bluuebunny/crossref_metadata_2025 huggingface dataset:
β-Carotene-Induced Alterations in Haemoglobin Affinity to O2
Authors: Fiedor Joanna , Przetocki Mateusz , Siniarski Aleksander , Gajos Grzegorz , Spiridis Nika , Freindl Kinga , Burda Kvetoslava
Published: March 2021

β-Carotene (β-Crt) can be dispersed in hydrophobic regions of the membrane of red blood cells (RBC). Its location, orientation and distribution strongly depend on carotenoid concentration. In the present pilot trial (six human subjects involved), it is demonstrated that incubation of RBCs with β-Crt (1.8 × 107 β-Crt molecules per RBC, 50 μmol/L) results in expansion of the membrane of RBCs and slight elongation of the cell. The changes are of statistical significance, as verified by the Wilcoxon test at p < 0.05. They indicate (i) a highly random orientation and location of β-Crt inside the membrane and (ii) a tendency for its interaction with membrane skeleton proteins. The accompanying effect of decreased RBC resistance to lysis is possibly a result of the incorrect functioning of ion channels due to their modification/disruption. At higher β-Crt concentrations, its clustering inside membranes may occur, leading to further alterations in the shape and size of RBCs, with the most pronounced changes observed at 1.8 × 108 β-Crt molecules per RBC (500 μmol/L). Due to the reduced permeability of ions, such membranes exhibit increased resistance to haemolysis. Finally, we show that interactions of β-Crt with the membrane of RBCs lead to an alteration in haemoglobin-oxygen affinity, shifting the oxyhaemoglobin dissociation curve toward higher oxygen partial pressures. If the impact of β-Crt on a curve course is confirmed in vivo, one may consider its role in the fine tuning of O2 transportation to tissues. Hence, at low concentrations, providing unchanged elastic and functional properties of RBCs, it could serve as a beneficial agent in optimising heart performance and cardiovascular load.
Paraoxonase-1 and Other HDL Accessory Proteins in Neurological Diseases
Authors: Marsillach Judit , Cervellati Carlo
Published: March 2021

The burden of neurological diseases continues to increase as they still are the leading cause of disability and the second-leading cause of death worldwide [...]
Changes in Gene Expression Profiling and Phenotype in Aged Multidrug Resistance Protein 4-Deficient Mouse Retinas
Authors: Kim Kyung Woo , Kusuhara Sentaro , Katsuyama-Yoshikawa Atsuko , Nobuyoshi Sho , Kitamura Megumi , Mori Sotaro , Sotani Noriyuki , Ueda Kaori , Matsumiya Wataru , Miki Akiko , Kurimoto Takuji , Imai Hisanori , Nakamura Makoto
Published: March 2021

Multidrug resistance protein 4 (MRP4) is an energy-dependent membrane transporter responsible for cellular efflux of a broad range of xenobiotics and physiological substrates. In this trial, we aimed to investigate the coeffects of aging and MRP4 deficiency using gene expression microarray and morphological and electrophysiological analyses of mouse retinas. Mrp4-knockout (null) mice and wild-type (WT) mice were reared in the same conditions to 8–12 weeks (young) or 45–55 weeks (aged). Microarray analysis identified 186 differently expressed genes from the retinas of aged Mrp4-null mice as compared to aged WT mice, and subsequent gene ontology and KEGG pathway analyses showed that differently expressed genes were related to lens, eye development, vision and transcellular barrier functions that are involved in metabolic pathways or viral infection pathways. No significant change in thickness was observed for each retinal layer among young/aged WT mice and young/aged Mrp4-null mice. Moreover, immunohistochemical analyses of retinal cell type did not exhibit an overt change in the cellular morphology or distribution among the four age/genotype groups, and the electroretinogram responses showed no significant differences in the amplitude or the latency between aged WT mice and aged Mrp4-null mice. Aging would be an insufficient stress to cause some damage to the retina in the presence of MRP4 deficiency.
13R,20-Dihydroxydocosahexaenoic Acid, a Novel Dihydroxy- DHA Derivative, Inhibits Breast Cancer Stemness through Regulation of the Stat3/IL-6 Signaling Pathway by Inducing ROS Production
Authors: Wang Lifang , Choi Hack Sun , Lee Binna , Choi Jong Hyun , Jang Yong-Suk , Seo Jeong-Woo
Published: March 2021

