bluuebunny/crossref_metadata_2025 huggingface dataset:
Combining Genetic and Multidimensional Analyses to Identify Interpretive Traits Related to Water Shortage Tolerance as an Indirect Selection Tool for Detecting Genotypes of Drought Tolerance in Wheat Breeding
Authors: Al-Ashkar Ibrahim , Al-Suhaibani Nasser , Abdella Kamel , Sallam Mohammed , Alotaibi Majed , Seleiman Mahmoud F.
Published: May 2021

Water shortages have direct adverse effects on wheat productivity and growth worldwide, vertically and horizontally. Productivity may be promoted using water shortage-tolerant wheat genotypes. High-throughput tools have supported plant breeders in increasing the rate of stability of the genetic gain of interpretive traits for wheat productivity through multidimensional technical methods. We used 27 agrophysiological interpretive traits for grain yield (GY) of 25 bread wheat genotypes under water shortage stress conditions for two seasons. Genetic parameters and multidimensional analyses were used to identify genetic and phenotypic variations of the wheat genotypes used, combining these strategies effectively to achieve a balance. Considerable high genotypic variations were observed for 27 traits. Eleven interpretive traits related to GY had combined high heritability (h2 > 60%) and genetic gain (>20%), compared to GY, which showed moderate values both for heritability (57.60%) and genetic gain (16.89%). It was determined that six out of eleven traits (dry leaf weight (DLW), canopy temperature (CT), relative water content (RWC), flag leaf area (FLA), green leaves area (GLA) and leaf area index (LAI)) loaded the highest onto PC1 and PC2 (with scores of >0.27), and five of them had a positive trend with GY, while the CT trait had a negative correlation determined by principal component analysis (PCA). Genetic parameters and multidimensional analyses (PCA, stepwise regression, and path coefficient) showed that CT, RWC, GLA, and LAI were the most important interpretive traits for GY. Selection based on these four interpretive traits might improve genetic gain for GY in environments that are vulnerable to water shortages. The membership index and clustering analysis based on these four traits were significantly correlated, with some deviation, and classified genotypes into five groups. Highly tolerant, tolerant, intermediate, sensitive and highly sensitive clusters represented six, eight, two, three and six genotypes, respectively. The conclusions drawn from the membership index and clustering analysis, signifying that there were clear separations between the water shortage tolerance groups, were confirmed through discriminant analysis. MANOVA indicated that there were considerable variations between the five water shortage tolerance groups. The tolerated genotypes (DHL02, DHL30, DHL26, Misr1, Pavone-76 and DHL08) can be recommended as interesting new genetic sources for water shortage-tolerant wheat breeding programs.
Polymer 3D Printing Review: Materials, Process, and Design Strategies for Medical Applications
Authors: Arefin Amit M. E. , Khatri Nava Raj , Kulkarni Nitin , Egan Paul F.
Published: May 2021

Polymer 3D printing is an emerging technology with recent research translating towards increased use in industry, particularly in medical fields. Polymer printing is advantageous because it enables printing low-cost functional parts with diverse properties and capabilities. Here, we provide a review of recent research advances for polymer 3D printing by investigating research related to materials, processes, and design strategies for medical applications. Research in materials has led to the development of polymers with advantageous characteristics for mechanics and biocompatibility, with tuning of mechanical properties achieved by altering printing process parameters. Suitable polymer printing processes include extrusion, resin, and powder 3D printing, which enable directed material deposition for the design of advantageous and customized architectures. Design strategies, such as hierarchical distribution of materials, enable balancing of conflicting properties, such as mechanical and biological needs for tissue scaffolds. Further medical applications reviewed include safety equipment, dental implants, and drug delivery systems, with findings suggesting a need for improved design methods to navigate the complex decision space enabled by 3D printing. Further research across these areas will lead to continued improvement of 3D-printed design performance that is essential for advancing frontiers across engineering and medicine.
Design of Kinematic Connectors for Microstructured Materials Produced by Additive Manufacturing
Authors: Silva Miguel R. , Dias-de-Oliveira João A. , Pereira António M. , Alves Nuno M. , Sampaio Álvaro M. , Pontes António J.
Published: May 2021

