateral vibration data obtained from such instruments. False positive track vibration defects detected by track quality instruments occur frequently. This results in considerable time and effort being expended needlessly because maintenance workers have to visit the railway track sites to check and review the track vibration defects. Therefore, we propose a model for data-driven bias correction and defect diagnosis for in-service vehicle acceleration measurements based on track degradation characteristics. Substantial amounts of historical track measurement data from different inspection methods were mined extensively to eliminate the false positive detection of track vibration defects and diagnose the causes of track vibration defects. Actual measurement data from the Lanxin Railway were used to validate our proposed model. The success rate achieved in identifying false positive track vibration defects was 84.1%, and that in track vibration defect diagnosis was 75.8%. These high success rates suggest that the proposed model can be of practical use in improving railway track maintenance management.
Deep Learning Assisted Buildings Energy Consumption Profiling Using Smart Meter Data
Authors: Ullah Amin , Haydarov Kilichbek , Ul Haq Ijaz , Muhammad Khan , Rho Seungmin , Lee Miyoung , Baik Sung Wook
Published: February 2020

The exponential growth in population and their overall reliance on the usage of electrical and electronic devices have increased the demand for energy production. It needs precise energy management systems that can forecast the usage of the consumers for future policymaking. Embedded smart sensors attached to electricity meters and home appliances enable power suppliers to effectively analyze the energy usage to generate and distribute electricity into residential areas based on their level of energy consumption. Therefore, this paper proposes a clustering-based analysis of energy consumption to categorize the consumers’ electricity usage into different levels. First, a deep autoencoder that transfers the low-dimensional energy consumption data to high-level representations was trained. Second, the high-level representations were fed into an adaptive self-organizing map (SOM) clustering algorithm. Afterward, the levels of electricity energy consumption were established by conducting the statistical analysis on the obtained clustered data. Finally, the results were visualized in graphs and calendar views, and the predicted levels of energy consumption were plotted over the city map, providing a compact overview to the providers for energy utilization analysis.
A Vision-Based Odometer for Localization of Omnidirectional Indoor Robots
Authors: Patruno Cosimo , Colella Roberto , Nitti Massimiliano , Renò Vito , Mosca Nicola , Stella Ettore
Published: February 2020

In this paper we tackle the problem of indoor robot localization by using a vision-based approach. Specifically, we propose a visual odometer able to give back the relative pose of an omnidirectional automatic guided vehicle (AGV) that moves inside an indoor industrial environment. A monocular downward-looking camera having the optical axis nearly perpendicular to the ground floor, is used for collecting floor images. After a preliminary analysis of images aimed at detecting robust point features (keypoints) takes place, specific descriptors associated to the keypoints enable to match the detected points to their consecutive frames. A robust correspondence feature filter based on statistical and geometrical information is devised for rejecting those incorrect matchings, thus delivering better pose estimations. A camera pose compensation is further introduced for ensuring better positioning accuracy. The effectiveness of proposed methodology has been proven through several experiments, in laboratory as well as in an industrial setting. Both quantitative and qualitative evaluations have been made. Outcomes have shown that the method provides a final positioning percentage error of 0.21% on an average distance of 17.2 m. A longer run in an industrial context has provided comparable results (a percentage error of 0.94% after about 80 m). The average relative positioning error is about 3%, which is still in good agreement with current state of the art.
A Maximum Likelihood Ensemble Filter via a Modified Cholesky Decomposition for Non-Gaussian Data Assimilation
Authors: Nino-Ruiz Elias David , Mancilla-Herrera Alfonso , Lopez-Restrepo Santiago , Quintero-Montoya Olga
Published: February 2020

This paper proposes an efficient and practical implementation of the Maximum Likelihood Ensemble Filter via a Modified Cholesky decomposition (MLEF-MC). The method works as follows: via an ensemble of model realizations, a well-conditioned and full-rank square-root approximation of the background error covariance matrix is obtained. This square-root approximation serves as a control space onto which analysis increments can be computed. These are calculated via Line-Search (LS) optimization. We theoretically prove the convergence of the MLEF-MC. Experimental simulations were performed using an Atmospheric General Circulation Model (AT-GCM) and a highly nonlinear observation operator. The results reveal that the proposed method can obtain posterior error estimates within reasonable accuracies in terms of ℓ − 2 error norms. Furthermore, our analysis estimates are similar to those of the MLEF with large ensemble sizes and full observational networks.
Open-Source Data Collection and Data Sets for Activity Recognition in Smart Homes
Authors: Köckemann Uwe , Alirezaie Marjan , Renoux Jennifer , Tsiftes Nicolas , Ahmed Mobyen Uddin , Morberg Daniel , Lindén Maria , Loutfi Amy
Published: February 2020

As research in smart homes and activity recognition is increasing, it is of ever increasing importance to have benchmarks systems and data upon which researchers can compare methods. While synthetic data can be useful for certain method developments, real data sets that are open and shared are equally as important. This paper presents the E-care@home system, its installation in a real home setting, and a series of data sets that were collected using the E-care@home system. Our first contribution, the E-care@home system, is a collection of software modules for data collection, labeling, and various reasoning tasks such as activity recognition, person counting, and configuration planning. It supports a heterogeneous set of sensors that can be extended easily and connects collected sensor data to higher-level Artificial Intelligence (AI) reasoning modules. Our second contribution is a series of open data sets which can be used to recognize activities of daily living. In addition to these data sets, we describe the technical infrastructure that we have developed to collect the data and the physical environment. Each data set is annotated with ground-truth information, making it relevant for researchers interested in benchmarking different algorithms for activity recognition.