Breast cancer is a major health problem worldwide. Cancer stem cells (CSCs) are known to mediate breast cancer metastasis and recurrence and are therefore a promising therapeutic target. In this study, we investigated the anti-inflammatory effect of 13R,20-dihydroxydocosahexaenoic acid (13R,20-diHDHA), a novel dihydroxy-DHA derivative, which was synthesized through an enzymatic reaction using cyanobacterial lipoxygenase. We found that 13R,20-diHDHA reduced the macrophage secretion of the inflammatory cytokines, IL-6 and TNF-α, and thus appeared to have anti-inflammatory effects. As the inflammatory tumor microenvironment is largely devoted to supporting the cancer stemness of breast cancer cells, we investigated the effect of 13R,20-diHDHA on breast cancer stemness. Indeed, 13R,20-diHDHA effectively inhibited breast cancer stemness, as evidenced by its ability to dose-dependently inhibit the mammospheres formation, colony formation, migration, and invasion of breast CSCs. 13R,20-diHDHA reduced the populations of CD44high/CD24low and aldehyde dehydrogenase (ALDH)-positive cells and the expression levels of the cancer stemness-related self-renewal genes, Nanog, Sox2, Oct4, c-Myc, and CD44. 13R,20-diHDHA increased reactive oxygen species (ROS) production, and the generated ROS reduced the phosphorylation of nuclear signal transducer and activator of transcription 3 (Stat3) and the secretion of IL-6 by mammospheres. These data collectively suggest that 13R,20-diHDHA inhibits breast cancer stemness through ROS production and downstream regulation of Stat3/IL-6 signaling, and thus might be developed as an anti-cancer agent acting against CSCs.
Genetic Diversity of Blueberry Genotypes Estimated by Antioxidant Properties and Molecular Markers
Authors: Bhatt Dhrumit S. , Debnath Samir C.
Published: March 2021

Blueberries (Vaccinium spp.) have gained much attention worldwide because of their potential health benefits and economic importance. Genetic diversity was estimated in blueberry hybrids, wild clones and cultivars by their antioxidant efficacy, total phenolic and flavonoid contents, and express sequence tag–simple sequence repeat (SSR) (EST–SSR), genomic (G)–SSR and express sequence tag–polymerase chain reaction (EST–PCR) markers. Wide diversity existed among the genotypes for antioxidant properties, with the highest variation for DPPH radical scavenging activity (20-fold), followed by the contents of total flavonoids (16-fold) and phenolics (3.8-fold). Although a group of 11 hybrids generated the maximum diversity for antioxidant activity (15-fold), wild clones collected from Quebec, Canada, had the maximum variation for total phenolic (2.8-fold) and flavonoid contents (6.9-fold). Extensive genetic diversity was evident from Shannon’s index (0.34 for EST–SSRs, 0.29 for G–SSR, 0.26 for EST–PCR) and expected heterozygosity (0.23 for EST–SSR, 0.19 for G–SSR, 0.16 for EST–PCR). STRUCTURE analysis separated the genotypes into three groups, which were in agreement with principal coordinate and neighbour-joining analyses. Molecular variance suggested 19% variation among groups and 81% among genotypes within the groups. Clustering based on biochemical data and molecular analysis did not coincide, indicating a random distribution of loci in the blueberry genome, conferring antioxidant properties. However, the stepwise multiple regression analysis (SMRA) revealed that 17 EST–SSR, G–SSR and EST–PCR markers were associated with antioxidant properties. The study is valuable to breeding and germplasm conservation programs.
Arabic Gloss WSD Using BERT
Authors: El-Razzaz Mohammed , Fakhr Mohamed Waleed , Maghraby Fahima A.
Published: March 2021

Word Sense Disambiguation (WSD) aims to predict the correct sense of a word given its context. This problem is of extreme importance in Arabic, as written words can be highly ambiguous; 43% of diacritized words have multiple interpretations and the percentage increases to 72% for non-diacritized words. Nevertheless, most Arabic written text does not have diacritical marks. Gloss-based WSD methods measure the semantic similarity or the overlap between the context of a target word that needs to be disambiguated and the dictionary definition of that word (gloss of the word). Arabic gloss WSD suffers from a lack of context-gloss datasets. In this paper, we present an Arabic gloss-based WSD technique. We utilize the celebrated Bidirectional Encoder Representation from Transformers (BERT) to build two models that can efficiently perform Arabic WSD. These models can be trained with few training samples since they utilize BERT models that were pretrained on a large Arabic corpus. Our experimental results show that our models outperform two of the most recent gloss-based WSDs when we test them against the same test data used to evaluate our model. Additionally, our model achieves an F1-score of 89% compared to the best-reported F1-score of 85% for knowledge-based Arabic WSD. Another contribution of this paper is introducing a context-gloss benchmark that may help to overcome the lack of a standardized benchmark for Arabic gloss-based WSD.
Construction of All-Wood Trusses with Plywood Nodes and Wooden Pegs: A Strategy towards Resource-Efficient Timber Construction
Authors: Kromoser Benjamin , Braun Matthias , Ortner Maximilian
Published: March 2021

Timber truss systems are very efficient load-bearing structures. They allow for great freedom in design and are characterised by high material use in combination with a low environmental impact. Unfortunately, the extensive effort in design and production have made the manufacturing and application of these structures, in this day and age, a rarity. In addition, the currently mainly used steel gusset plates adversely affect the costs and environmental impact of the trusses. The authors’ goals are to optimise the design of timber trusses and to solely use wood for all building components. The two research areas, (1) optimisation of the truss geometry and (2) optimisation of the joints by using solely wood–wood connections, are addressed in this paper. The numerical optimisation strategy is based on a parametric design of the truss and the use of a genetic solver for the optimisation regarding minimal material consumption. Furthermore, first results of the tensile and compression behaviour of the chosen wood–wood connections are presented. The basic idea for the joints is to use a plywood plate as a connector, which is inserted into the truss members and fixed with wooden pegs. The housing of the new robot laboratory located at BOKU Vienna is considered a special case study for the research and serves as an accompanying example for the application of the research within the present paper.
Self-Tuning Algorithm for Tuneable Clamping Table for Chatter Suppression in Blade Recontouring
Authors: Sanz-Calle Markel , Dombovari Zoltan , Munoa Jokin , Iglesias Alexander , López de Lacalle Luis Norberto
Published: March 2021