The main characteristic of materials with a functional gradient is the progressive composition or the structure variation across its geometry. This results in the properties variation in one or more specific directions, according to the functional application requirements. Cellular structure flexibility in tailoring properties is employed frequently to design functionally-graded materials. Topology optimisation methods are powerful tools to functionally graded materials design with cellular structure geometry, although continuity between adjacent unit-cells in gradient directions remains a restriction. It is mandatory to attain a manufacturable part to guarantee the connectedness between adjoining microstructures, namely by ensuring that the solid regions on the microstructure’s borders i.e., kinematic connectors) match the neighboring cells that share the same boundary. This study assesses the kinematic connectors generated by imposing local density restrictions in the initial design domain (i.e., nucleation) between topologically optimised representative unit-cells. Several kinematic connector examples are presented for two representatives unit-cells topology optimised for maximum bulk and shear moduli with different volume fractions restrictions and graduated Young’s modulus. Experimental mechanical tests (compression) were performed, and comparison studies were carried out between experimental and numerical Young’s modulus. The results for the single maximum bulk for the mean values for experimental compressive Young’s modulus (Ex¯) with 60%Vf show a deviation of 9.15%. The single maximum shear for the experimental compressive Young’s modulus mean values (Ex¯) with 60%Vf, exhibit a deviation of 11.73%. For graded structures, the experimental mean values of compressive Young’s moduli (Ex¯), compared with predicted total Young’s moduli (ESe), show a deviation of 6.96 for the bulk graded structure. The main results show that the single type representative unit-cell experimental Young’s modulus with higher volume fraction presents a minor deviation compared with homogenized data. Both (i.e., bulk and shear moduli) graded microstructures show continuity between adjacent cells. The proposed method proved to be suitable for generating kinematic connections for the design of shear and bulk graduated microstructured materials.
Incorporation of Elastin to Improve Polycaprolactone-Based Scaffolds for Skeletal Muscle via Electrospinning
Authors: Perez-Puyana Victor , Villanueva Paula , Jiménez-Rosado Mercedes , de la Portilla Fernando , Romero Alberto
Published: May 2021

Skeletal muscle regeneration is increasingly necessary, which is reflected in the increasing number of studies that are focused on improving the scaffolds used for such regeneration, as well as the incubation protocol. The main objective of this work was to improve the characteristics of polycaprolactone (PCL) scaffolds by incorporating elastin to achieve better cell proliferation and biocompatibility. In addition, two cell incubation protocols (with and without dynamic mechanical stimulation) were evaluated to improve the activity and functionality yields of the regenerated cells. The results indicate that the incorporation of elastin generates aligned and more hydrophilic scaffolds with smaller fiber size. In addition, the mechanical properties of the resulting scaffolds make them adequate for use in both bioreactors and patients. All these characteristics increase the biocompatibility of these systems, generating a better interconnection with the tissue. However, due to the low maturation achieved in biological tests, no differences could be found between the incubation with and without dynamic mechanical stimulation.
Production of Cyclic Anhydride-Modified Starches
Authors: Amos Ryan C. , Mesnager Julien , Kuska Michael , Gauthier Mario
Published: May 2021

Modified starches offer a biodegradable, readily available, and cost-effective alternative to petroleum-based products. The reaction of alkenylsuccinic anhydrides (ASAs), in particular, is an efficient method to produce amphiphilic starches with numerous applications in different areas. While ASAs are typically derived from petroleum sources, maleated soybean oil can also be used in an effort to produce materials from renewable sources. The reaction of gelatinized waxy maize starch with octenylsuccinic anhydride (OSA), dodecenylsuccinic anhydride (DDSA), a maleated fatty acid (TENAX 2010), phthalic anhydride (PA), 1,2,4-benzenetricarboxylic acid anhydride (trimellitic anhydride, TMA), and three maleated soybean oil samples, was investigated under different conditions. To minimize the reaction time and the amount of water required, the outcome of the esterification reaction was compared for starch dispersions in benchtop dispersed reactions, for starch melts in a heated torque rheometer, and for reactive extrusion in a pilot plant scale twin-screw extruder. The extent of reaction was quantified by 1H NMR analysis, and changes in molecular weight and diameter were monitored by gel permeation chromatography (GPC) analysis. The outcome of the reactions varied markedly in terms of reaction efficiency (RE), molecular weight distribution, and average hydrodynamic diameter, for the products derived from the different maleated reagents used, as well as for the different reaction protocols.
Effect of Deterpenated Origanum majorana L. Essential Oil on the Physicochemical and Biological Properties of Chitosan/β-Chitin Nanofibers Nanocomposite Films
Authors: Fernández-Marín Rut , Mujtaba Muhammad , Cansaran-Duman Demet , Ben Salha Ghada , Andrés Sánchez Mª Ángeles , Labidi Jalel , Fernandes Susana C. M.
Published: May 2021

Herein, the effect of three deterpenated fractions from Origanum majorana L. essential oil on the physicochemical, mechanical and biological properties of chitosan/β-chitin nanofibers-based nanocomposite films were investigated. In general, the incorporation of Origanum majorana L. original essential oil or its deterpenated fractions increases the opacity of the nanocomposite films and gives them a yellowish color. The water solubility decreases from 58% for chitosan/β-chitin nanofibers nanocomposite film to around 32% for the nanocomposite films modified with original essential oil or its deterpenated fractions. Regarding the thermal stability, no major changes were observed, and the mechanical properties decreased. Interestingly, data show differences on the biological properties of the materials depending on the incorporated deterpenated fraction of Origanum majorana L. essential oil. The nanocomposite films prepared with the deterpenated fractions with a high concentration of oxygenated terpene derivatives show the best antifungal activity against Aspergillus niger, with fungal growth inhibition of around 85.90%. Nonetheless, the only nanocomposite film that does not present cytotoxicity on the viability of L929 fibroblast cells after 48 and 72 h is the one prepared with the fraction presenting the higher terpenic hydrocarbon content (87.92%). These results suggest that the composition of the deterpenated fraction plays an important role in determining the biological properties of the nanocomposite films.
Influence of the Presence of Choline Chloride on the Classical Mechanism of “Gelatinization” of Starch
Authors: Crucean Doina , Pontoire Bruno , Debucquet Gervaise , Le-Bail Alain , Le-Bail Patricia
Published: May 2021