The production and repair of blades for aerospace engines and energy turbines is a complex process due their inherently low stiffness and damping properties. The final recontouring operation is usually performed by milling operations where regenerative chatter is one of the main productivity limiting factors. With the objective of avoiding specific stiffening fixtures for each blade geometry, this paper proposes a semi-active tuneable clamping table (TCT) based on mode tuning for blade machining. The active mode of the device can be externally controlled by means of a rotary spring and eddy current damping modules. Its in-series architecture allows damping to be introduced to the critical mode of the thin-walled part without any direct contact in the machining area and enables a more universal clamping. Its chatter suppression capabilities are maximized by means of a novel self-tuning algorithm that iteratively optimizes the tuning for the measured chatter frequency. The benefits of the iterative algorithm are validated through semidiscretization and initial value time-domain simulations, showing a clear improvement in blade recontouring stability compared to regular broad-bandwidth tuning methods.
Recovery of Forest Vegetation in a Burnt Area in the Republic of Korea: A Perspective Based on Sentinel-2 Data
Authors: Kim Yunhee , Jeong Myeong-Hun , Youm Minkyo , Kim Junkyeong , Kim Jinpyung
Published: March 2021

Forest fires are severe disasters that cause significant damage in the Republic of Korea and the entire world, and an effort is being made to prevent forest fires internationally. The Republic of Korea budgets 3.38 million USD every year to prevent forest fires. However, an average of 430 wildfires occur nationwide annually. Thirty-eight percent of the forest fire budget is used for forest restoration. Restoring afforestation in the affected areas is a top priority. This study aimed to estimate the degree of vegetative regeneration using the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil-Adjustment Vegetation Index (SAVI), and Normalized Burn Ratio (NBR). Although many studies have used NBR with NDVI to extract plant regeneration regions, they suffer from atmospheric effects and soil brightness. Thus, this study utilizes NBR with NDVI, EVI, and SAVI to accurately select areas for targeted forest restoration. Furthermore, this study applies clustering analysis to extract the spatial boundary of vegetative regenerative regions. The proposed method suggests a pixel range of vegetation indices. These ranges can be used as an indicator, such as the NBR’s Fire Severity Level, which reflects the mountain’s local characteristics, meaning that it can be useful after forest fires. Using the three vegetation indices can extract more accurate vegetation areas than using NBR with NDVI and can help determine a forest restoration target area.
Shape Optimization of an Open Photoacoustic Resonator
Authors: El-Busaidy Said , Baumann Bernd , Wolff Marcus , Duggen Lars
Published: March 2021

Photoacoustic (PA) measurements with open resonators usually provide poor detection sensitivity due to signal leakage at the resonator opening. We have recently demonstrated three different approaches for modelling the photoacoustic signal of open resonators. In this work, one of the approaches is applied for the optimization of the geometry of the T-shaped resonator for improved signal strength and thus sensitivity. The results from the numerical optimization show an increase in the photoacoustic signal by a factor of approximately 7.23. They are confirmed using numerical methods other than the one applied for the optimization and by experimental measurement. The measurement shows an increase in the photoacoustic signal by a factor of approximately 2.34.
Thermodynamic Analysis of Compressed Air Energy Storage (CAES) Reservoirs in Abandoned Mines Using Different Sealing Layers
Authors: Prado Laura Álvarez de , Menéndez Javier , Bernardo-Sánchez Antonio , Galdo Mónica , Loredo Jorge , Fernández-Oro Jesús Manuel
Published: March 2021

Million cubic meters from abandoned mines worldwide could be used as subsurface reservoirs for large scale energy storage systems, such as adiabatic compressed air energy storage (A-CAES). In this paper, analytical and three-dimensional CFD numerical models have been conducted to analyze the thermodynamic performance of the A-CAES reservoirs in abandoned mines during air charging and discharging processes. Unlike other research works, in which the heat transfer coefficient is considered constant during the operation time, in the present investigation a correlation based on both unsteady Reynolds and Rayleigh numbers is employed for the heat transfer coefficient in this type of application. A tunnel with a 35 cm thick concrete lining, 200 m3 of useful volume and typical operating pressures from 5 to 8 MPa were considered. Fiber-reinforced plastic (FRP) and steel were employed as sealing layers in the simulations around the fluid. Finally, the model also considers a 2.5 m thick sandstone rock mass around the concrete lining. The results obtained show significant heat flux between the pressurized air and the sealing layer and between the sealing layer and concrete lining. However, no temperature fluctuation was observed in the rock mass. The air temperature fluctuations are reduced when steel sealing layer is employed. The thermal energy balance through the sealing layer for 30 cycles, considering air mass flow rates of 0.22 kg s−1 (charge) and −0.45 kg s−1 (discharge), reached 1056 and 907 kWh for FRP and steel, respectively. In general, good agreements between analytical and numerical simulations were obtained.
Variable Speed Limit and Ramp Metering for Mixed Traffic Flows: A Review and Open Questions
Authors: Vrbanić Filip , Ivanjko Edouard , Kušić Krešimir , Čakija Dino
Published: March 2021

The trend of increasing traffic demand is causing congestion on existing urban roads, including urban motorways, resulting in a decrease in Level of Service (LoS) and safety, and an increase in fuel consumption. Lack of space and non-compliance with cities’ sustainable urban plans prevent the expansion of new transport infrastructure in some urban areas. To alleviate the aforementioned problems, appropriate solutions come from the domain of Intelligent Transportation Systems by implementing traffic control services. Those services include Variable Speed Limit (VSL) and Ramp Metering (RM) for urban motorways. VSL reduces the speed of incoming vehicles to a bottleneck area, and RM limits the inflow through on-ramps. In addition, with the increasing development of Autonomous Vehicles (AVs) and Connected AVs (CAVs), new opportunities for traffic control are emerging. VSL and RM can reduce traffic congestion on urban motorways, especially so in the case of mixed traffic flows where AVs and CAVs can fully comply with the control system output. Currently, there is no existing overview of control algorithms and applications for VSL and RM in mixed traffic flows. Therefore, we present a comprehensive survey of VSL and RM control algorithms including the most recent reinforcement learning-based approaches. Best practices for mixed traffic flow control are summarized and new viewpoints and future research directions are presented, including an overview of the currently open research questions.
Design, Manufacturing, Validation of a Multi-Orientation Tilt Test Bench for Testing Vehicles Rollover and Tests of ATV-Quad for Agricultural Applications
Authors: Bacaicoa Julen , Ballesteros Tomás , Arana Ignacio , Aginaga Jokin , Latorre-Biel Juan-Ignacio
Published: March 2021

A rollover test bench has been designed, manufactured, and validated for analyzing the all-terrain vehicle (ATV)-Quad overturn of diverse vehicles in different configurations, such as installing a rollover protection system (ROPS), considering drivers of different physical constitutions, the appropriate use of safety belts, or having a full or empty fuel tank. The main purpose of this research is to determine the tilt angle of the vehicle that triggers the ATV-Quad overturn. The scope of the design and development of the newly conceived bench include the mechanical structure, the electronics and the control. It can simulate static and dynamic rollover in different directions. As a main conclusion, it can be stated that the performance of the test bench was successful, since it allowed for the development of several ATV models, equipped with different dummies, in a variety of configurations. In particular, it was possible to assess the effectivity of the AD-ROPS system (automatically deployed ROPS), regarding the protection of the driver of the vehicle. Moreover, multiple tests, performed with diverse ATV-Quads in the developed bench and in different configurations, have been reported and their results discussed.
Synthesis and Physicochemical Properties of [(1R,2S,5R)-2-isopropyl-5-methylcyclohexyloxy]-thiophen-5-yl-substituted Tetrapyrazinoporphyrazine with Magnesium(II) Ion
Authors: Lijewski Sebastian , Tydlitát Jiří , Czarczynska-Goslinska Beata , Klikar Milan , Mielcarek Jadwiga , Goslinski Tomasz , Sobotta Lukasz
Published: March 2021

Tetrapyrazinoporphyrazine with peripheral menthol-thiophenyl substituents was synthesized using Linstead conditions and purified by flash column chromatography. The optimized synthetic and purification procedures allowed us to obtain a new macrocycle with 36% yield. Tetrapyrazinoporphyrazine derivative was characterized by UV–Vis and NMR spectroscopy, as well as MS spectrometry. Complex NMR studies using 1D and 2D NMR techniques allowed the analysis of the bulky menthol-thiophenyl substituted periphery of the new macrocycle. Further, photochemical stability and singlet oxygen quantum yield were determined by indirect method with diphenylisobenzofuran. The new tetrapyrazinoporphyrazine revealed low generation of singlet oxygen with a quantum yield of singlet oxygen formation at 2.3% in dimethylformamide. In turn, the macrocycle under irradiation with visible light presented very high stability with quantum yield for photostability of 9.59 × 10−6 in dimethylformamide, which figures significantly exceed the border for its classification as a stable porphyrinoid (10−4–10−5).
Numerical Analysis and Experimental Test for the Development of a Small Shaped Charge
Authors: Malesa Piotr , Sławiński Grzegorz , Pęcherzewska Karolina
Published: March 2021

Currently, shaped charges are widely used in many fields of science and industry. Due to the high efficiency of piercing materials with high strength and hardness, shaped charges are commonly used in mining, military and for structural damage. The main application area of shaped charges is the military industry, where they are used in missiles with warheads (torpedoes, rocket launchers) and for piercing vehicle armor or bunker walls. When analyzing the existing solutions of shaped charges, one can find many typical solutions designed for specific applications. However, there are no universal constructions which, after appropriate regulation, will fulfil their role in a wide range of applications. The subject of this article is a new solution for a shaped charge that is characterized by compact dimensions and a short preparation time. This article presents the results of experimental research and the numerical analyses of such a charge.
An Energy-Efficient Strategy for Microcontrollers
Authors: Wu Huanjie , Chen Chun , Weng Kai
Published: March 2021