The aim of this research is to contribute to a better understanding the destructuration of three native starches and a wheat flour in mixtures of water and choline chloride. Model systems have thus been defined to allow a better approach to hydrothermic transformations related to the interactions between choline chloride and starch. We have observed that choline chloride has an impact on the gelatinization of starch which corresponds to the stabilizing salts phenomenon. The depolymerization and dissolution of the starch have also been demonstrated and can there dominate the gelatinization. However, the results obtained in X-ray diffraction by heating cell have shown that the exotherm which appeared was not only related to the depolymerization of the starch, but that a stage of crystalline rearrangement of the starch coexisted with this phenomenon.
pH and Reduction Dual-Responsive Bi-Drugs Conjugated Dextran Assemblies for Combination Chemotherapy and In Vitro Evaluation
Authors: Xue Xiukun , Wu Yanjuan , Xu Xiao , Xu Ben , Chen Zhaowei , Li Tianduo
Published: May 2021

Polymeric prodrugs, synthesized by conjugating chemotherapeutic agents to functional polymers, have been extensively investigated and employed for safer and more efficacious cancer therapy. By rational design, a pH and reduction dual-sensitive dextran-di-drugs conjugate (oDex-g-Pt+DOX) was synthesized by the covalent conjugation of Pt (IV) prodrug and doxorubicin (DOX) to an oxidized dextran (oDex). Pt (IV) prodrug and DOX were linked by the versatile efficient esterification reactions and Schiff base reaction, respectively. oDex-g-Pt+DOX could self-assemble into nanoparticles with an average diameter at around 180 nm. The acidic and reductive (GSH) environment induced degradation and drug release behavior of the resulting nanoparticles (oDex-g-Pt+DOX NPs) were systematically investigated by optical experiment, DLS analysis, TEM measurement, and in vitro drugs release experiment. Effective cellular uptake of the oDex-g-Pt+DOX NPs was identified by the human cervical carcinoma HeLa cells via confocal laser scanning microscopy. Furthermore, oDex-g-Pt+DOX NPs displayed a comparable antiproliferative activity than the simple combination of free cisplatin and DOX (Cis+DOX) as the extension of time. More importantly, oDex-g-Pt+DOX NPs exhibited remarkable reversal ability of tumor resistance compared to the cisplatin in cisplatin-resistant lung carcinoma A549 cells. Take advantage of the acidic and reductive microenvironment of tumors, this smart polymer-dual-drugs conjugate could serve as a promising and effective nanomedicine for combination chemotherapy.
A Critical Review of the Time-Dependent Performance of Polymeric Pipeline Coatings: Focus on Hydration of Epoxy-Based Coatings
Authors: Zargarnezhad Hossein , Asselin Edouard , Wong Dennis , Lam C. N. Catherine
Published: May 2021

The barrier performance of organic coatings is a direct function of mass transport and long-term stability of the polymeric structure. A predictive assessment of the protective coating cannot be conducted a priori of degradation effects on transport. Epoxy-based powder coatings are an attractive class of coatings for pipelines and other structures because application processing times are low and residual stresses between polymer layers are reduced. However, water ingress into the polymeric network of these coatings is of particular interest due to associated competitive sorption and plasticization effects. This review examines common analytical techniques for identifying parameters involved in transport in wet environments and underscores the gaps in the literature for the evaluation of the long-term performance of such coating systems. Studies have shown that the extent of polymer hydration has a major impact on gas and ion permeability/selectivity. Thus, transport analyses based only on micropore filling (i.e., adsorption) by water molecules are inadequate. Combinatorial entropy of the glassy epoxy and water vapor mixture not only affects the mechanism of membrane plasticization, but also changes the sorption kinetics of gas permeation and causes a partial gas immobility in the system. However, diffusivity, defined as the product of a kinetic mobility parameter and a concentration-dependent thermodynamic parameter, can eventually become favorable for gas transport at elevated temperatures, meaning that increasing gas pressure can decrease selectivity of the membrane for gas permeation. On the other hand, reverse osmosis membranes have shown that salt permeation is sensitive to, among other variables, water content in the polymer and a fundamental attribute in ionic diffusion is the effective size of hydrated ions. In addition, external electron sources—e.g., cathodic protection potentials for pipeline structures—can alter the kinetics of this transport as the tendency of ions to dissociate increases due to electrostatic forces. Focusing primarily on epoxy-based powder coatings, this review demonstrates that service parameters such as humidity, temperature, and concentration of aggressive species can dynamically develop different transport mechanisms, each at the expense of others. Although multilayered coating systems decrease moisture ingress and the consequences of environmental exposure, this survey shows that demands for extreme operating conditions can pose new challenges for coating materials and sparse data on transport properties would limit analysis of the remaining life of the system. This knowledge gap impedes the prediction of the likelihood of coating and, consequently, infrastructure failures.
Synergistic Antibacterial Effect of Casein-AgNPs Combined with Tigecycline against Acinetobacter baumannii
Authors: Chen Yu-Hsuan , Wang Wei-Hsun , Lin Sheng-Hui , Yang-Wang Yuan-Ting , Tseng Sung-Pin , Chien Chi-Sheng , Shih Chi-Jen
Published: May 2021