Power saving has always been an important research direction in the field of microcontrollers. Dozens of low power technologies have been proposed to achieve the goal of reducing their power consumption. However, most of them focus mostly on lowering the consumption rate. It is well known that energy is the integral of power over time. Thus, our view is that both power and time should be carefully considered to achieve better energy efficiency. We reviewed some commonly used low power technologies and proposed our assumptions and strategy for improving energy efficiency. A series of test sets are designed to validate our hypotheses for improving energy efficiency. The experimental results suggest that time has no less impact on energy consumption than power. To support the operation of the processor, some peripheral components consume a constant amount of power regardless of the clock frequency, but the power consumption will be reduced when the processor enters low-power modes. This results in some interesting phenomena that are different from the usual thinking that energy can be saved by increasing processor clock frequency. For STM32F407 and Xtensa LX6 processors, this article also analyzes and calculates the minimum sleep time required for achieving energy saving based on our analytical models. Our energy efficiency strategy has been verified, and in some cases, it can indeed improve energy efficiency. We also proposed some suggestions on hardware design and software development for better energy efficiency.
Influence of Altitude and Image Overlap on Minimum Mapping Size of Chemical in Non-Destructive Trace Detection Using Hyperspectral Remote Sensing
Authors: Chaudhary Siddharth , Ninsawat Sarawut , Nakamura Tai
Published: March 2021

The increasing threat of explosives is a serious issue affecting socio-economy of many countries at multiple levels, such as public security, unused arable land, closing of trade routes, isolation of villages, and these act as a hindrance in the development of the country. Activities using explosives have increased in the last two decades making it a global threat that is challenging humanity. In this study, different chemicals such as Ammonium Nitrate (AN), Trinitrotoluene (TNT) and C4 along with soil as the background material were used for trace detection. The aim of this study was to determine an altitude for the sensor and to identify the minimum mapping size of the chemical at which the model can achieve 70% accuracy. To determine the altitude and minimum size of the chemical that can be detected in the acceptable range of accuracy, several experiments were performed in real ground conditions. This study focuses on the applicability of the proposed method in the real world. In the first set of experiments, the altitude of the sensor was varied from 40 cm to 150 cm and the accuracy of the model was determined. From the analysis, it was found that the model achieved more than 75% accuracy at an altitude of 90 cm with an image overlap of 70%. In the second set of experiments, the minimum size of chemical sample was varied from 0.25 cm to 1 cm. The accuracy of the model was more than 70% when the minimum sample size was 0.5 cm or greater. For various altitude determined, the speed of the vehicle was calculated. Therefore, to implement hyperspectral imaging system on the unarmed vehicle for real application, the suggested altitude and speed of the sensor should be around 90 cm and 10.5 cm/s at which detection limit would be equal or more than 0.5 cm with accuracy greater than 70%.
Reliability Evaluation of the Factors That Influenced COVID-19 Patients’ Condition
Authors: Levashenko Vitaly , Rabcan Jan , Zaitseva Elena
Published: March 2021

Health and safety is a problem that is intensively discussed nowadays. The failures in healthcare are called medical errors: if the patient’s condition worsens or he/she contracts an illness, then the actions that led to this are interpreted as medical errors. Medical errors can be the result of new procedures, extremes of age, complex or urgent care, improper documentation, illegible hand-writing, or patient actions. One of the ways to reduce medical error is an evaluation of its possibility, and then using the result of this evaluation to improve the medical organization units and processes in patient diagnosis, treatment, and care. This evaluation is possible based on methods of reliability engineering. The reliability engineering methods allow evaluating of different systems’ reliability and the influence of external and internal factors on system reliability. These methods’ application needs the system to be investigated or objective interpretation in terms of reliability engineering. Therefore, such a system in healthcare, for the diagnosis of disease, a patient’s treatment, the influence of different factors on a patient’s condition, and others, should be presented according to the rules and demands of reliability engineering. The first step is development of the mathematical representation of the investigated system or object according to the demands of the reliability analysis. One of the often-used mathematical representations in the reliability analysis of a system is the structure function. However, this mathematical representation needs completely specified initial data. The initial data from the healthcare domain for medical error analysis is uncertain and incompletely specified. Therefore, the development of this mathematical representation needs special methods. In this paper, a new method for the mathematical representation of system development based on uncertain and incompletely specified data is proposed. The system evaluation based on the structure function allows computing of many reliability indices and measures used in reliability engineering. The approbation of this method is considered based on an example of COVID-19 patients.
Leading Edge Blowing to Mimic and Enhance the Serration Effects for Aerofoil
Authors: Al-Okbi Yasir , Chong Tze Pei , Stalnov Oksana
Published: March 2021

Leading edge serration is now a well-established and effective passive control device for the reduction of turbulence–leading edge interaction noise, and for the suppression of boundary layer separation at high angle of attack. It is envisaged that leading edge blowing could produce the same mechanisms as those produced by a serrated leading edge to enhance the aeroacoustics and aerodynamic performances of aerofoil. Aeroacoustically, injection of mass airflow from the leading edge (against the incoming turbulent flow) can be an effective mechanism to decrease the turbulence intensity, and/or alter the stagnation point. According to classical theory on the aerofoil leading edge noise, there is a potential for the leading edge blowing to reduce the level of turbulence–leading edge interaction noise radiation. Aerodynamically, after the mixing between the injected air and the incoming flow, a shear instability is likely to be triggered owing to the different flow directions. The resulting vortical flow will then propagate along the main flow direction across the aerofoil surface. These vortical flows generated indirectly owing to the leading edge blowing could also be effective to mitigate boundary layer separation at high angle of attack. The objectives of this paper are to validate these hypotheses, and combine the serration and blowing together on the leading edge to harvest further improvement on the aeroacoustics and aerodynamic performances. Results presented in this paper strongly indicate that leading edge blowing, which is an active flow control method, can indeed mimic and even enhance the bio-inspired leading edge serration effectively.
In Vivo Positive Magnetic Resonance Imaging of Brain Cancer (U87MG) Using Folic Acid-Conjugated Polyacrylic Acid-Coated Ultrasmall Manganese Oxide Nanoparticles
Authors: Marasini Shanti , Yue Huan , Ho Son-Long , Park Ji-Ae , Kim Soyeon , Yang Ji-Ung , Cha Hyunsil , Liu Shuwen , Tegafaw Tirusew , Ahmad Mohammad Yaseen , Saidi Abdullah Khamis Ali Al , Zhao Dejun , Liu Ying , Chae Kwon-Seok , Chang Yongmin , Lee Gang-Ho
Published: March 2021

Ultrasmall nanoparticles are potential candidates for application as high-performance imaging agents. Herein, we present the synthesis and characterization of folic acid (FA)-conjugated polyacrylic acid (PAA)-coated MnO nanoparticles with an average particle diameter of 2.7 nm. FA conferred cancer-targeting ability, while PAA conferred good colloidal stability and low cellular cytotoxicity on the FA-PAA-coated MnO nanoparticles. Further, the nanoparticles exhibited a high relaxivity (r1) value of 9.3 s−1mM−1 (r2/r1 = 2.2). Their application potential as cancer-targeting T1 magnetic resonance imaging contrast agents was confirmed by their enhanced T1 contrast enhancements at the brain cancer (U87MG) site upon intravenous administration to mice tails.
In-Situ Photo-Dissociation and Polymerization of Carbon Disulfide with Vacuum Ultraviolet Microplasma Flat Lamp for Organic Thin Films
Authors: Kim Jinhong , Park Sung-Jin
Published: March 2021

Vacuum UV (VUV) photo-dissociation for a liquid phase organic compound, carbon disulfide (CS2), has been investigated. 172 nm (7.2 eV) VUV photons from Xe2* excimers in a microcavity plasma lamp irradiated free-standing liquid droplets on Si substrate in each a nitrogen environment and an atmospheric air environment. Selective and rapid dissociation of CS2 into C-C, C-S or C-O-S based fragments was observed in the different gas environments during the reaction. Thin-layered polymeric microdeposites have been identified by characterization with a Scanning electron microscope (SEM), Energy dispersive x-ray spectroscopy (EDX), Raman spectroscopy and X-ray photoelectron spectroscopy (XPS). This novel photo-process from the flat VUV microplasma lamp introduces another pathway of low-temperature organic (or synthetic) conversion for large area deposition. The in-situ, selective conversion of various organic precursors can be potentially used in optoelectronics and nanotechnology applications.
Kernel Point Convolution LSTM Networks for Radar Point Cloud Segmentation
Authors: Nobis Felix , Fent Felix , Betz Johannes , Lienkamp Markus
Published: March 2021

State-of-the-art 3D object detection for autonomous driving is achieved by processing lidar sensor data with deep-learning methods. However, the detection quality of the state of the art is still far from enabling safe driving in all conditions. Additional sensor modalities need to be used to increase the confidence and robustness of the overall detection result. Researchers have recently explored radar data as an additional input source for universal 3D object detection. This paper proposes artificial neural network architectures to segment sparse radar point cloud data. Segmentation is an intermediate step towards radar object detection as a complementary concept to lidar object detection. Conceptually, we adapt Kernel Point Convolution (KPConv) layers for radar data. Additionally, we introduce a long short-term memory (LSTM) variant based on KPConv layers to make use of the information content in the time dimension of radar data. This is motivated by classical radar processing, where tracking of features over time is imperative to generate confident object proposals. We benchmark several variants of the network on the public nuScenes data set against a state-of-the-art pointnet-based approach. The performance of the networks is limited by the quality of the publicly available data. The radar data and radar-label quality is of great importance to the training and evaluation of machine learning models. Therefore, the advantages and disadvantages of the available data set, regarding its radar data, are discussed in detail. The need for a radar-focused data set for object detection is expressed. We assume that higher segmentation scores should be achievable with better-quality data for all models compared, and differences between the models should manifest more clearly. To facilitate research with additional radar data, the modular code for this research will be made available to the public.
Multicriteria Analysis of Alternative Marine Fuels in Sustainable Coastal Marine Traffic
Authors: Mandić Nikola , Ukić Boljat Helena , Kekez Toni , Luttenberger Lidija Runko
Published: March 2021