Acinetobacter baumannii (A. baumannii) is a common and challenging pathogen of nosocomial infections, due to its ability to survive on inanimate objects, desiccation tolerance, and resistance to disinfectants. In this study, we investigated an antibacterial strategy to combat A. baumannii via the combination of antibiotics and silver protein. This strategy used a functional platform consisting of silver nanoparticles (AgNPs) resurrected from silver-based calcium thiophosphate (SSCP) through casein and arginine. Then, the silver protein was combined with tigecycline, the first drug in glycylcycline antibiotic, to synergistically inhibit the viability of A. baumannii. The synergistic antibacterial activity was confirmed by the 96-well checkerboard method to determine their minimum inhibitory concentrations (MIC) and calculated for the combination index (CI). The MIC of the combination of silver protein and tigecycline (0.31 mg/mL, 0.16 µg/mL) was significantly lower than that of the individual MIC, and the CI was 0.59, which indicates a synergistic effect. Consequently, we integrated the detailed synergistic antibacterial properties when silver protein was combined with tigecycline. The result could make for a promising approach for the treatment of A. baumannii.
Kenaf Fiber/Pet Yarn Reinforced Epoxy Hybrid Polymer Composites: Morphological, Tensile, and Flammability Properties
Authors: Suriani M. J. , Zainudin Hasliana Asyikin , Ilyas R. A. , Petrů Michal , Sapuan S. M. , Ruzaidi C. M. , Mustapha Rohani
Published: May 2021

The application of natural fibers is rapidly growing in many sectors, such as construction, automobile, and furniture. Kenaf fiber (KF) is a natural fiber that is in demand owing to its eco-friendly and renewable nature. Nowadays, there are various new applications for kenaf, such as in absorbents and building materials. It also has commercial applications, such as in the automotive industry. Magnesium hydroxide (Mg(OH)2) is used as a fire retardant as it is low in cost and has good flame retardancy, while polyester yarn (PET) has high tensile strength. The aim of this study was to determine the horizontal burning rate, tensile strength, and surface morphology of kenaf fiber/PET yarn reinforced epoxy fire retardant composites. The composites were prepared by hybridized epoxy and Mg(OH)2 PET with different amounts of KF content (0%, 20%, 35%, and 50%) using the cold press method. The specimen with 35% KF (epoxy/PET/KF-35) displayed better flammability properties and had the lowest average burning rate of 14.55 mm/min, while epoxy/PET/KF-50 with 50% KF had the highest tensile strength of all the samples. This was due to fewer defects being detected on the surface morphology of epoxy/PET/KF-35 compared to the other samples, which influenced the mechanical properties of the composites.
Research on the Nonlinear Characteristic of Flow in the Electro-Hydraulic Servo Pump Control System
Authors: Zhang Tiangui , Chen Gexin , Yan Guishan , Zhang Cheng , Li Yuan , Ai Chao
Published: May 2021

In an electro-hydraulic servo system, the system flow is used as an intermediate variable to control the output displacement/force/speed of the actuator, and it plays the role of a medium for energy transmission and conversion. Therefore, the control of the system flow is the essence of state control for the system actuator. The purpose of this paper is to study the non-linear characteristics of pump control system flow and pave the way for high-performance control of the system. Based on the flow partition idea, the system output flow is divided into the dead zone, load zone, and saturation zone. We establish a mathematical model of the hydraulic system to analyze and study the effect of the nonlinear characteristics of the servo motor and the positive displacement pump on the system flow partition characteristics. We focus on the effects of the positive displacement pump shear flow internal leakage, pressure difference flow internal leakage, oil compression, servo motor rotation friction, and servo motor torque reserve rate on the system flow output, and analyze the partition characteristics of the system flow under different working conditions. We provide a theoretical basis for a high-precision control strategy for a hydraulic system.
The God of the Covenant: Karl Barth on Creation Care
Authors: de Vera Nixon
Published: May 2021

This article seeks to explore the identity of the Creator God in Karl Barth’s doctrine of creation. Attention is given to his understanding of the eternal covenant God has made with humanity and how we are cared for within a covenantal fellowship. The study also concerns itself with how Barth’s distaste for the notion of analogia entis is somewhat unsustained in his treatment of creation. I argue that, to some extent, the analogy of being vis-à-vis the cosmos is complementarily employed with analogia fides in Barth’s articulation of creation care. This is the case as he reconfigures the talk on creation rigidly in and through Jesus Christ as Creator and creature.
Enhanced Pulsed-Source Localization with 3 Hydrophones: Uncertainty Estimates
Authors: Pavlidi Despoina , Skarsoulis Emmanuel K.
Published: May 2021

The uncertainty behavior of an enhanced three-dimensional (3D) localization scheme for pulsed sources based on relative travel times at a large-aperture three-hydrophone array is studied. The localization scheme is an extension of a two-hydrophone localization approach based on time differences between direct and surface-reflected arrivals, an approach with significant advantages, but also drawbacks, such as left-right ambiguity, high range/depth uncertainties for broadside sources, and high bearing uncertainties for endfire sources. These drawbacks can be removed by adding a third hydrophone. The 3D localization problem is separated into two, a range/depth estimation problem, for which only the hydrophone depths are needed, and a bearing estimation problem, if the hydrophone geometry in the horizontal is known as well. The refraction of acoustic paths is taken into account using ray theory. The condition for existence of surface-reflected arrivals can be relaxed by considering arrivals with an upper turning point, allowing for localization at longer ranges. A Bayesian framework is adopted, allowing for the estimation of localization uncertainties. Uncertainty estimates are obtained through analytic predictions and simulations and they are compared against two-hydrophone localization uncertainties as well as against two-dimensional localization that is based on direct arrivals.
Exploring Temporal Intra-Urban Travel Patterns: An Online Car-Hailing Trajectory Data Perspective
Authors: Shi Chaoyang , Li Qingquan , Lu Shiwei , Yang Xiping
Published: May 2021

Understanding intra-urban travel patterns is beneficial for urban planning and transportation management, among other fields. As an emerging travel mode, online car-hailing platforms provide massive and high-precision trajectory data, thus offering new opportunities for gaining insights into human mobility. This paper aims to explore temporal intra-urban travel patterns by fitting the distributions of mobility metrics and leveraging the boxplot. The statistical characteristics of daily and hourly travel distance are relatively stable, while those of travel time and speed have some fluctuations. More specifically, most residents travel between 2 and 10 km, with travel times ranging from 6.6 to 30 min, which is fairly consistent with our daily experience. Mainly attributed to travel cost, individuals seldom use online car-hailing for too short or long trips. It is worth mentioning that a weekly pattern can be found in all mobility metrics, in which the patterns of travel time and speed are more obvious than that of travel distance. In addition, since October has more rainy days than November, travel distances and travel times in October are higher than that in November, while the opposite is true for travel speed. This paper can provide a beneficial reference for understanding temporal human mobility patterns, and lays a solid foundation for future research.
A Robust Atmospheric Correction Procedure for Determination of Spectral Reflectance of Terrestrial Surfaces from Satellite Spectral Measurements
Authors: Katsev Iosif L. , Prikhach Alexander S. , Zege Eleonora P. , Kokhanovsky Alexander A.
Published: May 2021

In this work, we propose simple and robust technique for the retrieval of underlying surface spectral reflectance using spaceborne observations. It can be used to process both multispectral moderate resolution satellite data and also multi-zone high spatial resolution data. The technique can work automatically for different types of land surfaces without using huge databases accumulated in advance. The new cloud discrimination and retrieval of the water vapor content in atmosphere procedures are presented. The key point of the proposed atmospheric correction technique is the suggested single-wavelength method for determining the atmospheric aerosol optical thickness without reference to a specific type of underlying surface spectrum. The retrievals of spectral reflectance for various land surfaces with developed technique, performed using computer simulation and experimental data, have demonstrated a high retrieval accuracy.
Cement-Based Piezoelectric Ceramic Composites for Sensing Elements: A Comprehensive State-of-the-Art Review
Authors: Ding Weijian , Liu Yuqing , Shiotani Tomoki , Wang Quan , Han Ningxu , Xing Feng
Published: May 2021

Compatibility, a critical issue between sensing material and host structure, significantly influences the detecting performance (e.g., sensitive, signal-to-noise ratio) of the embedded sensor. To address this issue in concrete-based infrastructural health monitoring, cement-based piezoelectric composites (piezoelectric ceramic particles as a function phase and cementitious materials as a matrix) have attracted continuous attention in the past two decades, dramatically exhibiting superior durability, sensitivity, and compatibility. This review paper performs a synthetical overview of recent advances in theoretical analysis, characterization and simulation, materials selection, the fabrication process, and application of the cement-based piezoelectric composites. The critical issues of each part are also presented. The influencing factors of the materials and fabrication process on the final performance of composites are further discussed. Meanwhile, the application of the composite as a sensing element for various monitoring techniques is summarized. Further study on the experiment and simulation, materials, fabrication technique, and application are also pointed out purposefully.
Research on a Novel Locating Method for Track Inspection Based on Onboard Sensors in Maglev Train
Authors: Yuan Yihong , Luo Yanyun , Ye Feng , Zhu Zhiwei , Zeng Guofeng , Wang Guoqiang
Published: May 2021