Marine transportation is considered to be one of the most important aspects of global transportation services. Due to the increase in marine transportation, there are significant impacts on the marine environment. One of the possible measures for mitigation of the environmental impact could be switching to environmentally friendly fuel. However, the alternative fuel selection process is considered to be a problem due to various criteria to be considered and stakeholders that should be involved in the selection process. The aim of this paper is to demonstrate the application of multicriteria analysis as a decision-support tool for the alternative marine fuel selection problem in coastal marine traffic. The suggested methodology takes into account environmental, technological, and economic aspects, and ensures the participation of different stakeholders in the selection process. The priority ranking of the alternatives is based on a combination of the Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW). The implementation of this method considers the involvement of relevant stakeholders through evaluation of the criteria weights and performance of each alternative with respect to each criterion. The method is applied for the case study of Croatia, where the results demonstrated that the best alternative for all stakeholders is electric propulsion, even though there are differences in opinions and perceptions with respect to the objectives and criteria. The findings of this analysis, likely the first of this type in this area, can serve as a solid basis for strategic planning.
Decision Support for Removing Fractured Endodontic Instruments: A Patient-Specific Approach
Authors: Richert Raphaël , Farges Jean-Christophe , Villat Cyril , Valette Sébastien , Boisse Philippe , Ducret Maxime
Published: March 2021

The instrumental fracture is a common endodontic complication that is treated by surgical or non-surgical removal approaches. However, no tool exists to help the clinician to choose between available strategies, and decision-making is mostly based on clinical judgment. Digital solutions, such as Finite Element Analysis (FEA) and Virtual Treatment Planning (VTP), were recently proposed in maxillofacial surgery. The aim of the current study is to present a digital tool to help decide between non-surgical and surgical strategies in a clinical situation of a fractured instrument. Five models have been created: the initial state of the patient, two non-surgical removal strategies using a low or high root canal enlargement, and two surgical removal strategies using a 3- or 6-mm apicoectomy. Results of the VTP found a risk of perforation for the non-surgical strategies and sinus proximity for surgical ones. FEA showed the lowest mechanical risk for the apicoectomy strategy. A 3-mm apicoectomy approach was finally chosen and performed. In conclusion, this digital approach could offer a promising decision support for instrument removal by planning the treatment and predicting the mechanical impact of each strategy, but further investigations are required to confirm its relevance in endodontic practice.
Gross Ammonification and Nitrification Rates in Soil Amended with Natural and NH4-Enriched Chabazite Zeolite and Nitrification Inhibitor DMPP
Authors: Ferretti Giacomo , Galamini Giulio , Deltedesco Evi , Gorfer Markus , Fritz Jennifer , Faccini Barbara , Mentler Axel , Zechmeister-Boltenstern Sophie , Coltorti Massimo , Keiblinger Katharina Maria
Published: March 2021

Using zeolite-rich tuffs for improving soil properties and crop N-use efficiency is becoming popular. However, the mechanistic understanding of their influence on soil N-processes is still poor. This paper aims to shed new light on how natural and NH4+-enriched chabazite zeolites alter short-term N-ammonification and nitrification rates with and without the use of nitrification inhibitor (DMPP). We employed the 15N pool dilution technique to determine short-term gross rates of ammonification and nitrification in a silty-clay soil amended with two typologies of chabazite-rich tuff: (1) at natural state and (2) enriched with NH4+-N from an animal slurry. Archaeal and bacterial amoA, nirS and nosZ genes, N2O-N and CO2-C emissions were also evaluated. The results showed modest short-term effects of chabazite at natural state only on nitrate production rates, which was slightly delayed compared to the unamended soil. On the other hand, the addition of NH4+-enriched chabazite stimulated NH4+-N production, N2O-N emissions, but reduced NO3−-N production and abundance of nirS-nosZ genes. DMPP efficiency in reducing nitrification rates was dependent on N addition but not affected by the two typologies of zeolites tested. The outcomes of this study indicated the good compatibility of both natural and NH4+-enriched chabazite zeolite with DMPP. In particular, the application of NH4+-enriched zeolites with DMPP is recommended to mitigate short-term N losses.
An Improved VGG19 Transfer Learning Strip Steel Surface Defect Recognition Deep Neural Network Based on Few Samples and Imbalanced Datasets
Authors: Wan Xiang , Zhang Xiangyu , Liu Lilan
Published: March 2021

The surface defects’ region of strip steel is small, and has various defect types and, complex gray structures. There tend to be a large number of false defects and edge light interference, which lead traditional machine vision algorithms to be unable to detect defects for various types of strip steel. Image detection techniques based on deep learning require a large number of images to train a network. However, for a dataset with few samples with category imbalanced defects, common deep learning neural network training tasks cannot be carried out. Based on rapid image preprocessing algorithms (improved gray projection algorithm, ROI image augmentation algorithm) and transfer learning theory, this paper proposes a set of processes for complete strip steel defect detection. These methods achieved surface rapid screening, defect feature extraction, sample dataset’s category balance, data augmentation, defect detection, and classification. Through verification of the mixed dataset, composed of the NEU surface dataset and dataset in this paper, the recognition accuracy of the improved VGG19 network in this paper reached 97.8%. The improved VGG19 network performs slightly better than the baseline VGG19 in six types of defects, but the improved VGG19 performs significantly better in the surface seams defects. The convergence speed and accuracy of the improved VGG19 network were taken into account, and the detection rate was greatly improved with few samples and imbalanced datasets. This paper also has practical value in terms of extending its method of strip steel defect detection to other products.
Turn-to-Turn Fault Diagnosis on Three-Phase Power Transformer Using Hybrid Detection Algorithm
Authors: Liu Chien-Hsun , Muda Willybrordus H. P. , Kuo Cheng-Chien
Published: March 2021