Guideway inspection is of great significance to the operation safety and riding quality of a commercial high-speed maglev transportation system. When analyzing guideway inspection data, it is important to obtain the location information for each piece of raw data and convert it from the time domain to the spatial domain for the analysis afterward. Previous studies have used the method of adding additional hardware such as GPS (global positioning system) receivers, LRF (location reference flag) readers, or onboard CAN (controller area network) bus adaptors to obtain location information. This paper presents a novel method for indirectly obtaining the location information via the use of data from the levitation and guidance control sensors perpendicular to the longitudinal direction to extract the characteristic information from the track. The method can be used for a long stator linear motor-driven maglev system and similar contactless rail transit systems. The results showed that the method could accurately identify the required location information in each stator tooth during the entire operation simultaneously with the operating information such as train velocity, direction, and track ID, without additional hardware installation and vehicle network connection. Thus, it could improve the pertinence of the results of guideway inspection equipment, and at the same time, facilitate the miniaturization and independence of guideway inspection equipment.
Ethoflow: Computer Vision and Artificial Intelligence-Based Software for Automatic Behavior Analysis
Authors: Bernardes Rodrigo Cupertino , Lima Maria Augusta Pereira , Guedes Raul Narciso Carvalho , da Silva Clíssia Barboza , Martins Gustavo Ferreira
Published: May 2021

Manual monitoring of animal behavior is time-consuming and prone to bias. An alternative to such limitations is using computational resources in behavioral assessments, such as tracking systems, to facilitate accurate and long-term evaluations. There is a demand for robust software that addresses analysis in heterogeneous environments (such as in field conditions) and evaluates multiple individuals in groups while maintaining their identities. The Ethoflow software was developed using computer vision and artificial intelligence (AI) tools to monitor various behavioral parameters automatically. An object detection algorithm based on instance segmentation was implemented, allowing behavior monitoring in the field under heterogeneous environments. Moreover, a convolutional neural network was implemented to assess complex behaviors expanding behavior analyses’ possibilities. The heuristics used to generate training data for the AI models automatically are described, and the models trained with these datasets exhibited high accuracy in detecting individuals in heterogeneous environments and assessing complex behavior. Ethoflow was employed for kinematic assessments and to detect trophallaxis in social bees. The software was developed in desktop applications and had a graphical user interface. In the Ethoflow algorithm, the processing with AI is separate from the other modules, facilitating measurements on an ordinary computer and complex behavior assessing on machines with graphics processing units. Ethoflow is a useful support tool for applications in biology and related fields.
Recognizing Manual Activities Using Wearable Inertial Measurement Units: Clinical Application for Outcome Measurement
Authors: El Khoury Ghady , Penta Massimo , Barbier Olivier , Libouton Xavier , Thonnard Jean-Louis , Lefèvre Philippe
Published: May 2021

The ability to monitor activities of daily living in the natural environments of patients could become a valuable tool for various clinical applications. In this paper, we show that a simple algorithm is capable of classifying manual activities of daily living (ADL) into categories using data from wrist- and finger-worn sensors. Six participants without pathology of the upper limb performed 14 ADL. Gyroscope signals were used to analyze the angular velocity pattern for each activity. The elaboration of the algorithm was based on the examination of the activity at the different levels (hand, fingers and wrist) and the relationship between them for the duration of the activity. A leave-one-out cross-validation was used to validate our algorithm. The algorithm allowed the classification of manual activities into five different categories through three consecutive steps, based on hands ratio (i.e., activity of one or both hands) and fingers-to-wrist ratio (i.e., finger movement independently of the wrist). On average, the algorithm made the correct classification in 87.4% of cases. The proposed algorithm has a high overall accuracy, yet its computational complexity is very low as it involves only averages and ratios.
Attention Networks for the Quality Enhancement of Light Field Images
Authors: Schiopu Ionut , Munteanu Adrian
Published: May 2021

In this paper, we propose a novel filtering method based on deep attention networks for the quality enhancement of light field (LF) images captured by plenoptic cameras and compressed using the High Efficiency Video Coding (HEVC) standard. The proposed architecture was built using efficient complex processing blocks and novel attention-based residual blocks. The network takes advantage of the macro-pixel (MP) structure, specific to LF images, and processes each reconstructed MP in the luminance (Y) channel. The input patch is represented as a tensor that collects, from an MP neighbourhood, four Epipolar Plane Images (EPIs) at four different angles. The experimental results on a common LF image database showed high improvements over HEVC in terms of the structural similarity index (SSIM), with an average Y-Bjøntegaard Delta (BD)-rate savings of 36.57%, and an average Y-BD-PSNR improvement of 2.301 dB. Increased performance was achieved when the HEVC built-in filtering methods were skipped. The visual results illustrate that the enhanced image contains sharper edges and more texture details. The ablation study provides two robust solutions to reduce the inference time by 44.6% and the network complexity by 74.7%. The results demonstrate the potential of attention networks for the quality enhancement of LF images encoded by HEVC.
Systematic Review of Fault Tolerant Techniques in Underwater Sensor Networks
Authors: Vihman Lauri , Kruusmaa Maarja , Raik Jaan
Published: May 2021

Sensor networks provide services to a broad range of applications ranging from intelligence service surveillance to weather forecasting. While most of the sensor networks are terrestrial, Underwater Sensor Networks (USN) are an emerging area. One of the unavoidable and increasing challenges for modern USN technology is tolerating faults, i.e., accepting that hardware is imperfect, and coping with it. Fault Tolerance tends to have more impact in underwater than in terrestrial environment as the latter is generally more forgiving. Moreover, reaching the malfunctioning devices for replacement and maintenance under water is harder and more costly. The current paper is the first to provide an overview of fault-tolerant, particularly cross-layer fault-tolerant, techniques in USNs. In the paper, we present a systematic survey of the techniques, introduce a taxonomy of the Fault Tolerance tasks, present a categorized list of articles, and list the open research issues within the area.
A Compact High-Quality Image Demosaicking Neural Network for Edge-Computing Devices
Authors: Wang Shuyu , Zhao Mingxin , Dou Runjiang , Yu Shuangming , Liu Liyuan , Wu Nanjian
Published: May 2021