A power transformer (PT) in power generation or transmission is critical to maintaining electrical continuity. Fault detection on a PT is needed, especially of incipient faults, which are often caused by a turn-to-turn fault (TTF) before it develops into a more severe fault. We use a hybrid algorithm between conventional and modern techniques to detect a developing fault in a PT. The current response signals from a negative sequence current directional algorithm, extended park vector algorithm (EPVA), differential negative sequence current, and EPVA-fuzzy system are combined to distinguish the possibility of a TTF. The subalgorithms are combined using a hybrid detection algorithm to distinguish the faults. The model is a 10 MVA, three-phase PT with Δ-Y configuration 150/300 kV, simulated using MATLAB Simulink software. The results show that by combining the subalgorithms, several limitations are distinguished within the TTF with a slight increase in accuracy.
Automated Structural Damage Identification Using Data Normalization and 1-Dimensional Convolutional Neural Network
Authors: Won Jongbin , Park Jong-Woong , Jang Soojin , Jin Kyohoon , Kim Youngbin
Published: March 2021

In the field of structural-health monitoring, vibration-based structural damage detection techniques have been practically implemented in recent decades for structural condition assessment. With the development of deep-learning networks that make automatic feature extraction and high classification accuracy possible, deep-learning-based structural damage detection has been gaining significant attention. The deep-learning neural networks come with fixed input and output size, and input data must be downsampled or cropped to the predetermined input size of the networks to obtain desired output of the network. However, the length of input data (i.e., sensing data) is associated with the excitation quality of a structure, adjusting the size of the input data while maintaining the excitation quality is critical to ensure high accuracy of the deep-learning-based structural damage detection. To address this issue, natural-excitation-technique-based data normalization and the use of 1-D convolutional neural networks for automated structural damage detection are presented. The presented approach converts input data to predetermined size using cross-correlation and uses convolutional network to extract damage-sensitive feature for automated structural damage identification. Numerical simulations were conducted on a simply supported beam model excited by random and traffic loadings, and the performance was validated under various scenarios. The proposed method successfully detected the location of damage on a beam under random and traffic loadings with accuracies of 99.90% and 99.20%, respectively.
Additive Manufacturing of Porous Ti6Al4V Alloy: Geometry Analysis and Mechanical Properties Testing
Authors: Hudák Radovan , Schnitzer Marek , Králová Zuzana Orságová , Gorejová Radka , Mitrík Lukáš , Rajťúková Viktória , Tóth Teodor , Kovačević Mila , Riznič Marcel , Oriňaková Renáta , Živčák Jozef
Published: March 2021

This work is devoted to the research of porous titanium alloy structures suitable for use in biomedical applications. Mechanical properties were examined on six series of samples with different structures and porosity via static compressive test to identify the type of structure suitable for elimination of the “stress shielding” effect. In addition, high porosity is desirable due to the overgrowth of bone tissue into the internal structure of the implant. The samples were made of titanium alloy Ti6Al4V by using selective laser melting (SLM) additive manufacturing. The series of samples differ from each other in pore size (200, 400, and 600 µm) and porous structure topology (cubic or trabecular). The actual weight of all samples, which plays an important role in identifying other characteristics, was determined. Compressive tests were focused on the detection of maximum stress. The highest porosity and thus the lowest weight were achieved in the samples with a trabecular structure and 600 µm pore size. All tested samples reached optimal values of maximum stress and tensile strength. The most appropriate mechanical properties were observed for samples with a 200 µm pore diameter and cubic structure.
Design of a Fuzzy Optimization Control Structure for Nonlinear Systems: A Disturbance-Rejection Method
Authors: Charfeddine Samia , Boudjemline Attia , Ben Aoun Sondess , Jerbi Houssem , Kchaou Mourad , Alshammari Obaid , Elleuch Zied , Abbassi Rabeh
Published: March 2021

This paper tackles the control problem of nonlinear disturbed polynomial systems using the formalism of output feedback linearization and a subsequent sliding mode control design. This aims to ensure the asymptotic stability of an unstable equilibrium point. The class of systems under investigation has an equivalent Byrnes–Isidori normal form, which reveals stable zero dynamics. For the case of modeling uncertainties and/or process dynamic disturbances, conventional feedback linearizing control strategies may fail to be efficient. To design a robust control strategy, meta-heuristic techniques are synthesized with feedback linearization and sliding mode control. The resulting control design guarantees the decoupling of the system output from disturbances and achieves the desired output trajectory tracking with asymptotically stable dynamic behavior. The effectiveness and efficiency of the designed technique were assessed based on a benchmark model of a continuous stirred tank reactor (CSTR) through numerical simulation analysis.