Image demosaicking has been an essential and challenging problem among the most crucial steps of image processing behind image sensors. Due to the rapid development of intelligent processors based on deep learning, several demosaicking methods based on a convolutional neural network (CNN) have been proposed. However, it is difficult for their networks to run in real-time on edge computing devices with a large number of model parameters. This paper presents a compact demosaicking neural network based on the UNet++ structure. The network inserts densely connected layer blocks and adopts Gaussian smoothing layers instead of down-sampling operations before the backbone network. The densely connected blocks can extract mosaic image features efficiently by utilizing the correlation between feature maps. Furthermore, the block adopts depthwise separable convolutions to reduce the model parameters; the Gaussian smoothing layer can expand the receptive fields without down-sampling image size and discarding image information. The size constraints on the input and output images can also be relaxed, and the quality of demosaicked images is improved. Experiment results show that the proposed network can improve the running speed by 42% compared with the fastest CNN-based method and achieve comparable reconstruction quality as it on four mainstream datasets. Besides, when we carry out the inference processing on the demosaicked images on typical deep CNN networks, Mobilenet v1 and SSD, the accuracy can also achieve 85.83% (top 5) and 75.44% (mAP), which performs comparably to the existing methods. The proposed network has the highest computing efficiency and lowest parameter number through all methods, demonstrating that it is well suitable for applications on modern edge computing devices.
Exploring a Novel Multiple-Query Resistive Grid-Based Planning Method Applied to High-DOF Robotic Manipulators
Authors: Huerta-Chua Jesus , Diaz-Arango Gerardo , Vazquez-Leal Hector , Flores-Mendez Javier , Moreno-Moreno Mario , Ambrosio-Lazaro Roberto C. , Hernandez-Mejia Carlos
Published: May 2021

The applicability of the path planning strategy to robotic manipulators has been an exciting topic for researchers in the last few decades due to the large demand in the industrial sector and its enormous potential development for space, surgical, and pharmaceutical applications. The automation of high-degree-of-freedom (DOF) manipulator robots is a challenging task due to the high redundancy in the end-effector position. Additionally, in the presence of obstacles in the workspace, the task becomes even more complicated. Therefore, for decades, the most common method of integrating a manipulator in an industrial automated process has been the demonstration technique through human operator intervention. Although it is a simple strategy, some drawbacks must be considered: first, the path’s success, length, and execution time depend on operator experience; second, for a structured environment with few objects, the planning task is easy. However, for most typical industrial applications, the environments contain many obstacles, which poses challenges for planning a collision-free trajectory. In this paper, a multiple-query method capable of obtaining collision-free paths for high DOF manipulators with multiple surrounding obstacles is presented. The proposed method is inspired by the resistive grid-based planner method (RGBPM). Furthermore, several improvements are implemented to solve complex planning problems that cannot be handled by the original formulation. The most important features of the proposed planner are as follows: (1) the easy implementation of robotic manipulators with multiple degrees of freedom, (2) the ability to handle dozens of obstacles in the environment, (3) compatibility with various obstacle representations using mathematical models, (4) a new recycling of a previous simulation strategy to convert the RGBPM into a multiple-query planner, and (5) the capacity to handle large sparse matrices representing the configuration space. A numerical simulation was carried out to validate the proposed planning method’s effectiveness for manipulators with three, five, and six DOFs on environments with dozens of surrounding obstacles. The case study results show the applicability of the proposed novel strategy in quickly computing new collision-free paths using the first execution data. Each new query requires less than 0.2 s for a 3 DOF manipulator in a configuration space free-modeled by a 7291 × 7291 sparse matrix and less than 30 s for five and six DOF manipulators in a configuration space free-modeled by 313,958 × 313,958 and 204,087 × 204,087 sparse matrices, respectively. Finally, a simulation was conducted to validate the proposed multiple-query RGBPM planner’s efficacy in finding feasible paths without collision using a six-DOF manipulator (KUKA LBR iiwa 14R820) in a complex environment with dozens of surrounding obstacles.
Non-Local and Multi-Scale Mechanisms for Image Inpainting
Authors: He Xu , Yin Yong
Published: May 2021

Recently, deep learning-based techniques have shown great power in image inpainting especially dealing with squared holes. However, they fail to generate plausible results inside the missing regions for irregular and large holes as there is a lack of understanding between missing regions and existing counterparts. To overcome this limitation, we combine two non-local mechanisms including a contextual attention module (CAM) and an implicit diversified Markov random fields (ID-MRF) loss with a multi-scale architecture which uses several dense fusion blocks (DFB) based on the dense combination of dilated convolution to guide the generative network to restore discontinuous and continuous large masked areas. To prevent color discrepancies and grid-like artifacts, we apply the ID-MRF loss to improve the visual appearance by comparing similarities of long-distance feature patches. To further capture the long-term relationship of different regions in large missing regions, we introduce the CAM. Although CAM has the ability to create plausible results via reconstructing refined features, it depends on initial predicted results. Hence, we employ the DFB to obtain larger and more effective receptive fields, which benefits to predict more precise and fine-grained information for CAM. Extensive experiments on two widely-used datasets demonstrate that our proposed framework significantly outperforms the state-of-the-art approaches both in quantity and quality.
Towards Characterizing and Developing Formation and Migration Cues in Seafloor Sand Waves on Topology, Morphology, Evolution from High-Resolution Mapping via Side-Scan Sonar in Autonomous Underwater Vehicles
Authors: Nian Rui , Zang Lina , Geng Xue , Yu Fei , Ren Shidong , He Bo , Li Xishuang
Published: May 2021

Sand waves constitute ubiquitous geomorphology distribution in the ocean. In this paper, we quantitatively investigate the sand wave variation of topology, morphology, and evolution from the high-resolution mapping of a side scan sonar (SSS) in an Autonomous Underwater Vehicle (AUV), in favor of online sequential Extreme Learning Machine (OS-ELM). We utilize echo intensity directly derived from SSS to help accelerate detection and localization, denote a collection of Gaussian-type morphological templates, with one integrated matching criterion for similarity assessment, discuss the envelope demodulation, zero-crossing rate (ZCR), cross-correlation statistically, and estimate the specific morphological parameters. It is demonstrated that the sand wave detection rate could reach up to 95.61% averagely, comparable to deep learning such as MobileNet, but at a much higher speed, with the average test time of 0.0018 s, which is particularly superior for sand waves at smaller scales. The calculation of morphological parameters primarily infer a wave length range and composition ratio in all types of sand waves, implying the possible dominant direction of hydrodynamics. The proposed scheme permits to delicately and adaptively explore the submarine geomorphology of sand waves with online computation strategies and symmetrically integrate evidence of its spatio-temporal responses during formation and migration.
Thermal Face Verification through Identification
Authors: Grudzień Artur , Kowalski Marcin , Pałka Norbert
Published: May 2021

This paper reports on a new approach to face verification in long-wavelength infrared radiation. Two face images were combined into one double image, which was then used as an input for a classification based on neural networks. For testing, we exploited two external and one homemade thermal face databases acquired in various variants. The method is reported to achieve a true acceptance rate of about 83%. We proved that the proposed method outperforms other studied baseline methods by about 20 percentage points. We also analyzed the issue of extending the performance of algorithms. We believe that the proposed double image method can also be applied to other spectral ranges and modalities different than the face.
3D Printing Techniques and Their Applications to Organ-on-a-Chip Platforms: A Systematic Review
Authors: Carvalho Violeta , Gonçalves Inês , Lage Teresa , Rodrigues Raquel O. , Minas Graça , Teixeira Senhorinha F. C. F. , Moita Ana S. , Hori Takeshi , Kaji Hirokazu , Lima Rui A.
Published: May 2021

Three-dimensional (3D) in vitro models, such as organ-on-a-chip platforms, are an emerging and effective technology that allows the replication of the function of tissues and organs, bridging the gap amid the conventional models based on planar cell cultures or animals and the complex human system. Hence, they have been increasingly used for biomedical research, such as drug discovery and personalized healthcare. A promising strategy for their fabrication is 3D printing, a layer-by-layer fabrication process that allows the construction of complex 3D structures. In contrast, 3D bioprinting, an evolving biofabrication method, focuses on the accurate deposition of hydrogel bioinks loaded with cells to construct tissue-engineered structures. The purpose of the present work is to conduct a systematic review (SR) of the published literature, according to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, providing a source of information on the evolution of organ-on-a-chip platforms obtained resorting to 3D printing and bioprinting techniques. In the literature search, PubMed, Scopus, and ScienceDirect databases were used, and two authors independently performed the search, study selection, and data extraction. The goal of this SR is to highlight the importance and advantages of using 3D printing techniques in obtaining organ-on-a-chip platforms, and also to identify potential gaps and future perspectives in this research field. Additionally, challenges in integrating sensors in organs-on-chip platforms are briefly investigated and discussed.
“System Conditions”, System Failure, Structural Racism and Anti-Racism in the United Kingdom: Evidence from Education and Beyond
Authors: Miller Paul
Published: May 2021

Racism in any society is fuelled by a number of factors, often acting independently of each other, or, at times, in concert with each other. On the one hand, anti-racism efforts rely on the alignment of four “system conditions” to stand a chance of successfully engaging and tackling racism. On the other hand, where these “system conditions” are not present, or where they are not in sync, this leads to “system failure”—a situation where racism is writ large in society and in the institutions therein, and where anti-racism efforts are severely hampered. Drawing on evidence from within the education sector and elsewhere in UK society, this paper examines how a lack of alignment between “system conditions” hampers antiracism efforts, and simultaneously reinforces racism in society and in institutions—leading to gridlock or “system failure” around anti-racism.